U.S. patent application number 16/833580 was filed with the patent office on 2020-10-01 for performance grinding solutions.
The applicant listed for this patent is Saint-Gobain Abrasifs, Saint Gobain Abrasives, Inc.. Invention is credited to Christopher Arcona, Robin Chandras Jayaram, Anna Maassel, Thomas H. Osborn, Joseph P. Sullivan, Rajappa Tadepalli, Brahmanandam V. Tanikella, Arunvel Thangamani.
Application Number | 20200306927 16/833580 |
Document ID | / |
Family ID | 1000004793863 |
Filed Date | 2020-10-01 |
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United States Patent
Application |
20200306927 |
Kind Code |
A1 |
Tanikella; Brahmanandam V. ;
et al. |
October 1, 2020 |
Performance Grinding Solutions
Abstract
The present application relates to systems and methods for
obtaining real-time abrasion data. An example computer-implemented
method could include receiving, at a computing device, sensor data
from one or more sensors. The one or more sensors are disposed in
proximity to an abrasive product or a workpiece associated with the
abrasive product. The one or more sensors are configured to collect
abrasion operational data associated with an abrasive operation
involving the abrasive product or the workpiece. The
computer-implemented method could further include training, based
on the sensor data, a machine learning system to determine product
specific information of the abrasive product and/or workpiece
specific information. The computer-implemented method could also
include providing the trained machine learning system using the
computing device.
Inventors: |
Tanikella; Brahmanandam V.;
(Northborough, MA) ; Arcona; Christopher;
(Northborough, MA) ; Osborn; Thomas H.;
(Worcester, MA) ; Sullivan; Joseph P.; (Worcester,
MA) ; Maassel; Anna; (Worcester, MA) ;
Thangamani; Arunvel; (Chennai, IN) ; Jayaram; Robin
Chandras; (Chennai, IN) ; Tadepalli; Rajappa;
(Northborough, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Saint Gobain Abrasives, Inc.
Saint-Gobain Abrasifs |
Worcester |
MA |
US |
|
|
Family ID: |
1000004793863 |
Appl. No.: |
16/833580 |
Filed: |
March 28, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62826971 |
Mar 29, 2019 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06K 9/6257 20130101;
B24B 49/006 20130101; G06N 5/04 20130101; G06K 2209/19 20130101;
B24B 49/003 20130101; G06N 20/00 20190101; B24B 49/00 20130101;
G06K 9/6253 20130101 |
International
Class: |
B24B 49/00 20060101
B24B049/00; G06N 20/00 20060101 G06N020/00; G06N 5/04 20060101
G06N005/04; G06K 9/62 20060101 G06K009/62 |
Foreign Application Data
Date |
Code |
Application Number |
May 24, 2019 |
IN |
201941020668 |
Claims
1. A computer-implemented method, comprising: receiving, at a
computing device, sensor data from one or more sensors, wherein the
one or more sensors are disposed in proximity to an abrasive
product or a workpiece associated with the abrasive product,
wherein the one or more sensors are configured to collect abrasion
operational data associated with an abrasive operation involving
the abrasive product or the workpiece; training, by way of the
computing device and based on the sensor data, a machine learning
system to determine product specific information of the abrasive
product or workpiece specific information of the workpiece; and
providing, by way of the computing device, the trained machine
learning system.
2. The computer-implemented method of claim 1, further comprising
tagging at least a portion of the sensor data to provide tagged
sensor data, wherein the tagged sensor data comprises one or more
tags, each tag identifying different product specific information
for the abrasive product.
3. The computer-implemented method of claim 2, wherein the one or
more tags identify a pattern of the abrasion operation data from a
duration of time prior to an abrasive product related event.
4. The computer-implemented method of claim 3, wherein the pattern
of the abrasion operation data comprises one or more stages, each
stage associated with one or more sensor threshold values, wherein
the one or more tags are associated with a stage based on the one
or more sensor threshold values.
5. The computer-implemented method of claim 1, wherein training the
machine learning system comprises training one or more machine
learning models, wherein each model is trained with sensor data
from abrasive products having unique identifiers from a shared
identifier set.
6. The computer-implemented method of claim 1, wherein the sensor
data from one or more sensors is aggregated by a local computing
device to provide aggregated sensor data, and wherein receiving the
sensor data comprises receiving the aggregated sensor data from the
local computing device.
7. The computer-implemented method of claim 1, wherein the one or
more sensors are disposed in a wearable device, wherein the sensor
data from one or more sensors is aggregated by the wearable device
to provide aggregated sensor data, and wherein receiving the sensor
data comprises receiving the aggregated sensor data from the
wearable device.
8. The computer-implemented method of claim 7, wherein the sensor
data comprises information indicative of a rotation per minute
(RPM) value of the abrasive product.
9. A computer-implemented method, comprising: receiving, at a
computing device, sensor data from one or more sensors, wherein the
one or more sensors are disposed in proximity to an abrasive
product or a workpiece associated with the abrasive product,
wherein the one or more sensors are configured to collect abrasion
operational data associated with an abrasive operation involving
the abrasive product or the workpiece, and wherein the computing
device has access to a trained machine learning system configured
to receive input sensor data and output product specific
information of the abrasive product or workpiece specific
information of the workpiece; determining, by applying the trained
machine learning system on the sensor data, product specific
information of the abrasive product or workpiece specific
information of the workpiece; and providing, to one or more client
devices, the product specific information or the workpiece specific
information.
10. The computer-implemented method of claim 9, wherein the one or
more sensors are disposed in a wearable device, wherein the sensor
data from one or more sensors is aggregated by the wearable device
to provide aggregated sensor data, and wherein receiving the sensor
data comprises receiving the aggregated sensor data from the
wearable device.
11. The computer-implemented method of claim 10, wherein the sensor
data comprises information indicative of a rotation per minute
(RPM) value of the abrasive product.
12. The computer-implemented method of claim 9, wherein the one or
more client devices comprises at least one of: a wearable device, a
mobile device, a dashboard device, a web server, an analytics
processing engine, or a third party server.
13. The computer-implemented method of claim 9, wherein providing
the product specific information comprises providing information
associated with a new abrasive product or an updated abrasive
product, wherein the information includes, at least in part,
instructions for constructing the new abrasive product or the
updated abrasive product.
14. The computer-implemented method of claim 9, wherein providing
the product specific information or the workpiece specific
information comprises providing, to the one or more client devices,
a notification.
15. The computer-implemented method of claim 9, wherein providing
the product specific information or the workpiece specific
information comprises providing, to the one or more client devices,
one or more product specific solutions for resolving issues with
the abrasive product.
16. The computer-implemented method of claim 15, wherein the one or
more client devices are configured to: display, on a graphical user
interface, the one or more product specific solutions, receive, via
the graphical user interface, a selection of one of the one or more
product specific solutions; determine training data for the trained
machine learning system based on the selected product specific
solution; and transmit, to the computing device, the training
data.
17. The computer-implemented method of claim 9, wherein the
abrasive product is a handheld abrasive product operated by a
user.
18. The computer-implemented method of claim 17, wherein the
abrasive operational data comprises at least one of: vibration data
associated with the handheld abrasive product or acceleration data
associated with the handheld abrasive product.
19. The computer-implemented method of claim 17, wherein providing
the product specific information or the workpiece specific
information comprises providing a notification to a graphical
interface of a wearable device worn by the user.
20. The computer-implemented method of claim 17, wherein the
product specific information comprises at least one of: time spent
performing tasks assigned to the user, idle time of the user, or
productive time of the user.
21. The computer-implemented method of claim 17, wherein the
product specific information comprises at least one of: a working
angle of the user with respect to the handheld abrasive product, a
working angle of the handheld abrasive product with respect to the
workpiece, a grip tightness of the user on the handheld abrasive
product, or an applied pressure of the user on the handheld
abrasive product.
22. The computer-implemented method of claim 17, wherein the
product specific information comprises an end of life estimate of
the handheld abrasive product, wherein the end of life estimate
comprises an estimated amount of time the user can safely use the
handheld abrasive product.
23. A computer-implemented method of claim 9, wherein the abrasive
product is an automated abrasive product operated by a
controller.
24. The computer-implemented method of claim 23, wherein the one or
more sensors comprises a spark-invariant sensor configured to
collect operational speeds of the automated abrasive product.
25. The computer-implemented method of claim 23, wherein providing
the product specific information comprises providing a
determination that one or more abrasive articles of the automated
abrasive product are damaged or malfunctioning.
26. The computer-implemented method of claim 25, wherein upon
providing the determination, the computing device is further
configured to: identify, by a product database, one or more
replacement abrasive articles for the automated abrasive product;
and responsive to identifying the one or more replacement abrasive
articles, place a request for the one or more replacement articles
or a refurbishment treatment.
27. The computer-implemented method of claim 23, wherein providing
the product specific information comprises transmitting at least
one control instruction to the controller of the automated abrasive
product, wherein the at least one control instruction comprises at
least one of: adjust a rotational speed of the automated abrasive
product, provide a notification to the automated abrasive product,
turn on the automated abrasive product, or turn off the automated
abrasive product.
28. The computer-implemented method of claim 9, wherein determining
the product specific information of the abrasive product or
workpiece specific information of the workpiece comprises
determining one or more of: a predicted future condition of the
abrasive product or a predicted future condition of the
workpiece.
29. The computer-implemented method of claim 28, wherein providing
the product specific information or the workpiece specific
information comprises providing at least one of the predicted
future condition of the abrasive product or the predicted future
condition of the workpiece.
30. The computer-implemented method of claim 9, wherein determining
the product specific information of the abrasive product or
workpiece specific information of the workpiece comprises
determining a prescriptive action.
31. The computer-implemented method of claim 30, wherein providing
the product specific information or the workpiece specific
information comprises providing the prescriptive action, wherein
the prescriptive action comprises at least one of: adjusting an
operational parameter of an abrasive machine, performing a
maintenance operation, redressing the abrasive product, or
replacing the abrasive product.
32. A computing system, comprising: a trained machine learning
system configured to receive input sensor data and output, based on
the input sensor data, product specific information or workpiece
specific information; and a computing device configured to: receive
sensor data from one or more sensors, wherein the one or more
sensors are disposed in proximity to an abrasive product or a
workpiece associated with the abrasive product, wherein the one or
more sensors are configured to collect abrasion operational data
associated with an abrasive operation involving the abrasive
product or the workpiece; determine, by applying the trained
machine learning system on the sensor data, product specific
information of the abrasive product or workpiece specific
information of the workpiece; and provide, to one or more client
devices, the product specific information or the workpiece specific
information.
33. The computing system of claim 32, further comprising: a display
configured to provide a graphical user interface with a search
interface, wherein the search interface comprises a plurality of
user-selectable criteria, wherein the user-selectable criteria
comprise at least one of: a location menu, a device menu, a date
range, or a workpiece menu, wherein the graphical user interface is
configured to: receive user-selected search criteria from the
user-selectable criteria; determine, based on the user-selected
search criteria, one or more metrics; and display, via the
graphical user interface, the one or more metrics.
34. The computing system of claim 33, wherein the one or more
metrics comprise at least one of: a grinding time metric, an
optimal grinding metric, a vibration metric, a depth of cut, a
current trace, a tool identifier, or a part count.
35. The computing system of claim 33, wherein the graphical user
interface is further configured to receive a desired metric
selected from a metric menu, wherein displaying the one or more
metrics is based on the desired metric.
36. The computing system of claim 33, wherein the display is
further configured to provide, via the graphical user interface, a
cycle comparison interface, wherein the cycle comparison interface
is configured to display at least a portion of the sensor data in
an overlapping arrangement of a plurality of periodic time
series.
37. The computing system of claim 32, wherein the computing device
is further configured to: compare a plurality of periodic time
series of at least a portion of the sensor data, wherein
determining the product specific information of the abrasive
product or workpiece specific information of the workpiece, is
based, at least in part, on the comparison.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims priority to U.S. Provisional
Patent Application No. 62/826,971, filed on Mar. 29, 2019, the
contents of which are entirely incorporated by reference herein.
The present application further claims priority to India Patent
Application No. 201941020668, filed on May 24, 2019, the contents
of which are entirely incorporated by reference herein.
BACKGROUND
[0002] Abrasive tools can be used in various material removal
operations. Such tools have been equipped with sensors that may
monitor the usage of the tools. For example, a power sensor may be
incorporated into a tool in order to monitor the electrical power
that is consumed by the load. Although a power sensor incorporated
into the tool may provide a user of the tool with useful
information related to the tool, the sensor may not fully capture
the operation of the tool and/or the experience of the user. For
example, power sensor data cannot effectively be used to determine
whether a component of the tool has been damaged or is
malfunctioning.
SUMMARY
[0003] The present disclosure generally relates to systems and
methods for obtaining, analyzing, and utilizing real-time data in
abrasive and abrasive device applications.
[0004] In a first aspect, a computer-implemented method is
provided. The computer-implemented method includes receiving, at a
computing device, sensor data from one or more sensors. The one or
more sensors are a disposed in proximity to at least one of: one or
more abrasive products or one or more workpieces. The one or more
sensors are configured to collect abrasion operational data
associated with an abrasive operation involving the one or more
abrasive products or the one or more workpieces. The
computer-implemented method includes training, using the computing
device, a machine learning system to determine product-specific
information of the one or more abrasive products and/or
workpiece-specific information based on the sensor data. The
computer-implemented method also includes providing the trained
machine learning system using the computing device.
[0005] In a second aspect, a computer-implemented method is
provided. The computer-implemented method includes receiving, at a
computing device, sensor data from one or more sensors. The one or
more sensors are a disposed in proximity to at least one of: an
abrasive product or a workpiece. The one or more sensors are
configured to collect abrasion operational data associated with an
abrasive operation involving the abrasive product or the workpiece.
The computing device has access to a trained machine learning
system configured to receive input sensor data and output
product-specific information of the abrasive product or
workpiece-specific information based on the input sensor data. The
computer-implemented method includes determining, by the trained
machine learning system, product-specific information of the
abrasive product or workpiece-specific information based of the
sensor data. The computer-implemented method additionally includes
providing the product-specific information of the abrasive product
or workpiece-specific information to one or more client
devices.
[0006] In a third aspect, a system is provided. The system includes
a trained machine learning system configured to receive input
sensor data and output at least one of: product-specific
information of abrasive products or workpiece-specific information
based on the input sensor data. The system also includes a
computing device configured to receive, from a client device,
sensor data from one or more sensors. The one or more sensors are
disposed in proximity to an abrasive product or a workpiece. The
one or more sensors are configured to collect abrasion operational
data associated with an abrasive operation involving the abrasive
product or the workpiece. The computing device is also configured
to determine, by applying the trained machine learning system on
the sensor data, product-specific information of the abrasive
product or workpiece-specific information. The computing device is
further configured to provide, the product-specific information of
the abrasive product and/or workpiece-specific information to one
or more client devices.
[0007] In a fourth aspect, a computer-implemented method is
provided. The computer-implemented method includes receiving, at a
computing device, sensor data from one or more sensors. The one or
more sensors are a disposed in proximity to a handheld abrasive
product operated by a user. The one or more sensors are configured
to collect abrasion operational data associated with an abrasive
operation involving the handheld abrasive product. The computing
device has access a trained machine learning system configured to
receive input sensor data and output product-specific information
of the abrasive product based on the input sensor data. The
computer-implemented method includes determining, by the trained
machine learning system, product-specific information of the
abrasive product information based of the sensor data. The
computer-implemented method also includes providing the
product-specific information relating to the abrasive operation of
the abrasive product to one or more client devices.
[0008] In a fifth aspect, a computer-implemented method is
provided. The computer-implemented method includes receiving, at a
computing device, sensor data from one or more sensors. The one or
more sensors are a disposed in proximity to an abrasive product
operated by an automated process. The one or more sensors are
configured to collect abrasion operational data associated with an
abrasive operation involving the abrasive product. The computing
device has access a trained machine learning system configured to
receive input sensor data and output product-specific information
of the abrasive product based on the input sensor data. The
computer-implemented method includes determining, by the trained
machine learning system, product-specific information of the
abrasive product based of the sensor data. The computer-implemented
method also includes providing the product-specific information
relating to the abrasive product to one or more client devices.
[0009] In a sixth aspect, a computer-implemented method is
provided. The computer-implemented method includes receiving, at a
computing device, sensor data from one or more sensors. The one or
more sensors are a disposed in proximity to one or more abrasive
products. The one or more sensors are configured to collect
abrasion operational data associated with an abrasive operation
involving the one or more abrasive products. The one or more
abrasive products are located across a plurality of enterprises.
The computer-implemented method further includes training, using
the computing device, a machine learning system to determine
product-specific information or workpiece-specific information of
the one or more abrasive products based on the sensor data. The
computer-implemented method additionally includes providing the
trained machine learning system using the computing device.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1A includes a perspective view illustration of a shaped
abrasive particle, according to an example embodiment.
[0011] FIG. 1B includes a top-down illustration of the shaped
abrasive particle of FIG. 1A, according to an example
embodiment.
[0012] FIG. 1C includes a perspective view illustration of a shaped
abrasive particle, according to an example embodiment.
[0013] FIG. 2A includes a perspective view illustration of a
controlled height abrasive particle (CHAP), according to an example
embodiment.
[0014] FIG. 2B includes a perspective view illustration of a
non-shaped particle, according to an example embodiment.
[0015] FIG. 3 includes a cross-sectional illustration of a coated
abrasive article incorporating particulate material, according to
an example embodiment.
[0016] FIG. 4 includes a top view of a portion of a coated
abrasive, according to an example embodiment.
[0017] FIG. 5 illustrates a cross-sectional of a portion of a
coated abrasive, according to an example embodiment.
[0018] FIG. 6 includes a perspective view illustration of a bonded
abrasive article, according to an example embodiment.
[0019] FIG. 7A illustrates a cross-sectional view of an abrasive
article, according to an example embodiment.
[0020] FIG. 7B illustrates a cross-sectional view of an electronic
assembly, according to an example embodiment.
[0021] FIG. 8A illustrates a top-down view of a releasable coupling
of an electronic assembly on a body, according to an example
embodiment.
[0022] FIG. 8B illustrates an abrasive system, according to an
example embodiment.
[0023] FIGS. 9A-9C illustrate microscopic interactions, according
to an example embodiment.
[0024] FIG. 10 illustrates a block diagram of a computing device,
according to an example embodiment.
[0025] FIG. 11 illustrates an arrangement of a machine learning
platform, according to an example embodiment.
[0026] FIG. 12 depicts a scenario containing an enterprise, a
machine learning system, an input request, and output predictions,
according to an example embodiment.
[0027] FIG. 13 illustrates a scenario representing input devices to
a machine learning system, according to an example embodiment.
[0028] FIG. 14 depicts devices that receive predictions from a
machine learning system, according to an example embodiment.
[0029] FIG. 15 is a flowchart of method, in accordance with example
embodiments.
[0030] FIG. 16 is a flowchart of method, in accordance with example
embodiments.
[0031] FIG. 17 is a flowchart of method, in accordance with example
embodiments.
[0032] FIG. 18A illustrates an abrasive wheel, according to an
example embodiment.
[0033] FIG. 18B illustrates an abrasive wheel, according to an
example embodiment.
[0034] FIG. 19 illustrates components of a tag, according to
example embodiments.
[0035] FIG. 20 illustrates a communication environment, according
to an example embodiment.
[0036] FIG. 21 illustrates a communication environment, according
to an example embodiment.
[0037] FIG. 22 illustrates a method, according to an example
embodiment.
[0038] FIG. 23 illustrates a state diagram, according to an example
embodiment.
[0039] FIGS. 24A-24C illustrate views of a mobile device, according
to an example embodiment.
[0040] FIG. 25 illustrates a model, according to an example
embodiment.
[0041] FIG. 26 illustrates a view of a web application, according
to an example embodiment.
[0042] FIG. 27 illustrates several displays of a wearable device,
according to an example embodiment.
[0043] FIGS. 28A-28C illustrate panes of an analytics platform,
according to an example embodiment,
[0044] FIG. 29 illustrates a graph, according to an example
embodiment.
[0045] FIG. 30 illustrates a graph, according to an example
embodiment.
DETAILED DESCRIPTION
[0046] Example methods, devices, and systems are described herein.
It should be understood that the words "example" and "exemplary"
are used herein to mean "serving as an example, instance, or
illustration." Any embodiment or feature described herein as being
an "example" or "exemplary" is not necessarily to be construed as
preferred or advantageous over other embodiments or features. Other
embodiments can be utilized, and other changes can be made, without
departing from the scope of the subject matter presented
herein.
[0047] Thus, the example embodiments described herein are not meant
to be limiting. Aspects of the present disclosure, as generally
described herein, and illustrated in the figures, can be arranged,
substituted, combined, separated, and designed in a wide variety of
different configurations, all of which are contemplated herein.
[0048] Further, unless context suggests otherwise, the features
illustrated in each of the figures may be used in combination with
one another. Thus, the figures should be generally viewed as
component aspects of one or more overall embodiments, with the
understanding that not all illustrated features are necessary for
each embodiment.
I. Overview
[0049] Abrasive products are used extensively in a wide variety of
industrial and domestic operations, ranging from home improvement
projects to highly technical precision engineering. With the
assistance of these products, operators can perform grinding,
polishing, buffing, and other operations to shape and
surface-finish many different types of materials.
[0050] Often, an abrasive product manufacturer collects operational
data from customers to improve the productivity and safety of their
abrasive products. For instance, a manufacturer can equip an
abrasive product with sensors to generate data streams on inputs
(e.g., components that form the product), operations (e.g., the
power, speed, and/or vibration of the product), and outputs (e.g.,
the end-material surface-finish) of the abrasive product. By
combining multiple data streams, for example, through Internet of
Things (IoT) aggregation tools, the manufacturer can acquire
diagnostic information from a wide range of abrasive products,
enhancing the manufacturer's ability to perform process monitoring,
address customer issues, and improve the development of future
abrasive products.
[0051] To provide further value, it may be beneficial for the
manufacturer to convert diagnostic information into a form easily
usable for customers. For example, given that a customer can be an
enterprise, a production manager from the enterprise may be
concerned with productivity and quality information of an abrasive
product, whereas an operator from the enterprise may be concerned
with real-time safety information. As such, it may be advantageous
for the manufacturer to convert the diagnostic information into a
text notification and transmit the notification to graphical
interfaces used by the production manager and operator.
[0052] To efficiently convert and transmit the diagnostic
information, the manufacturer may benefit from a remotely-hosted
platform that can understand the individuals/entities operating
within the enterprise and distribute, in real-time, diagnostic
information to the relevant individuals/entities. The goal of such
a platform would be to develop a predictive intelligence and
analytical framework for a customer's procedures so that the
customer can focus on producing high-value materials with the
products rather than wasting time analyzing abrasive product
data.
[0053] In order to achieve this goal, a machine learning platform
is described herein, which may intelligently provide predictive
analytics to customers of an abrasive product manufacturer. Such a
machine learning platform can be hosted remotely from customers,
but may access data and services from the customers by way of
secure connections. The machine learning platform may be web-based
and accessible from a variety of Internet-enabled client devices.
For example, the machine learning platform could have a mobile
applications component (iOS/Android) and a web services component
that allows customers to easily access the features provided by the
platform.
[0054] Such a machine learning platform may have several desirable
capabilities and characteristics. For example, by utilizing
aggregate diagnostic information across multiple customers, the
machine learning platform can develop deep insights that provide
real-time feedback to recommend and/or adjust customer operations
and forecasted statistics in real-time to drive a customer's future
business decisions. These characteristics of the machine learning
platform may also be leveraged by the manufacturer to develop new
business models, including abrasive services and abrasive products,
thereby driving further growth for the manufacturer. Other
features, functionality, and benefits of such a machine learning
platform may exist and will be appreciated and understood from the
discussion below.
[0055] Accordingly, disclosed herein are methods and systems for
using abrasive operational data indicative of a behavior of an
abrasive product. As described herein, the abrasive operational
data could be sent to a machine learning platform to train one or
more machine learning models. Each machine learning model may be
configured to predict one or more behaviors of an abrasive product
based on the abrasive operational data. The machine learning
platform may transmit to the interface on the abrasive product, to
a mobile computing device, or to an analytics platform
product-specific information or workpiece-specific information
related to the abrasive product. This information may include
providing ergonomic recommendations to an operator using the
product, determining the end of life of the product, and/or
determining operational improvements (e.g., workflow best
practices).
II. Example Abrasive Particles
[0056] As used herein, the term abrasive tool includes any tool
configured to be used with an abrasive article. An abrasive article
can include a fixed abrasive article including at least a substrate
and abrasive particles connected to (e.g., contained within or
overlying) the substrate. The abrasive articles of the embodiments
herein can be bonded abrasives, coated abrasive, non-woven
abrasives, thin wheels, cut-off wheels, reinforced abrasive
articles, superabrasives, single-layered abrasive articles and the
like. Such abrasive articles can include one or more various types
of abrasive particles, including for example, but not limited to,
shaped abrasive particles, constant height abrasive particles,
unshaped abrasive particles (e.g., crushed, extruded, or exploded
abrasive particles) and the like.
[0057] FIG. 1A includes a perspective view illustration of a shaped
abrasive particle in accordance with an embodiment. The shaped
abrasive particle 100 can include a body 101 including a major
surface 102, a major surface 103, and a side surface 104 extending
between the major surfaces 102 and 103. As illustrated in FIG. 1A,
the body 101 of the shaped abrasive particle 100 can be a
thin-shaped body, wherein the major surfaces 102 and 103 are larger
than the side surface 104. Moreover, the body 101 can include a
longitudinal axis 110 extending from a point to a base and through
the midpoint 150 on a major surface 102 or 103. The longitudinal
axis 110 can define the longest dimension of the body along a major
surface and through the midpoint 150 of the major surface 102.
[0058] In certain particles, if the midpoint of a major surface of
the body is not readily apparent, one may view the major surface
top-down, draw a closest-fit circle around the two-dimensional
shape of the major surface and use the center of the circle as the
midpoint of the major surface.
[0059] FIG. 1B includes a top-down illustration of the shaped
abrasive particle of FIG. 1A. Notably, the body 101 includes a
major surface 102 having a triangular two-dimensional shape. The
circle 160 is drawn around the triangular shape to facilitate
location of the midpoint 150 on the major surface 102.
[0060] Referring again to FIG. 1A, the body 101 can further include
a lateral axis 111 defining a width of the body 101 extending
generally perpendicular to the longitudinal axis 110 on the same
major surface 102. Finally, as illustrated, the body 101 can
include a vertical axis 112, which in the context of thin shaped
bodies can define a height (or thickness) of the body 101. For
thin-shaped bodies, the length of the longitudinal axis 110 is
greater than the vertical axis 112. As illustrated, the thickness
along the vertical axis 112 can extend along the side surface 104
between the major surfaces 102 and 103 and perpendicular to the
plane defined by the longitudinal axis 110 and lateral axis 111. It
will be appreciated that reference herein to length, width, and
height of the abrasive particles may be reference to average values
taken from a suitable sampling size of abrasive particles of a
larger group, including for example, a group of abrasive particle
affixed to a fixed abrasive.
[0061] The shaped abrasive particles of the embodiments herein,
including thin shaped abrasive particles can have a primary aspect
ratio of length:width such that the length can be greater than or
equal to the width. Furthermore, the length of the body 101 can be
greater than or equal to the height. Finally, the width of the body
101 can be greater than or equal to the height. In accordance with
an embodiment, the primary aspect ratio of length:width can be at
least 1:1, such as at least 1.1:1, at least 1.2:1, at least 1.5:1,
at least 1.8:1, at least 2:1, at least 3:1, at least 4:1, at least
5:1, at least 6:1, or even at least 10:1. In another non-limiting
embodiment, the body 101 of the shaped abrasive particle can have a
primary aspect ratio of length:width of not greater than 100:1, not
greater than 50:1, not greater than 10:1, not greater than 6:1, not
greater than 5:1, not greater than 4:1, not greater than 3:1, not
greater than 2:1, or even not greater than 1:1. It will be
appreciated that the primary aspect ratio of the body 101 can be
within a range including any of the minimum and maximum ratios
noted above.
[0062] However, in certain other embodiments, the width can be
greater than the length. For example, in those embodiments wherein
the body 101 is an equilateral triangle, the width can be greater
than the length. In such embodiments, the primary aspect ratio of
length:width can be at least 1:1.1 or at least 1:1.2 or at least
1:1.3 or at least 1:1.5 or at least 1:1.8 or at least 1:2 or at
least 1:2.5 or at least 1:3 or at least 1:4 or at least 1:5 or at
least 1:10. Still, in a non-limiting embodiment, the primary aspect
ratio length:width can be not greater than 1:100 or not greater
than 1:50 or not greater than 1:25 or not greater than 1:10 or not
greater than 5:1 or not greater than 3:1. It will be appreciated
that the primary aspect ratio of the body 101 can be within a range
including any of the minimum and maximum ratios noted above.
[0063] Furthermore, the body 101 can have a secondary aspect ratio
of width:height that can be at least 1:1, such as at least 1.1:1,
at least 1.2:1, at least 1.5:1, at least 1.8:1, at least 2:1, at
least 3:1, at least 4:1, at least 5:1, at least 8:1, or even at
least 10:1. Still, in another non-limiting embodiment, the
secondary aspect ratio width:height of the body 101 can be not
greater than 100:1, such as not greater than 50:1, not greater than
10:1, not greater than 8:1, not greater than 6:1, not greater than
5:1, not greater than 4:1, not greater than 3:1, or even not
greater than 2:1. It will be appreciated the secondary aspect ratio
of width:height can be within a range including any of the minimum
and maximum ratios of above.
[0064] In another embodiment, the body 101 can have a tertiary
aspect ratio of length:height that can be at least 1.1:1, such as
at least 1.2:1, at least 1.5:1, at least 1.8:1, at least 2:1, at
least 3:1, at least 4:1, at least 5:1, at least 8:1, or even at
least 10:1. Still, in another non-limiting embodiment, the tertiary
aspect ratio length:height of the body 101 can be not greater than
100:1, such as not greater than 50:1, not greater than 10:1, not
greater than 8:1, not greater than 6:1, not greater than 5:1, not
greater than 4:1, not greater than 3:1. It will be appreciated that
the tertiary aspect ratio the body 101 can be within a range
including any of the minimum and maximum ratios and above.
[0065] The abrasive particles of the embodiments herein, including
the shaped abrasive particles can include a crystalline material,
and more particularly, a polycrystalline material. Notably, the
polycrystalline material can include abrasive grains. In one
embodiment, the body of the abrasive particle, including for
example, the body of a shaped abrasive particle can be essentially
free of an organic material, such as, a binder. In at least one
embodiment, the abrasive particles can consist essentially of a
polycrystalline material. In another embodiment, the abrasive
particles, such as shaped abrasive particles can be free of silane,
and particularly, may not have a silane coating.
[0066] The abrasive particles may be made of certain material,
including but not limited to nitrides, oxides, carbides, borides,
oxynitrides, oxyborides, diamond, carbon-containing materials, and
a combination thereof. In particular instances, the abrasive
particles can include an oxide compound or complex, such as
aluminum oxide, zirconium oxide, titanium oxide, yttrium oxide,
chromium oxide, strontium oxide, silicon oxide, magnesium oxide,
rare-earth oxides, and a combination thereof. The abrasive
particles may be superabrasive particles.
[0067] In one particular embodiment, the abrasive particles can
include a majority content of alumina. For at least one embodiment,
the abrasive particle can include at least 80 wt % alumina, such as
at least 90 wt % alumina, at least 91 wt % alumina, at least 92 wt
% alumina, at least 93 wt % alumina, at least 94 wt % alumina, at
least 95 wt % alumina, at least 96 wt % alumina, or even at least
97 wt % alumina. Still, in at least one particular embodiment, the
abrasive particle can include not greater than 99.5 wt % alumina,
such as not greater than 99 wt % alumina, not greater than 98.5 wt
% alumina, not greater than 97.5 wt % alumina, not greater than 97
wt % alumina not greater than 96 wt % alumina, or even not greater
than 94 wt % alumina. It will be appreciated that the abrasive
particles of the embodiments herein can include a content of
alumina within a range including any of the minimum and maximum
percentages noted above. Moreover, in particular instances, the
shaped abrasive particles can be formed from a seeded sol-gel. In
at least one embodiment, the abrasive particles can consist
essentially of alumina and certain dopant materials as described
herein.
[0068] The abrasive particles of the embodiments herein can include
particularly dense bodies, which may be suitable for use as
abrasives. For example, the abrasive particles may have a body
having a density of at least 95% theoretical density, such as at
least 96% theoretical density, at least 97% theoretical density, at
least 98% theoretical density or even at least 99% theoretical
density.
[0069] The abrasive grains (i.e., crystallites) contained within
the body of the abrasive particles may have an average grain size
(i.e., average crystal size) that is generally not greater than
about 100 microns. In other embodiments, the average grain size can
be less, such as not greater than about 80 microns or not greater
than about 50 microns or not greater than about 30 microns or not
greater than about 20 microns or not greater than about 10 microns
or not greater than 6 microns or not greater than 5 microns or not
greater than 4 microns or not greater than 3.5 microns or not
greater than 3 microns or not greater than 2.5 microns or not
greater than 2 microns or not greater than 1.5 microns or not
greater than 1 micron or not greater than 0.8 microns or not
greater than 0.6 microns or not greater than 0.5 microns or not
greater than 0.4 microns or not greater than 0.3 microns or even
not greater than 0.2 microns. Still, the average grain size of the
abrasive grains contained within the body of the abrasive particle
can be at least about 0.01 microns, such as at least about 0.05
microns or at least about 0.06 microns or at least about 0.07
microns or at least about 0.08 microns or at least about 0.09
microns or at least about 0.1 microns or at least about 0.12
microns or at least about 0.15 microns or at least about 0.17
microns or at least about 0.2 microns or even at least about 0.3
microns. It will be appreciated that the abrasive particles can
have an average grain size (i.e., average crystal size) within a
range between any of the minimum and maximum values noted
above.
[0070] The average grain size (i.e., average crystal size) can be
measured based on the uncorrected intercept method using scanning
electron microscope (SEM) photomicrographs. Samples of abrasive
grains are prepared by making a bakelite mount in epoxy resin then
polished with diamond polishing slurry using a Struers Tegramin 30
polishing unit. After polishing the epoxy is heated on a hot plate,
the polished surface is then thermally etched for 5 minutes at
150.degree. C. below sintering temperature. Individual grains (5-10
grits) are mounted on the SEM mount then gold coated for SEM
preparation. SEM photomicrographs of three individual abrasive
particles are taken at approximately 50,000.times. magnification,
then the uncorrected crystallite size is calculated using the
following steps: 1) draw diagonal lines from one corner to the
opposite corner of the crystal structure view, excluding black data
band at bottom of photo 2) measure the length of the diagonal lines
as L1 and L2 to the nearest 0.1 centimeters; 3) count the number of
grain boundaries intersected by each of the diagonal lines, (i.e.,
grain boundary intersections I1 and I2) and record this number for
each of the diagonal lines, 4) determine a calculated bar number by
measuring the length (in centimeters) of the micron bar (i.e., "bar
length") at the bottom of each photomicrograph or view screen, and
divide the bar length (in microns) by the bar length (in
centimeters); 5) add the total centimeters of the diagonal lines
drawn on photomicrograph (L1+L2) to obtain a sum of the diagonal
lengths; 6) add the numbers of grain boundary intersections for
both diagonal lines (I1+I2) to obtain a sum of the grain boundary
intersections; 7) divide the sum of the diagonal lengths (L1+L2) in
centimeters by the sum of grain boundary intersections (I1+I2) and
multiply this number by the calculated bar number. This process is
completed at least three different times for three different,
randomly selected samples to obtain an average crystallite
size.
[0071] In accordance with certain embodiments, certain abrasive
particles can be composite articles including at least two
different types of grains within the body of the abrasive particle.
It will be appreciated that different types of grains are grains
having different compositions with regard to each other. For
example, the body of the abrasive particle can be formed such that
is includes at least two different types of grains, wherein the two
different types of grains can be nitrides, oxides, carbides,
borides, oxynitrides, oxyborides, diamond, and a combination
thereof.
[0072] In accordance with an embodiment, the abrasive particles can
have an average particle size, as measured by the largest dimension
(i.e., length) of at least about 100 microns. In fact, the abrasive
particles can have an average particle size of at least about 150
microns, such as at least about 200 microns, at least about 300
microns, at least about 400 microns, at least about 500 microns, at
least about 600 microns, at least about microns, at least about 800
microns, or even at least about 900 microns. Still, the abrasive
particles of the embodiments herein can have an average particle
size that is not greater than about 5 mm, such as not greater than
about 3 mm, not greater than about 2 mm, or even not greater than
about 1.5 mm. It will be appreciated that the abrasive particles
can have an average particle size within a range between any of the
minimum and maximum values noted above.
[0073] FIG. 1A includes an illustration of a shaped abrasive
particle having a two-dimensional shape as defined by the plane of
the upper major surface 102 or major surface 103, which has a
generally triangular two-dimensional shape. It will be appreciated
that the shaped abrasive particles of the embodiments herein are
not so limited and can include other two-dimensional shapes. For
example, the shaped abrasive particles of the embodiment herein can
include particles having a body with a two-dimensional shape as
defined by a major surface of the body from the group of shapes
including polygons, regular polygons, irregular polygons, irregular
polygons including arcuate or curved sides or portions of sides,
ellipsoids, numerals, Greek alphabet characters, Latin alphabet
characters, Russian alphabet characters, Kanji characters, complex
shapes having a combination of polygons shapes, shapes including a
central region and a plurality of arms (e.g., at least three arms)
extending from a central region (e.g., star shapes), and a
combination thereof. Particular polygonal shapes include
rectangular, trapezoidal, quadrilateral, pentagonal, hexagonal,
heptagonal, octagonal, nonagonal, decagonal, and any combination
thereof. In another instance, the finally-formed shaped abrasive
particles can have a body having a two-dimensional shape such as an
irregular quadrilateral, an irregular rectangle, an irregular
trapezoid, an irregular pentagon, an irregular hexagon, an
irregular heptagon, an irregular octagon, an irregular nonagon, an
irregular decagon, and a combination thereof. An irregular
polygonal shape is one where at least one of the sides defining the
polygonal shape is different in dimension (e.g., length) with
respect to another side. As illustrated in other embodiments
herein, the two-dimensional shape of certain shaped abrasive
particles can have a particular number of exterior points or
external corners. For example, the body of the shaped abrasive
particles can have a two-dimensional polygonal shape as viewed in a
plane defined by a length and width, wherein the body comprises a
two-dimensional shape having at least 4 exterior points (e.g., a
quadrilateral), at least 5 exterior points (e.g., a pentagon), at
least 6 exterior points (e.g., a hexagon), at least 7 exterior
points (e.g., a heptagon), at least 8 exterior points (e.g., an
octagon), at least 9 exterior points (e.g., a nonagon), and the
like.
[0074] FIG. 1C includes a perspective view illustration of a shaped
abrasive particle according to another embodiment. Notably, the
shaped abrasive particle 170 can include a body 171 including a
surface 172 and a surface 173, which may be referred to as end
surfaces 172 and 173. The body can further include major surfaces
174, 175, 176, 177 extending between and coupled to the end
surfaces 172 and 173. The shaped abrasive particle of FIG. 1C is an
elongated shaped abrasive particle having a longitudinal axis 180
that extends along the major surface 175 and through the midpoint
184 between the end surfaces 172 and 173. For particles having an
identifiable two-dimensional shape, such as the shaped abrasive
particles of FIGS. 1A-C, the longitudinal axis is the dimension
that would be readily understood to define the length of the body
through the midpoint on a major surface. For example, in FIG. 1C,
the longitudinal axis 180 of the shaped abrasive particle 170
extends between the end surfaces 172 and 173 parallel to the edges
defining the major surface as shown. Such a longitudinal axis is
consistent with how one would define the length of a rod. Notably,
the longitudinal axis 180 does not extend diagonally between the
corners joining the end surfaces 172 and 173 and the edges defining
the major surface 175, even though such a line may define the
dimension of greatest length. To the extent that a major surface
has undulations or minor imperfections from a perfectly planar
surface, the longitudinal axis can be determined using a top-down,
two-dimensional image that ignores the undulations.
[0075] It will be appreciated that the surface 175 is selected for
illustrating the longitudinal axis 180, because the body 171 has a
generally square cross-sectional contour as defined by the end
surfaces 172 and 173. As such, the surfaces 174, 175, 176, and 177
can be approximately the same size relative to each other. However,
in the context of other elongated abrasive particles, the surfaces
172 and 173 can have a different shape, for example, a rectangular
shape, and as such, at least one of the surfaces 174, 175, 176, and
177 may be larger relative to the others. In such instances, the
largest surface can define the major surface and the longitudinal
axis would extend along the largest of those surfaces through the
midpoint 184 and may extend parallel to the edges defining the
major surface. As further illustrated, the body 171 can include a
lateral axis 181 extending perpendicular to the longitudinal axis
180 within the same plane defined by the surface 175. As further
illustrated, the body 171 can further include a vertical axis 182
defining a height of the abrasive particle, were in the vertical
axis 182 extends in a direction perpendicular to the plane defined
by the longitudinal axis 180 and lateral axis 181 of the surface
175.
[0076] It will be appreciated that like the thin shaped abrasive
particle of FIGS. 1A-B, the elongated shaped abrasive particle of
FIG. 1C can have various two-dimensional shapes, such as those
defined with respect to the shaped abrasive particle of FIGS. 1A-B.
The two-dimensional shape of the body 171 can be defined by the
shape of the perimeter of the end surfaces 172 and 173. The
elongated shaped abrasive particle 170 can have any of the
attributes of the shaped abrasive particles of the embodiments
herein.
[0077] FIG. 2A includes a perspective view illustration of a
controlled height abrasive particle according (CHAP) to an
embodiment. As illustrated, the CHAP 200 can include a body 201
including a first major surface 202, a second major surface 203,
and a side surface 204 extending between the first and second major
surfaces 202 and 203. As illustrated in FIG. 2A, the body 201 can
have a thin, relatively planar shape, wherein the first and second
major surfaces 202 and 203 are larger than the side surface 204 and
substantially parallel to each other. Moreover, the body 201 can
include a longitudinal axis 210 extending through the midpoint 220
and defining a length of the body 201. The body 201 can further
include a lateral axis 211 on the first major surface 202, which
extends through the midpoint 220 of the first major surface 202,
perpendicular to the longitudinal axis 210, and defining a width of
the body 201.
[0078] The body 201 can further include a vertical axis 212, which
can define a height (or thickness) of the body 201. As illustrated,
the vertical axis 212 can extend along the side surface 204 between
the first and second major surfaces 202 and 203 in a direction
generally perpendicular to the plane defined by the axes 210 and
211 on the first major surface. For thin-shaped bodies, such as the
CHAP 200 illustrated in FIG. 2A, the length can be equal to or
greater than the width and the length can be greater than the
height. It will be appreciated that reference herein to length,
width, and height of the abrasive particles may be referenced to
average values taken from a suitable sampling size of abrasive
particles of a batch of abrasive particles.
[0079] Unlike the shaped abrasive particles of FIGS. 1A, 1B, and
1C, the CHAP 200 of FIG. 2A does not have a readily identifiable
two-dimensional shape based on the perimeter of the first or second
major surfaces 202 and 203. Such abrasive particles may be formed
in a variety of ways, including but not limited to, fracturing of a
thin layer of material to form abrasive particles having a
controlled height but with irregularly formed, planar, major
surfaces. For such particles, the longitudinal axis is defined as
the longest dimension on the major surface that extends through a
midpoint on the surface. To the extent that the major surface has
undulations, the longitudinal axis can be determined using a
top-down, two-dimensional image that ignores the undulations.
Moreover, as noted above in FIG. 1B, a closest-fit circle may be
used to identify the midpoint of the major surface and
identification of the longitudinal and lateral axes.
[0080] FIG. 2B includes an illustration of a non-shaped particle,
which may be an elongated, non-shaped abrasive particle or a
secondary particle, such as a diluent grain, a filler, an
agglomerate, or the like. Shaped abrasive particles may be formed
through particular processes, including molding, printing, casting,
extrusion, and the like. Shaped abrasive particles can be formed
such that the each particle has substantially the same arrangement
of surfaces and edges relative to each other. For example, a group
of shaped abrasive particles generally have the same arrangement
and orientation and or two-dimensional shape of the surfaces and
edges relative to each other. As such, the shaped abrasive
particles have a relatively high shape fidelity and consistency in
the arrangement of the surfaces and edges relative to each other.
Moreover, constant height abrasive particles (CHAPs) can also be
formed through particular processes that facilitate formation of
thin-shaped bodies that can have irregular two-dimensional shapes
when viewing the major surface top-down. CHAPs can have less shape
fidelity than shaped abrasive particles, but can have substantially
planar and parallel major surfaces separated by a side surface.
[0081] By contrast, non-shaped particles can be formed through
different processes and have different shape attributes compared to
shaped abrasive particles and CHAPs. For example, non-shaped
particles are typically formed by a comminution process wherein a
mass of material is formed and then crushed and sieved to obtain
abrasive particles of a certain size. However, a non-shaped
particle will have a generally random arrangement of surfaces and
edges, and generally will lack any recognizable two-dimensional or
three dimensional shape in the arrangement of the surfaces and
edges. Moreover, non-shaped particles do not necessarily have a
consistent shape with respect to each other, and therefore have a
significantly lower shape fidelity compared to shaped abrasive
particles or CHAPs. The non-shaped particles generally are defined
by a random arrangement of surfaces and edges for each particle and
with respect to other non-shaped particles
[0082] FIG. 2B includes a perspective view illustration of a
non-shaped particle. The non-shaped particle 250 can have a body
251 including a generally random arrangement of edges 255 extending
along the exterior surface of the body 251. The body can further
include a longitudinal axis 252 defining the longest dimension of
the particle. The longitudinal axis 252 defines the longest
dimension of the body as viewed in two-dimensions. Thus, unlike
shaped abrasive particles and CHAPs, where the longitudinal axis is
measured on the major surface, the longitudinal axis of a
non-shaped particle is defined by the points on the body furthest
from each other as the particle is viewed in two-dimensions using
an image or vantage that provides a view of the particle's longest
dimension. That is, an elongated particle, but non-shaped
particles, such as illustrated in FIG. 2B, should be viewed in a
perspective that makes the longest dimension apparent to properly
evaluate the longitudinal axis. The body 251 can further include a
lateral axis 253 extending perpendicular to the longitudinal axis
252 and defining a width of the particle. The lateral axis 253 can
extend perpendicular to the longitudinal axis 252 through the
midpoint 256 of the longitudinal axis in the same plane used to
identify the longitudinal axis 252. The abrasive particle may have
a height (or thickness) as defined by the vertical axis 254. The
vertical axis 254 can extend through the midpoint 256 but in a
direction perpendicular to the plane used to define the
longitudinal axis 252 and lateral axis 253. To evaluate the height,
one may have to change the perspective of view of the abrasive
particle to look at the particle from a different vantage than is
used to evaluate the length and width.
[0083] As will be appreciated, the abrasive particle can have a
length defined by the longitudinal axis 252, a width defined by the
lateral axis 253, and a vertical axis 254 defining a height. As
will be appreciated, the body 251 can have a primary aspect ratio
of length:width such that the length is equal to or greater than
the width. Furthermore, the length of the body 251 can be equal to
or greater than or equal to the height. Finally, the width of the
body 251 can be greater than or equal to the height. In accordance
with an embodiment, the primary aspect ratio of length:width can be
at least 1.1:1, at least 1.2:1, at least 1.5:1, at least 1.8:1, at
least 2:1, at least 3:1, at least 4:1, at least 5:1, at least 6:1,
or even at least 10:1. In another non-limiting embodiment, the body
251 of the elongated shaped abrasive particle can have a primary
aspect ratio of length:width of not greater than 100:1, not greater
than 50:1, not greater than 10:1, not greater than 6:1, not greater
than 5:1, not greater than 4:1, not greater than 3:1, or even not
greater than 2:1. It will be appreciated that the primary aspect
ratio of the body 251 can be within a range including any of the
minimum and maximum ratios noted above.
[0084] Furthermore, the body 251 can include a secondary aspect
ratio of width:height that can be at least 1.1:1, such as at least
1.2:1, at least 1.5:1, at least 1.8:1, at least 2:1, at least 3:1,
at least 4:1, at least 5:1, at least 8:1, or even at least 10:1.
Still, in another non-limiting embodiment, the secondary aspect
ratio width:height of the body 251 can be not greater than 100:1,
such as not greater than 50:1, not greater than 10:1, not greater
than 8:1, not greater than 6:1, not greater than 5:1, not greater
than 4:1, not greater than 3:1, or even not greater than 2:1. It
will be appreciated the secondary aspect ratio of width:height can
be with a range including any of the minimum and maximum ratios of
above.
[0085] In another embodiment, the body 251 can have a tertiary
aspect ratio of length:height that can be at least 1.1:1, such as
at least 1.2:1, at least 1.5:1, at least 1.8:1, at least 2:1, at
least 3:1, at least 4:1, at least 5:1, at least 8:1, or even at
least 10:1. Still, in another non-limiting embodiment, the tertiary
aspect ratio length:height of the body 251 can be not greater than
100:1, such as not greater than 50:1, not greater than 10:1, not
greater than 8:1, not greater than 6:1, not greater than 5:1, not
greater than 4:1, not greater than 3:1, It will be appreciated that
the tertiary aspect ratio the body 251 can be with a range
including any of the minimum and maximum ratios and above.
[0086] The non-shaped particle 250 can have any of the attributes
of abrasive particles described in the embodiments herein,
including for example but not limited to, composition,
microstructural features (e.g., average grain size), hardness,
porosity, and the like.
[0087] The abrasive articles of the embodiments herein may
incorporate different types of particles, including different types
of abrasive particles, different types of secondary particles, or
any combination thereof. For example, in one embodiment, the coated
abrasive article can include a first type of abrasive particle
comprising shaped abrasive particles and a second type of abrasive
particle. The second type of abrasive particle may be a shaped
abrasive particle or a non-shaped abrasive particle.
[0088] FIG. 3 includes a cross-sectional illustration of a coated
abrasive article incorporating particulate material in accordance
with an embodiment. As illustrated, the coated abrasive 300 can
include a substrate 301 and a make coat 303 overlying a surface of
the substrate 301. The coated abrasive 300 can further include a
first type of particulate material 305 in the form of a first type
of shaped abrasive particle, a second type of particulate material
306 in the form of a second type of shaped abrasive particle, and a
third type of particulate material 307, which may be a secondary
particle, such as a diluent abrasive particle, a non-shaped
abrasive particle, a filler, and the like. The coated abrasive 300
may further include size coat 304 overlying and bonded to the
abrasive particulate materials 305, 306, 307, and the size coat
304. It will be appreciated that other layers or materials may be
added to the substrate other component layers, including for
example, but not limited to, a frontfill, a backfill, and the like
as known to those of ordinary skill in the art.
[0089] According to one embodiment, the substrate 301 can include
an organic material, inorganic material, and a combination thereof.
In certain instances, the substrate 301 can include a woven
material. However, the substrate 301 may be made of a non-woven
material. Particularly suitable substrate materials can include
organic materials, including polymers, and particularly, polyester,
polyurethane, polypropylene, polyimides such as KAPTON from DuPont,
paper or any combination thereof. Some suitable inorganic materials
can include metals, metal alloys, and particularly, foils of
copper, aluminum, steel, and a combination thereof. In the context
of a non-woven substrate, which may be open web of fibers, the
abrasive particles may be adhered to the fibers by one or more
adhesive layers. In such non-woven products, the abrasive particles
are coating the fibers, but not necessarily forming a conformal
layer overlying a major surface of the substrate as illustrated in
FIG. 3. It will be appreciated that such non-woven products are
included in the embodiments herein.
[0090] The make coat 303 can be applied to the surface of the
substrate 301 in a single process, or alternatively, the
particulate materials 305, 306, 307 can be combined with a make
coat 303 material and the combination of the make coat 303 and
particulate materials 305-307 can be applied as a mixture to the
surface of the substrate 301. In certain instances, controlled
deposition or placement of the particles 305-307 in the make coat
may be better suited by separating the processes of applying the
make coat 303 from the deposition of the abrasive particulate
materials 305-307 in the make coat 303. Still, it is contemplated
that such processes may be combined. Suitable materials of the make
coat 303 can include organic materials, particularly polymeric
materials, including for example, polyesters, epoxy resins,
polyurethanes, polyamides, polyacrylates, polymethacrylates,
polyvinylchlorides, polyethylene, polysiloxane, silicones,
cellulose acetates, nitrocellulose, natural rubber, starch,
shellac, and mixtures thereof. In one embodiment, the make coat 303
can include a polyester resin. The coated substrate can then be
heated in order to cure the resin and the abrasive particulate
material to the substrate. In general, the coated substrate 301 can
be heated to a temperature of between about 100.degree. C. to less
than about 250.degree. C. during this curing process.
[0091] The particulate materials 305-307 can include different
types of abrasive particles according to embodiments herein. The
different types of abrasive particles can include different types
of shaped abrasive particles, different types of secondary
particles or a combination thereof. The different types of
particles can be different from each other in composition,
two-dimensional shape, three-dimensional shape, grain size,
particle size, hardness, friability, agglomeration, and a
combination thereof. As illustrated, the coated abrasive 300 can
include a first type of shaped abrasive particle 305 having a
generally pyramidal shape and a second type of shaped abrasive
particle 306 having a generally triangular two-dimensional shape.
The coated abrasive 300 can include different amounts of the first
type and second type of shaped abrasive particles 305 and 306. It
will be appreciated that the coated abrasive may not necessarily
include different types of shaped abrasive particles, and can
consist essentially of a single type of shaped abrasive particle.
As will be appreciated, the shaped abrasive particles of the
embodiments herein can be incorporated into various fixed abrasives
(e.g., bonded abrasives, coated abrasive, non-woven abrasives, thin
wheels, cut-off wheels, reinforced abrasive articles, and the
like), including in the form of blends, which may include different
types of shaped abrasive particles, secondary particles, and the
like.
[0092] The particles 307 can be secondary particles different than
the first and second types of shaped abrasive particles 305 and
306. For example, the secondary particles 307 can include crushed
abrasive grit representing non-shaped abrasive particles.
[0093] After sufficiently forming the make coat 303 with the
abrasive particulate materials 305-307 contained therein, the size
coat 304 can be formed to overlie and bond the abrasive particulate
material 305 in place. The size coat 304 can include an organic
material, may be made essentially of a polymeric material, and
notably, can use polyesters, epoxy resins, polyurethanes,
polyamides, polyacrylates, polymethacrylates, poly vinyl chlorides,
polyethylene, polysiloxane, silicones, cellulose acetates,
nitrocellulose, natural rubber, starch, shellac, and mixtures
thereof.
III. Example Abrasive Articles
[0094] FIG. 4 includes a top view of a portion of a coated abrasive
according to an embodiment. The coated abrasive 400 can include a
plurality of regions, such as a first region 410, a second region
420, a third region 430 and a fourth region 440. Each of the
regions 410, 420, 430, and 440 can be separated by a channel region
450, wherein the channel region 450 defines a region the backing
that is free of particles. The channel region 450 can have any size
and shape and may be particularly useful for removing swarf and
improved grinding operations. The channel region may have a length
(i.e., longest dimension) and width (i.e., shortest dimension
perpendicular to the length) that is greater than the average
spacing between immediately adjacent abrasive particles within any
of the regions 410, 420, 430, and 440. The channel region 450 is an
optional feature for any of the embodiments herein.
[0095] As further illustrated, the first region 410 can include a
group of shaped abrasive particles 411 having a generally random
rotational orientation with respect to each other. The group of
shaped abrasive particles 411 can be arranged in a random
distribution relative to each other, such that there is no
discernable short-range or long-range order with regard to the
placement of the shaped abrasive particles 411. Notably, the group
of shaped abrasive particles 411 can be substantially homogenously
distributed within the first region 410, such that the formation of
clumps (two or more particles in contact with each other) is
limited. It will be appreciated that the grain weight of the group
of shaped abrasive particles 411 in the first region 410 can be
controlled based on the intended application of the coated
abrasive.
[0096] The second region 420 can include a group of shaped abrasive
particles 421 arranged in a controlled distribution relative to
each other. Moreover, the group of shaped abrasive particles 421
can have a regular and controlled rotational orientation relative
to each other. As illustrated, the group of shaped abrasive
particles 421 can have generally the same rotational orientation as
defined by the same rotational angle on the backing of the coated
abrasive 401. Notably, the group of shaped abrasive particles 421
can be substantially homogenously distributed within the second
region 420, such that the formation of clumps (two or more
particles in contact with each other) is limited. It will be
appreciated that the grain weight of the group of shaped abrasive
particles 421 in the second region 420 can be controlled based on
the intended application of the coated abrasive.
[0097] The third region 430 can include a plurality of groups of
shaped abrasive particles 421 and secondary particles 432. The
group of shaped abrasive particles 431 and secondary particles 432
can be arranged in a controlled distribution relative to each
other. Moreover, the group of shaped abrasive particles 431 can
have a regular and controlled rotational orientation relative to
each other. As illustrated, the group of shaped abrasive particles
431 can have generally one of two types of rotational orientations
on the backing of the coated abrasive 401. Notably, the group of
shaped abrasive particles 431 and secondary particles 432 can be
substantially homogenously distributed within the third region 430,
such that the formation of clumps (two or more particles in contact
with each other) is limited. It will be appreciated that the grain
weight of the group of shaped abrasive particles 431 and secondary
particles 432 in the third region 430 can be controlled based on
the intended application of the coated abrasive.
[0098] The fourth region 440 can include a group of shaped abrasive
particles 441 and secondary particles 442 having a generally random
distribution with respect to each other. Additionally, the group of
shaped abrasive particles 441 can have a random rotational
orientation with respect to each other. The group of shaped
abrasive particles 441 and secondary particles 442 can be arranged
in a random distribution relative to each other, such that there is
no discernable short-range or long-range order. Notably, the group
of shaped abrasive particles 441 and the secondary particles 442
can be substantially homogenously distributed within the fourth
region 440, such that the formation of clumps (two or more
particles in contact with each other) is limited. It will be
appreciated that the grain weight of the group of shaped abrasive
particles 441 and secondary particles 442 in the fourth region 440
can be controlled based on the intended application of the coated
abrasive.
[0099] As illustrated in FIG. 4, the coated abrasive article 400
can include different regions 410, 420, 430, and 440, each of which
can include different groups of particles, such as shaped particles
and secondary particles. The coated abrasive article 400 is
intended to illustrate the different types of groupings,
arrangements and distributions of particles that may be created
using the systems and processes of the embodiments herein. The
illustration is not intended to be limited to only those groupings
of particles and it will be appreciated that coated abrasive
articles can be made including only one region as illustrated in
FIG. 4. It will also be understood that other coated abrasive
articles can be made including a different combination or
arrangement of one or more of the regions illustrated in FIG.
4.
[0100] According to another embodiment, a coated abrasive article
may be formed that includes different groups of abrasive particles,
wherein the different groups have different tilt angles with
respect to each other. For example, as illustrated in FIG. 5, a
cross-sectional illustration of a portion of a coated abrasive is
provided. The coated abrasive 500 can include a backing 501 and a
first group of abrasive particles 502, wherein each of the abrasive
particles in the first group of abrasive particles 502 have a first
average tilt angle. The coated abrasive 500 can further include a
second group of abrasive particles 503, wherein each of the
abrasive particles in the second group of abrasive particles 503
have a second average tilt angle. According to one embodiment the
first group of abrasive particles 502 and the second group of
abrasive particles 503 can be separated by a channel region 505.
Moreover, the first average tilt angle can be different than the
second average tilt angle. In a more particular embodiment, the
first group of abrasive particles may be oriented in an upright
orientation and the second group of abrasive particles may be
oriented in a slanted orientation. Without wishing to be tied to a
particular theory, it is thought that controlled variation of the
tilt angle for different groups of abrasive particles in different
regions of the coated abrasive may facilitate improved performance
of the coated abrasive.
[0101] According to one particular aspect, the content of abrasive
particles overlying the backing can be controlled based on the
intended application. For example, the abrasive particles can be
overlying at least 5% of the total surface area of the backing,
such as at least 10% or at least 20% or at least 30% or at least
40% or at least 50% or at least 60% or at least 70% or at least 80%
or at least 90%. In still another embodiment, the coated abrasive
article may be essentially free of silane.
[0102] Furthermore, the abrasive articles of the embodiments herein
can have a particular content of particles overlying the substrate.
Moreover, it is noted that for certain contents of particles on the
backing, such as open coat densities, the industry has found it
challenging to obtain certain contents of particles in desired
vertical orientations. In one embodiment, the particles can define
an open coat abrasive product having a coating density of particles
(i.e., abrasive particles, secondary particles, or both abrasive
particles and secondary particles) of not greater than about 70
particles/cm.sup.2. In other instances, the density of shaped
abrasive particle per square centimeter of the abrasive article may
be not greater than about 65 particles/cm.sup.2, such as not
greater than about 60 particles/cm.sup.2, not greater than about 55
particles/cm.sup.2, or even not greater than about 50
particles/cm.sup.2. Still, in one non-limiting embodiment, the
density of the open coat coated abrasive using the shaped abrasive
particle herein can be at least about 5 particles/cm.sup.2, or even
at least about 10 particles/cm.sup.2. It will be appreciated that
the density of shaped abrasive particles per square centimeter of
abrasive article can be within a range between any of the above
minimum and maximum values.
[0103] In certain instances, the abrasive article can have an open
coat density of not greater than about 50% of particles (i.e.,
abrasive particles or secondary particles or the total of abrasive
particles and secondary particles) covering the exterior abrasive
surface of the article. In other embodiments, the area of the
abrasive particles relative to the total area of the surface on
which the particles are placed can be not greater than about 40%,
such as not greater than about 30%, not greater than about 25%, or
even not greater than about 20%. Still, in one non-limiting
embodiment, the percentage coating of the particles relative to the
total area of the surface can be at least about 5%, such as at
least about 10%, at least about 15%, at least about 20%, at least
about 25%, at least about 30%, at least about 35%, or even at least
about 40%. It will be appreciated that the percent coverage of the
particles for the total area of abrasive surface can be within a
range between any of the above minimum and maximum values.
[0104] Some abrasive articles may have a particular content of
particles (i.e., abrasive particles or secondary particles or the
total of abrasive particles and secondary particles) for a given
area (e.g., ream, wherein 1 ream=30.66 m.sup.2) of the backing. For
example, in one embodiment, the abrasive article may utilize a
normalized weight of particles of at least about 1 lbs/ream (14.8
grams/m.sup.2), such as at least 5 lbs/ream or at least 10 lbs/ream
or at least about 15 lbs/ream or at least about 20 lbs/ream or at
least about 25 lbs/ream or even at least about 30 lbs/ream. Still,
in one non-limiting embodiment, the abrasive article can include a
normalized weight of particles of not greater than about 90
lbs/ream (1333.8 grams/m.sup.2), such as not greater than 80
lbs/ream or not greater than 70 lbs/ream or not greater than 60
lbs/ream or not greater than about 50 lbs/ream or even not greater
than about 45 lbs/ream. It will be appreciated that the abrasive
articles of the embodiments herein can utilize a normalized weight
of particles within a range between any of the above minimum and
maximum values.
[0105] In certain instances, the abrasive articles can be used on
particular workpieces. A suitable exemplary workpiece can include
an inorganic material, an organic material, a natural material, and
a combination thereof. According to a particular embodiment, the
workpiece can include a metal or metal alloy, such as an iron-based
material, a nickel-based material, and the like. In one embodiment,
the workpiece can be steel, and more particularly, can consist
essentially of stainless steel (e.g., 304 stainless steel).
[0106] In another embodiment, the fixed abrasive article may be a
bonded abrasive, including abrasive particles contained within the
three-dimensional volume of the bond material, which can be
distinct from certain other fixed abrasive articles including, for
example, coated abrasive articles, which generally include a single
layer of abrasive particles contained within a binder, such as a
make coat and/or size coat. Furthermore, coated abrasive articles
generally include a backing as a support for the layer of abrasive
particles and binder. By contrast, bonded abrasive articles are
generally self-supporting articles including a three-dimensional
volume of abrasive particles, bond material, and optionally some
porosity. Bonded abrasive articles may not necessarily include a
substrate, and can be essentially free of a substrate.
[0107] FIG. 6 includes a perspective view illustration of a bonded
abrasive article in accordance with an embodiment. As illustrated,
the bonded abrasive article 620 can have a body 601 of a generally
cylindrical shape including an upper surface 624, a bottom surface
626, and a side surface 603 extending between the upper surface 624
and bottom surface 626. It will be appreciated that the fixed
abrasive article of FIG. 6 is a non-limiting example, and other
shapes of the body may be utilized including, but not limited to,
conical, cup-shaped, depressed center wheels (e.g., T42), and the
like. Finally, as further illustrated, the body 601 can include a
central opening 685 which may be configured to accept an arbor or
shaft for mounting of the body 601 on a machine configured to
rotate the body 601 and facilitate a material removal
operation.
[0108] The bonded abrasive article 620 can have a body 601
including abrasive particles, including for example, the groups of
abrasive particles 605 and 628, contained within the volume of the
body 601. The abrasive particles may be contained within the
three-dimensional volume of the body 601 by a bond material 607
that can extend throughout the three-dimensional volume of the body
601. In accordance with an embodiment, the bond material 607 can
include materials such as vitreous, polycrystalline,
monocrystalline, organic (e.g., resin), metal, metal alloys, and a
combination thereof.
[0109] In a particular embodiment, the abrasive particles may be
encapsulated within the bond material 607. As used herein,
"encapsulated" refers to a condition whereby at least one of the
abrasive particles is fully surrounded by a homogenous, or
generally homogenous, composition of bond material. In an
embodiment, the bonded abrasive article 620 can be essentially free
of a fixation layer. In a particular instance, the bonded abrasive
article 620 can be substantially uniform throughout a volume of the
body 601. In more particular instances, the body 601 can have a
substantially homogenous composition throughout the volume of the
body 601.
[0110] In accordance with an embodiment, the abrasive particles
contained within the bonded abrasive article 620 can include
abrasive materials in accordance with those described in
embodiments herein.
[0111] The bonded abrasive article 620 can include a combination of
abrasive particles, including one or more types of abrasive
particles, such as primary and secondary types of abrasive
particles. Primary and secondary types may refer to the content of
the abrasive particles within the body of the fixed abrasive
article, wherein the primary type abrasive particles are present in
a higher content than the secondary type of abrasive particles. In
other instances, the distinction between primary and secondary
types of abrasive particles may be based upon the position of the
abrasive particle within the body, wherein the primary abrasive
particles may be positioned to conduct an initial stage of material
removal or conduct the majority of material removal compared to the
secondary abrasive particles. In still other instances, the
distinction between primary and secondary abrasive particles may
pertain to the abrasive nature (e.g., hardness, friability,
fracture mechanics, etc.) of the abrasive particles, wherein the
abrasive nature of the primary particles is typically more robust
as compared to the secondary type of abrasive particles. Some
suitable examples of abrasive particles that may be considered as a
secondary type of abrasive particle include diluent particles,
agglomerated particles, unagglomerated particles, naturally
occurring materials (e.g., minerals), synthetic materials, and a
combination thereof.
[0112] In certain instances, the bonded abrasive article 620 can
include a particular content of abrasive particles within the body
601 that may facilitate suitable material removal operations. For
example, the body 601 can include a content of abrasive particles
of at least 0.5 vol % and not greater than 60 vol % for a total
volume of the body.
[0113] Furthermore, the body 601 of the bonded abrasive article 620
can include a particular content of bond material 607 that may
facilitate suitable operation of the bonded abrasive article 620.
For example, the body 601 can include a content of bond material
607 of at least 0.5 vol % and not greater than about 90 vol % for a
total volume of the body.
[0114] In certain instances, the fixed abrasive article can have a
body 601 including a content of porosity. The porosity can extend
throughout at least a portion of the entire volume of the body 601,
and in certain instances, may extend substantially uniformly
throughout the entire volume of the body 601. For example, the
porosity can include closed porosity or open porosity. Closed
porosity can be in the form of discrete pores that are isolated
from each other by bond material and/or abrasive particles. Such
closed porosity may be formed by pore formers. In other instances,
the porosity may be open porosity defining an interconnected
network of channels extending throughout at least a portion of the
three-dimensional volume of the body 601. It will be appreciated
that the body 601 may include a combination of closed porosity and
open porosity.
[0115] In accordance with an embodiment, the fixed abrasive article
can have a body 601 including a particular content of porosity that
can facilitate suitable material removal operations. For example,
the body 601 can have a porosity of at least 0.5 vol % and not
greater than 80 vol % for a total volume of the body.
[0116] In accordance with another embodiment, it will be
appreciated that the bonded abrasive article 620 can include a body
601 including certain additives that may facilitate certain
grinding operations. For example, the body 601 can include
additives such as fillers, grinding aids, pore inducers, hollow
materials, catalysts, coupling agents, curants, antistatic agents,
suspending agents, anti-loading agents, lubricants, wetting agents,
dyes, fillers, viscosity modifiers, dispersants, defoamers, and a
combination thereof.
[0117] As further illustrated in FIG. 6, the body 601 can have a
diameter 683, which may be varied according to the desired material
removal operation. The diameter can refer to the maximum diameter
of the body, particularly in those cases where the body 601 has a
conical or cup-shaped contour.
[0118] Moreover, the body 601 can have a particular thickness 681
extending along the side surface 603 between the upper surface 624
and the bottom surface 626 along the axial axis 680. The body 601
can have a thickness 681, which may be an average thickness of the
body 601, which can be not greater than 1 m.
[0119] In accordance with an embodiment, the body 601 may have a
particular relationship between the diameter 683 and thickness 681,
defining a ratio of diameter:thickness that may be suitable for
certain material removal operations. For example, the body 601 can
have a ratio of diameter:thickness of at least 10:1, such as at
least 15:1, at least 20:1, at least 50:1, or even at least 100:1.
It will be appreciated that the body may have a ratio of
diameter:thickness of not greater than 10,000:1 or not greater than
1000:1.
[0120] The bonded abrasive article 620 may include at least one
reinforcing member 641. In particular instances, the reinforcing
material 641 can extend for a majority of the entire width (e.g.,
the diameter 683) of the body 601. However, in other instances, the
reinforcing member 641 may extend for only a fraction of the entire
width (e.g., diameter 183) of the body 601. In certain instances,
the reinforcing member 641 may be included to add suitable
stability to the body for certain material removal operations. In
accordance with an embodiment, the reinforcing member 641 can
include a material such as a woven material, a nonwoven material, a
composite material, a laminated material, a monolithic material, a
natural material, a synthetic material, and a combination thereof.
More particularly, in certain instances, the reinforcing member 641
can include a material such as a monocrystalline material, a
polycrystalline material, a vitreous material, an amorphous
material, a glass (e.g., a glass fiber), a ceramic, a metal, an
organic material, an inorganic material, and a combination thereof.
In particular instances, the reinforcing material 641 may include
fiberglass, and may be formed essentially from fiberglass.
[0121] In particular instances, the reinforcing material 641 can be
substantially contained within the three-dimensional volume of the
body 601, more particularly, within the three-dimensional volume of
the bond material 607. In certain instances, the reinforcing
material 641 may intersect an exterior surface of the body 601
including, but not limited to, the upper surface 624, side surface
603, and/or bottom surface 626. For example, the reinforcing
material 641 can intersect the upper surface 624 or bottom surface
626. In at least one embodiment, the reinforcing material 641 may
define the upper surface 624 or bottom surface 626 of the body 601,
such that the bond material 607 is disposed between one or more
reinforcing materials. It will be appreciated that while a single
reinforcing member 641 is illustrated in the embodiment of FIG. 6 a
plurality of reinforcing members may be provided within the body
601 in a variety of arrangements and orientations suitable for the
intended material removal application.
[0122] As further illustrated, the body 601 can include certain
axes and planes defining the three-dimensional volume of the body
601. For example, the body 601 of the fixed abrasive article 620
can include an axial axis 680. As further illustrated along the
axial axis 680, the body 601 can include a first axial plane 631
extending along the axial axis 680 and through a particular
diameter of the body 601 at a particular angular orientation,
designated herein as 0.degree.. The body 601 can further include a
second axial plane 632 distinct from the first axial plane 631. The
second axial plane 632 can extend along the axial axis 680 and
through a diameter of the body 601 at an angular position, as
designated by example herein as 30.degree.. The first and second
axial planes 631 and 632 of the body 601 may define particular
axial collections of abrasive particles within the body 601
including, for example, the axial collection of abrasive particles
691 within the axial plane 631 and the axial collection of abrasive
particles 692 within the axial plane 632. Furthermore, the axial
planes of the body 601 may define sectors there between, including
for example, sector 684 defined as the region between the axial
planes 631 and 632 within the body 601. The sectors can include a
particular group of abrasive particles that may facilitate improved
material removal operations. Reference herein to features of
portions of abrasive particles within the body, including for
example, abrasive particles within axial planes will also be
relevant to groups of abrasive particles contained within one or
more sectors of the body.
[0123] As further illustrated, the body 601 can include a first
radial plane 621 extending along a plane that is substantially
parallel to the upper surface 624 and/or bottom surface 626 at a
particular axial location along the axial axis 680. The body can
further include a second radial plane 622, which can extend in a
substantially parallel manner to the upper surface 624 and/or
bottom surface 626 at a particular axial location along the axial
axis 680. The first radial plane 621 and second radial plane 622
can be separated from each other within the body 601, and more
particularly, the first radial plane 621 and second radial plane
622 can be axially separated from each other. As further
illustrated, in certain instances, one or more reinforcing members
641 may be disposed between the first and second radial planes 621
and 622. The first and second radial planes 621 and 622 may include
one or more particular groups of abrasive particles including, for
example, the group of abrasive particles 628 of the first radial
plane 621 and the group of abrasive particles 605 of the second
radial plane 622, which may have certain features relative to each
other that may facilitate improved grinding performance.
[0124] The abrasive particles of the embodiments herein can include
particular types of abrasive particles. For example, the abrasive
particles may include shaped abrasive particles and/or elongated
abrasive particles, wherein the elongated abrasive particles may
have an aspect ratio of length:width or length:height of at least
1.1:1. Various methods may be utilized to obtain shaped abrasive
particles. The particles may be obtained from a commercial source
or fabricated. Some suitable processes used to fabricate the shaped
abrasive particles can include, but is not limited to, depositing,
printing (e.g., screen-printing), molding, pressing, casting,
sectioning, cutting, dicing, punching, pressing, drying, curing,
coating, extruding, rolling, and a combination thereof. Similar
processes may be utilized to obtain elongated abrasive particles.
Elongated un-shaped abrasive particles may be formed through
crushing and sieving techniques.
[0125] FIG. 7A illustrates a cross-sectional view of an abrasive
article 730, according to an example embodiment. The abrasive
article 730 includes an abrasive portion 732 and a non-abrasive
portion 731, and an electronic assembly 720 coupled to the
non-abrasive portion 731 of an abrasive article 730. The
non-abrasive portion 731 can have a first surface 733, a second
surface 734, and a side surface 735 extending between the first
surface 733 and second surface 734. The first and second surfaces
733 and 734 may be major planar surfaces. The second surface 734
may be a major planar surface of the same size or different size
relative to the first surface 733. As further illustrated, the
non-abrasive portion 731 may include an opening 705, such as an
arbor hole. The electronic assembly 720 can be coupled to the first
surface 733. The electronic assembly 720 may include an electronic
device 722 and package 721 as described in embodiments herein. In
one embodiment, the electronic assembly 720 can include at least
one electronic device 722 that may be contained within a package
721. The package 721 may be suitable for attaching the electronic
assembly 720 to the body of the abrasive article 730, and may
provide some suitable protection of the one or more electronic
devices contained therein. In particular examples, the electronic
device 722 can be encapsulated within the package 721.
[0126] According to one embodiment, the electronic device 722 can
be configured to be written-to with information, store information,
or provide information to other objects during a read operation.
Such information may be relevant to the manufacturing of the
abrasive article, operation of the abrasive article or conditions
encountered by the electronic assembly 720. Reference herein to the
electronic device will be understood to be reference to at least
one electronic device, which can include one or more electronic
devices. In at least one embodiment, the electronic device 722 can
include at least one device selected from the group including an
integrated circuit and chip, data transponder, a radio frequency
based tag or sensor with or without chip, an electronic tag,
electronic memory, a sensor, an analog to digital converter, a
transmitter, a receiver, a transceiver, a modulator circuit, a
multiplexer, an antenna, a near-field communication device, a power
source, a display (e.g., LCD or OLED screen), optical devices
(e.g., LEDs), global positioning system (GPS) or device, or any
combination thereof. In some instances, the electronic device may
optionally include a substrate, a power source, or both. In one
particular embodiment, the electronic device 722 can include a tag,
such as a passive radio frequency identification (RFID) tag. In
another embodiment, the electronic device 722 can include an active
radio frequency identification (RFID) tag. An active RFID tag can
include a power supply, such as a batter or inductive capacitive
(LC) tank circuit. In a further embodiment, the electronic device
1722 can be wired or wireless.
[0127] According to one aspect, the electronic device 722 can
include a sensor. The sensor may be selectively operated by any
system and/or individual within the supply chain. For example, the
sensor can be configured to sense one or more processing conditions
during the formation of the abrasive article. In another
embodiment, the sensor may be configured to sense a condition
during use of the abrasive article. In yet another embodiment, the
sensor can be configured to sense a condition in the environment of
the abrasive article. The sensor can include an acoustic sensor
(e.g., ultrasound sensor), force sensor, vibration sensor,
temperature sensor, moisture sensor, pressure sensor, gas sensor,
timer, accelerometer, gyroscope, or any combination thereof. The
sensor can be configured to alert any system and/or individual
associated with the abrasive article, such as a manufacturer and/or
customer to a particular condition sensed by the sensor. The sensor
may be configured to generate an alarm signal to one or more
systems and/or individuals in the supply chain, including but not
limited to, manufacturers, distributors, customers, users, or any
combination thereof.
[0128] In another embodiment, the electronic device 722 may include
a near-field communication device. A near field communication
device can be any device capable of transmitting information via
electromagnetic radiation within a certain defined radius of the
device, typically less than 20 meters. The near-field communication
device can be coupled to one or more electronic devices, including
for example a sensor. In one particular embodiment, a sensor can be
coupled to the near-field communication device and configured to
relay information to one or systems and/or individuals in the
supply chain via the near-field communication device.
[0129] In an alternative embodiment, the electronic device 722 can
include a transceiver. A transceiver can be a device that can
receive information and/or transmit information. Unlike passive
RFID tags or passive near-field communication devices, which are
generally read-only devices that store information for a read
operation, a transceiver can actively transmit information without
having to conduct an active read operation. Moreover, the
transceiver may be capable of transmitting information over various
select frequencies, which may improve the communication
capabilities of the electronic assembly with a variety of systems
and/or individuals in the supply chain.
[0130] FIG. 7B illustrates a cross-sectional view of an electronic
assembly, according to an example embodiment. In accordance with
one aspect, the electronic assembly 720 may include one or more
electronic devices, including for example electronic device 756 and
electronic device 757. In certain instances, the electronic
assembly 720 may include a substrate 759 upon which the one or more
electronic devices 756 and 757 can be disposed. In yet other
instances, the electronic assembly 720 may further include a first
portion 771 and a second portion 772. The first portion 771 and
second portion 772 may be part of a package that may overlie at
least a portion of the electronic assembly 720. The package 721 may
consist essentially of the first and second portions 771 and 772.
For example, as illustrated in FIG. 7B, the first portion 771 can
be underlying the substrate 759 and one or more electronic devices
756 and 757. In certain instances, the first portion 771 can be
coupled to, such as directly contacting, the second portion 772. In
still another embodiment, electronic assembly 720 may include a
first portion 771 that is underlying and partially enveloping at
least a portion of the substrate 1759 and the one or more
electronic devices 756 and 757. The second portion 772 can be
overlying at least a portion of the one or more electronic devices
756 and 757. The second portion 772 may be indirectly coupled or
directly coupled (e.g., directly contacting or bonded to) the first
portion 771. As illustrated, the first portion 771 and second
portion 772 can substantially surround the entire one or more
electronic devices 756 and 757 as well as substrate 759.
[0131] The first portion 771 can underlie at least a portion, such
as at least 50% of the electronic device 757. The first portion 771
can electrically insulate and isolate the electronic device 757
from the non-abrasive portion to which it is coupled. In particular
instances, the first portion 771 can be disposed between and
electrically insulating at least one of the at least one or more
electronic devices 756 and 757 from the body of the abrasive
article. More particularly, the electronic devices 756 and/or 757
may include at least one antenna, and the first portion 771 can be
disposed between and electrically insulating the antenna from the
body of the abrasive article.
[0132] In certain instances, second portion 772 may act as a
protective layer. In some instances, the substrate can serve as a
protective layer or facilitate bonding of the electronic assembly
to a body to obviate the use of a protective layer that is disposed
underlying the substrate. In another instance, the protective layer
may be disposed to underlie the electronic device and an upper
surface and side surfaces of the electronic devices 757 or 756 may
not be covered by the protective layer. In a further embodiment,
the electronic assembly 720 can include an extra protection layer
that is disposed over and/or under the second portion for
additional protection. The second portion 772 can act as a
protective layer to limit impact of coolant and swarf on the
electronic assembly. In other instances, the protective layer may
protect the electronic devices from mechanical damage or chemical
damage during re-profiling, dressing, maintenance of the abrasive
portion or non-abrasive portion, and the like.
[0133] FIG. 8A illustrates a top-down view of a releasable coupling
of an electronic assembly on a body, according to an example
embodiment. As illustrated, the body 801 can include an upper
surface 802. An electronic assembly 803 may be contained within a
cavity 820 in the body 801. The electronic assembly 803 may be
press-fit in the cavity 820. A securing assembly 830 including a
securing element 831 may be configured to translate from and
engaged position to a disengaged position. In an engaged position,
as illustrated in FIG. 8A, the securing element 831 can be
overlying and engaging the electronic assembly 803, thus securing
the electronic assembly 803 to the body 801. In a disengaged
position, the securing element 831 may be spaced apart from and
disengaged from the electronic assembly 803. The securing element
831 may articulate between the engaged position and the disengaged
position by translating in the Y-direction. In the disengaged
position, the electronic assembly 803 is in a non-secure position
and may be readily removed from the body 801. In such instances,
removal of electronic assembly 803 from the body 801 may be
accomplished without need for applying heat or other chemical
additives to remove or dissolve an adhesive.
[0134] FIG. 8B illustrates an abrasive system 850, according to an
example embodiment. The abrasive system 850 includes a housing 851
and a body 852 contained within the housing 851. The body 852 may
include an electronic assembly 853 coupled to the body 952. The
body 852, as illustrated, may be a particular type of edge grinding
tool, wherein the workpiece 1961 may be a piece of glass. The
housing 851 may further include coolant 854 that is applied to the
grinding interface during a material removal operation. In one
embodiment, the housing 851 may include at least one electronic
device 855. The at least one electronic device 855 can be coupled
to a surface or embedded in the material of the housing 851. The
electronic assembly 853 includes one or more electronic devices
configured to communicate with the one or more electronic devices
855 in the housing 851. Information received by the electronic
device 855 may be related to a remote electronic device 856 which
is positioned outside of the housing 851.
[0135] As further illustrated, the workpiece 861 may include one or
more electronic devices 857 coupled to the workpiece 861 and
configured to transmit and/or receive information from one of the
other electronic devices, such as the electronic assembly 853, the
electronic device 855, and/or the electronic device 856. In
particular instances, it may be suitable that the electronic
assembly 853 include a protective layer configured to protect
against corrosive effects of the coolant 854.
[0136] In an alternative embodiment, the electronic assembly 853
may also be coupled to, partially embedded, or fully embedded, in a
surface 858 of the body 852. The placement and position of the
electronic assembly may facilitate improved communication with the
electronic devices 855, 856, and/or 857. Moreover, in certain
instances, of the electronic devices 855, 856, 857 and/or
electronic assembly 853 may utilize a vertically polarized antenna,
booster antenna, 3D polarized antenna, or any combination thereof.
It will also be appreciated that in certain instances, it may be
suitable to use a plurality of electronic assemblies located at
different positions and orientations on the body 852.
IV. Example Microscopic Interactions
[0137] FIG. 9A depicts various types of interactions associated
with grinding processes. Each type of interaction could be
incorporated into the analytical models and/or machine learning
methods and systems described herein. For example, an abrasive-type
interaction could include cutting (material removal), plowing
(material displacement), or sliding (surface modification) effects.
Abrasion processes include sliding a hard material such as an
abrasive grain, against a softer material, during which the softer
material undergoes deformation and surface modification. In some
cases, this may be due to deep scratches or plowing, such as work
material being displaced without material removal or sliding
between abrasive grain and work piece. If the depth of penetration
of abrasive grains against the work material is sufficiently
intense, then the abrasive grain can act as a cutting edge, leading
to the generation of a fresh surface and removal of debris called
"chips" from the work surface. If the depth of penetration is not
sufficient, it is likely that the hard abrasive grain will locally
deform the work material. This interaction or deformation is often
termed as plowing. Finally, if the depth of penetration of abrasive
grain against work material is extremely shallow, the result would
be the sliding of abrasive grain against work material, although at
high contact stress. The surface generated at the end is the
cumulative effect of all these abrasive/work interactions during
the grinding process. Additionally or alternatively, various
chip/bond, chip/workpiece, bond/workpiece, and/or other sliding
interactions are possible and contemplated.
[0138] FIG. 9B depicts example force interactions and associated
analytical models on a grinding process. As an example, the
material removal rate (MRR) could increase proportionally to the
tangential force and/or the normal force. Furthermore, FIG. 9B
illustrates MRR as a function of the components of applied force
(e.g., tangential force Ft and normal force Fn), which follow
relationships that are governed by the principles of machining and
tribology. As an example, for a given material removal rate,
tangential force Ft=the force required to form chips,
Ft.sub.c+frictional force, Ft.sub.f+threshold force at time zero,
Ft.sub.th(0)+threshold force at time t, Ft.sub.th(t). A similar
relationship applies for the normal force component.
[0139] FIG. 9C depicts example power interactions and associated
analytical models of a grinding process. For example, FIG. 9C
illustrates the variation in grinding power after time "t" at a
given MRR and its associated four components: (1) initial threshold
power P.sub.th(0), (2) change in threshold power with time
P.sub.th(t), (3) power for cutting or chip making P.sub.c, and (4)
change in P.sub.c caused by chip friction effects, P.sub.f(t). It
should also be appreciated that FIGS. 9A-9C are presented as
conceptual representations of example microscopic interactions and
are not intended to be limiting with respect to the types of
microscopic interactions, analytical models, or grinding processes
that may be used in the current disclosure.
V. Example Computing Devices
[0140] FIG. 10 illustrates a block diagram of a computing device 10
00, according to an example embodiment. In particular, computing
device 1000 can be configured to perform at least the functions
related to the components of and/or related to machine learning
platform 1110, enterprise 1120, outside vendors 1130, 3.sup.rd
party users 1140, machine learning system 1210, manual abrasive
device 1310, wearable device 1320, automated abrasive device 1330,
server device 1340, other sensors 1350, abrasive products 1410,
remote devices 1420, vendors 1430, analytics platform 1440, method
1500, method 1600, method 1700, microcontroller 1810B, controller
2020, remote network 2110, client network 2120, mobile device 2400,
and/or other elements described herein.
[0141] Computing device 1000 may include one or more sensors 1016
for collecting data, a data storage 1004, which may store the
collected data and may include instructions 1014, one or more
processor(s) 1002, a communication interface 1006 for communicating
with a remote source (e.g., a server or another device/sensor), and
a display 1008. Additionally, computing device 1000 may include an
audio output device (e.g., a speaker) and a haptic feedback device
(e.g., an eccentric rotating mass (ERM) actuator, linear resonant
actuator (LRA), or piezoelectric actuators, among other
examples).
[0142] Processor 1002 may include one or more general purpose
processors or special purpose processors (e.g., GPUs). Processor
1002 may be configured to execute computer-readable instructions
1014. For example, processor 1002 may control the one or more
sensors 1016 based, at least in part, on computer-readable
instructions 1014. Processor 1002 may be configured to process the
real-time data collected by the one or more sensors 1016.
[0143] Data storage 1004 is a non-transitory computer-readable
medium that may include, without limitation, magnetic disks,
optical disks, organic memory, and/or any other volatile (e.g. RAM)
or non-volatile (e.g. ROM) storage system readable by the processor
1002. Data storage 1004 may include a data storage to store
indications of data, such as sensor readings, machine learning
models, program settings (e.g., to adjust behavior of the computing
device 1000), user inputs (e.g., from a user interface on the
device 1000 or communicated from a remote device), etc. Data
storage 1004 may also include program instructions 1014 for
execution by the processor 1002 to cause the device 1000 to perform
operations specified by the instructions. The operations may
include any of the methods described herein.
[0144] Communication interface 1006 may include hardware to enable
communication within the computing device 1000 and/or between the
computing device 1000 and one or more other devices. The hardware
can include transmitters, receivers, and antennas, for example.
Communication interface 1006 can be configured to facilitate
communication with one or more other devices, in accordance with
one or more wired or wireless communication protocols. For example,
the communication interface 1006 can be configured to facilitate
wireless data communication for computing device 1000 according to
one or more wireless communication standards, such as one or more
IEEE 801.11 standards, ZigBee standards, Bluetooth standards, etc.
For instance, communication interface 1006 could include WiFi
connectivity and access to cloud computing and/or cloud storage
capabilities. As another example, communication interface 1006 can
be configured to facilitate wired data communication with one or
more other devices.
[0145] Display 1008 can be any type of display component configured
to display data. As one example, display 1008 can include a
touchscreen display. As another example, the display 1008 can
include a flat-panel display, such as a liquid-crystal display
(LCD) or a light-emitting diode (LED) display.
[0146] User interface 1010 can include one or more pieces of
hardware used to provide data and control signals to computing
device 1000. For instance, user interface 1010 can include a mouse
or a pointing device, a keyboard or a keypad, a microphone, a
touchpad, or a touchscreen, among other possible types of user
input devices. Generally, user interface 1010 can enable an
operator to interact with a graphical user interface (GUI) provided
by computing device 1000 (e.g., displayed by the display 1008). As
an example, user interface 1010 may allow an operator to provide
input data to computing device 1000. As another example, the
operator may provide an input indicative of a product to be used to
perform the operation and/or an input indicative of a workpiece on
which the operator may perform the abrasive operation.
[0147] In some embodiments, a user could utilize the GUI to provide
a desired operation level (e.g., maximum desired vibration level,
maximum desired noise level, etc.), which could be based on, for
example, user preference and/or user comfort. It will be understood
that a user could provide information indicative of the desired
operation level by other means as well.
[0148] One or more sensors 1016 may be configured to collect data
in real-time from or associated with an environment of the
computing device 1000. Real-time collection of data may involve the
sensors periodically or continuously collecting data. For example,
the one or more sensors 1016 may include a sound detection device
(e.g., a microphone) that is configured to detect sound in the
environment of the sensor (e.g., from an abrasive device operating
in proximity of the sensor). Additionally and/or alternatively, the
sensors 1016 may be configured to collect data from or associated
with an operator of computing device 1000. For example, the one or
more sensors 1016 may include an accelerometer. As described
herein, the data collected by the one or more sensors 1016 may be
used to determine abrasive operational data, which could then be
used for obtaining real-time data about grinding/abrasive
operations, capturing a user experience of a user that is using the
device, and/or determining operational and/or or enterprise
improvements (e.g., based on data collected over a period of
time).
[0149] The one or more sensors 1016 may also include other sensors
for detecting movement, such IMUs and gyroscopes. Further, the one
or more sensors 1016 may include other types of sensors such as
location-tracking sensors (e.g., a GPS or other positioning
device), light intensity sensors, thermometers, clocks, force
sensors, pressure sensors, photo-sensors, Hall sensors, vibration
sensors, sound-pressure sensors, a magnetometer, an infrared
sensor, cameras, and piezo sensors, among other examples. Sensors
and their components may be miniaturized.
VI. Example Machine Learning Platforms
[0150] FIG. 11 illustrates arrangement 1100 of machine learning
platform 1110, according to an example embodiment. As shown in FIG.
11, machine learning platform 1110 may be communicatively coupled
to enterprise 1120, outside vendors 1130, and 3.sup.rd party users
1140. Machine learning platform 1110 may include, for example,
machine learning system 1112, database devices 1114, server devices
1116, and analytics platform 1118. Machine learning platform 1110
may utilize machine learning to process and/or analyze the sensor
data collected by the enterprise 1120. Machine learning platform
1110 may store the received sensor data and then analyze the data
to provide product-specific information of the abrasive products
and/or workpiece-specific information associated with an abrasive
product on enterprise 1120. As used herein, product-specific
information may refer to any information related to elements of an
abrasive product/device or elements of any abrasive
operations/processes performed by the abrasive product/device. For
example, machine learning platform 1110 may determine best
operational practices for enterprise 1120. In another example, the
machine learning platform 1110 may determine different value
metrics (e.g., productivity, product life, etc.) for different
abrasive products. As used herein, abrasive product may refer to an
apparatus associated with or embodied by an abrasive tool.
[0151] Machine learning system 1112 may include one or more machine
learning models configured to receive sensor data from enterprise
1120. For example, sensor data may be related to an abrasive
product and correlated with a grinding operation mode, a particular
workpiece, a particular abrasive tool, or a particular grinding
condition from enterprise 1120. In response to receiving the sensor
data, machine learning system 1112 may train the one or more
machine learning models to predict product-specific information
and/or workpieces specific information related to the received
sensor data. After one or more machine learning models have been
trained, the machine learning system 1112 may be applied at
run-time to predict or infer a prediction condition based on the
real-time data received from enterprise 1120. As described herein,
the predicted condition could trigger, prompt, or initiate various
events such as a notification, a report, an order, or another type
of action
[0152] Database devices 1114 may include one or more computing
devices configured to store data into one or more databases. For
example, database devices may include one or more relational
databases (e.g., SQL), graph databases (e.g., neo4j), document
databases (e.g., MongoDB), column databases (e.g., Cassandra),
and/or other database models. Database devices 1114 may act as data
storage for components of machine learning platform 1110. An as
example, database devices 1114 may be configured to receive and
store sensor data from enterprise 1120 and provide the sensor data
to machine learning system 1112 for training one or more machine
learning models. In some examples, database devices 1114 may be
configured to act as the primary data source for analytics platform
1118. In other examples, database devices 1114 may be configured to
store one or more trained models (e.g., learned parameters).
[0153] Server devices 1116 may include one or more web servers,
file servers, and/or computational servers. Server devices may
facilitate communication between machine learning platform 1110 and
enterprise 1120, outside vendors 1130, and 3.sup.rd party users
1140. Communication may be facilitated by known web communication
protocols, such as TCP/IP. In some embodiments, server devices 1116
may be utilized by machine learning system 1112 or analytics
platform 1118 for computational tasks. For example, devices in
server devices 1116 may be part of a MapReduce cluster that is used
as part of a distributed training architecture for machine learning
system 1112.
[0154] Analytics platform 1118 may include a web application
configured to utilize information collected from machine learning
system 1112 and database devices 1116. After processing the
collected information, analytics platform 1118 could generate
various predicted future conditions for enterprise 1120 as well as
various prescriptive actions for enterprise 1120. As used herein, a
predicted future condition refers to an estimate about a future
event that could occur at enterprise 1120. Examples of future
events may include a predicted failure of an abrasive
product/workpiece, a prediction of potential damage to an abrasive
product/workpiece, or a prediction that the quality of a workpiece
does not meet a predetermined quality level, among other
possibilities. Further, as used herein, a prescriptive action
refers a recommendation of a best course of action given a current
state and/or current situation of an abrasive product and/or given
a current state and/or current situation of enterprise 1120.
Examples of prescriptive actions may include a command to shut off
an abrasive product if the abrasive product is displaying aberrant
behavior, a command to adapt the speed rate of an abrasive wheel, a
notification to change an abrasive article of an abrasive product,
or a notification to dress a damaged abrasive product, among other
possibilities.
[0155] In some embodiments, analytics platform 1118 includes a
simulation environment programmed with digital versions (e.g.,
"digital twins") of physical abrasive products used by enterprise
1120. The simulation environment could use these digital versions
to estimate productivity, costs, and/or injuries resulting from
adding/reconfiguring/removing different digital abrasive products
from the stimulation environment. In some embodiments, analytics
platform 1118 is configured to graphically display metrics
associated with one or more abrasive products and/or one or more
workpieces in enterprise 1120. More details on analytics platform
1118 are provided below.
[0156] Notably, the configuration of machine learning platform 1110
is provided as an example. In some cases, machine learning platform
1110 may include one or more additional devices. For example,
machine learning platform 1110 may include a firewall to allow
access from authorized users, deny access from unauthorized users,
provide intrusion detection, facilitate virus scanning, and/or
provide other network security services. As another example,
machine learning platform 1110 may include one or more load
balancers to distribute incoming network traffic or requests across
multiple computing devices within machine learning platform 1110
(e.g., such that no single devices is overwhelmed with task
requests). In other examples, machine learning platform 1110 may
include one or more routers, virtual machines, proxy servers,
and/or other common network devices. Machine learning platform 1110
may also be connected to one more client devices (e.g., personal
computers or mobile phones). In some examples, machine learning
platform 1110 may offer virtual private network (VPN) services.
[0157] Additionally and/or alternatively, components of machine
learning platform 1110 may be replicated across multiple computing
devices to provide data duplication and increase capacity of
services. These computing devices may be located at different
physical locations to ensure high availability in case of failure
at one location. As such, machine learning platform 1110 may be
configured across different physical locations and hundreds of
computing devices.
[0158] Enterprise 1120 may include, for example, one or more
abrasive products 1122, wearable devices 1124, server devices 1126,
and remote devices 1128. Enterprise 1120 may represent a single
geographic location containing multiple abrasive machines or may
represent multiple abrasive machines located across several
geographic locations. Moreover, enterprise 1120 may represent a
single enterprise of a plurality of enterprises that utilize
products manufactured or maintained by the entity operating machine
learning platform 1110. As such, machine learning platform 1110 may
act as a remote customer support system for these products.
[0159] Abrasive products 1122 may include one or more devices or
tools that perform grinding operations on a workpiece. As described
above, abrasive products 1122 may be manufactured or maintained by
the entity operating machine learning platform 1110. Abrasive
products 1122 may contain one or more sensors that collect abrasion
operational data associated with grinding operations or the
involving the workpiece being grinded on. For example, the one or
more sensors may transmit the collected abrasion operational data,
via Bluetooth, TCP/IP or other networking protocols, to server
devices 1126. In another example, the one or more sensors may
transmit the collected abrasion operational data to machine
learning platform 1110.
[0160] Wearable devices 1124 may include wearable computing devices
with one or more sensors that continuously or periodically collect
data from or associated with an environment of abrasive products
1122 and/or data from or associated with operators' abrasive
products 1122. For example, the data collected by the wearable
devices 1124 may be used to determine abrasive operational data. In
some examples, the collected data may be sent to server devices
1126, for example, via Bluetooth, TCP/IP, or other networking
protocols. In other examples, the collected data may be transmitted
directly to machine learning platform 1110.
[0161] Server devices 1126 may include one or more computing
devices located on enterprise 1120. Server devices may be
configured to receive and aggregate sensor data from abrasive
products 1122 and wearable devices 1124. Server devices 1126 may be
operated by machine learning platform 1110 or by enterprise 1120.
Upon receiving sensor data, server devices 1126 may apply data
filters to the sensor data, such as removing outlier sensor data
and/or ignoring sensor data from one or more wearable devices 1124
or abrasive products 1122. In some examples, server devices 1126
may be configured to convert sensor data into a different data
format more suitable for machine learning platform 1110, for
example into JavaScript Object Notation (JSON). As another example,
server devices 1126 may allow a human operator to tag sensor data
with labels, as further described herein. Server devices 1126 may
receive product-specific information and/or workpiece-specific
information from machine learning platform 1110 and distribute this
information to remote devices 1128, abrasive products 1122,
wearable devices 1124, or may store this data for later access by
members of enterprise 1120.
[0162] In some embodiments, server devices 1126 may provide sensor
data to machine learning platform 1110 by grouping data in batches.
Batches may be transmitted periodically, for example, every 10
minutes or 30 minutes. In other examples, server devices 1126 may
send sensor data machine learning platform 1110 in a real time,
streaming format. In some embodiments, server devices 1126 may be
configured to monitor the sensors disposed in abrasive products
1122 and wearable devices 1124. For example, server devices 1126
may send heartbeat messages to the sensors, which in turn may be
configured to respond with a response heartbeat message. This may
ensure that sensors are operable and have not stopped sending data
to server devices 1126, for example, because of malfunction or loss
of power.
[0163] Remote devices 1128 may include interfaces located on one or
more computing devices in enterprise 1120. For example, remote
devices 1128 may include on wearable devices (e.g., smart watches),
mobile devices (e.g., mobile phones or tablets), and/or monitors
(e.g., computer screens). Remote devices 1128 may receive data from
server devices 1126 or machine learning platform 1110 and display
output data on a graphical user interface (GUI) or emit an alarm,
an alert, a notification, a report, an order, and/or another type
of action.
[0164] Outside vendors 1130 may represent one or more computing
systems managed by partners of the entity operating machine
learning platform 1110. In example embodiments, machine learning
platform 1110 may transmit to outside vendors 1130 new order
requests, delivery requests, and/or other logistics requests based
on predictions made by machine learning system 1112. These requests
may be made automatically by machine learning platform 1110 on the
behalf of enterprise 1120.
[0165] 3.sup.rd party users 1140 may include one or more
individuals or organizations that utilize the capabilities of
analytics platform 1118. For example, 3.sup.rd party users 1140 may
access analytics platform 1118 via a web browser and may be able to
access data provided to analytics platform 1118 by machine learning
platform 1110. 3.sup.rd party users 1140 may be granted access, for
example, through a subscription based model. Analytics platform
1118 may provide multiple levels of access to 3.sup.rd party users
1140, each based on the subscription purchased by 3.sup.rd party
users 1140. For example, each level of access may provide more
sensitive or larger amounts of data.
[0166] Notably, the components of arrangement 1100 are used for the
purpose of example. Other components and arrangements are
possible.
VII. Example Machine Learning Systems
[0167] FIG. 12 depicts scenario 1200 containing enterprise 1202,
machine learning system 1210, input request 1220, and output
predictions 1230, according to an example embodiment. Scenario 1200
may occur as part of arrangement 1100. As such, enterprise 1202 may
be embodied by enterprise 1120, machine learning system 1210 may be
embodied by machine learning system 1112, and training data 1212
may be embodied by database devices 1114.
[0168] Enterprise 1202 may represent, for example, organization
that utilizes products manufactured or maintained by the operator
of machine learning system 1210. As discussed above, enterprise
1202 may contain sensors that produce abrasion operational data
associated with an abrasive operation involving one or more
abrasive products or one or more workpieces. The data from these
sensors are may be transmitted from enterprise 1202 to machine
learning system 1210 in order to determine product specific
information of the one or more abrasive products and
workpiece-specific information based on the sensor data.
[0169] Machine learning system 1210 may utilize machine learning
techniques to train one or more machine learning models with
training data to detect patterns and provide output predictions
about the training data. The resulting trained machine learning
model may be referred to as trained machine learning models. For
example, scenario 1200 illustrates machine learning models 1214
being trained on training data 1212 to become trained machine
learning models 1216. During prediction time, trained machine
learning models 1216 may receive input request 1220 and
responsively provide output predictions 1230.
[0170] Training data 1212 may include one or more databases
designed to receive and store sensor data from enterprise 1202 and
provide the sensor data to train one or more machine learning
models. For example, training data 1212 may include relational
databases (e.g., SQL), graph databases (e.g., neo4j), document
databases (e.g., MongoDB), column databases (e.g., Cassandra)
and/or other database models.
[0171] Machine learning models 1214 may include, but are not
limited to, algorithms such as: logistic or linear regression,
support vector machines (SVM), Bayesian networks, artificial neural
networks (ANNs), (including convolutional neural networks (CNNs)
and recurrent neural networks (RNNs)), Naive Bayes classifiers, K
nearest neighbors, auto-encoders, hidden Markov models (HMM),
Markov decision process, decision trees, random forests, ensemble
methods (including boosting and bagging) and/or heuristic machine
learning models. Machine learning models that incorporate
rule-based algorithms (e.g., association rule models, learning
classifier models, etc.) or iterative algorithms (e.g.,
expectation-maximization algorithms) are also contemplated and
possible within the scope of the present application.
[0172] Machine learning models 1214 may be trained may utilize
online or offline learning. During training, machine learning
models 1214 may utilize the training data 1212 to adjust weights
and/or other parameters of one or more models. Machine learning
models 1214 may use various regularization techniques during
training, such as L1 regularization, L2 regularization, early
termination of training and/or dropout methods, to reduce
overfitting. Machine learning models 1214 may use various
optimization methods during training, such as gradient based
methods (batch gradient descent, stochastic gradient descent, Adam
optimization), search based techniques (genetic algorithms, grid
search, random search), and/or other techniques to learn open or
more parameters and/or hyper parameters.
[0173] In some embodiments, machine learning models 1214 may use
supervised learning to determine output predictions based on
labeled training data 1212. These output predictions may be
accepted or corrected based on correct results associated with
labels on training data 1212. As an example, linear regression may
be used to predict the idle time of an operator of an abrasive
product given input RPM data of the abrasive product. This may
include, for example, optimizing a square loss function based on a
difference between the predicted idle time of an operator and the
actual labeled idle time of an operator associated with the input
RPM data.
[0174] In some embodiments, machine learning models 1214 may use
unsupervised learning to learn patterns, structures, of features
based on unlabeled training data 1212. As an example, clustering
algorithms (e.g.: k-means clustering, hierarchical clustering) may
be used to group sensor data with similar features into clusters,
which may be used to for anomaly detection (e.g., sensors with
features that do not fall into any cluster may be alerted to). In
another examples, auto-encoders may be used learn new
representations (typically with reduced dimensionality) of the
sensor data. These new representations of the sensor data may then
be used for classification tasks.
[0175] In some embodiments, machine learning models 1214 may use
semi-supervised learning by having labels for part, but not all of
training data 1212. As such, supervised learning may be used for a
portion of training data 1212 having labels and unsupervised
learning may be used for a portion of training data 1212 not having
labels. In some embodiments, machine learning models 1214 may use
reinforcement learning to receive a reward value in response to an
action in an environment. For example, during reinforcement
learning, machine learning models 1214 may take an action, such
providing a text notification to a client device indicating end of
life, and receive a reward value from enterprise 1202. In response
to the reward, machine learning models 1214 may try to maximize the
reward value by taking additional actions (providing another text
notification) or exploring new potential actions (providing a
different type of notification to the client device).
[0176] In some embodiments, machine learning models 1214 could be
trained based on various user experiences and/or knowledge. For
example, operational limits could be set or suggested based on
experiences from research and development personnel and/or an
application engineer. In such scenarios, the machine learning
models 1214 could utilize organizational "know-how" and/or feedback
about some or all aspects of the abrasive operation, such as, but
not limited to, operator experiences, manager experiences, and/or
customer feedback.
[0177] In some embodiments, the training of machine learning models
1214 may be accelerated using specialized processors, such as
graphic processing units (GPUs), tensor processing units (TPUs),
and/or digital signal processors (DSPs). In some embodiments,
training machine learning models 1214 may include training a
different machine learning model for different sets of sensor data.
Sets of sensor data may be based on the unique identifier of the
abrasive product from which sensor data is collected from. For
example, a set of sensor data may include all sensor data gathered
from abrasive products with a unique identifier starting with
"X_12", which may represent abrasive products sharing similar
characteristics. As another example, a set of sensor data may
include all sensor data gathered from abrasive products that share
the same amount and types of sensors, such as vibration
sensors.
[0178] In some embodiments, machine learning models 1214 may be
configured to be periodically retrained with new sensor data
received from enterprise 1202. For example, sensor data from
enterprise 1202 may associated with timestamps indicating the time
the data was collected by the sensor. Thus, machine learning models
1214 may be configured to be retrained on a subset of new sensor
data determined by the timestamps, such as all data collected in
the last week, month, or year. In some examples, periodically
retraining machine learning models 1214 may include removing old
sensor data from the training data 1212 based on the timestamp of
the old sensor data. For example, sensor data collect more than
two, three, or four years may be removed from training data
1212.
[0179] After training machine learning models 1214 are trained on
training data 1212 to become trained machine learning models 1216,
trained machine learning models 1216 may reside and execute on
machine learning system 1210 to provide predictions for requests
from one or more computing devices. In some cases, trained machine
learning models 1216 may reside and execute on one or more
computing devices to make predictions for requests from the one or
more computing devices.
[0180] In some embodiments, trained machine learning models 1216
may receive input requests 1220 from enterprise 1202, generate one
or more output predictions 1230 about input request 1220, and
provide the one or more output predictions to enterprise 1202. For
example, input request 1220 may be a request from enterprise 1202
to predict the end of life of an abrasive device. Thus, input
request 1220 may include RPM data for the abrasive device. In such
an example, enterprise 1202 may be the provider of training data
1212. In other embodiments, trained machine learning models 1216
may receive input requests 1220 from external source 1240, generate
one or more output predictions 1230 about input request 1220, and
transmit the one or more output predictions to external source
1240. For example, input request 1220 may be a request from
external source 1240 to predict the end of life of an abrasive
device. Thus, input request 1220 may include RPM data for the
abrasive device. In such an example, external source 1240 may not
be the provider of training data 1212 and thus may rely on the
training data from enterprise 1202 to make the prediction.
[0181] In some embodiments, output predictions 1230 may be labeled
and be utilized during a subsequent training phase to further
improve trained machine learning model 1216. For example, output
predictions 1230 may be provided as one or more product-specific
solutions for resolving issues with an abrasive product. These
product-specific solutions may be provided to enterprise 1202,
which in turn select of a product-specific solution from the one or
more product-specific solutions. Upon the selection of the
solution, enterprise 1202 may record the selected product-specific
solution and may determine the label for the abrasive product as
the selected solution. This may then be transmitted to training
data 1212 for use in improving the trained machine learning models
1216.
VIII. Example Inputs
[0182] FIG. 13 illustrates scenario 1300 representing input devices
to a machine learning system, according to an example embodiment.
Scenario 1300 may occur in enterprise 1120 and include manual
abrasive device 1310, wearable device 1320, automated abrasive
device 1330, and a server device 1340 As such, manual abrasive
device 1310 and automated abrasive device 1330 may be embodied by
abrasive products 1122 and server device 1340 may be embodied by
server devices 1126. As used herein, abrasive device may refer to a
device associated with or embodied by an abrasive tool.
[0183] Manual abrasive device 1310 may be any tool that is
configured to perform manual grinding operations on a work piece
(not illustrated in FIG. 13). Such grinding operations could
include grinding, polishing, buffing, honing, cutting, drilling,
sharpening, filing, lapping, sanding, and/or other similar tasks.
However, other types of manual mechanical operations that may
include vibration and/or noise are contemplated. For example,
hammering, chiseling, crimping, striking, or other manual
operations are possible within the context of the current
disclosure. Accordingly, the manual abrasive device 1310 may be a
device that is configured to perform one or more of the abrasive
operations. For example, the manual abrasive device 1310 may be a
right angle grinding tool, a power drill, a hammer drill and/or
percussion hammer, a saw, a plane, a screwdriver, a router, a
sander, an angle grinder, a garden appliance and/or a multifunction
tool, among other examples.
[0184] Automated abrasive device 1330 may be any tool that is
configured to perform automated grinding operations on a workpiece
(not illustrated in FIG. 13). Such grinding operations could
include grinding, polishing, buffing, honing, cutting, drilling,
sharpening, filing, lapping, sanding, and/or other similar tasks.
For example, the automated abrasive device 1330 may include a
cutting tool, a sanding tool, a buffing tool, and/or a polishing
tool.
[0185] Whereas an operator generally operates manual abrasive
device 1310, in the present disclosure, a controller operates
automated abrasive device 1330. The controller could take the form
of computing device 1000 and could be configured to perform various
actions, such as turning on automated abrasive device 1330, turning
off automated abrasive device 1330, setting a rotational speed for
automated abrasive device 1330, applying a desired grinding or
cutting force at a desired location and/or angle with respect to
the workpiece, among other possibilities. In some implementations,
the actions of the controller are configured based on output from
machine learning system 1210. For example, machine learning system
1210 could make predictions based on abrasive operational data
(e.g., vibration data, electrical current data, wheel speed data,
etc.) received from a controller of automated abrasive device 1330.
After making the predictions, machine learning system 1210 could
reconfigure the controller in accordance with the predictions, for
example, by setting a new maximum rotational speed at the
controller. In some examples, the automated abrasive device 1330
could take the form of a computer numerical control (CNC) grinding
machine, and the aforementioned controller could be a CNC
controller. However, other types of automated abrasive devices are
possible and contemplated.
[0186] Manual abrasive device 1310 and automated abrasive device
1330 may include one or more components that enable the tool to
perform one or more of the abrasive operations. In particular,
manual abrasive device 1310 and automated abrasive device 1330 may
include an abrasive article for performing the one or more
operations described. The abrasive article may include one or more
materials that may be used to shape or finish a workpiece. The one
or more materials may include an abrasive mineral such as calcite
(calcium carbonate), emery (impure corundum), diamond, CBN, diamond
dust (e.g., synthetic diamonds), novaculite, pumice, rouge, sand,
corundum, garnet, sandstone, tripoli, powdered feldspar,
staurolite, borazon, ceramic, ceramic aluminum oxide, ceramic iron
oxide, corundum, glass powder, steel abrasive, silicon carbide
(carborundum), zirconia alumina, boron carbide, and slags.
Additionally and/or alternatively, the one or more materials may
include a composite material that includes a coarse-particle
aggregate that is pressed and bonded together using a bond. The
composite material may include clay, a resin, a glass, a rubber,
aluminum oxide, silicon carbide, tungsten carbide, garnet, and/or
garnet ceramic.
[0187] Furthermore, the abrasive article may have one of many
shapes. For instance, the article may take the form of a sheet, a
block, a stick, a wheel, a ring, or a disc, among other examples.
In the example shown in FIG. 13, the manual abrasive device 1310
may include a wheel shaped abrasive article 1316. Additionally,
manual abrasive device 1310 and automated abrasive device 1330 may
include a power source that may be configured to actuate the
abrasive article to perform an operation. Within examples, the
power source may be an electric motor, a petrol engine, or
compressed air. The manual abrasive device 1310 and automated
abrasive device 1330 may also include a housing that houses the
power source. The housing may be formed from hard plastic, phenolic
resin, or medium-hard rubber, among other examples.
[0188] In some embodiments, manual abrasive device 1310 and
automated abrasive device 1330 may include an identifying feature,
such as a scannable identifier (e.g., QR code, barcode, serial
number, etc.) that may be engraved in or affixed. The identifying
feature may be used to identify a type of the tool, a manufacturer
of the tool, a model of the tool, and/or a unique identifier of the
tool. Additionally and/or alternatively, the individual components
of manual abrasive device 1310 and automated abrasive device 1330
may include an identifying feature. For example, an abrasive
article may include an identifying feature that is engraved in
and/or affixed to the abrasive article. The identifying feature may
be used to identify a type of the abrasive article, a manufacturer
of the abrasive article, a model of the abrasive article, and/or a
unique identifier of the abrasive article.
[0189] Manual abrasive device 1310 and automated abrasive device
1330 may include one or more sensors that can collect data in
real-time from an environment of the tool and/or from the tool
itself (e.g., within a handle, a body of the tool, and/or coupled
to an abrasive product). In some embodiments, the system may
additionally include remote sensors that are disposed in an
environment in which an operation is being performed. These sensors
and the embedded sensor may be configured to transmit collected
data to server device 1340. Sensors may be configured to transmit
operational data of the tool and the unique identifier of the
tool.
[0190] Wearable device 1320 may be in the form of a wrist-mountable
device that is mounted onto a wrist of a user's hand 1322. The
user's hand 1322 may be a dominant hand of the operator that is
favored by the operator when performing tasks. Here, the operator
may use hand 1322 (on which the wearable device 1320 is mounted) to
grasp handle 1312 of manual abrasive device 1310. Wearable device
1320 may be configured obtain real-time data that may be used to
determine abrasive operational data. To obtain real-time data,
wearable device 1320 may include sensors that can collect data in
real-time from an environment of an abrasive product and/or from
the abrasive product itself. Wearable device 1320 may be configured
to communicate with remote sensors on an abrasive product and/or
with the one or more sensors associated with the abrasive product.
Additionally, the wearable device may include a communication
interface to transmit collected data to server device 1340, for
example, in a real-time and/or asynchronous fashion. In some
embodiments, wearable device 1320 could be operable to run web
applications, which could include event-driven scripts operating in
a Node.js (e.g., JavaScript everywhere) runtime environment, among
other possibilities.
[0191] In some embodiment, sensors of the wearable device 1320 may
be configured to read or scan an identifying feature of manual
abrasive device 1310 or an abrasive article of manual abrasive
device 1310. For example, sensors may include an image capture
device (e.g., a camera) that may capture and analyze images of
manual abrasive device 1310 in order to determine a type or may
scan identifying feature 1314. Identifying the tool and/or the
components thereof may allow wearable device 1320 to provide an
operator with information associated with the tool and/or the
components thereof. Additionally and/or alternatively, the
identification may allow wearable device 1320 to associate data
collected by sensors in the environment with manual abrasive device
1310 and/or the particular component being used to perform the
desired operation.
[0192] In the scenario 1300, the sensors of manual abrasive device
1310, wearable device 1320, automated abrasive device 1330, and
other sensors 1350 may continuously or periodically collect data
from or associated with the operation of manual abrasive device
1310 and automated abrasive device 1330. For example, the collected
abrasive operational data may include sound data, acceleration
data, vibration data, gyroscope data and/or data extrapolated from
the sound, acceleration, and/or vibration data (e.g., applied force
data, RPM data, usage rate, etc.). In example embodiments,
collected abrasive operational data may be correlated with a
material, a material removal rate, an operating condition, an
expended power, or a specific grinding energy.
[0193] Additionally, sensors of manual abrasive device 1310,
wearable device 1320, automated abrasive device 1330, and other
sensors 1350 may be positioned in various locations with respect to
the abrasive device/tool and/or the workpiece operated on. For
example, a vibration sensor, gyroscope, microphone, and/or any
other sensor may be embedded within a tool or a handle of a tool.
In other examples, sensors could be located nearby the tool and/or
workpiece. In yet another example, sensors could be mounted on a
work surface on which the workpiece may lay. In even further
examples, sensors could be mounted at a wall or ceiling location.
It will be understood that multiple sensors could be located at
various locations nearby a tool and/or workpiece to provide
"stereo" or multi-sensor combinations. Such multiple sensor
combinations could provide information on which tool is being used
and/or disambiguate particular sounds based on stereoscopic or
multiscopic sensing.
[0194] In some embodiments, sensors may include an accelerometer
that may be operable to measure and record acceleration information
in three axes (x, y, and z). For example, wearable device 1320 may
include an accelerometer configured to collect acceleration data
related to the user's hand 1322 when operating manual abrasive
device 1310. Accordingly, the accelerometer may measure the hand's
acceleration as a result of the vibration. Because the hand's
vibration is a result of the tool's vibration, the acceleration
information collected by the accelerometer may be indicative of the
vibration of the tool. In this scenario, the acceleration
information may be used to determine an extent of vibration of the
tool. The vibration data, which is an example of abrasive
operational data, could be used to extrapolate other abrasive
operational data. As an example, the vibration data may be used to
determine operational information of the tool, such as an
operational status and operational hours. For instance, the
operational status could include "OFF", "IDLE", and "SANDING",
among other possibilities. As another example, the vibration data
may be used to determine grinding information of the performed
abrasive operation, such as a working angle, a grip tightness, an
applied pressure, an angular velocity (e.g., revolutions per
minute, RPM), among other variables.
[0195] In some embodiments, sensors could include microphones
configured to collect sound data from an abrasive operation. The
collected sound data may be used to determine an RPM at which an
abrasive product is operating by analyzing the amplitude of the
sound data for correlation with known sound amplitudes for
different RPM values of the abrasive product.
[0196] In some embodiments sensors could include an optical or
magnetic sensor operable configured to detect visible/magnetic
attachments to a grinding wheel of disc and provide information
about an angular velocity (RPM) of a grinding wheel or disc.
[0197] In some embodiments, sensors may include spark-invariant
sensors configured to detect rotation of sensing targets attached
to a grinding wheel or disc and provide information about an
angular velocity (RPM) of the grinding wheel or disc. In some
embodiments sensors could include pressure sensors to determine air
pressure applied to a grinding tool.
[0198] In example embodiments, determined RPM values may be used to
extrapolate abrasive operational data. For example, RPM values may
determine a grinding power of a tool by using a data (e.g., a
table) indicative of a correlation between an RPM of a particular
tool and the grinding power exerted by the tool. As another
example, the RPM value may determine a force that is applied to the
workpiece by using a data (e.g., a table) indicative of a
correlation between an RPM of a particular tool and the grinding
power exerted by the tool.
[0199] In some embodiments, sensors may collect information
indicative of the workpiece. For example, sensors could include an
image capture device (e.g., a camera) configured to capture an
image of the workpiece. The image may be analyzed in order to
determine a status of the workpiece, including a type of the
workpiece, a material of the workpiece, dimensions of the
workpiece, surface characteristics of the workpiece, and/or an
arrangement of the workpiece in the environment (e.g., orientation,
angle, position with respect to a reference point in the
environment). In some embodiments, information about the workpiece
could be determined based on other types of sensor information. For
example, while grinding is in process, interactions between the
grinding tool and various workpiece materials could provide
different noise and vibration data. In example embodiments, a
database of tagged noise and vibration data could specifically
identify various workpiece materials. In such a scenario,
subsequently captured noise and vibration data could be compared to
entries in the tagged noise and vibration database so as to provide
information about the material of a given workpiece.
[0200] In some embodiments, sensors may collect information
indicative of the microscopic interactions (e.g., tribology)
occurring in a grinding zone. (i.e., the region between a grinding
wheel and work material). For example, sensors may include a
vibration sensor configured to extrapolate the depth of penetration
of an abrasive grain against a workpiece during each step of a
grinding process. Each depth of penetration may then be linked to a
specific microscopic interaction, such as cutting (e.g., material
removal), plowing (e.g., material displacement), sliding (e.g,
surface modifications). Notably, the surface generated at the end
of the grinding process may be the cumulative effect of the
different types of microscopic interactions.
[0201] Server device 1340 may include one or more computing devices
configured to receive and aggregate sensor data from sensors of
manual abrasive device 1310, wearable device 1320, automated
abrasive device 1330, and other sensors 1350. Communication between
these sensors and server device 1340 may be facilitated via a
wireless fidelity (Wi-Fi) connection, a Bluetooth connection, a
light fidelity (Li-Fi) connection, an infrared connection, a near
field communication (NFC) connection, or some other wireless
connection. In some embodiments, sensors could be configured to
communicate with the server device 1340 using Message Queuing
Telemetry Transport (MQTT) or another type of messaging
protocol.
[0202] Upon receiving sensor data, server device 1340 may apply
data filters to the sensor data or convert sensor data into a
different data format. Server device 1340 may transmit sensor data
to a machine learning platform, such as machine learning platform
1110, in order determine product-specific information and/or
workpiece-specific information related manual abrasive device 1310,
wearable device 1320, automated abrasive device 1330, and other
sensors 1350.
[0203] In some embodiments, server device 1340 may be configured to
allow a human operator to tag sensor data. In particular, tags may
be utilized as labels or additional training features for training
one or more machine learning models. For instance, tags may
identify product specific information of the one or more abrasive
products associated with the sensor data. This may include
operational statuses related to vibration data. As an example, an
operational status of "walking" may be assigned to vibration data
with small peaks. In another example, an operational status of
"idle" may be assigned to vibration data with a stable slope.
[0204] In some embodiments, tags may identify patterns of abrasion
operation data immediately before an abrasive product related
event. For example, an abrasive product related event may include a
serious injury of an operator using an abrasive product. As such,
tags may be associated with vibration sensor data from a wearable
device of the operator before injury. For instance, a "critical"
tag may be associated with the vibration data an hour prior to the
injury and a "danger" tag may be associated with the vibration data
minutes or less than a minute prior to the injury.
[0205] In some embodiments, server device 1340 may provide graphs
of sensor data to aid a human operator with extrapolating patterns
that are indicative of a particular condition or a performance
indicator of abrasive product. As explained herein, a human
operator may use one or more data analysis methods to extrapolate
the patterns. As an example, a sensor graph may be used to
extrapolate a correlation between a power signal supplied to a tool
during an operation and vibration of the tool during the operation
in order to assign tags to the vibration data. In particular,
vibration data with amplitude greater than a threshold during a
period of time/stage may be indicative of assigning the abrasive
product a "powered on" tag for that period time/stage of time.
Furthermore, vibration data with an amplitude greater than a second
threshold for a period of time/stage may be indicative of assigning
the abrasive product a "severe conditions" tag during that period
of time/stage. In another example, operating the abrasive product
under severe conditions, acceleration data may include higher peaks
than when operating the abrasive product under normal conditions.
Accordingly, peaks greater than a threshold in the vibration data
may be indicative of a "severe condition" tag.
[0206] In some embodiments, the human operator may use methods such
as machine learning (e.g., Bayesian classifiers, support vector
machines, linear classifiers, k-nearest-neighbor classifiers,
decision trees, random forests), Fast Fourier Transform (FFT),
artificial intelligence (AI) methods (e.g., neural networks, fuzzy
logic, cluster analysis, or pattern recognition), filtering, peak
value, mean, standard deviation, skewness, and/or kurtosis to
assist with tagging the sensor data. For example, the human
operator may use a machine learning platform, such as machine
learning platform 1110, to assist creating tags for sensor data.
Notably, the created tags may then be used to train one more
machine learning models on the same machine learning platform.
[0207] Other sensors 1350 may include other sensors disposed in the
environment around manual abrasive device 1310, wearable device
1320, and automated abrasive device 1330. For example, other sensor
may include temperature sensors to detect to the temperature of the
environment around manual abrasive device 1310 and automated
abrasive device 1330. In some examples, sensors in manual abrasive
device 1310, wearable device 1320, and automated abrasive device
1330, and other sensors 1350 may communicate directly with a
machine learning platform, such as machine learning platform
1110.
IX. Example Outputs
[0208] FIG. 14 depicts scenario 1400 representing devices that
receive predictions from machine learning system 1210, according to
an example embodiment. Scenario 1400 may include abrasive products
1410, remote devices 1420, vendors 1430, and analytics platform
1440. Notably, abrasive products 1410 may be embodied by abrasive
products 1122, analytics platform 1440 may be embodied by analytics
platform 1118, vendors 1430 may be embodied by outside vendors
1130, and remote devices 1420 may be embodied by remote devices
1128.
[0209] Abrasive products 1410 may include, for example, automated
abrasive devices 1412, manual abrasive devices 1414, and wearable
devices 1416 that are communicatively coupled to manual abrasive
devices 1414, as described herein. As such, machine learning system
1210 may output predictive information relating to abrasive
operational data of abrasive products 1410. Such abrasive
operational data may be gathered by sensors disposed on or near
abrasive products 1410. For example, abrasive operational data may
include information about an angular velocity (RPM) of a grinding
wheel, severity of the operation, and shocks experienced by the
tool. In another example, the abrasive operational data may include
grinding parameters of the manual abrasive devices 1414, including
working angle, grip tightness, and applied pressure. As another
example, noise sensors and spark-invariant sensors may provide
electrical power information of automated abrasive devices 1412
and/or manual abrasive devices 1414. In a further example, the
abrasive operational data may include gyroscope data indicative of
the work piece on which the operation is being performed (e.g.,
based on sensor data, such as an image, indicative of the
workpiece).
[0210] Based on this abrasive operational data, machine learning
system 1210 may determine product-specific information of abrasive
products 1410 and/or workpiece-specific information of the
workpiece operated on by abrasive products 1410. Machine learning
system 1210 may then provide abrasive products 1410 with one or
more notifications alerting the product-specific information or
workpiece-specific information. In response to receiving the
indication, abrasive products 1410 may output a visual, haptic,
and/or audio alert or may take an automated action. For example,
abrasive products 1410 may include a graphical user interface (GUI)
or an attached indicator light visual for notifications to be
displayed. In another example, abrasive products 1410 may include
an apparatus that converts electrical impulses into sound, such as
an embedded speaker, for audio alerts to be heard. As a further
example, abrasive products 1410 may include an embedded computing
device that may automatically alter the abrasive operation of
abrasive products 1410 in response to an indication from machine
learning system 1210. In yet another example, wearable device 1416
may include a vibration mechanism such that alerts can be
communicated as vibrations to the wearable device 1416.
[0211] In some embodiments, machine learning system 1210 may
determine whether the operating speed of a particular abrasive
wheel of abrasive products 1410 is within a safe, or productive
operating range for the particular workpiece being operated on, the
particular abrasive wheel, or a combination thereof. For example,
machine learning system 1210 may provide a warning signal to
abrasive products 1410 in the form of warning light. In another
example, machine learning system 1210 may provide an automatic
instruction to abrasive products 1410 to adjust an RPM, turn on,
and/or turn off. For instance, this automatic instruction may be a
signal to a fluid control valve, e.g., an air valve, to adjust the
speed of the grinder or an indication to use a grinding dresser to
dress the surface of the abrasive wheel.
[0212] In some embodiments, the machine learning system 1210 may
determine that an abrasive article of abrasive products 1410
damaged or malfunctioning. For instance, the machine learning
system 1210 may analyze acceleration and/or noise data to determine
that the abrasive article is damaged and/or malfunctioning. This
may involve detecting one or more patterns in the acceleration
and/or noise data that may be indicative of a damaged or
malfunctioning abrasive article. For instance, a first pattern of
spikes or peaks may be indicative of a damaged abrasive product and
a second pattern of spikes or peaks may be indicative of a
malfunctioning abrasive product. After making a determination,
machine learning system 1210 may provide abrasive products 1410
with an indication that the abrasive article is damaged or
malfunctioning. Such an indication may be a visual, haptic, and/or
audio alert. In some embodiments, machine learning system 1210 is
configured to send an indication upon the acceleration and/or noise
data exceeding predefined threshold (e.g., maximum desired tool
vibration level, maximum desired tool noise level, etc.). In such
embodiments, the indication could be instruction to shut of one or
more abrasive products 1410. Additionally, the alert may provide
the user with an option to order a replacement article or to
request maintenance for the article via a GUI.
[0213] In some embodiments, machine learning system 1210 may
determine, based on the gyroscope data, that a user is positioning
manual abrasive device 1414 at an angle that is different from a
recommended angle. Based on this abrasive operational data, machine
learning system 1210 may determine that the user of manual abrasive
device 1414 is incorrectly performing an operation. Machine
learning system 1210 may then provide the manual abrasive device
1414 and/or wearable device 1416 with an indication that a user is
performing the operation incorrectly. In response to receiving the
indication, manual abrasive device 1414 and/or wearable device 1416
may output a visual, haptic, and/or audio alert that indicates to
the user that the user is performing the operation incorrectly.
Additionally and/or alternatively, the machine learning system 1210
may continuously provide the user with feedback indicative of
correct performance of the operation by providing notifications
related to the abrasive operations.
[0214] In some embodiments, machine learning system 1210 may
determine an ergonomic status of the user operating manual abrasive
device 1414. For instance, the determination may be based on an
analysis of the data captured from manual abrasive device 1414.
Machine learning system 1210 may determine that the user has been
performing operations for a period of time greater than a
recommended time and/or may determine that the user is operating
manual abrasive device 1414 at a force, vibration, and/or RPM level
higher/lower than desired. For instance, an operator and/or an
abrasive product manufacturer could provide machine learning system
1210 with upper and lower limits for force, vibration, and/or RPM.
The upper and lower limits could be determined via machine learning
system 1210, analytics platform (e.g., analytics platform 1118)
and/or could be based on an occupational safety standard, either
enforced today or in the future. For example, the upper and lower
limits may be based on standards set by the Occupational Safety and
Health Administration (OSHA), the National Institute for
Occupational Safety and Health (NIOSH), the European Agency for
Safety and Health at Work (EU-OSHA), or the International
Organization for Standardization (ISO). Accordingly, machine
learning system 1210 could determine whether the force, vibration,
and/or RPM data collected from the user operating manual abrasive
device 1414 is within an "optimal zone", e.g., falls between the
upper and lower limits. If the percentage of time that the user
operates within the optimal zone is sufficiently low, machine
learning system 1210 can provide manual abrasive device 1414 and/or
wearable device 1416 with information to increase the percentage of
time in the "optimal zone", in some embodiments, by outputting a
visual, haptic, and/or audio alert that provides operational
improvements, recommended angles of operation, and so on. In such a
manner, systems and methods described herein could monitor
operating conditions relevant to worker safety and/or compliance
with international, federal (e.g., OSHA), state, and/or local rules
and guidelines. In some embodiments, a safety margin (e.g., 1-10%
of maximum limit value) could be included so as to ensure
compliance and to avoid inadvertent violations due to, for example,
sensor miscalibration or other minor sensor errors.
[0215] In some embodiments, optimal zones relating to different
abrasive operations and/or tools could be operationally dependent.
For example, the optimal zone for vibration data received from a
manual abrasive device may be different than the optimal zone for
vibration data received from a handheld abrasive device. As another
example, the optimal zone for vibration data received from a
lighter handheld abrasive device might be different than the
optimal zone for vibration data received from a heavier handheld
abrasive device.
[0216] Further, the optimal zones could be measurements that are
calculated based on a maximum metric of an abrasive device. For
example, if an abrasive device had a maximum RPM of MAX_PRM, than
the optimal zone of that abrasive device could be calculated as
anywhere between 0.6*MAX RPM to 0.7*MAX RPM.
[0217] Note that while optimal zones were discussed based on data
collected from manual abrasive device 1414, optimal zones could
similarly be developed for data collected from automated abrasive
device 1412.
[0218] In some embodiments, machine learning system 1210 may
determine an end of life estimate for abrasive products 1410. For
example, the determination may be based on an analysis of
historical RPM data or product life data of similar abrasive
products that failed and/or were decommissioned. Machine learning
system 1210 may then provide abrasive products 1410 or a supervisor
of the operator of abrasive products 1410 with an indication that
abrasive products 1410 are near the end of life. This indication
may include an estimated amount of time that an operator can safely
use abrasive products 1410. In some cases, machine learning system
1210 may also be configured to automatically order a replacement
device for abrasive products 1410.
[0219] Remote devices 1420 may include, for example, mobile
computing devices, database devices, tablet computing devices,
and/or other computing devices that facilitate operations including
aggregating received data, filtering received data, and/or
displaying receiving data. Remote devices 1420 may also include
capabilities to execute web applications. Machine learning system
1210 may output predictive information relating to the aggregate
abrasive operational data of multiple abrasive devices in an
enterprise to remote devices 1420. As such, this information may be
displayed by remote devices 1420 for analysis by one or more
supervisors. For example, the aggregate abrasive operational data
may include length of time spent performing assigned tasks, idle
time of abrasive device operators, and/or productive time of
abrasive device operators. For instance, the sound data and/or the
vibration data may be used to determine an abrasive device is in
operation.
[0220] Machine learning system 1210 may be configured to collect
the aggregate abrasive operational data and maintain categorize the
collected data by abrasive operation performed, length of the
abrasive operation, workpiece associated with the abrasive
operation, the abrasive device used, the operator performing the
abrasive operation, and the time the abrasive operation was
performed, feedback on the abrasive operation (e.g., from a manger
or customer), vibration, noise, productivity, product life,
etc.
[0221] Based on the aggregate abrasive operational data collected
from multiple abrasive devices in an enterprise, machine learning
system 1210 may provide predictions for improved
workplace/enterprise operations. For example, machine learning
system 1210 may predict workflows and/or best practices for
performing a particular type of task by utilizing a database of
knowledge base articles that include information related to tasks,
information related to best practices when performing tasks, and
information describing how to use certain abrasive devices to
complete a task. In another example, machine learning system 1210
may provide metrics associated with one or more abrasive devices in
an enterprise. The metrics may include a usage rates, total
operation times, downtime, number of malfunctions, number of repair
requests, and so on. These metrics may be compared across abrasive
devices used in a given task and across operators.
[0222] In example implementations, remote devices 1420 could be
programmed to display the predictions of best practices, workflows,
and metrics received from machine learning system 1210. These could
include graphical visualizations (e.g., histograms, bar charts,
plot trends across time), key performance indicators (KPIs) of the
enterprise, and so on. In some embodiments, remote devices 1420
could be programmed to display virtual rewards that appear in
response to positive action performed by an abrasive device
operator, such as a positive safety actions (e.g., modifying a
grinding angle, performing a recommended safety check, performing
recommended maintenance, etc.) or a positive operational action
(e.g., having the highest/fastest throughput, producing the most
parts per minute, etc.), among other possibilities. In such
embodiments, a central server device (e.g., server devices 1126 or
machine learning platform 1110) could establish among remote
devices 1420 virtual competitions in which the virtual rewards
associated with each remote device 1420 are compared to one
another, thus possibly encouraging each operator to increase their
virtual rewards by performing further positive actions. Remote
devices 1420 may also have access to analytics platform 1440, as
discussed below.
[0223] Vendors 1430 may represent one or more computing systems
managed by the operator of machine learning system 1210, partners
of the operator of machine learning system 1210, and/or partners of
the enterprise using machine learning system 1210. For example,
vendors 1430 may include computing systems associated with
manufacturers responsible for assembling components of one or more
abrasive devices, logistics centers responsible for delivering
components to/from the enterprise, research and development
(R&D) centers responsible for improving abrasive device
operations, OSHA inspectors responsible for abrasive device safety,
abrasive device repair technicians responsible for repairing
defective abrasive devices, and so on. To allow flexibility across
potential vendors, machine learning system 1210 may provide the
ability to add and/or remove vendors from communication. Adding a
vendor may involve configuring machine learning system 1210 to
process data in a format provided by a vendor and transmitting data
in a format known to the vendor. Adding a vendor may also involve
configuring machine learning system 1210 to resolve conflicting
actions between vendors. For instance, machine learning system 1210
may resolve conflicting actions by defaulting to actions to a
primary vendor.
[0224] Communication with vendors 1430 may enable machine learning
system 1210 to automatically adjust supply chain/manufacturing
operations in response to predicted outputs. As an example, machine
learning system 1210 may collect temporal abrasive operational data
over the lifetime of an operator or abrasive device to provide an
estimate for abrasive product life, abrasive product condition,
and/or operator conditions. Based on the predicted output from the
temporal abrasive operational data, machine learning system 1210
may be configured to transmit commands directly to vendors 1430.
Such commands may include an automatic order of new components for
an abrasive product nearing a predicted end of life, an automatic
request for an OSHA inspection to comply with regulatory laws based
on a predicted safety hazard, and/or a cancelation of an order
based on a predicted extension of an end of life.
[0225] In some examples, machine learning system 1210 may collect
data associated with microscopic interactions between an abrasive
device and workpiece during a grinding process. Each set of
microscopic interactions may be tagged with a type of surface
generated at the end of the grinding process. Accordingly, machine
learning system 1210 may utilize this type of surface generated to
predict how microscopic interactions can be manipulated
individually or in some combination, based on machine tool
capabilities, abrasive grains, bonds, the grain/bond interaction
between the surface and the tool, or structure, work material
properties, abrasive device features and specifications, as well as
operational factors such as truing and dressing, cycle design,
coolant application, to create a desired surface. Additionally, the
power of abrasive device, grinding efficiency of the device tool,
and cutting efficiency of the device may be collected and used to
create a desired surface.
[0226] In example embodiments, machine learning system 1210 may
utilize such relationships, provide predictions based on the
relationships, and/or transmit the predictions to an abrasive
manufacturing vendor to help produce improved abrasive products or
grinding processes that achieve a specific set of microscopic
interactions, construct new abrasive grains or abrasive tools that
have different microscopic interactions with a workpiece, and/or
create custom abrasive products and grinding processes to address a
customer's requirements.
[0227] In some embodiments, machine learning system 1210 may
provide enterprises the option to configure the types of automatic
alerts sent to vendors 1430. For example, machine learning system
1210 may be configured to only order new components based on the
type of abrasive article that components are needed for, the cost
of components, the delivery time of the components, and so on.
[0228] Analytics platform 1440 may include a web application
configured to receive, across multiple enterprises, abrasion
operational data and predictions made by machine learning system
1210. By utilizing data across multiple enterprises, analytics
platform 1440 may provide services to simulate the operation of
abrasive products, estimate costs of purchasing new abrasive
products, and/or perform other analytical operations. For example,
enterprises without access to a specific abrasive product may
utilize analytics platform 1440 to estimate the costs or
productivity rates associated with the specific abrasive products.
Analytics platform 1440 may be accessed by one or more users though
a web browser.
[0229] In some embodiments, analytics platform 1440 may provide
tiered services. Each tiered may provide different portions of
information to a user. For instance a higher tier may give a user
access to all services on the analytics platform 1440 while a lower
tier may only give a user access to a portion of services on
analytic platform 1440. Tiers may be assigned to users based on a
fee or subscription basis or another paid for service
mechanism.
[0230] In some embodiments, analytics platform 1440 may be
configured to anonymize received abrasion operational data and
predictions from machine learning system 1210. For example,
de-identification methods such as k-anonymization may be utilized
disassociate abrasion operational data from an enterprise by
ensuring that data from at least k enterprises are
undistinguishable. As another example, analytics platform 1440 may
be configured to take restrictions from each enterprise, the
restrictions indicating the types of abrasion operational data that
may be shared and the types of abrasion operational data that may
not be shared.
[0231] In various embodiments, analytics platform 1440 includes a
simulation environment programmed with digital versions (e.g.,
"digital twins") of physical abrasive products used by an
enterprise. The simulation environment could use these digital
versions to estimate productivity, costs, and/or injuries resulting
from adding/reconfiguring/removing different digital abrasive
products from the stimulation environment. The simulation
environment may be modeled, for instance, by modeling software such
as ProModel.TM..
[0232] In some examples, the simulation environment could use a
digital version of a physical abrasive product to generate
synthetic sensor data. The synthetic sensor data could mirror
actual sensor data generated from a physical abrasive product.
However, unlike actual sensor data, the synthetic sensor data could
have the advantage of being quick and easy to produce. The
synthetic sensor data could be provided to an enterprise for cost
estimates, throughput analysis, energy usage rates, and so on.
Additionally, the synthetic sensor data could be provided to a
machine learning system (e.g., machine learning system 1210) and
used to train one or more machine learning models.
[0233] Further, the simulation environment could provide the
ability to simulate the construction of a new abrasive product. For
example, the simulation environment may provide the ability to
model, via a computer aided design (CAD) program such as
AutoCAD.TM., a completely new abrasive product or a new variation
of an existing abrasive product. Machine learning system 1210 could
then estimate abrasive operational data, such as RPM, and related
predictions (e.g., cost, idle time) that could result from
operations of the new abrasive product. This may aid the user in
deciding whether to actually create and manufacture the new
abrasive product.
X. Example Methods
[0234] FIG. 15 is a flowchart of method 1500, in accordance with
example embodiments. Method 1500 may start at block 1502, where a
wearable device, such as wearable device 1320, may collect sensor
data from an abrasive operation. For example, the abrasive
operation may include an operator with a wearable device using a
manual angle grinder to grind on a workpiece (e.g., a piece of
sheet metal). During the grinding operation, the wearable device
may determine, based on an embedded gyroscope sensor, angular
velocity values of the wearable device with respect to the X, Y,
and Z axis. These values may be used to estimate user's positioning
of the manual angle grinder. Moreover, the wearable device may
determine, based on communication with a sound sensor embedded in
the angle grinder, the frequency, amplitude, and wavelength of
sounds emitted from a rotating grinding wheel on the angle grinder.
This data may be used to estimate the operational status of the
angle grinder. Notably, sound data and gyroscope data are just
examples of sensor data that may be collected at block 1502. In
reality, any sensor data described with respect to FIG. 13 and FIG.
14 may be utilized.
[0235] At block 1504, machine learning system 1210 may receive the
sensor data from one or more server devices configured aggregate
sensor data from the wearable device, such as server device 1340,
or directly from the one or more sensors on the wearable device.
Machine learning system 1210 may then convert the received sensor
data into feature vectors for input into one or more machine
learning models. For example, machine learning system 1210 may be
configured to create a feature vector (with an associated label)
for sensor data collected at a given timestamp. In other words,
machine learning system 1210 may create feature vector V_1 to
describe sensor data collected at time T_1 and may create a feature
vector V_2 to describe data collected at time T_2. Machine learning
system 1210 may also convert categorical data collected from the
sensors, such as product type, into one hot encodings. At block
1506, machine learning system 1210 may preprocess the feature
vectors to enable more accurate predictions. For example,
preprocessing may include normalization techniques (scaling to unit
norm), discretization of continuous features (e.g., binning),
and/or other known methodologies.
[0236] At block 1508, machine learning system 1210 may assign the
feature vector to a trained machine learning model for execution.
As noted above, machine learning system 1210 may contain a
plurality of trained machine learning models, each trained to make
predictions on a specific subset of abrasive products. For example,
machine learning system 1210 may use a unique identifier, such as
identifying feature 1314 (which may have been transmitted along
with the sensor data by the wearable device) to recognize that the
input feature vector corresponds to a manual angle grinder. With
this, machine learning system 1210 may select a machine learning
model that has been trained with sensor data from comparable manual
angle grinders. Continuing from the above example, the selected
machine learning model may be a feedforward Artificial Neural
Network (ANN) model that predicts whether the operator of the
identified abrasive product is performing an unsafe abrasive
operation. However, any machine learning model as described with
respect to FIG. 12 may be utilized.
[0237] At blocks 1510, machine learning system 1210 may execute the
selected machine learning model with the input feature vector to
predict one or more outputs. In accordance with the present
example, the ANN model may use a softmax function to map the
non-normalized output of the ANN to a probability distribution over
predicted outputs. For example, predicted outputs may include
operator conditions such as "OK", "APPROACHING CRITICAL", and
"CRITICAL". At block 1512, the predicted probabilities may be
ranked and the highest probability output or any probability output
greater than a predefined threshold may be provided to the wearable
device. Moreover, based on this predicted output, machine learning
system 1210 may be configured to provide a recommendation to the
wearable device to resolve any harmful conditions that are
predicted. Other predicted outputs are possible.
[0238] At block 1514, the wearable device may receive a
notification from machine learning system 1210 with one or more
recommendations to provide to the operator. For example, the
wearable device may include a graphical user interface for a visual
notification to be displayed. This indication may be a flashing
warning indicating that the operator is performing a dangerous
operation using the abrasive device. In response this indication,
the operator may change the position of the angle grinder. This
change in positioning may result in another prediction by machine
learning system 1210 to determine whether the changed position has
resolved the hazard. Notably, other types of notifications and
display mechanisms as described with respect to FIG. 15 are
possible.
[0239] FIG. 16 is a flowchart of method 1600, in accordance with
example embodiments. Method 1600 may start at block 1602, where an
automated abrasive device, such as automated abrasive device 1330,
may collect sensor data from an abrasive operation. For example,
the abrasive operation may include the automated abrasive device
operating on a workpiece (e.g., a piece of sheet metal). During the
grinding operation, the automated abrasive device may determine,
based on an embedded spark-invariant sensors, the RPM of a grinding
wheel of the automated abrasive device. For example, the spark
invariant sensor may be configured to detect rotation of sensing
targets attached to a grinding wheel. Notably, RPM data is just an
example of sensor data that may be collected at block 1602. In
reality, any sensor data described with respect to FIG. 13 and FIG.
14 may be utilized.
[0240] At block 1604, machine learning system 1210 may receive the
sensor data from one or more server devices configured aggregate
sensor data from the automated abrasive device, such as server
device 1340, or directly from the one or more sensors of the
automated abrasive device. Machine learning system 1210 may then
convert the received sensor data into feature vectors for input
into one or more machine learning models. For example, machine
learning system 1210 may be configured to create a feature vector
(with an associated label) for sensor data collected at a given
timestamp. In other words, machine learning system 1210 may create
feature vector V_1 to describe sensor data collected at time T_1
and may create a feature vector V_2 to describe data collected at
time T_2. Machine learning system 1210 may also convert categorical
data collected from the sensors, such as product type, into one hot
encodings. At block 1606, machine learning system 1210 may
preprocess the feature vectors to enable more accurate predictions.
For example, preprocessing may include normalization techniques
(scaling to unit norm), discretization of continuous features
(e.g., binning), and/or other known methodologies.
[0241] At block 1608, machine learning system 1210 may assign the
feature vector to a trained machine learning model for execution.
As noted above, machine learning system 1210 may contain a
plurality of trained machine learning models, each trained to make
predictions on a specific subset of abrasive products. For example,
machine learning system 1210 may use a unique identifier to
recognize that the input feature vector corresponds to the
automated abrasive device. With this, machine learning system 1210
may select a machine learning model that has been trained with
sensor data from comparable automated abrasive devices. Continuing
from the above example, the selected machine learning model may be
a support vector machine (SVM) that predicts whether the automated
abrasive device is operating is malfunctioning or defective.
However, any machine learning model as described with respect to
FIG. 12 may be utilized.
[0242] At blocks 1610, machine learning system 1210 may execute the
selected machine learning model with the input feature vector to
predict one or more outputs. In accordance with the present
example, the SVM model may be multiclass SVMs that utilizes a one
vs. one training mechanism (e.g., each pair of classes/predicted
outputs is assigned to one SVM of the multi-class SVM) to determine
a score for each of predicted outputs. For example, predicted
outputs may include device conditions such as "OK",
"MALFUNCTIONING", "OVERHEATING", and "DANGER". At block 1612, the
scores may be ranked and the predicted output with the highest
score or any predicted output with a score greater than a
predefined threshold may be provided to the automated abrasive
device. Moreover, based on this predicted output, machine learning
system 1210 may be configured to provide a control instruction to
the automated abrasive device to resolve any harmful conditions.
Other predicted outputs are possible.
[0243] At block 1614, the automated abrasive device may receive a
notification from machine learning system 1210 with one or more
control instructions. For example, the automated abrasive device
may include an embedded computing device that may control the
operations of the automated abrasive device. This embedded
computing device may receive the notification and adjust a
rotational speed, provide a notification, turn on tool, or turn off
tool. Alternatively and/or additionally, machine learning system
1210, upon predicting that the automated abrasive device is
malfunctioning, may be configured to place an automated request to
order new parts to fix the automated abrasive device. Notably,
other types of notifications and display mechanisms as described
with respect to FIG. 16 are possible.
[0244] FIG. 17 is a flowchart of method 1700, in accordance with
example embodiments. Method 1700 may start at block 1702, where
machine learning system 1210 outputs one or more predictions
corresponding to sensor data received from one or more enterprises.
For example, predictions may include average estimated idle times
of operators performing abrasive operations in an enterprise. Other
predictions as described herein are possible.
[0245] At block 1704, the predictions from block 1702 may be
aggregated together to calculate a global average prediction. For
example, the average estimated idle time of operators performing
abrasive operations may be aggregated across multiple enterprises.
At block 1706, one or more global average predictions may be
utilized as inputs into a simulation engine that mimics abrasive
operations of an artificial enterprise over a predefined time
period (e.g., 10 years, 20 years). As such, the simulation engine
may rely on the one or more global average predictions as
parameters to guide the simulation and produce synthetic abrasive
operational data.
[0246] At block 1708, output statistics from the simulation
determined from the synthetic abrasive operational data, such as
number of injuries, total productive time, and/or total cost, are
provided. These output statistics may aid a user of analytics
platform 1440 with making decisions such as whether to order more
abrasive devices, whether to hire more operators, or whether to
create a new supply chain strategy. Other options are possible.
XI. Example Abrasive Wheels
[0247] During an assembly process, an abrasive product manufacturer
may couple identification tags (or colloquially referred to as
"tags") to abrasive wheels. Through these tags, information unique
to each abrasive wheel can be ascertained. For example, tags may
provide details regarding an abrasive wheel's manufacturing date,
surface material, GPS position within an environment, size, and/or
expiration date. Suitably, the abrasive product manufacturer and/or
customers of the abrasive product manufacture may utilize tags to
make informed decisions.
[0248] In some aspects, tags may be used to track movement of
abrasive wheels within an enterprise environment. For example, the
enterprise environment may be equipped with several tag readers (or
colloquially referred to as "readers") within a given environment.
These readers may communicate with tags to establish which abrasive
wheels are entering a given region of the environment and which
abrasive wheels are leaving such regions.
[0249] In some aspects, tags may improve the setup efficiency of
abrasive devices. For example, upon attaching an abrasive wheel to
an abrasive device, the abrasive device may automatically
communicate with the abrasive wheel's tag to determine features
(e.g., diameter, structure, material) of the abrasive wheel. Using
this information, the abrasive device may determine the best
operating parameters (e.g., RPM speed, applied pressure, feed rate,
depth of cut, traverse rate, coolant factors, dressing factors,
etc.) to correctly operate the abrasive wheel.
[0250] In some aspects, tags may increase the safety of abrasive
devices. For example, upon attaching an abrasive wheel to an
abrasive device, the abrasive device may automatically communicate
with the abrasive wheel's tag to determine a level of deterioration
of the abrasive wheel (e.g., the waning of the abrasive wheel's
outer coating). If the deterioration is beyond a threshold level,
the abrasive device may notify a supervisor or a device operator
that the deteriorated abrasive wheel should be replaced with a new
abrasive wheel.
[0251] In some aspects, tags may improve the logistical aspects of
an enterprise. For example, an enterprise may use tags to
dynamically maintain part inventory and determine whether and/or
when new abrasive wheels should be ordered.
[0252] In some aspects, tags may be combined with software
applications that can display tag information in a user friendly
format. Moreover, the software applications may offer users the
capability to perform actions, such as ordering new abrasive
wheels, in response to obtaining the tag information.
[0253] As an example operation, an abrasive product manufacturer
may wish to track the usage of an abrasive wheel within a
customer's environment. To do so, the abrasive product manufacturer
may couple a tag to the abrasive wheel and may provide the customer
with a software application to communicate with the tag. Then, when
using the abrasive wheel, the customer may update, via software
application, the tag to reflect how many workpieces the abrasive
wheel has operated on, how long the abrasive wheel was being used,
and so on. Such usage information could be made available to the
abrasive product manufacture by the customer. In such scenarios, if
the abrasive wheel is transported back to the abrasive product
manufacturer for repairs and/or refurbishment, the abrasive product
manufacturer may be knowledgeable to the history of the abrasive
wheel and, in turn, may determine an optimal repair strategy to
address the abrasive wheel's usage history and particular use
case.
[0254] Other features, functionality, and benefits of tags may
exist and will be appreciated and understood from the discussion
below.
[0255] FIGS. 18A and 18B illustrate an abrasive wheel 1800,
according to example embodiments. Abrasive wheel 1800 may take on
all or some of the properties of the abrasive wheels or grinding
wheels previously described herein. In examples, abrasive wheel
1800 contains tag 1810 and connection mechanism 1812. Abrasive
wheel 1800 may serve as a grinding component of an abrasive device
or abrasive product, such as manual abrasive device 1310 or
automated abrasive device 1330, and may be physically connected to
the abrasive device, perhaps via connection mechanism 1812. In some
embodiments, tag 1810 could include a quick response (QR) code, bar
code, a radio-frequency identification (RFID) tag (both active and
passive), a near field communication (NFC) tag, a BLUETOOTH LOW
ENERGY (BLE) device, or another type of tag. In examples, tag 1810
may contain information regarding abrasive wheel 1800 and/or may
include a unique identifier, such as a universally unique
identifier (UUID), which could be used as a pointer reference. The
pointer reference could direct a computing device to information
regarding abrasive wheel 1800 that is stored on a database device
or elsewhere. While abrasive wheel 1800 is presently described, it
will be understood that other types of abrasive products, such as
bonded abrasives, coated abrasives, non-woven abrasives, thin
wheels, cut-off wheels, reinforced abrasive articles,
superabrasives, single-layered abrasive articles and multi-layered
abrasive articles are all possible and contemplated herein. Any of
these other types of abrasive products could also include an
identifier and could be utilized as described below.
[0256] FIG. 19 illustrates components of tag 1810, according to
example embodiments. In particular, tag 1810 is shown to include
one or more sensors 1810A, microcontroller 1810B, RFID integrated
circuit (IC) 1810C, and antenna 1810D. In some examples, tag 1810
can have more, fewer, and/or different types of components than
indicated in FIG. 19.
[0257] Sensor(s) 1810A may take on all or some of the properties of
the sensors previously described herein. In some embodiments,
sensor(s) 1810A may be physically outside the confines of tag 1810,
but may be communicatively coupled to components within tag 1810.
In other embodiments, sensor(s) 1810A may be physically inside the
confines of tag 1810, as depicted in FIG. 19.
[0258] In some embodiments, sensor(s) 1810A may include
magnetometers configured to sense a surrounding magnetic field of a
workpiece operated on by abrasive wheel 1800. The magnetic field
may be converted into an analog or digital electronic signal and
transmitted to microcontroller 1810B, which may be configured to
convert the magnetic field data to equivalent orientation data.
[0259] In some embodiments, sensor(s) 1810A may include temperature
and humidity sensors configured to provide information about the
environmental temperature and humidity levels around abrasive wheel
1800. The readings may be converted into equivalent analog or
digital electronic signals and transmitted to microcontroller
1810B.
[0260] In some embodiments, sensor(s) 1810A may include
accelerometers configured to measure the vibration, orientation,
surface acoustic levels, revolutions per min (RPM), and/or angular
acceleration of abrasive wheel 1800. These measurements may be
converted into analog or digital electronic signals and transmitted
to microcontroller 1810B.
[0261] In some embodiments, sensor(s) 1810A may include a
capacitive input interface capable of measuring variations in
material density or potential damages related to abrasive wheel
1800 by way of fluctuations in capacitance between capacitive
plates and/or wires. These measurements may be converted into
digital electronic signals and transmitted to microcontroller
1810B.
[0262] Microcontroller 1810B may take on all or some of the
properties of computing device 1000. In some embodiments,
microcontroller 1810B may be physically outside the confines of tag
1810, but may be communicatively connected to the components within
tag 1810. In examples, microcontroller 1810B may be configured to
receive digital electronic signals from sensor(s) 1810A and write
the receive signals as data into the memory of RFID IC 1810C. In
some embodiments, microcontroller 1810B may be physically outside
the confines of tag 1810, but may be communicatively coupled to
components within tag 1810. In other embodiments, microcontroller
1810B may be physically inside the confines of tag 1810, as
depicted in FIG. 19.
[0263] RFID IC 1810C may be an integrated circuit that stores and
processes information as well as modulates/demodulates signals.
RFID IC 1810C may take on all or some of the properties of
computing device 1000. In examples, RFID IC 1810C may contain
information regarding unique tag identifiers, unique tag serial
numbers, passwords, or perhaps product-related information such as
a stock number, lot or batch number, production date, or other
specific information related to abrasive wheel 1800. Additionally,
RFID IC 1810C may be able to store information provided by
microcontroller 1810B, such as data collected from sensor(s) 1810A.
The power to operate RFID IC 1810C may come from a battery unit
attached to tag 1810 or may be obtained from the operations of
antenna 1810D. In some embodiments, RFID IC 1810C may be physically
outside the confines of tag 1810, but may be communicatively
coupled to components within tag 1810. In other embodiments, RFID
IC 1810C may be physically inside the confines of tag 1810, as
depicted in FIG. 19.
[0264] Antenna 1810D may include an antenna structure and
associated circuitry used by tag 1810 that is configured to receive
and transmit signals. In examples, antenna 1810D may be
communicatively coupled to RFID IC 1810C. In some embodiments,
antenna 1810D may include an induced antenna coil that provides
power to the components of tag 1810. Antenna 1810D can be made from
a variety of materials and can be printed, etched, stamped, or
vapor deposited onto tag 1810. In some embodiments, antenna 1810D
may be physically outside the confines of tag 1810, but may be
communicatively coupled to components within tag 1810. In other
embodiments, antenna 1810D may be physically inside the confines of
tag 1810, as depicted in FIG. 19.
[0265] FIG. 20 illustrates a communication environment 2000 between
tag 1810 and reader 2022, according to example embodiments.
Communications between tag 1810 and reader 2022 may be over
communication medium 2010, which may include RFID, NFC, and/or BLE
communications over ultra high (e.g., at or near 900 megahertz),
high (e.g., at or near 14 megahertz), or low (e.g., at or near 130
kilohertz) frequencies, where the physical distance during
communication between tag 1810 and reader 2022 may vary based on
the frequency and type of communication medium 2010. The data
received by reader 2022 may be information related to abrasive
wheel 1800 and/or a unique identifier of abrasive wheel 1800.
[0266] In some examples, reader 2022 may take on the form of a
portable, wireless reader system. In operations, reader 2022 may
receive information from tag 1810 and immediately transmit the
information to controller 2020 or a portable mobile device via
wireless protocols, such as Bluetooth or Wi-Fi.
[0267] In some examples, reader 2022 may take on the form of a
portable, wireless reader physically connected to a mobile device.
In operations, reader 2022 may receive information from tag 1810
and immediately transmit the information to the mobile device via
USB connections, micro USB connections, or similar physical
connection mechanisms.
[0268] In some examples, reader 2022 may take on the form of a
fixed reader system equipped with antennas that can communicate
with tag 1810. In operations, reader 2022 may receive information
from tag 1810 and subsequently transmit the information to a mobile
device or controller 2020 via wireless protocols discussed
above.
[0269] FIG. 21 illustrates a communication environment 2100 between
client network 2120 and remote network 2110, according to an
example embodiment. Client network 2120 may take on all or some of
the properties of enterprise 1120, as illustrated and described in
reference to FIG. 11. Remote network 2110 may take on all or some
of the properties of machine learning platform 1110, as illustrated
and described in reference to FIG. 11. In examples, client network
2120 may be a computer network used by an entity to manage abrasive
operations. Client network 2120 may include controller 2020, data
storage 2122 (e.g., database devices, file systems), server devices
2124 (e.g., remotely hosted server devices, on premise server
devices, virtual machines, etc.), abrasive devices 2126 (e.g.,
handheld grinding devices, automated grinding devices, including
manual abrasive device 1310 or automated abrasive device 1330), and
user device(s) 2128 (e.g., mobile devices and/or wearable device
1320 as illustrated and described in reference to FIG. 13). Client
network 2120 may be communicatively coupled to remote network 2110,
which may be configured to manage aspects of client network
2120.
[0270] It should be noted that any component on remote network 2110
and/or client network 2120 could be replicated across multiple
computing devices and/or hosted by third-party networks (e.g.,
cloud networks) to provide data duplication and increase capacity
of services. Replicated components may be located at various
computing locations to ensure high availability in case of power
failure at one computing location. In some cases, remote network
2110 and/or client network 2120 may consist of a few devices and a
small number of components. In other deployments, remote network
2110 and/or client network 2120 may span across multiple physical
locations and could include hundreds, thousands, or more devices
and other components. In some cases, specific components on client
network 2120 may be managed by remote network 2110. For example,
one or more server devices 2124 or controller 2020 may be installed
onto client network 2120 by remote network 2110 in order to support
the various embodiments herein.
[0271] FIG. 22 illustrates method 2200, according to an example
embodiment. In particular, method 2200 may represent a specific
sequence or series of actions that, when performed, allows remote
network 2110 to gather tag information from client network 2120 and
provide updated status information to user device(s) 2128. By way
of example, method 2200 may utilize remote network 2110, client
network 2120, and user device(s) 2128 during operation. However,
additional components, steps, or blocks, may be added to method
2200. For example, the steps described within respect to remote
network 2110 may take place wholly or in part on a server device
(e.g., server devices 2124) located on client network 2120 or
third-party networks (e.g., AMAZON WEB SERVICES.TM.) operated by
client network 2120. Such a scenario may be referred to as a
"locally hosted solution" and may allow the client network 2102 to
execute the operations of method 2200 while maintaining high levels
of security and flexibility by having all or some of the entities
executing method 2200 within client network 2120.
[0272] Method 2200 may begin at step 2202, when client network 2120
gathers data from one or more tags, such as tag 1810. As described
above, several means may be utilized to gather tag data from
abrasive wheels. For example, a portable, wireless reader may be
used to collect tag data and transmit the tag data to controller
2020. As another example, a fixed reader system may be used to
collect tag data. In another example, a user of a mobile device may
manually input tag data, perhaps after physically inspecting the
properties of an abrasive wheel. Other possibilities may also
exist. Moreover, step 2202 may exemplify a continuous procedure
taking place on client network 2120, in which one or more readers,
such as reader 2022, continuously operate to collect data from one
or more tags within client network 2120.
[0273] At step 2204, client network 2120 may transmit tag data to
remote network 2110. Step 2204 may be facilitated by several
entities within client network 2102. For example, controller 2020
may communicate data to remote network 2110, user device(s) 2128
may communicate data to remote network 2110, and/or reader 2022 may
communicate tag data to remote network 2110. In examples,
transmission of data from client network 2120 to remote network
2110 may occur based on a predetermined frequency interval (e.g.,
every 1 ms, or 1 s) or based upon an event occurring on client
network 2120 (e.g., any time new tag data is collected).
[0274] At step 2206, remote network 2110 may update internal
information to reflect the received tag data from step 2204.
Accordingly, step 2206 may include updating databases records, file
entries, software parameters, and so on. Subsequently, at step
2208, remote network 2101 may provide an updated user interface to
user device(s) 2128. The user interface may be represented by way
of a web page or series of web pages hosted by remote network 2110
and provided to user device(s) 2128. For example, in some
embodiments, the user interface presented to user device(s) 2128
may provide an overview of all abrasive wheels currently used
within client network 2120 as a series of selectable items. In some
such scenarios, the selectable items may be configured to allow a
user to view the collected tag data for each respective abrasive
wheel. Additionally, the user interface may provide key performance
indicators (KPIs), such as overall inventory level, as well as user
implementable actions.
[0275] At step 2210, upon users selecting or otherwise choosing at
least one implementable action, the at least one action may be
transmitted by user device(s) 2128 to remote network 2110. In
response, at step 2212, the at least one action transmitted at step
2210 may be executed by remote network 2110.
[0276] In some cases, executing the at least one action can include
one or more interactions with abrasive devices 2126 on the client
network 2120. For instance, remote network 2110 can establish
communication with abrasive devices 2126 on client network 2120 to
send requests to halt the operations of certain abrasive devices,
resume the operations of abrasive devices, and so on.
[0277] In some cases, executing the at least one action could
include interacting with a third-party vendor, such as outside
vendor 1130, to order replacement parts or repair/refurbish a
malfunctioning or used abrasive wheel. In some cases, executing the
at least one action can involve remote network 2110 placing a
request to ship new abrasive wheels, for example, in the case of an
inventory shortage determined by client network 2120.
[0278] In some cases, executing the at least one action can involve
providing alerts in the form of a text message, email alert,
another other type of communication to other user device(s) 2128 on
client network 2120. For example, an administrator from client
network 2120 could transmit a message to all users of a certain
product line to halt operations due to a potential production
problem.
[0279] Notably, the actions discussed above with respect to 2212
are not intended to be limiting. In practice, other actions may
also be possible.
[0280] FIG. 23 illustrates state diagram 2320, according to an
example embodiment. In particular, state diagram 2320 conceptually
demonstrates how abrasive wheel 1800 may move between various
operative states during the course of its lifespan. As used herein,
operative state may refer to a current location or function being
performed by or on abrasive wheel 1800. Some operative states may
be visited zero times or more than one time. Also, some operative
states may have more than one possible next state, thus
representing a decision to be made based information within on tag
1810. Note that upon entering a new state within state diagram
2320, tag 1810 may be "tagged" to reflect this new state of
abrasive wheel 1800.
[0281] Abrasive wheel 1800 may start in manufacturer state 2322,
which may represent a physical environment associated with remote
network 2110. Here, abrasive wheel 1800 may be constructed and
coupled with tag 1810.
[0282] From manufacturer state 2322, abrasive wheel 1800 may move
to gate state 2324, which may represent the arrival of abrasive
wheel 1800 to a physical environment associated with client network
2120. At gate state 2324, one or more readers, such as reader 2222,
may be utilized to communicate and obtain information from tag 1810
regarding the unique identifier of abrasive wheel 1800 and/or the
order number associated with abrasive wheel 1800. If the unique
identifier and order number match the records of client network
2120 (e.g., client network 2120 has records that they actually
purchased abrasive wheel 1800 from remote network 2310), abrasive
wheel 1800 may continue to shop floor state 2326. In some examples,
abrasive wheel 1800 may be untagged from gate state 2324 and
returned to manufacturer state 2322.
[0283] Shop floor state 2326 may represent the arrival of abrasive
wheel 1800 to a grinding environment associated with client network
2120. Once in shop floor state 2326, one or more readers, such as
reader 2222, may be utilized to communication and obtain
information from tag 1810 regarding the dimensions and appropriate
operating speeds of abrasive wheel 1800. If the dimensions and
appropriate operating speeds match records of client network 2120
(e.g., client network 2120 determines that the dimension and
appropriate operating speeds of abrasive wheel 1800 match the
acceptable parameters of at least one abrasive device operating
within the grinding environment associated with client network
2120), abrasive wheel 1800 may continue to wheel balancing state
2328. Otherwise, abrasive wheel 1800 may be untagged from shop
floor state 2326 and return to manufacturer state 2322.
[0284] At wheel balancing state 2328, abrasive wheel 1800 may be
inspected for imbalance issues that could potentially harm abrasive
device operators/damage workpieces. If the imbalance issues exceed
a predefined tolerance level, abrasive wheel 1800 may be untagged
from wheel balancing state 2328 and sent back to shop floor state
2326 for repairs. Otherwise, abrasive wheel 1800 may continue to
operation state 2330.
[0285] At operation state 2330, abrasive wheel 1800 may be attached
to an abrasive device, for example a manual abrasive device 1310 or
an automated abrasive device 1330, to perform abrasive operations.
During operation state 2330, tag 1810 may be updated to reflect how
many workpieces the abrasive wheel 1800 has operated on, how long
the abrasive wheel 1800 is being used, and so on. After abrasive
wheel 1800 starts to show deterioration, one or more readers may be
utilized to communicate with and obtain information from tag 1810
regarding the expiration date and manufacturing date of abrasive
wheel 1800. In some embodiments, if abrasive wheel 1800 is near its
end of life, abrasive wheel 1800 may be untagged from operation
state 2330 may enter a scrap state 2332, which may signify an end
of life state for abrasive wheel 1800. In other examples, abrasive
wheel 1800 may be untagged from operation state 2330 and enter
manufacturer state 2322 for repairs.
XII. Example Mobile Devices
[0286] FIG. 24A illustrates a view of mobile device 2400, according
to example embodiments. In examples, mobile device 2400 can be
configured with authentication mechanisms, which may include a
passcode, two-factor authentication, fingerprint identification,
facial recognition, or verification of other biometric information.
Such authentication mechanisms may provide varying levels or types
of user access. For examples, the levels of user access may include
levels of access for users from client network 2120, administrators
from client network 2120, and/or non-administrators from client
network 2120. Based on the present user's level of access, the
mobile device 2400 may display a different arrangement of
information, provide access to different types of information,
and/or offer varying functionality.
[0287] Information on mobile device 2400 may include maintenance
information, information related to the state of client network
2120 (e.g., the number of abrasive wheels available, the
service-life of abrasive wheels, etc.), and so on. As noted above,
mobile device 2400 may also contain selectable options to perform
actions. As examples, the actions could include reviewing product
information 2402 and/or reviewing current stock information 2404.
It will be understood that mobile device 2400 could include a
smartphone, tablet, laptop computer, or another type of computing
device. Yet further, mobile device 2400 could include, for example,
a head-mountable display (HMD), a heads-up display (HUD), or
another type of portable computing device with or without a user
interface.
[0288] Such embodiments may provide an easier way for customers to
reorder abrasive products. In such scenarios, upon receiving
information of an impending end-of-useful life of a given abrasive
product, the mobile device 2400 could provide a notification of
such information to a user. Additionally, or alternatively, the
mobile device 2400 could be configured to display a product catalog
and/or products that may functionally replace the abrasive product
that is reaching the end of its useful life. Accordingly, in such
examples, a user could interact with the mobile device 2400 to
request a given abrasive article, a quantity, shipping type,
desired delivery date, etc. The mobile device 240 could forward the
request to the client network 2120 and/or the remote network 2110.
Such a system and method could reduce manual ordering efforts
and/or paper work, which may be more environmentally-friendly.
Reorder requests made via mobile device 2400 could be authorized by
a user with appropriate permissions (e.g., administrator, manager,
and/or sales personnel).
[0289] FIG. 24B illustrates a second view of mobile device 2400,
according to example embodiments. In particular, FIG. 24B
illustrates how mobile device 2400 may include multiple navigation
menus to assist users in performing various functions. For example,
inventory menu 2406 may provide an interface that displays
information regarding all abrasive wheels that have been purchased
by a particular user. Operation menu 2408 may provide an interface
that displays data about the operations of abrasive wheels, perhaps
displaying data collected by sensor(s) 1810A. Gate entry 2410 may
provide an interface enables users interact with tags coupled to
abrasive wheels, perhaps by enabling a reader embedded onto mobile
device 2400 or communicatively connected to mobile device 2400.
Shop floor menu 2412 may provide an interface that displays
information regarding which abrasive wheels are currently within in
a shop floor state or operation state, as previously discussed in
connection with state diagram 2320. Discard menu 2414 may provide
an interface that allows users to untag an abrasive wheel, perhaps
in connection with scrap state 2332 in state diagram 2320. Notably,
other navigation menus are also possible.
[0290] FIG. 24C illustrates a third view of mobile device 2400,
according to example embodiments. In particular, FIG. 24C
illustrates several, non-limiting examples of information items
that may ascertained with respect to a particular abrasive wheel.
For example, FIG. 24C illustrates that information regarding an
abrasive wheel's product ID, order ID, product name, product type,
dimensions, manufacturing date, expiration date, customer ID,
customer name, operating speed, inventory time, inventory storage
costs, storage wait time, and storage costs can be ascertained.
Notably, other information items (e.g., GPS location of the
abrasive wheel) are also possible.
XIII. Example Web Applications and Data Models
[0291] As described above, a web application may be configured to
display information about remote sensors, wearable devices,
abrasive devices, abrasive device operators, and so on. This may be
accomplished by way of a web page or series of web pages hosted by
a cloud computing device and provided to users upon request. The
layout and compilation of information in these web pages may enable
efficient review of pertinent information about the remote sensors,
wearable devices, abrasive devices, abrasive device operators, and
so on. Additionally, the web pages may organize and arrange the
information using graphics with intuitive visuals and easy to
understand metrics.
[0292] As an additional feature, the web application may allow
users to make associations between abrasive devices, wearable
devices, abrasive device operators, and plants (e.g., an
environment in which abrasive operations are being performed). For
example, a user may associate plant P1 with abrasive device AT1 to
indicate that abrasive device AT1 is operating within plant P1. The
user may then associate abrasive device AT1 with wearable device
WD2 to indicate that the data collected by wearable device WD2 is
with respect to the operations of abrasive device AT1. Finally, the
user may associate wearable device WD1 with operator O1 to indicate
that operator O1 is wearing wearable device WD1. In this way,
abrasive devices, wearable devices, abrasive device operators, and
plants become distinct logical entities on the web application
which can be mixed in matched with each other.
[0293] Having distinct logical entities may have numerous benefits.
For example, suppose that wearable device WD1 was permanently
associated with operator O1. If operator O1 suddenly became
unavailable, then no data could be collected from wearable device
WD1 during the unavailability. On the other hand, suppose that
wearable device WD1 was a distinct logical entity from operator O1.
If operator O1 became unavailable, then wearable device WD1 could
quickly be associated with operator O3 and data could still be
collected for wearable device WD1. Advantageously, data can be
collected from wearable device WD1 regardless of operator O1 or
operator O3. Other advantages are also possible.
[0294] FIG. 25 illustrates model 2500, in accordance with example
embodiments. Model 2500 may include four base tables--plant table
2510, device table 2530, wearable table 2550, and operator table
2550--and three linking tables--plant device table 2520, device
wearable table 2540, and operator wearable table 2560. As a unit,
these tables provide the necessary information to capture the
relationships between plants, abrasive devices, wearable devices,
and operators. In some examples, model 2500 can have more, fewer,
and/or different types of tables than indicated in FIG. 25.
Moreover, the tables in model 2500 may be abridged for the purposes
of clarity. But in practice, these tables may contain more, fewer,
and/or different entries.
[0295] Plant table 2510 can include entries for plants. In
particular, each entry in plant table 2510 may have a unique
identifier for a plant and associated information for the plant. In
some examples, a user may input, for example through a web page or
series of web pages provided by a cloud computing device, the
information to populate plant table 2510.
[0296] Plant device table 2520 can include entries that map a given
plant from plant table 2510 to an abrasive device from device table
2530 that operates in the given plant. In particular, the web
application described above may provide means for dynamically
populating the entries in plant devices table 2520. For example,
the web application may provide a series of dropdown menus to allow
users to make associations between plants and abrasive devices that
operate within those plants.
[0297] Device table 2530 can include entries for abrasive devices,
such as manual abrasive device 1310. In particular, each entry in
device table 2530 may have a unique identifier for an abrasive
device and associated information for the abrasive device. In some
examples, a user may input, for example through a web page or
series of web pages provided by a cloud computing device, the
information to populate device table 2530. In other examples, the
information in device table 2530 can be populated from the remote
sensors and/or wearable devices as described above.
[0298] Device wearable table 2540 can include entries that map an
abrasive device from device table 2530 to a wearable from wearable
table 2550 that collects data associated with that abrasive device.
In particular, the web application described above may provide
means for dynamically populating the entries in device wearable
table 2540. For example, the web application may provide a series
of dropdown menus to allow users to make associations between
abrasive devices and wearable devices. In some cases, entries in
device wearable table 2540 can be automatically populated through
the readers as described above. For example, an abrasive device may
include an RFID tag, such as identifying feature 1314, and a
wearable device may include an RFID reader that can read the RFID
tag of the abrasive device to associate the wearable device with
the abrasive device.
[0299] Wearable table 2550 can include entries for wearable
devices, such as wearable device 1320. In particular, each entry in
wearable table 2550 may have a unique identifier for a wearable
device and associated information for the wearable device. In some
examples, a user may input, for example through a web page or
series of web pages provided by a cloud computing device, the
information to populate wearable table 2550. In other examples, the
information in wearable table 2550 can be populated from the remote
sensors as described above.
[0300] Operator wearable table 2560 can include entries that map a
wearable device from wearable table 2550 to an operator from
operator table 2570 that wears the wearable device. In particular,
the web application described above may provide means for
dynamically populating the entries in operator wearable table 2560.
For example, the web application may provide a series of dropdown
menus to allow users to make associations between wearable devices
and operators. In some cases, entries in operator wearable table
2560 can be automatically populated through the readers as
described above. For example, a wearable device may include an RFID
tag and an operator may have an RFID reader that can read the RFID
tag of the wearable device to associate the wearable device with
the operator.
[0301] Operator table 2570 can include entries for operators that
wear wearable devices. In particular, each entry in operator table
2570 may have a unique identifier for an operator and associated
information for the operator. In some examples, a user may input,
for example through a web page or series of web pages provided by a
cloud computing device, the information to populate operator table
2570.
[0302] Taken together, the tables of model 2500 provide information
to establish (i) which operators are associated with which wearable
devices, (ii) which wearable deices are associated with which
abrasive devices, and (iii) which abrasive devices are associated
with which plants. In some cases, a web application can use this
information to provide metrics related to plants, wearable devices,
abrasive devices, and operators.
[0303] FIG. 26 illustrates web page 2600, in accordance with
example embodiments. Web page 2600 may be provided to a user by the
web application described above. In particular, web page 2600
provides metrics related to plants, wearable devices, abrasive
devices, and operators.
[0304] As shown in FIG. 26, plant dropdown 2610 allows a user to
indicate a plant from a plurality of plants for which they want to
receive metrics on. Devices dropdown 2620 allows a user to select
one or more devices for which they want to receive metrics on. The
devices available in devices dropdown 2620 may be based on the
user's selection on plant dropdown 2610 and on the entries in plant
device table 2520. Date range 2630 allows a user to select the date
range for which they want to receive metrics on. After making
selections for plant dropdown 2610, devices dropdown 2620, and date
range 2630, the user can continue by pressing "Search". This action
may display one or more entries corresponding to the information in
the plant dropdown 2610, devices dropdown 2620, and the date range
2630 (e.g., entry 2640).
[0305] Entry 2640 includes metrics related a particular operator
using a device selected from device dropdown 2620, within a plant
selected from plant dropdown 2610, and during the time range
selected from date range 2630. The particular operator may be
determined based on entries in operator wearable table 2560,
wearable table 2550, and device wearable table 2540. Entry 2640
shows grind time metric 2650, optimal grinding metric 2660, and
vibration exposure metric 2670 for the particular operator.
[0306] Grind time metric 2650 displays a bar graph of total
grinding time of the particular operator during the date range
2630. In particular, grind time metric 2650 may be determined using
the embodiments with respect to graph 2900 and graph 3000, as
further described below.
[0307] Optimal grinding metric 2660 displays a bar graph of time
spent by the particular operator while grinding within the optimal
grinding parameters. In particular, optimal grinding metric 2660
may be determined using the embodiments described with respect to
graph 2900 and graph 3000. While optimal grinding metric 2660 is
illustrated as a bar graph, it will be understood that an amount of
time or percentage or ratio of such time while grinding within
optimal grinding parameters could be represented and/or displayed
in a variety of different forms. For example, the optimal grinding
metric 2660 could be represented as a pie chart, a radar chart, a
line graph, or another type of information representation or
infographic.
[0308] Vibration exposure metric 2670 displays a pie chart of
vibration exposure time for the particular operator in three
categories. In particular, vibration exposure metric 2670 may be
determined using the embodiments described with respect to graph
2900 and graph 3000. While the vibration exposure metric 2670 is
illustrated as a pie chart, it will be understood that an amount of
time under respective vibration exposure conditions could be
represented and/or displayed in a variety of different forms. For
example, the vibration exposure metric 2670 could be represented as
a bar graph, a radar chart, a line graph, or another type of
information representation or infographic.
[0309] RPM graph 2680 is a plot indicating RPM levels of the
devices specified in devices dropdown 2620. The x-axis of RPM graph
2680 corresponds to time values, while the y-axis of RPM graph 2680
corresponds to RPM values.
[0310] It will be understood that web page 2600 is presented for
the purpose of example. In other embodiments, web page 2600 may
provide other types of metrics and alternative methods of
displaying such metrics.
[0311] FIG. 27 illustrates displays 2700, 2710, 2720, and 2730 of
wearable device 1320, according to example embodiments. In
particular, the displays shown in FIG. 27 illustrate different
views that may appear on a user interface component of wearable
device 1320. However, note that the displays shown in FIG. 27 are
not limiting; other displays are contemplated and possible within
the scope of the present disclosure.
[0312] Display 2700 provides visual cues about the average
vibration of wearable device 1320, the battery life (shown at the
top left), the current time (shown at the top middle), and whether
a WiFi signal is present on wearable device 1320 (shown at the top
right).
[0313] Display 2710 also depicts the battery life, current time,
and WiFi signal of wearable device 1320, but additionally shows a
time of grinding metric, which may be calculated, for example,
using the graph 2900 and graph 3000 discussed in FIGS. 29 and
30.
[0314] Display 2720 also depicts the battery life, current time,
and WiFi signal of wearable device 1320, but additionally shows an
optimal grinding time metric, which may be calculated, for example,
using the using the graph 2900 and graph 3000 discussed in FIGS. 29
and 30.
[0315] Display 2730 also depicts the battery life, current time,
and WiFi signal of wearable device 1320, but additionally shows an
instantaneous view of current RPM and vibration as the operator is
performing abrasive operations.
XIV. Example Analytics Dashboards
[0316] In line with the discussion above, analytics platform 1118
may be configured to display metrics associated with one or more
abrasive products and/or one or more workpieces in enterprise 1120.
For example, analytics platform 1118 could display technically
oriented metrics (e.g., the surface quality of a workpiece,
abrasive device/tools that are near end of life, etc.), financially
oriented metrics (e.g., estimated costs per abrasive tool,
estimated shop floor efficiency, estimated throughput levels,
etc.), and/or other types of metrics.
[0317] Analytics platform 1118 may display such metrics by way of a
GUI containing one or more panes. As described herein, the term
"pane" may refer to a GUI component that contains one or more
locations in which to display information, and/or one or more
user-selectable items such as buttons or tabs. In some embodiments,
a pane may be equivalent to, or contained within, a page or a GUI
window, though such a window may contain multiple panes. Buttons
and/or tabs may be graphical control elements that display
additional information within the pane.
[0318] Example panes are illustrated below in FIGS. 28A, 28B, and
28C. These example panes organize and arrange information using
graphics with intuitive visuals and easy to understand diagrams. As
a result, a user of analytics platform 1118 can quickly and
efficiently review pertinent information about abrasive operations
occurring at enterprise 1120. Notably, the examples of panes below
are merely for purposes of illustration and not intended to be
limiting. Other panes including alternative arrangements of
information may exist.
[0319] FIG. 28A depicts current time series pane 2810, in
accordance with example embodiments. Current time series pane 2810
includes machine dropdown 2812, date range 2814, current trace
graph 2820, tool ID graph 2822, and part count graph 2824. Current
time series pane 2810 also includes navigation bar 2810, which
includes tabs for "Current Time Series", "Operational Factors",
"Cycle Comparisons", and "Performance Metrics". Notably, the tab
for "Current Time Series" is shown in a dashed line to indicate
that this tab's information is currently displayed.
[0320] Machine dropdown 2812 allows a user to select one or more
abrasive devices for which they want to receive metrics on. The
devices available in machine dropdown 2812 may include all devices
operating in (or that have operated in) enterprise 1120. In the
example shown in FIG. 28A, the abrasive device "13333" has been
selected.
[0321] Date range 2814 allows a user to select the date range for
which they want to receive metrics on. In the example shown in FIG.
28A, the date range is from "11/01/2019" to "11/02/2019".
[0322] After selections for machine dropdown 2812 and date range
2814 have been made, current time series pane 2810 may responsively
display metrics related to the selection in trace graph 2820, tool
ID graph 2822, and part count graph 2824.
[0323] Trace graph 2820 is a plot depicting the current experienced
by abrasive device "13333" over time. The x-axis of trace graph
2820 corresponds to time values, while the y-axis corresponds to
current values (in amperes). As shown, the current for abrasive
device "13333" varies over time. At some periods of time, the
current is high. These could correspond to periods of time that
abrasive device "13333" is performing an abrasive operation. At
other periods of time, the current is low. These could correspond
to periods of time that abrasive device "13333" is not performing
an abrasive operation. Further, trace graph 2820 shows many
repeating patterns. For example, pattern 2820A is similar to
pattern 2820B. These repeating patterns could correspond to periods
of time that abrasive device "13333" is performing similar abrasive
operations.
[0324] In line with the discussion above, in some embodiments,
analytics platform 1118 may be configured to allow an operator to
tag various traces in trace graph 2820. For example, an operational
status of "grinding" may be assigned to traces in trace graph 2820
with large peaks. As another example, an operational status of
"idle" may be assigned to traces in trace graph 2820 with a stable
slope. The tags may be utilized as labels or additional training
features for training one or more machine learning models, as
previously described herein.
[0325] Tool ID graph 2822 is a plot depicting the abrasive tool
(e.g., abrasive wheel) used by abrasive device "13333" over time.
The x-axis of tool ID graph 2822 corresponds to time values, while
the y-axis corresponds to abrasive tool ID values. As shown, the
abrasive tool used by abrasive device "13333" varies over time. For
example, during the time period corresponding to pattern 2820A,
abrasive device "13333" uses both abrasive tool 2822A and abrasive
tool 2822B.
[0326] Part count graph 2824 is a plot depicting the workpiece
being operated on by abrasive device "13333" over time. The x-axis
of part count graph 2824 corresponds to time values, while the
y-axis corresponds to workpiece ID values. As shown, the workpiece
being operated on by abrasive device "13333" varies over time. For
example, during the time period corresponding to pattern 2820A,
abrasive device "13333" was operating on workpiece 2824A, whereas
during the time period corresponding to pattern 2820B, abrasive
device "13333" was operating on workpiece 2824B.
[0327] In example embodiments, trace graph 2820, tool ID graph
2822, and part count graph 2824 could be utilized to understand the
effects of modifications an abrasive operation. For example,
pattern 2820A may correspond to a time period before a modification
to an abrasive operation and pattern 2820B may correspond to a time
period after the modification. If pattern 2820A and pattern 2820B
exhibit similar traces in trace graph 2820, then it may be
determined that the modification did not have any substantial
effect on the abrasive operation. On the other hand, if pattern
2820A and pattern 2820B exhibit difference traces in trace graph
2820, then it may be determined that the modification did have a
substantial effect. Example modifications to an abrasive operation
may include changing operators, changing operating speeds, and so
on.
[0328] In some embodiments, an operator of an abrasive device can
utilize trace graph 2820, tool ID graph 2822, and part count graph
2824 to make real time adjustments to an abrasive operation. For
example, if it determined that every time abrasive device "13333"
is using abrasive tool 2822A, the current value for abrasive device
"13333" on trace graph 2820 increases, that may indicate that
abrasive tool 2822A is dulling (e.g., requires more energy to
operate at a same speed). Accordingly, an operator may perform a
dressing processes on abrasive tool 2822A to get it sharpened and
back into operational condition. As another example, if the current
value for abrasive device "13333" on trace graph 2820 exceeds a
preset upper limit, then the operator may shut down abrasive device
"13333". Other examples are also possible.
[0329] In some embodiments, trace graph 2820, tool ID graph 2822,
and part count graph 2824 could be utilized to understand the
economic metrics related an abrasive operation. For example, if it
is determined that time it takes to grind workpiece 2824A with
abrasive tool 2822A is less than the time it takes to grind
workpiece 2824A with abrasive tool 2822B, then it could be
economically advantageous to order more of abrasive tool 2822A
rather than abrasive tool 2822B, as using abrasive tool 2822A would
reduce overall grind cycle time. Other examples of economic metrics
are also possible.
[0330] FIG. 28B depicts cycle comparisons pane 2802, in accordance
with example embodiments. Like current time series pane 2800, cycle
comparisons pane 2802 includes machine dropdown 2812 and date range
2814. However, unlike current time series pane 2800, cycle
comparisons pane 2802 includes cycle comparison graph 2830 and
metric dropdown 2832. Cycle comparisons pane 2802 also contains
navigation bar 2810, which includes tabs for "Current Time Series",
"Operational Factors", "Cycle Comparisons", and "Performance
Metrics". Notably, the tab for "Cycle Comparisons" is shown in a
dashed line to indicate that this tab's information is currently
displayed.
[0331] Cycle comparison graph 2830 includes a data plot displaying
current usage for various abrasive devices over some period of
time. The x-axis of cycle comparison graph 2830 corresponds to time
values, while the y-axis corresponds to current values (in
amperes). As shown in FIG. 28B, cycle comparison graph 2830
simultaneously plots the current usage for two abrasive devices:
abrasive device "43128" (the orange colored plot) and abrasive
device "43131" (the blue colored plot). Cycle comparison graph 2830
may be beneficial as it allows a user to concurrently view metrics
associated with several abrasive devices on one data plot, thus
enabling a visual comparison between the performances of those
abrasive devices.
[0332] If a user decides to select an additional abrasive device
from machine dropdown 2812, an additional plot of that additional
abrasive device would be added to cycle comparison graph 2830. In
theory, cycle comparison graph 2830 could include data plots for
tens, if not hundreds of abrasive devices.
[0333] Metric dropdown 2832 may be a dropdown menu that allows a
user to select an alternative metric to display on cycle comparison
graph 2830. Example metrics could include current, vibration, feed
rate, or RPM, among other possibilities. In example embodiments,
cycle comparison graph 2830 may automatically update itself in
response to a selection at metric dropdown 2832. For example
instead of displaying current values in the y-axis of cycle
comparison graph 2830, to the value selected via metric dropdown
2832 may be displayed on the y-axis.
[0334] FIG. 28C depicts performance metrics pane 2804, in
accordance with example embodiments. Like current time series pane
2800, performance metrics pane 2804 includes machine dropdown 2812
and date range 2814. However, unlike current time series pane 2800,
performance metrics pane 2804 includes performance metrics graph
2840. Further, performance metrics pane 2804 contains navigation
bar 2810, which includes tabs for "Current Time Series",
"Operational Factors", "Cycle Comparisons", and "Performance
Metrics". Notably, the tab for "Performance Metrics" is shown in a
dashed line to indicate that this tab's information is currently
displayed.
[0335] Performance metrics graph 2840 may include various plots
that track the integrated current of several abrasive tools over
time. The x-axis of performance metrics graph 2840 corresponds to
time values, while the y-axis corresponds to current values (in
amperes). Similar to cycle comparison graph 2830, performance
metrics graph 2840 may be beneficial as it allows a user to
concurrently view metrics associated with several abrasive tools on
one data plot, thus enabling a visual comparison between the
performances of those abrasive tools.
XV. Example Systems and Methods for Calculation
[0336] As previously discussed, an abrasive product/tool can
include sensors that detect an angular velocity (RPM) of a grinding
wheel or disc. Wearable device 1320 can communicate with these
sensors to receive RPM information and determine a grinding power
and/or applied grinding force of the abrasive product/tool.
Additionally and/or alternatively, wearable device 1320 may use
sound data to determine the RPM of a grinding wheel or disc. In
particular, wearable device 1320 may analyze an amplitude of the
sound data and then use a correlation table to map the sound
amplitude to an estimated RPM value. The mapping between the sound
amplitude and the estimated RPM value may vary depending on the
type of abrasive product/tool.
[0337] In either of the above scenarios, wearable device 1320
relies on communication with sensors or the type of abrasive
product/tool (e.g., for the mapping) to determine RPM information.
Yet it may be advantageous to decouple the reliance of wearable
device 1320 from the abrasive product/tool. Doing so, for example,
may allow wearable device 1320 to determine RPM for any grinding
wheel or disc, independent of the how the abrasive product/tool is
being held by the user of wearable device 1320, regardless of the
type of abrasive product/tool being held, and regardless if any
communication sensors are present on the abrasive product/tool.
[0338] To independently determine RPM, a vibration signal may be
used. In particular, the vibration signal may be determined from an
accelerometer of wearable device 1320. As noted above, the
accelerometer collects acceleration data related to vibration of
the user's hand. Because the hand's vibration results from the
abrasive product/tool's vibration, the acceleration data indicates
the vibration of the abrasive product/tool. The acceleration data
may then be used to calculate a gRMS value over time, resulting in
a vibration signal. Notably, the calculation of gRMS could be
performed on wearable device 1320, on a remote device such as the
aforementioned cloud computing devices, or partially on wearable
device 1320 and partially on a remote device.
[0339] FIG. 29 illustrates graph 2900, according to an example
embodiment. As illustrates in FIG. 29, graph 2900 includes signal
2902, which represents the vibration of wearable device 1320 over
time. Namely, signal 2902 results from the vibration experienced by
a user when wearing wearable device 1320 and using an abrasive
product/tool. The x-axis of graph 2900 corresponds to time values,
while the y-axis corresponds to vibration values (in gRMS).
[0340] An important point to recognize is that since the RPM of a
grinding wheel or disc contributes to the signal 2902, a Fourier
transformation (e.g., Fast Fourier transformation (FFT), short-time
Fourier transform (STFT), etc.) can be performed on signal 2902 to
determine the RPM value. For example, software embedded on wearable
device 1320 can perform a Fourier transformation on signal 2902
from the time period between t0 and t3 to determine the RPM of the
grinding wheel or disc from t0 to t3.
[0341] In some embodiments, the RPM of the grinding wheel of disc
may vary over time. For example, a user can push a grinding wheel
or disc harder into a workpiece (the friction of the workpiece
thereby slowing the rotational speed), the power levels of the
abrasive device/tool can change, and so on. To account for this,
signal 2902 may be divided/sampled into shorter segments and then
software embedded on wearable device 1320 can compute the Fourier
transformations on each shorter segment. For example, a Fourier
transformation on signal 2902 can be performed from the time period
between t0 and t1, from a time period between t1 and t2, and so on.
The RPM for each time segment may be plotted to determine a graph
of RPM over time (as shown in FIG. 30).
[0342] In some embodiments, signal 2902 may be composed of multiple
underlying frequencies and/or may have confounding/alias
frequencies. To determine the exact frequency that corresponds to
the RPM of the grinding wheel or disc, a frequency with the highest
amplitude or a frequency with an amplitude within a predetermined
range may be used. Alternatively, in scenarios in which signal 2902
is divided into shorter segments, the RPM for a given time segment
may be determined based on a frequency with an amplitude that shows
little deviation from a previous time segment. Other methods are
also possible.
[0343] In some embodiments, signal 2902 represents the vibration of
wearable device 1320 with respect to a given axis (e.g., the
accelerometer may be operable to measure and record vibration data
in three axes (x, y, and z)). In these situations, a vibration
signal may be determined for each axis and an aggregate/composite
vibration signal for the grinding wheel or disc may be determined
by weighting/combining the individual vibration signals for each
axis. In some examples, the weighting/combining may be based on an
occupational safety standard, such as the ISO 5349 standard
discussed herein. To illustrate, applying the ISO 5349 standard may
involve combining the vibration signal from each axis by way of a
root mean squared calculation, where each axis is weighted
differently in the composite vibration signal. However, other
occupational safety standards and their corresponding algorithms
for determining the aggregate/composite vibration signals are also
contemplated herein. Wearable device 1320 could be configured to
carry out those algorithms additionally and/or alternatively to the
ISO 5349 standard.
[0344] As discussed above, limits may be placed on the signal 2902.
More specifically, upper limit 2904 and lower limit 2906 may be
used to represent upper and lower limits of vibration, with the
region between upper limit 2904 and lower limit 2906 being an
"optimal zone" of vibration for the abrasive product/tool. In some
embodiments, upper limit 2904 and lower limit 2906 may be
determined by the manufacturer of wearable device 1320 or the
manufacturer of the abrasive product/tool. In other embodiments,
upper limit 2904 and lower limit 2906 may be based on an
occupational safety standard, either enforced today or in the
future. For example, upper limit 2904 and lower limit 2906 may be
based on standards set by the Occupational Safety and Health
Administration (OSHA), the National Institute for Occupational
Safety and Health (NIOSH), the European Agency for Safety and
Health at Work (EU-OSHA), or the International Organization for
Standardization (ISO). In some cases, upper limit 2904 and lower
limit 2906 may be based on the ISO 5349 exposure risks.
[0345] In some embodiments, upper limit 2904 and lower limit 2906
can be determined based on values installed into the firmware of
wearable device 1320 upon manufacturing or user defined values that
are dynamically loaded into the firmware of wearable device 1320.
In examples, user defined values can be communicated to wearable
device 1320 via a user interface component of wearable device 1320,
can be communicated to wearable device 1320 via a web application,
such as the web applications described below, or communicated to
wearable device 1320 from a cloud computing device, such as the
cloud computing devices described above. Other possibilities also
exist.
[0346] Since keeping the vibration of the abrasive product/tool
within the optimal zone can be valuable to the user, wearable
device 1320 may determine deviations from the optimal zone. For
example, wearable device 1320 may determine exposure time 2908,
which corresponds to a length of time which vibrations are in the
optimal zone. Exposure time 2908 can be compared to a total time of
operation (e.g., t3-t0) to determine the percentage of time within
the optimal zone. If the percentage of time within the optimal zone
is sufficiently low, wearable device 1320 can provide information
to increase the percentage of time, perhaps by outputting a visual,
haptic, and/or audio alert that provides operational improvements,
recommended angles of operation, and so on.
[0347] As another example, wearable device 1320 can determine
critical exposure time 2910, which represents a period of vibration
above upper limit 2904. Since operations in excess of critical
exposure time 2910 could be detrimental to users, wearable device
1320 can provide information to decrease critical exposure time
2910, perhaps by outputting a visual, haptic, and/or audio alert as
similarly described above.
[0348] FIG. 30 illustrates graph 3000, according to an example
embodiment. As illustrated in FIG. 30, graph 3000 includes signal
3002, which may represent the RPM of a grinding wheel or disc over
time. Namely, signal 3002 may result from a Fourier transformation
performed on signal 2902 from graph 2900. The x-axis of graph 3000
corresponds to a time value, while the y-axis corresponds to a RPM
value (in gRMS).
[0349] Similarly to graph 2900, graph 3000 contains upper limit
3004 and lower limit 3006, respectively representing the upper and
lower limits of RPM, The region between upper limit 3004 and lower
limit 3006 is an "optimal zone" of RPM for the grinding wheel or
disc. In some embodiments, upper limit 3004 and lower limit 3006
may be determined by the manufacturer of wearable device 1320 or
the manufacturer of the abrasive product/tool. In other
embodiments, upper limit 3004 and lower limit 3006 may be based on
occupational safety standards, either enforced today or in the
future.
[0350] In some embodiments, upper limit 3004 and lower limit 3006
can be determined based on values installed into the firmware of
wearable device 1320 upon manufacturing or user defined values that
are dynamically loaded into the firmware of wearable device 1320.
In examples, user defined values can be communicated to wearable
device 1320 via a user interface component of wearable device 1320,
can be communicated to wearable device 1320 via a web application,
such as the web applications described below, or communicated to
wearable device 1320 from a cloud computing device, such as the
cloud computing devices described above. Other possibilities also
exist.
[0351] Much like graph 2900, keeping the RPM within the optimal
zone of graph 3000 can be valuable to the user. Thus, wearable
device 1320 may operate to determine deviations of RPM from the
optimal zone. For example, wearable device 1320 may determine
critical time 3008, which corresponds to a length of time for which
RPM was above upper limit 3004. Likewise, wearable device 1320 may
operate to determine low use time 3010, which corresponds to a
length of time for which RPM was below lower limit 3006. In either
case, wearable device 1320 can provide information to decrease
critical time 3008 and low use time 3010, perhaps by outputting a
visual, haptic, and/or audio alert that provides operational
improvements, recommended angles of operation, and so on.
[0352] In some embodiments, data from graph 2900 and/or graph 3000
may be transmitted by wearable device 1320 to a cloud computing
device for storage and additional computation. For example, the
cloud computing device can execute the machine learning algorithms
discussed above to discover patterns (e.g., grinding time, optimal
RPM time, overload time, optimum vibration time, etc.) with regard
to signal 2902 and/or signal 3002. Discovered patterns can then be
transmitted to a web application that provides information to the
user. Additionally and/or alternatively, the web application may
include of plots of the vibration of wearable device 1320 over time
(e.g., graph 2900) and/or may include of plots of the RPM of
wearable device 1320 over time (e.g., graph 3000) The web
application may be auto-scalable--capable of being viewed on a
tablet device, desktop computing device, mobile device, and so on.
Further, the web application may be configured to establish
dedicated accounts for various users and may have security measures
in place to isolate each user's data and ensure privacy. In some
embodiments, the cloud computing device or web application can be
used to update the firmware of wearable device 1320, for example,
by transmitting software updates to communication interface 106 of
wearable device 1320.
[0353] Notably, while the embodiments above are discussed with
regard to vibration and RPM data, other types of data are also
contemplated in the disclosure herein.
[0354] In one example, temperature sensors/relative humidity
sensors may be used to provide data about environment temperatures
and humidity levels around wearable device 1320. In turn, the data
collected by the temperature sensors/relative humidity sensors may
be used to measure thermal exposure times for an abrasive
product/tool being operated on by the user of the wearable device
1320. For instance, the temperature sensors/relative humidity
sensors may calculate that an abrasive product/tool operated in a
55.degree. F. environment for 2 hours and then operated in a
105.degree. F. environment for 6 hours. The calculated thermal
exposure times could then be used to determine the remaining
product life/productivity for the abrasive product/tool. For
instance, if the abrasive product/tool frequently operated in a
high temperature environment, then the projected product life of
the abrasive product/tool may shorter than if the abrasive
product/tool frequently operated in a moderate temperature
environment.
[0355] In another example, magnetometers may be used to provide
data about surrounding magnetic fields/orientations of wearable
device 1320 or workpieces operated on by the user of wearable
device 1320.
[0356] In yet another example, capacitance sensors may be used to
provide data about material density or potential damages related to
wearable device 1320 or abrasive tools.
[0357] In a further example, current measurements may be obtained
from abrasive tools and converted into power data. The power data
be used to provide grinding cycle data for the abrasive tools and,
in some cases, may be compared with the aforementioned vibration
and RPM data to gain further insights on an abrasive operation.
Moreover, the data described above data, along with data from other
sensors such as inertial sensors, pressure sensors, and/or force
sensors may be graphed, transformed, displayed on a dashboard, such
as displays 2700, 2710, 2720, and 2730 described above, and
associated with upper and lower threshold limits as similarly
described with respect to graph 2900 and graph 3000.
XVI. Enumerated Example Embodiments
[0358] Embodiments of the present disclosure may relate to one of
the enumerated example embodiments (EEEs) listed below.
[0359] EEE 1 is a computer-implemented method, comprising: [0360]
receiving, at a computing device, sensor data from one or more
sensors, wherein the one or more sensors are disposed in proximity
to an abrasive product or a workpiece associated with the abrasive
product, wherein the one or more sensors are configured to collect
abrasion operational data associated with an abrasive operation
involving the abrasive product or the workpiece; [0361] training,
by way of the computing device and based on the sensor data, a
machine learning system to determine product specific information
of the abrasive product or workpiece specific information of the
workpiece; and [0362] providing, by way of the computing device,
the trained machine learning system.
[0363] EEE 2 is the computer-implemented method of EEE 1, further
comprising tagging at least a portion of the sensor data to provide
tagged sensor data, wherein the tagged sensor data comprises one or
more tags, each tag identifying different product specific
information for the abrasive product.
[0364] EEE 3 is the computer-implemented method of EEE 2, wherein
the one or more tags identify a pattern of the abrasion operation
data from a duration of time prior to an abrasive product related
event.
[0365] EEE 4 is the computer-implemented method of EEE 3, wherein
the pattern of the abrasion operation data comprises one or more
stages, each stage associated with one or more sensor threshold
values, wherein the one or more tags are associated with a stage
based on the one or more sensor threshold values.
[0366] EEE 5 is the computer-implemented method of EEE 1, wherein
training the machine learning system comprises training one or more
machine learning models, wherein each model is trained with sensor
data from abrasive products having unique identifiers from a shared
identifier set.
[0367] EEE 6 is the computer-implemented method of EEE 1, wherein
the sensor data from one or more sensors is aggregated by a local
computing device to provide aggregated sensor data, and wherein
receiving the sensor data comprises receiving the aggregated sensor
data from the local computing device.
[0368] EEE 7 is the computer-implemented method of EEE 1, wherein
the one or more sensors are disposed in a wearable device, wherein
the sensor data from one or more sensors is aggregated by the
wearable device to provide aggregated sensor data, and wherein
receiving the sensor data comprises receiving the aggregated sensor
data from the wearable device.
[0369] EEE 8 is the computer-implemented method of EEE 7, wherein
the sensor data comprises information indicative of a rotation per
minute (RPM) value of the abrasive product.
[0370] EEE 9 is a computer-implemented method, comprising: [0371]
receiving, at a computing device, sensor data from one or more
sensors, wherein the one or more sensors are disposed in proximity
to an abrasive product or a workpiece associated with the abrasive
product, wherein the one or more sensors are configured to collect
abrasion operational data associated with an abrasive operation
involving the abrasive product or the workpiece, and wherein the
computing device has access to a trained machine learning system
configured to receive input sensor data and output product specific
information of the abrasive product or workpiece specific
information of the workpiece; [0372] determining, by applying the
trained machine learning system on the sensor data, product
specific information of the abrasive product or workpiece specific
information of the workpiece; and [0373] providing, to one or more
client devices, the product specific information or the workpiece
specific information.
[0374] EEE 10 is the computer-implemented method of EEE 9, wherein
the one or more sensors are disposed in a wearable device, wherein
the sensor data from one or more sensors is aggregated by the
wearable device to provide aggregated sensor data, and wherein
receiving the sensor data comprises receiving the aggregated sensor
data from the wearable device.
[0375] EEE 11 is the computer-implemented method of EEE 10, wherein
the sensor data comprises information indicative of a rotation per
minute (RPM) value of the abrasive product.
[0376] EEE 12 is the computer-implemented method of EEE 9, wherein
the one or more client devices comprises at least one of: a
wearable device, a mobile device, a dashboard device, a web server,
an analytics processing engine, or a third party server.
[0377] EEE 13 is the computer-implemented method of EEE 9, wherein
providing the product specific information comprises providing
information associated with a new abrasive product or an updated
abrasive product, wherein the information includes, at least in
part, instructions for constructing the new abrasive product or the
updated abrasive product.
[0378] EEE 14 is the computer-implemented method of EEE 9, wherein
providing the product specific information or the workpiece
specific information comprises providing, to the one or more client
devices, a notification.
[0379] EEE 15 is the computer-implemented method of EEE 9, wherein
providing the product specific information or the workpiece
specific information comprises providing, to the one or more client
devices, one or more product specific solutions for resolving
issues with the abrasive product.
[0380] EEE 16 is the computer-implemented method of EEE 15, wherein
the one or more client devices are configured to: [0381] display,
on a graphical user interface, the one or more product specific
solutions, [0382] receive, via the graphical user interface, a
selection of one of the one or more product specific solutions;
[0383] determine training data for the trained machine learning
system based on the selected product specific solution; and [0384]
transmit, to the computing device, the training data.
[0385] EEE 17 is the computer-implemented method of EEE 9, wherein
the abrasive product is a handheld abrasive product operated by a
user.
[0386] EEE 18 is the computer-implemented method of EEE 17, wherein
the abrasive operational data comprises at least one of: vibration
data associated with the handheld abrasive product or acceleration
data associated with the handheld abrasive product.
[0387] EEE 19 is the computer-implemented method of EEE 17, wherein
providing the product specific information or the workpiece
specific information comprises providing a notification to a
graphical interface of a wearable device worn by the user.
[0388] EEE 20 is the computer-implemented method of EEE 17, wherein
the product specific information comprises at least one of: time
spent performing tasks assigned to the user, idle time of the user,
or productive time of the user.
[0389] EEE 21 is the computer-implemented method of EEE 17, wherein
the product specific information comprises at least one of: a
working angle of the user with respect to the handheld abrasive
product, a working angle of the handheld abrasive product with
respect to the workpiece, a grip tightness of the user on the
handheld abrasive product, or an applied pressure of the user on
the handheld abrasive product.
[0390] EEE 22 is the computer-implemented method of EEE 17, wherein
the product specific information comprises an end of life estimate
of the handheld abrasive product, wherein the end of life estimate
comprises an estimated amount of time the user can safely use the
handheld abrasive product.
[0391] EEE 23 is the computer-implemented method of EEE 9, wherein
the abrasive product is an automated abrasive product operated by a
controller.
[0392] EEE 24 is the computer-implemented method of EEE 23, wherein
the one or more sensors comprises a spark-invariant sensor
configured to collect operational speeds of the automated abrasive
product.
[0393] EEE 25 is the computer-implemented method of EEE 23, wherein
providing the product specific information comprises providing a
determination that one or more abrasive articles of the automated
abrasive product are damaged or malfunctioning.
[0394] EEE 26 is the computer-implemented method of EEE 25, wherein
upon providing the determination, the computing device is further
configured to: [0395] identify, by a product database, one or more
replacement abrasive articles for the automated abrasive product;
and [0396] responsive to identifying the one or more replacement
abrasive articles, place a request for the one or more replacement
articles or a refurbishment treatment.
[0397] EEE 27 is the computer-implemented method of EEE 23, wherein
providing the product specific information comprises transmitting
at least one control instruction to the controller of the automated
abrasive product, wherein the at least one control instruction
comprises at least one of: adjust a rotational speed of the
automated abrasive product, provide a notification to the automated
abrasive product, turn on the automated abrasive product, or turn
off the automated abrasive product.
[0398] EEE 28 is the computer-implemented method of EEE 9, further
comprising: [0399] providing, via a graphical user interface, a
search interface, wherein the search interface comprises a
plurality of user-selectable criteria, wherein the user-selectable
criteria comprise at least one of: a location menu, a device menu,
a date range, or a workpiece menu; [0400] receiving, via the
graphical user interface, user-selected search criteria from the
user-selectable criteria; [0401] determining, based on the
user-selected search criteria, one or more metrics; an [0402]
displaying, via the graphical user interface, the one or more
metrics.
[0403] EEE 29 is the computer-implemented method EEE 28, wherein
the one or more metrics comprise at least one of: a grinding time
metric, an optimal grinding metric, a vibration metric, a depth of
cut, a current trace, a tool identifier, or a part count.
[0404] EEE 30 is the computer-implemented method of EEE 29, further
comprising receiving, via the graphical user interface, a desired
metric selected from a metric menu, wherein displaying the one or
more metrics is based on the desired metric.
[0405] EEE 31 is the computer-implemented method of EEE 9, further
comprising: providing, via a graphical user interface, a cycle
comparison interface, wherein the cycle comparison interface is
configured to display at least a portion of the sensor data in an
overlapping arrangement of a plurality of periodic time series.
[0406] EEE 32 is the computer-implemented method of EEE 9, further
comprising: comparing a plurality of periodic time series of at
least a portion of the sensor data, wherein determining the product
specific information of the abrasive product or workpiece specific
information of the workpiece, is based, at least in part, on the
comparison.
[0407] EEE 33 is the computer-implemented method of EEE 9, wherein
determining the product specific information of the abrasive
product or workpiece specific information of the workpiece
comprises determining one or more of: a predicted future condition
of the abrasive product or a predicted future condition of the
workpiece.
[0408] EEE 34 is the computer-implemented method of EEE 33, wherein
providing the product specific information or the workpiece
specific information comprises providing at least one of the
predicted future condition of the abrasive product or the predicted
future condition of the workpiece.
[0409] EEE 35 is the computer-implemented method of EEE 9, wherein
determining the product specific information of the abrasive
product or workpiece specific information of the workpiece
comprises determining a prescriptive action.
[0410] EEE 36 is the computer-implemented method of EEE 35, wherein
providing the product specific information or the workpiece
specific information comprises providing the prescriptive action,
wherein the prescriptive action comprises at least one of:
adjusting an operational parameter of an abrasive machine,
performing a maintenance operation, redressing the abrasive
product, or replacing the abrasive product.
[0411] EEE 37 is a computing system, comprising: [0412] one or more
processors; and [0413] data storage, wherein the data storage has
stored computer-executable instructions that, when executed by the
one or more processors, cause the computing system to carry out
operations, the operations including: [0414] receiving sensor data
from one or more sensors, wherein the one or more sensors are
disposed in proximity to an abrasive product or a workpiece
associated with the abrasive product, wherein the one or more
sensors are configured to collect abrasion operational data
associated with an abrasive operation involving the abrasive
product or the workpiece; [0415] training, based on the sensor
data, a machine learning system to determine product specific
information of the abrasive product or workpiece specific
information of the workpiece; and [0416] providing the trained
machine learning system.
[0417] EEE 38 is the computing system of EEE 37, wherein the
operations further including: tagging at least a portion of the
sensor data to provide tagged sensor data, wherein the tagged
sensor data comprises one or more tags, each tag identifying
different product specific information for the abrasive
product.
[0418] EEE 39 is the computing system of EEE 38, wherein the one or
more tags identify a pattern of the abrasion operation data from a
duration of time prior to an abrasive product related event.
[0419] EEE 40 is the computing system of EEE 39, wherein the
pattern of the abrasion operation data comprises one or more
stages, each stage associated with one or more sensor threshold
values, wherein the one or more tags are associated with a stage
based on the one or more sensor threshold values.
[0420] EEE 41 is the computing system of EEE 37, wherein training
the machine learning system comprises training one or more machine
learning models, wherein each model is trained with sensor data
from abrasive products having unique identifiers from a shared
identifier set.
[0421] EEE 42 is the computing system of EEE 37, wherein the sensor
data is aggregated by a local computing device to provide
aggregated sensor data, and wherein receiving the sensor data
comprises receiving the aggregated sensor data from the local
computing device.
[0422] EEE 43 is the computing system of EEE 37, wherein the one or
more sensors are disposed in a wearable device, wherein the sensor
data from one or more sensors is aggregated by the wearable device
to provide aggregated sensor data, and wherein receiving the sensor
data comprises receiving the aggregated sensor data from the
wearable device.
[0423] EEE 44 is the computing system of EEE 37, wherein the sensor
data comprises information indicative of a rotation per minute
(RPM) value of the abrasive product.
[0424] EEE 45 is the computing system, comprising: [0425] a trained
machine learning system configured to receive input sensor data and
output, based on the input sensor data, product specific
information or workpiece specific information; and [0426] a
computing device configured to: [0427] receive sensor data from one
or more sensors, wherein the one or more sensors are disposed in
proximity to an abrasive product or a workpiece associated with the
abrasive product, wherein the one or more sensors are configured to
collect abrasion operational data associated with an abrasive
operation involving the abrasive product or the workpiece; [0428]
determine, by applying the trained machine learning system on the
sensor data, product specific information of the abrasive product
or workpiece specific information of the workpiece; and [0429]
provide, to one or more client devices, the product specific
information or the workpiece specific information.
[0430] EEE 46 is the computing system of EEE 45, wherein the one or
more client devices comprises at least one of: a wearable device, a
mobile device, a dashboard device, a web server, an analytics
processing engine, or a third party server.
[0431] EEE 47 is the computing system of EEE 45, wherein providing
the product specific information comprises providing information
associated with a new abrasive product or an updated abrasive
product, wherein the information includes, at least in part,
instructions for constructing the new abrasive product or the
updated abrasive product.
[0432] EEE 48 is the computing system of EEE 45, wherein providing
the product specific information or the workpiece specific
information comprises providing, to the one or more client devices,
a notification.
[0433] EEE 49 is the computing system of EEE 45, wherein providing
the product specific information or the workpiece specific
information comprises providing, to the one or more client devices,
one or more product specific solutions for resolving issues with
the abrasive product.
[0434] EEE 50 is the computing system of EEE 49, wherein the one or
more client devices are configured to: [0435] display, on a
graphical user interface, the one or more product specific
solutions, [0436] receive, via the graphical user interface, a
selection of one of the one or more product specific solutions;
[0437] determine training data for the trained machine learning
system based on the selected product specific solution; and [0438]
transmit, to the computing device, the training data.
[0439] EEE 51 is the computing system of EEE 45, wherein the
abrasive product is a handheld abrasive product operated by a
user.
[0440] EEE 52 is the computing system of EEE 51, wherein the
abrasive operational data comprises at least one of: vibration data
associated with the handheld abrasive product or acceleration data
associated with the handheld abrasive product.
[0441] EEE 53 is the computing system of EEE 51, wherein providing
the product specific information or the workpiece specific
information comprises providing a notification to a graphical
interface of a wearable device worn by the user.
[0442] EEE 54 is the computing system of EEE 51, wherein the
product specific information comprises at least one of: time spent
performing tasks assigned to the user, idle time of the user, or
productive time of the user.
[0443] EEE 55 is the computing system of EEE 51, wherein the
product specific information comprises at least one of: a working
angle of the user with respect to the handheld abrasive product, a
working angle of the handheld abrasive product with respect to the
workpiece, a grip tightness of the user on the handheld abrasive
product, or an applied pressure of the user on the handheld
abrasive product.
[0444] EEE 56 is the computing system of EEE 51, wherein the
product specific information comprises an end of life estimate of
the handheld abrasive product, wherein the end of life estimate
comprises an estimated amount of time the user can safely use the
handheld abrasive product.
[0445] EEE 57 is the computing system of EEE 45, wherein the
abrasive product is an automated abrasive product operated by a
controller.
[0446] EEE 58 is the computing system of EEE 57, wherein the one or
more sensors comprises a spark-invariant sensor configured to
collect operational speeds of the automated abrasive product.
[0447] EEE 59 is the computing system of EEE 57, wherein providing
the product specific information comprises providing a
determination that one or more abrasive articles of the automated
abrasive product are damaged or malfunctioning.
[0448] EEE 60 is the computing system of EEE 59, wherein upon
providing the determination, the computing device is further
configured to: [0449] identify, by a product database, one or more
replacement abrasive articles for the automated abrasive product;
and [0450] responsive to identifying the one or more replacement
abrasive articles, place a request for the one or more replacement
articles.
[0451] EEE 61 is the computing system of EEE 57, wherein providing
the product specific information comprises transmitting at least
one control instruction to the controller of the automated abrasive
product, wherein the at least one control instruction comprises at
least one of: adjust a rotational speed of the automated abrasive
product, provide a notification to the automated abrasive product,
turn on the automated abrasive product, or turn off the automated
abrasive product.
[0452] EEE 62 is the computing system of EEE 45, wherein the one or
more sensors are disposed in a wearable device, wherein the sensor
data from one or more sensors is aggregated by the wearable device
to provide aggregated sensor data, and wherein receiving the sensor
data comprises receiving the aggregated sensor data from the
wearable device.
[0453] EEE 63 is the computing system of EEE 59, wherein the sensor
data comprises information indicative of a rotation per minute
(RPM) value of the abrasive product.
[0454] EEE 64 is the computing system of EEE 45, further
comprising: [0455] a display configured to provide a graphical user
interface with a search interface, wherein the search interface
comprises a plurality of user-selectable criteria, wherein the
user-selectable criteria comprise at least one of: a location menu,
a device menu, a date range, or a workpiece menu, wherein the
graphical user interface is configured to: [0456] receive
user-selected search criteria from the user-selectable criteria
[0457] determine, based on the user-selected search criteria, one
or more metrics; an [0458] display, via the graphical user
interface, the one or more metrics.
[0459] EEE 65 is the computing system m of EEE 64, wherein the one
or more metrics comprise at least one of: a grinding time metric,
an optimal grinding metric, a vibration metric, a depth of cut, a
current trace, a tool identifier, or a part count.
[0460] EEE 66 is the computing system of EEE 65, wherein the
graphical user interface is further configured to receive a desired
metric selected from a metric menu, wherein displaying the one or
more metrics is based on the desired metric.
[0461] EEE 67 is the computing system of EEE 64, wherein the
display is further configured to provide, via the graphical user
interface, a cycle comparison interface, wherein the cycle
comparison interface is configured to display at least a portion of
the sensor data in an overlapping arrangement of a plurality of
periodic time series.
[0462] EEE 68 is the computing system of EEE 45, wherein the
computing device is further configured to: [0463] compare a
plurality of periodic time series of at least a portion of the
sensor data, wherein determining the product specific information
of the abrasive product or workpiece specific information of the
workpiece, is based, at least in part, on the comparison.
[0464] EEE 69 is the computing system of EEE 45, wherein the
product specific information or the workpiece specific information
comprises information indicative of one or more operating
parameters of a remote device.
[0465] EEE 70 is computing system, comprising: [0466] a trained
machine learning system configured to receive input sensor data and
output, based on the input sensor data, product specific
information related to an abrasive product or workpiece specific
information related to a workpiece associated with the abrasive
product; and [0467] a computing device configured to: [0468]
receive, from one or more sensors, sensor data, wherein the one or
more sensors are disposed in proximity to a plurality of abrasive
products, and wherein the one or more sensors are configured to
collect abrasion operational data associated with abrasive
operations involving the plurality of abrasive products; [0469]
determine, by applying the trained machine learning system on the
sensor data, product specific information of the plurality of
abrasive products or workpiece specific information of a plurality
of workpieces associated with the plurality of abrasive products;
and [0470] provide, to one or more client devices, the product
specific information or the workpiece specific information.
[0471] EEE 71 is the computing system of EEE 70, wherein the
plurality of abrasive products are located across a plurality of
enterprises.
[0472] EEE 72 is the computing system of EEE 71, wherein the
computing device is configured to anonymize the sensor data so as
to disassociate the sensor data from each of the plurality of
enterprises.
[0473] EEE 73 is the computing system of EEE 70, wherein the sensor
data comprises synthetic sensor data, wherein the synthetic sensor
data is generated from digital twins of the plurality of abrasive
products.
[0474] EEE 74 is a computing device, comprising:
[0475] one or more processors; and
[0476] data storage, wherein the data storage has stored thereon
computer-executable instructions that, when executed by the one or
more processors, cause the computing device to carry out functions
comprising the computer-implemented method of any one of EEEs
1-36.
[0477] EEE 75 is the article of manufacture comprising one or more
computer readable media having non-transitory computer-readable
instructions stored that, when executed by one or more processors
of a computing device, cause the computing device to carry out
functions that comprise the computer-implemented method of any one
of EEEs 1-36.
[0478] EEE 76 is a computing device, comprising:
[0479] means for carrying out the computer-implemented method of
any one of EEEs 1-36.
[0480] EEE 77 is a system, comprising:
an abrasive product comprising a tag, wherein the abrasive product
is associated with a universally unique identifier (UUID); a mobile
device comprising a user interface and a tag reader, wherein the
mobile device is configured to carry out operations, the operations
comprising:
[0481] interrogating, with the tag reader, the tag to determine the
UUID;
[0482] based on the UUID, performing at least one of: [0483]
displaying, via the user interface, stock information about similar
abrasive products; [0484] displaying, via the user interface,
product information or GPS information about similar abrasive
products; or [0485] generating a reorder request for a similar
abrasive product and transmitting the reorder request to a client
network or a remote network.
[0486] EEE 78 is a method, comprising:
[0487] interrogating, with a tag reader of a mobile device, a tag
of an abrasive product to determine an associated universally
unique identifier (UUID);
[0488] based on the UUID, performing at least one of: [0489]
displaying, via a user interface of the mobile device, stock
information about similar abrasive products; [0490] displaying, via
the user interface, product information or GPS information about
similar abrasive products; or [0491] generating a reorder request
for a similar abrasive product and transmitting the reorder request
to a client network or a remote network.
[0492] EEE 79 is a method, comprising:
[0493] obtaining, by a tag reader, tag information from a client
network, wherein the tag information is associated with a tag of an
abrasive product having a universally unique identifier (UUID);
[0494] transmitting the tag information, to a remote network;
[0495] updating at least one database with the tag information;
[0496] displaying the tag information with a user interface;
[0497] selecting at least one implementable action based on a user
interaction with the user interface, wherein the at least one
implementable action comprises at least one of: [0498] establishing
communications with abrasive devices associated with the abrasive
product; [0499] adjusting an operation of the abrasive devices
associated with the abrasive product; [0500] notifying a vendor to
order one or more replacement parts; [0501] notifying a vendor to
repair or refurbish the abrasive product; or [0502] notifying a
user of the abrasive product by way of a text message or email.
[0503] EEE 80 is a method, comprising:
[0504] interrogating, with a tag reader of a mobile device, a tag
of an abrasive product to determine an associated universally
unique identifier (UUID);
[0505] determining, based on the UUID, an operative state of the
abrasive product from among a plurality of possible operative
states, wherein the plurality of possible operative states
comprises at least one of: [0506] a manufacturer state; [0507] a
gate state; [0508] a shop floor state; [0509] a wheel balancing
state; [0510] an operation state; and [0511] a scrap state; and
[0512] storing the operative state of the abrasive product in at
least one of: a product database or a memory associated with the
tag.
[0513] EEE 81 is a mobile device, comprising:
[0514] an authentication system configured to determine a level of
access from among a plurality of possible levels of access based on
a user identity; and
[0515] a user interface configured to provide abrasive product
information based on the determined level of access, wherein the
abrasive product information comprises at least one of: [0516]
maintenance information; [0517] GPS information; [0518] a state of
a client network; [0519] abrasive product details; or [0520]
current stock information.
* * * * *