U.S. patent application number 16/731141 was filed with the patent office on 2020-07-16 for method for predicting martensitic transformation rate and method for setting processing condition.
This patent application is currently assigned to The University of Tokyo. The applicant listed for this patent is The University of Tokyo TOYOTA JIDOSHA KABUSHIKI KAISHA. Invention is credited to Hiroyuki IKUTA, Takahiro ISHIGURO, Dai KOBUCHI, Kenshiro MIMURA, Jun YANAGIMOTO.
Application Number | 20200224289 16/731141 |
Document ID | / |
Family ID | 71517453 |
Filed Date | 2020-07-16 |
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United States Patent
Application |
20200224289 |
Kind Code |
A1 |
YANAGIMOTO; Jun ; et
al. |
July 16, 2020 |
METHOD FOR PREDICTING MARTENSITIC TRANSFORMATION RATE AND METHOD
FOR SETTING PROCESSING CONDITION
Abstract
A method for predicting a martensitic transformation rate and a
method for setting processing conditions capable of improving the
accuracy of a prediction of a martensitic transformation rate when
a steel material is subjected to deformation processing as well as
to heat treatment are provided. A method for predicting a
martensitic transformation rate according to an embodiment includes
predicting a rate of a transformation to a martensitic phase that
appears when a steel material is subjected to deformation
processing as well as to heat treatment in which a temperature of
the steel material is changed, in which a martensitic
transformation rate Vm is calculated by using a prediction formula,
the method further including identifying parameters m and n of the
prediction formula, and calculating the martensitic transformation
rate at a predetermined temperature and a predetermined strain rate
by using the prediction formula into which the identified
parameters are substituted.
Inventors: |
YANAGIMOTO; Jun; (Bunkyo-ku,
JP) ; MIMURA; Kenshiro; (Toyota-shi, JP) ;
KOBUCHI; Dai; (Nagoya-shi, JP) ; IKUTA; Hiroyuki;
(Nisshin-shi, JP) ; ISHIGURO; Takahiro;
(Chiryu-shi, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The University of Tokyo
TOYOTA JIDOSHA KABUSHIKI KAISHA |
Bunkyo-ku
Toyota-shi |
|
JP
JP |
|
|
Assignee: |
The University of Tokyo
Bunkyo-ku
JP
TOYOTA JIDOSHA KABUSHIKI KAISHA
Toyota-shi
JP
|
Family ID: |
71517453 |
Appl. No.: |
16/731141 |
Filed: |
December 31, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C21D 2211/008 20130101;
C21D 11/00 20130101; C21D 9/32 20130101 |
International
Class: |
C21D 11/00 20060101
C21D011/00; C21D 9/32 20060101 C21D009/32 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 10, 2019 |
JP |
2019-002574 |
Claims
1. A method for predicting a martensitic transformation rate
comprising predicting a rate of a transformation to a martensitic
phase that appears when a steel material is subjected to
deformation processing as well as to heat treatment in which a
temperature of the steel material is changed, wherein a martensitic
transformation rate V.sub.m in calculated by using a below-shown
Expression (1): V n = ( 1 - V .alpha. - V p - V .beta. ) [ { 1 -
exp [ - 0 . 0 1 1 ( M S - T ) ] } + .alpha. i = t o t n ( . i * ) n
] ( .rho. .rho. 0 ) m ( 1 ) ##EQU00007## where: V.sub..alpha. is a
ferrite rate; V.sub.p is a pearlite rate; V.sub..beta. is a bainite
rate; and M.sub.S satisfies a below-shown Expression (2):
M.sub.S=550-350.times.[C]%-40.times.[Mn]%-35.times.[V]%-20.times.[Cr]%
(2) where: T is a temperature in the heat treatment;
.epsilon..sub.i with a dot (i.e., .epsilon..sub.i with " " added
thereon) is a strain rate of the steel material; .epsilon.* is a
normalization constant; t.sub.0 is a start time of the deformation
processing; t.sub.n is an end time of the deformation processing;
.rho. is an average dislocation density of the steel material;
.rho..sub.0 is an initial dislocation density of the steel
material; and .alpha., m and n are parameters.
2. The method for predicting a martensitic transformation rate
according to claim 1, comprising: identifying the parameters
.alpha., m and n of the Expression (1); and calculating the
martensitic transformation rate at a predetermined temperature and
a predetermined strain rate by using the Expression (1) into which
the identified parameters .alpha., m and n are substituted.
3. The method for predicting a martensitic transformation rate
according to claim 2, wherein the identifying the parameters
.alpha., m and n of the Expression (1) comprises: obtaining a
measured value of the martensitic transformation rate by performing
a compression test of the steel material; calculating the
martensitic transformation rate from the Expression (1) in which
the parameters are changed; and comparing the measured value with
the calculated value, and identifying, as the parameters .alpha., m
and n of the Expression (1), parameters with which an error between
the measured value and the calculated value falls within a
predetermined range.
4. A method for setting a processing condition, comprising setting
a temperature and a strain rate at the time when the steel material
is subjected to the deformation processing by using the method for
predicting a martensitic transformation rate according to claim 2
so that the resultant steel material has a predetermined
martensitic transformation rate.
5. The method for setting a processing condition according to claim
4, wherein the steel material is a material for a gear, the
deformation processing is performed by a rotating die, and when the
strain rate is set, a rotation condition of the die for forming a
predetermined part of the gear is set.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is based upon and claims the benefit of
priority from Japanese patent application No. 2019-002574, filed on
Jan. 10, 2019, the disclosure of which is incorporated herein in
its entirety by reference.
BACKGROUND
[0002] The present disclosure relates to a method for predicting a
martensitic transformation rate and a method for setting processing
conditions in a steel material processing process.
[0003] As a calculation formula for calculating a martensitic
transformation rate in heat treatment of a steel material, there is
a calculation formula disclosed in Magee, C. L. (for example, The
Nucleation of Martensite, Ch. 3. ASM, New York, 1968., Materials
Science Research International, Vol. 3, No. 4 pp. 193-203, 1997.,
ISIJ International, Vol. 32 No. 3, pp. 306-315, 1992. etc).
SUMMARY
[0004] It has been demonstrated that when the calculation formula
disclosed in Magee, C. L. is used, predicted transformation rates
of a steel material in the so-called static heat treatment roughly
agree with actual behavior of the steel material. This is because
the temperature dependence of the transformation becomes dominant
in the static heat treatment. In contrast, predicted transformation
rates in a process in which deformation processing of a steel
material is combined with heat treatment thereof (e.g., a component
rolling process of a gear material) widely differ from actual
behavior of the steel material. It is considered that this is
because the calculation formula disclosed in Magee, C. L. does not
take account of the so-called dynamic factors, such as changes in
energy caused by the deformation processing, in the occurrence of
the transformation.
[0005] The present disclosure has been made in order to solve the
above-described problem and provides a method for predicting a
martensitic transformation rate and a method for setting processing
conditions capable of improving the accuracy of a prediction of a
martensitic transformation rate when a steel material is subjected
to deformation processing as well as to heat treatment as compared
with the prior-art calculation method.
[0006] In a first exemplary aspect, a method for predicting a
martensitic transformation rate is a method for predicting a rate
of a transformation to a martensitic phase that appears when a
steel material is subjected to deformation processing as well as to
heat treatment in which a temperature of the steel material is
changed, in which a martensitic transformation rate is calculated
by using a prediction formula. By adopting the above-described
feature, it is possible to improve the accuracy of a prediction of
a martensitic transformation rate when a steel material is
subjected to deformation processing as well as to heat treatment as
compared with the prior-art calculation method.
[0007] Further, another exemplary aspect is a method for setting a
processing condition, in which a temperature and a strain rate at
the time when a steel material is subjected to deformation
processing are set by using a method for predicting a martensitic
transformation rate so that the resultant steel material has a
predetermined martensitic transformation rate. By adopting the
above-described feature, it is possible to process a steel material
into one having a desired martensitic transformation rate and
thereby to make each part of the steel material have a different
strength.
[0008] According to the present disclosure, it is possible to
provide a method for predicting a martensitic transformation rate
and a method for setting processing conditions capable of improving
the accuracy of a prediction of a martensitic transformation rate
when a steel material is subjected to deformation processing as
well as to heat treatment as compared with the prior-art
calculation method.
[0009] The above and other objects, features and advantages of the
present disclosure will become more fully understood from the
detailed description given hereinbelow and the accompanying
drawings which are given by way of illustration only, and thus are
not to be considered as limiting the present disclosure.
BRIEF DESCRIPTION OF DRAWINGS
[0010] FIG. 1 is a graph showing an example of a shift of a
transformation nose caused by deformation processing in a
martensitic transformation rate prediction formula according to a
first embodiment, in which a horizontal axis indicates time and a
vertical axis indicates temperatures;
[0011] FIG. 2 is a flowchart showing an example of a method for
predicting a martensitic transformation rate according to an
embodiment;
[0012] FIG. 3 is a flowchart showing an example of a method for
identifying parameters of a prediction formula in a method for
predicting a martensitic transformation rate according to an
embodiment;
[0013] FIG. 4 shows an example of an unprocessed workpiece in a
compression test in a method for predicting a martensitic
transformation rate according to an embodiment;
[0014] FIG. 5 shows an example of a processed workpiece in a
compression test in a method for predicting a martensitic
transformation rate according to an embodiment;
[0015] FIG. 6 shows an example of a cross section of a processed
workpiece in a compression test in a method for predicting a
martensitic transformation rate according to an embodiment;
[0016] FIG. 7 shows an example of an actually-measured structure on
a cross section of a workpiece compressed at a high temperature in
a method for predicting a martensitic transformation rate according
to an embodiment;
[0017] FIG. 8 is a calculation result showing an example of
martensitic transformation rates on a cross section of a workpiece
compressed at a high temperature, predicted by using a prediction
formula in a method for predicting a martensitic transformation
rate according to an embodiment;
[0018] FIG. 9 is a graph showing an example of calculated values of
martensitic transformation rates on a cross section of a workpiece
compressed at a high temperature, predicted by using a prediction
formula in a method for predicting a martensitic transformation
rate according to an embodiment, calculated values of martensitic
transformation rates predicted by using an existing formula, and
actually-measured values of martensitic transformation rates, in
which a horizontal axis indicates distances from the upper surface
of the workpiece and a vertical axis indicates martensitic
transformation rates;
[0019] FIG. 10 shows an example of an actually-measured structure
on a cross section of a workpiece compressed at a low temperature
in a method for predicting a martensitic transformation rate
according to an embodiment;
[0020] FIG. 11 is a calculation result showing an example of
martensitic transformation rates on a cross section of a workpiece
compressed at a low temperature, predicted by using a prediction
formula in a method for predicting a martensitic transformation
rate according to an embodiment;
[0021] FIG. 12 is a graph showing an example of calculated values
of martensitic transformation rates on a cross section of a
workpiece compressed at a low temperature, predicted by using a
prediction formula in a method for predicting a martensitic
transformation rate according to an embodiment, calculated values
of martensitic transformation rates predicted by using an existing
formula, and actually-measured values of martensitic transformation
rates, in which a horizontal axis indicates distances from the
upper surface of the workpiece and a vertical axis indicates
martensitic transformation rates;
[0022] FIG. 13 shows an example of needs for gears of EVs (Electric
Vehicles); and
[0023] FIG. 14 shows an example as to how to make each part of a
gear have a different strength in a method for predicting a
martensitic transformation prediction rate according to an
embodiment.
DESCRIPTION OF EMBODIMENTS
[0024] Specific embodiments for implementing the present disclosure
will be described hereinafter with reference to the drawings.
However, the present disclosure is not limited to the below-shown
embodiments. Further, the following descriptions and drawings are
simplified as appropriate for clarifying the explanation.
Embodiment
[0025] A method for predicting a martensitic transformation rate
according to an embodiment will be described. The method for
predicting a martensitic transformation rate will be described
hereinafter by dividing it into a <Prediction Formula>
section, a <Method for Predicting Martensitic Transformation
Rate> section, and a <Method for Setting Processing
Conditions> section. In the <Prediction Formula> section,
a prediction formula used for predicting a martensitic
transformation rate will be described. Further, a comparison of
this prediction formula with an existing formula will also be
described. In the <Method for Predicting Martensitic
Transformation Rate> section, a method for predicting a
martensitic transformation rate by using the prediction formula
will be described. In the <Method for Setting Processing
Conditions> section, a method for setting processing conditions
by using the method for predicting a martensitic transformation
rate will be described.
[0026] <Prediction Formula>
[0027] A method for predicting a martensitic transformation rate
according to this embodiment is a method for predicting a rate of a
transformation to a martensitic phase that appears when a steel
material is subjected to deformation processing as well as to heat
treatment in which the temperature of the steel material is
changed, and in which a martensitic transformation rate V.sub.m is
calculated by using a prediction formula expressed by the
below-shown Expression (1).
[ Expression 1 ] y m = ( 1 - V .alpha. - V p - V .beta. ) [ { 1 -
exp [ - 0 . 0 1 1 ( M S - T ) ] } + .alpha. i = t o t n ( . i * ) n
] ( .rho. .rho. 0 ) m ( 1 ) ##EQU00001##
[0028] In the expression, V.sub..alpha. is a ferrite rate and
V.sub.p is a pearlite rate. Further, V.sub..beta. is a bainite
rate. For example, each of the martensitic transformation rate
V.sub.m, the ferrite rate V.sub.u, the pearlite rate V.sub.p, and
the bainite rate V.sub..beta. is a volume fraction. M.sub.S
satisfies the below-shown Expression (2).
[Expression 2]
M.sub.S=.sub.550-350.times.[C]%-40.times.[Mn]%-35.times.[V]%-20.times.[C-
r]% (2)
[0029] T is a temperature in the heat treatment; .epsilon..sub.i
with a dot (i.e., .epsilon..sub.i with "-" added thereon,
hereinafter expressed as .epsilon. .sub.i) is a strain rate of the
steel material; .epsilon.* is a normalization constant [/s]; to is
a start time of the deformation processing; t.sub.n is an end time
of the deformation processing; .rho. is an average dislocation
density of the steel material; .rho..sub.0 is an initial
dislocation density of the steel material; .alpha. is a material
constant; m is a dislocation dependence index; n is a strain-rate
dependence index; and .alpha., m and n are parameters dependent on
the temperature and the strain rate.
[0030] The ferrite rate V.alpha. is calculated by the below-shown
Expressions (3) to (5) as shown in, for example, M. Suehiro et al.
(M. Suehiro, K. Sato, H. Yada, T. Senuma and Y. Matsumura:
Transactions ISIJ, 27 (1987), 439.), T. Senuma et al. (T. Senuma,
M. Suehiro and H. Yada, ISIJ, Int., 32-3 (1992), 423.), J. Liu et
al. (J. Liu, A. Yanagida, S. Sugiyama and J. Yanagimoto; ISIJ,
Int., 41-12 (2001), 1510.)
[ Expression 3 ] V .alpha. = .intg. 0 .DELTA. X f dt ( 3 ) [
Expression 4 ] .DELTA. X F ( n ) = k 1 S v 2 G F n ( 1 - X F n - 1
) .DELTA. t n ( 4 ) [ Expression 5 ] G F n = 1 2 r n D n C
.gamma..alpha. n - C .gamma. n - 1 C .gamma. n - 1 C .alpha..gamma.
n ( 5 ) ##EQU00002##
[0031] The pearlite rate V.sub.p is calculated by, for example, the
below-shown Expressions (6) to (8) as shown in M. Suehiro et
al.
[Expression 6]
V.sub.p=.intg..sub.0.sup.t.DELTA.X.sub.pdt (6)
[Expression 7]
.DELTA.X.sub.p.sup.<n>=k.sub.4S.sub.v2G.sub.p.sup.<n>(1-X.su-
b.P.sup.<n-1>-X.sub.F.sup.total).DELTA.t.sup.<n>
(7)
[Expression 8]
G.sub.P.sup.<n>=.DELTA.T.sup.<n>D.sup.<n>(C.sub..gamma-
..alpha..sup.<n>-C.sub..gamma..beta..sup.<n>) (8)
[0032] The bainite rate V.sub..beta. is calculated by, for example,
the below-shown Expressions (9) to (11) as shown in M. Suehiro et
al.
[Expression 9]
V.sub..beta.=.intg..sub.0.sup.t.DELTA.X.sub.Bdt. (9)
[Expression 10]
.DELTA.X.sub.B.sup.<n>=k.sub.5S.sub.v2G.sub.B.sup.<n>(1-X.su-
b.B.sup.<n-1>-X.sub.F.sup.total-X.sub.P.sup.total).DELTA.t.sup.<n-
> (10)
[Expression 11]
B.sub.S(.degree. C.)=717.5-425[C](wt %)-42.5[Mn](wt %) (11)
[0033] The coefficients have, for example, values shown in the
below-shown Expression (12) as shown in J. Liu et al. The unit for
these coefficients is (Cal.sup.3/mol.sup.3).
[ Expression 12 ] k 1 = 8.933 .times. 10 - 12 exp ( 21100 T ) k 2 =
17476.0 k 3 = 1.305 .times. 10 7 k 4 = 3.0 .times. 10 3 k 5 = 6.816
.times. 10 - 4 exp ( 3431.5 T ) } ( 12 ) ##EQU00003##
[0034] The below-shown Expression (13) shows nucleation caused by
deformation energy.
[ Expression 13 ] .alpha. i = t 0 t n ( . i * ) n ( 13 )
##EQU00004##
[0035] The above-shown Expression (13) is a Zener-Hollomon
parameter. Therefore, it corresponds to the below-shown Expression
(14) describing dynamic recrystallization behavior.
[ Expression 14 ] Z = . exp ( Q RT ) ( 14 ) ##EQU00005##
[0036] The above-shown Expression (14) indicates that nucleation
occurs accidentally depending on the strain rate. The "exp" term
can be expressed, for example, as "a", i.e., as a temperature
dependence constant.
[0037] The below-shown Expression (15) indicates a shift of a
transformation nose. Note that the initial dislocation density
.rho..sub.0 is about 10.sup.8/cm.sup.2
(.rho..sub.0.apprxeq.10.sup.8/cm.sup.2).
[ Expression 14 ] ( .rho. .rho. 0 ) m ( 14 ) ##EQU00006##
[0038] FIG. 1 is a graph showing an example of a shift of a
transformation nose caused by deformation processing in a
martensitic transformation rate prediction formula according to a
first embodiment, in which a horizontal axis indicates time and a
vertical axis indicates temperatures. As shown in FIG. 1, when the
temperature of the steel material is changed during the deformation
processing thereof, the phase state of the steel material changes
according to the temperature. For example, the phase of the steel
material is a single phase of y-iron at 980.degree. C. or higher.
Further, the phase becomes a two-phase state including 7-iron
between 780.degree. C. and 980.degree. C. The above-shown
Expression (15) indicates that the dislocation has a function of
shifting the transformation nose in the non-martensitic region.
[0039] Next, an existing formula is described. The existing formula
is the below-shown Expression (16) as shown in Magee, C. L.
[Expression 16]
V.sub.m=(1-V.sub..alpha.-V.sub.p-V.sub..beta.){1-exp[-0.011(M.sub.S-T)]}
(16)
[0040] As compared with the prediction formula, the existing
formula does not include the terms shown in the above-shown
Expressions (13) and (15). In the case of static heat treatment,
i.e., heat treatment that does not include deformation processing,
i.e., includes only raising the temperature and cooling, it is
possible to accurately predict a martensitic transformation rate by
using the existing formula. This is because in the static heat
treatment, the appearance of the martensitic phase is significantly
dependent on the temperature.
[0041] In contrast, in the case of dynamic heat treatment, i.e.,
heat treatment that includes deformation processing, the appearance
of the martensitic phase is dependent on nucleation caused by
deformation energy and the shift of the transformation nose as well
as dependent on the temperature. Therefore, in the dynamic heat
treatment, it is impossible to accurately predict a martensitic
transformation rate by using the existing formula. This is because
neither the above-described nucleation caused by deformation energy
nor the shift of the transformation nose is taken into account in
the existing formula.
[0042] The terms shown in the above-shown Expressions (13) and (15)
are added (i.e., included) in the prediction formula according to
this embodiment. Therefore, the nucleation caused by deformation
energy and the shift of the transformation nose are also taken into
account in the prediction formula according to this embodiment.
Therefore, it is possible to accurately predict a martensitic
transformation rate in dynamic heat treatment.
[0043] <Method for Predicting Martensitic Transformation
Rate>
[0044] Next, a method for predicting a martensitic transformation
rate according to this embodiment is described. FIG. 2 is a
flowchart showing an example of a method for predicting a
martensitic transformation rate according to the embodiment. FIG. 3
is a flowchart showing an example of a method for identifying
parameters of a prediction formula in the method for predicting a
martensitic transformation rate according to the embodiment. FIG. 4
shows an example of an unprocessed workpiece (i.e., a workpiece
that has not yet been processed) in a compression test in the
method for predicting a martensitic transformation rate according
to the embodiment. FIG. 5 shows an example of a processed workpiece
(i.e., a workpiece that has already been processed) in a
compression test in the method for predicting a martensitic
transformation rate according to the embodiment. FIG. 6 shows an
example of a cross section of the processed workpiece in the
compression test in the method for predicting a martensitic
transformation rate according to the embodiment, and shows a cross
section taken along a line VI-VI in FIG. 5.
[0045] In order to predict a martensitic transformation rate, first
of all, parameters .alpha., m and n of the prediction formula are
identified (i.e., determined) as shown in a step S11 in FIG. 2. In
order to identify the parameters .alpha., m and n of the prediction
formula, firstly, a cylindrical compression test of a workpiece is
performed as shown in a step S21 in FIG. 3.
[0046] As shown in FIG. 4, a workpiece 10 used in the cylindrical
compression test is a cylindrical steel material of which a
diameter of a bottom surface (i.e., a diameter of the upper surface
11 and the lower surface 12) is 8 (mm) and a height is 12 (mm). The
above-described workpiece 10 is deformed by applying a stress to
each of the bottom surfaces (i.e., the upper and lower surfaces) so
as to compress the workpiece 10. As a result, the workpiece 10 is
deformed into a cylinder having a height of 3 (mm) as shown in FIG.
5. Then, as shown in FIG. 6, the deformed workpiece 10 is
vertically severed at the center, so that a cross section of the
workpiece 10 perpendicular to the bottom surface thereof is
exposed.
[0047] Next, as shown in a step S22 in FIG. 3, hardness in the
central part on the cross section of the workpiece 10 is measured.
When the hardness is measured, the cross section is divided into
minute parts in a mesh pattern and hardness of each minute part is
measured. Next, as shown in a step S23, the measured hardness of
each minute part on the cross section is converted into a
martensitic transformation rate. For example, the measured hardness
is converted into a martensitic transformation rate by using a
degree of a strain obtained based on the measured hardness and the
crystalline structure. By doing so, a distribution of martensitic
transformation rates on the cross section is obtained. In this way,
measured values of martensitic transformation rates are obtained by
performing the compression test of the steel material in the steps
S21 to S23.
[0048] Meanwhile, a cylindrical compression analysis is performed
as shown in a step S24. In the cylindrical compression analysis,
martensitic transformation rates are calculated by using the
prediction formula. In this process, martensitic transformation
rates are calculated for a plurality of parameters .alpha., m and n
as shown in a step S26 while changing the parameters .alpha., m and
n as shown in a step S25. When the martensitic transformation rates
are calculated, the workpiece 10 is divided into minute parts in a
mesh pattern and a martensitic transformation rate is calculated
for each minute part by using a strain rate of that minute part.
For example, the strain rate is derived from the processing speed
of the cylindrical compression. Specifically, the strain rate is
calculated by summing up strain rates in respective minute time
periods from a time to at which the processing is stared to a time
t, at which the processing is finished. In this way, through the
steps S24 to S26, calculated values of martensitic transformation
rates are obtained by the prediction formula in which the
parameters .alpha., m and n are changed.
[0049] Note that the steps S24 to S26 may be performed after the
steps S21 to S23, or the steps S21 to S23 may be performed after
the steps S24 to S26. Alternatively, the steps S24 to S26 may be
performed in parallel with the steps S21 to S23.
[0050] Next, as shown in a step S27, the measured value of the
martensitic transformation rate is compared with the calculated
value. Then, for example, when an error (i.e., a difference)
between these values exceeds a predetermined range, the process
returns to the step S25, in which the parameters .alpha., m and n
are changed. Further, the steps S26 and S27 are repeated. On the
other hand, when the error between these values is within the
predetermined range in the step S27, the parameters at that time
are identified (i.e., determined) as the parameters .alpha., m and
n of the prediction formula. Note that the predetermined range for
the error between these values is, for example, 10%. However, the
predetermined range is not limited to this example.
[0051] Next, as shown in a step S12 in FIG. 2, a martensitic
transformation rate at a predetermined temperature and a
predetermined strain rate is calculated by using the prediction
formula into which the identified parameters .alpha., m, and n have
been substituted. In this way, it is possible to predict a rate of
a transformation of the steel material to the martensitic phase,
which appears when the steel material is subjected to deformation
processing as well as to heat treatment in which the temperature of
the steel material is changed.
[0052] Martensitic transformation rates that are obtained after
subjecting the steel material to deformation processing at high and
low temperatures are compared by using the prediction formula
according to this embodiment and the existing formula. The
deformation processing at the high temperature is, for example,
deformation processing performed at a temperature of 1,050
[.degree. C.] and a strain rate of 50 [/s]. The deformation
processing at the high temperature is significantly dependent on
the temperature. Therefore, such deformation processing corresponds
to a static heat treatment state. When the deformation processing
corresponds to the static heat treatment, it is possible to predict
martensitic transformation rates by using the existing formula.
[0053] FIG. 7 shows an example of an actually-measured structure on
a cross section of a workpiece compressed at a high temperature in
the method for predicting a martensitic transformation rate
according to the embodiment. FIG. 8 is a calculation result showing
an example of martensitic transformation rates on a cross section
of a workpiece compressed at a high temperature, predicted by using
the prediction formula in the method for predicting a martensitic
transformation rate according to the embodiment. FIG. 9 is a graph
showing an example of calculated values of martensitic
transformation rates on a cross section of a workpiece compressed
at a high temperature, predicted by using the prediction formula in
the method for predicting a martensitic transformation rate
according to the embodiment, calculated values of martensitic
transformation rates predicted by using the existing formula, and
actually-measured values of martensitic transformation rates, in
which a horizontal axis indicates distances from the upper surface
of the workpiece and a vertical axis indicates martensitic
transformation rates.
[0054] As shown in FIG. 7, the measured values are derived by
measuring hardness on the cross section of the workpiece 10.
Specifically, the measured values are obtained in the steps S22 and
S23. As shown in FIG. 8, the calculated values of the prediction
formula are obtained in the steps S24 to S26. A distribution of
martensitic transformation rates on the cross section of the
workpiece 10 are shown in a gray scale. As shown in FIG. 9, the
measured value of the martensitic transformation rate near the
upper surface is 0.8. In a range of distances from 0 (mm) to 2 (mm)
as measured from the upper surface, the martensitic transformation
rate slightly increases while oscillating between 0.7 and 0.85. In
a range of distances from 2 (mm) to 3 (mm), the martensitic
transformation rate slightly increases from 0.85 to 0.94.
[0055] Regarding the calculated value by the prediction formula
according to this embodiment, the martensitic transformation rate
near the upper surface is 0.75. In a range of distances from 0 (mm)
to 0.5 (mm) as measured from the upper surface, the martensitic
transformation rate slightly increases. In a range of distances
from 0.5 (mm) to 3 (mm), the martensitic transformation rate is
roughly constant (i.e., unchanged) at 0.85.
[0056] Regarding the calculated value by the existing formula, the
martensitic transformation rate near the upper surface is 0.8. In a
range of distances from 0 (mm) to 0.5 (mm) as measured from the
upper surface, the martensitic transformation rate slightly
decreases. In a range of distances from 0.5 (mm) to 3 (mm), the
martensitic transformation rate is roughly constant (i.e.,
unchanged) at 0.75.
[0057] As described above, in the deformation processing at a high
temperature, the prediction formula according to this embodiment
satisfactorily agrees with measured values of martensitic
transformation rates. Therefore, the prediction formula according
to this embodiment can predict the martensitic transformation rate
in the deformation processing at a high temperature. Further, since
the deformation processing at a high temperature corresponds to
static heat treatment, which is significantly dependent on the
temperature, the martensitic transformation rate can also be
predicted by the existing formula as well as by the prediction
formula according to this embodiment.
[0058] Meanwhile, the deformation processing at the low temperature
is, for example, deformation processing performed at a temperature
of 750 (.degree. C.) and a strain rate of 50 (/s). The deformation
processing at the low temperature is not significantly dependent on
the temperature. Therefore, such deformation processing corresponds
to a dynamic heat treatment. In the case of the dynamic heat
treatment, it is impossible to predict the martensitic
transformation rate by using the existing formula.
[0059] FIG. 10 shows an example of an actually-measured structure
on a cross section of a workpiece compressed at a low temperature
in the method for predicting a martensitic transformation rate
according to the embodiment. FIG. 11 is a calculation result
showing an example of martensitic transformation rates on a cross
section of a workpiece compressed at a low temperature, predicted
by using the prediction formula in the method for predicting a
martensitic transformation rate according to the embodiment. FIG.
12 is a graph showing an example of calculated values of
martensitic transformation rates on a cross section of a workpiece
compressed at a low temperature, predicted by using the prediction
formula in the method for predicting a martensitic transformation
rate according to the embodiment, calculated values of martensitic
transformation rates predicted by using the existing formula, and
actually-measured values of martensitic transformation rates, in
which a horizontal axis indicates distances from the upper surface
of the workpiece and a vertical axis indicates martensitic
transformation rates.
[0060] As shown in FIG. 10, the measured values are derived by
measuring hardness on the cross section of the workpiece 10.
Changes in the structure are observed in the central part on the
cross section shown in FIG. 10. As shown in FIG. 11, calculated
values of the prediction formula is shown in a gray scale. Changes
in the martensitic transformation rate are observed in the central
part on the cross section of the workpiece 10.
[0061] As shown in FIG. 12, the measured value of the martensitic
transformation rate near the upper surface is 0.05. In a range of
distances from 0 (mm) to 1.6 (mm) as measured from the upper
surface, the martensitic transformation rate fluctuates around or
below 0.1. The martensitic transformation rate sharply increases at
the distance of 1.6 (mm) and has a peak value of 0.9 at a distance
of 1.75 (mm). The martensitic transformation rate sharply decreases
at a distance of 2 (mm) and fluctuates around 0.1 in a range of
distances from 2 (mm) to 3 (mm).
[0062] Regarding the calculated value by the prediction formula
according to this embodiment, the martensitic transformation rate
near the upper surface is 0. In a range of distances from 0 (mm) to
1.5 (mm) as measured from the upper surface, the martensitic
transformation rate slightly increases to 0.2. The martensitic
transformation rate sharply increases at the distance of 1.6 (mm)
and has a peak value of 0.85 at a distance of 1.75 (mm). The
martensitic transformation rate sharply decreases at a distance of
2 (mm) and decreases to 0.1 in a range of distances from 2 (mm) to
3 (mm).
[0063] Regarding the calculated value by the existing formula, the
martensitic transformation rate near the upper surface is 0.87. In
a range of distances from 0 (mm) to 1.0 (mm) as measured from the
upper surface, the martensitic transformation rate decreases to
0.5. In a range of distances from 1.0 (mm) to 2.5 (mm), the
martensitic transformation rate fluctuates around 0.45. In a range
of distances from 2.5 (mm) to 3.0 (mm), the martensitic
transformation rate increases to 0.65.
[0064] As described above, in the case of the deformation
processing at a low temperature, the prediction formula according
to this embodiment satisfactorily agrees with measured values of
martensitic transformation rates. For example, the prediction
formula can reproduce behavior in which the martensitic
transformation rate increases in the central part between the upper
surface 11 of the workpiece 10 and the lower surface 12 thereof.
Therefore, the prediction formula can accurately predict
martensitic transformation rates in the deformation processing at a
low temperature. In contrast, in the deformation processing at a
low temperature, since the transformation is not significantly
dependent on the temperature, it is impossible to predict the
martensitic transformation rate by using the existing formula. For
example, the existing formula cannot reproduce the behavior in
which the martensitic transformation rate increases in the central
part of the workpiece.
[0065] In principle, no martensitic phase appears when only heat
treatment at 750.degree. C. is performed. In order to form a
martensitic phase, heat treatment at 900.degree. C. or higher has
to be performed to form austenite in which carbon or the like is
contained in the form of solid solution in y-iron having an fcc
structure. By cooling the austenite, a martensitic phase having a
bct structure in which carbon is contained in the form of solid
solution in a bcc structure appears.
[0066] In contrast, in heat treatment at 750.degree. C. in which
deformation processing is also performed, a crystal lattice is
distorted due to energy of the deformation processing. Further,
carbon enters the distorted crystal lattice. Part of the carbon
that has entered the distorted crystal lattice and has not escaped
therefrom is trapped therein after the deformation processing.
Further, it is considered that it consequently transforms into
martensite. Since such a martensitic transformation is not taken
into account in the existing formula, it cannot predict a
martensitic transformation rate.
[0067] The deformation processing at a low temperature has the
following advantages. That is, the oxidation of the steel material
can be quantitatively reduced. Further, the thermal expansion of
the steel material can be quantitatively reduced. Further, the
residual T-iron can be reduced. Therefore, the deformation
processing at a low temperature is becoming more important as a
method for processing a component made of a steel material. The
prediction formula according to this embodiment can be applied to
such important processing methods.
[0068] According to the method for predicting a martensitic
transformation rate in accordance with this embodiment, it is
possible to improve the accuracy of a prediction of a martensitic
transformation rate when a steel material is subjected to
deformation processing as well as to heat treatment as compared
with the prior-art calculation method. In particular, in the case
of the deformation processing at a low temperature, which
corresponds to the dynamic heat treatment, while the existing
formula cannot predict the martensitic transformation rate, the
prediction formula according to this embodiment can predict the
martensitic transformation rate.
[0069] <Method for Setting Processing Condition>
[0070] Next, a method for setting processing conditions to which
the method for predicting a martensitic transformation rate
according to this embodiment is applied is described. In the method
for setting processing conditions according to this embodiment, a
temperature and a strain rate at the time when a steel material is
subjected to deformation processing are set by using the
above-described method for predicting a martensitic transformation
rate so that the resultant steel material has a predetermined
martensitic transformation rate. The method for setting processing
conditions according to this embodiment is applied to, for example,
the setting of processing conditions for strengthening a gear. Due
to the needs from EVs (Electric Vehicles) and their systems, a gear
strengthening technique is essential for gears in order to achieve
a reduction in size and an improvement in efficiency.
[0071] FIG. 13 shows an example of needs for gears of EVs (Electric
Vehicles). As shown in FIG. 13, as needs for vehicles and their
systems, for example, it has been desired to reduce the amount (or
the number) of batteries mounted in the vehicle and increase the
power efficiency (i.e., the electric mileage). To that end, there
is a need to reduce rolling losses of tires and it has been desired
to lower the position of the center of gravity, reduce the size,
and reduce the difference between the weight on the left wheel and
that on the right wheel. Further, there is a need to reduce a T/A
loss and it has been desired to improve the efficiency. Further,
there is a need for easiness for mounting and it has been desired
to reduce the size.
[0072] In order to achieve the above-described low position of the
center of gravity, the reduction in the difference between weights
on the left and right wheels, the high efficiency, and the
reduction in size, as needs for the T/A, it has been desired to
reduce the height, reduce the width, reduce the weight, lower the
viscosity of oil, reduce the volume of lubricating oil, facilitate
the conversion into ball bearings, and reduce the length. Further,
all of these needs for the T/A are directly related to the
strengthening of gears.
[0073] As a method for strengthening a gear, there is a method for
forming a gear out of a high-strength material. However, if this
method is implemented as the extension of the prior art, there is a
limit to the achieved strength. Further, if the whole part of a
gear is strong, its torsional rigidity (dynamic rigidity) becomes
so high that it adversely affects its vibration. Therefore, it is
necessary to strengthen only a part(s) of a gear where strength is
required and make the other parts have tenacity. That is, it is
necessary to change the strength of each part of the gear according
to the place of that part in the gear.
[0074] FIG. 14 shows an example as to how to make each part of a
gear have a different strength in a method for predicting a
martensitic transformation prediction rate according to an
embodiment. As shown in FIG. 14, a tooth surface 21 of a gear 20 in
which the tooth of the gear 20 comes into contact with that of
another gear needs to be strong. Further, a tooth base 22 of the
gear 20 at which the tooth is bent also needs to be strong.
Meanwhile, a central part 23 of the tooth needs to have tenacity
rather than strength.
[0075] Since it is necessary to change the strength of each part of
the gear 20 according to the place of that part as described above,
it is necessary to perform thermo-mechanical heat treatment in
which the strengthening of the material by deformation processing
is combined with the strengthening thereof by heat treatment. As a
result, it is possible to aim to develop strengths that cannot be
achieved by the prior-art. In this embodiment, a temperature and a
strain rate at the time when a steel material is subjected to
deformation processing are set by using a method for predicting a
martensitic transformation rate so that the resultant steel
material has a predetermined martensitic transformation rate. For
example, the deformation processing for the steel material, i.e.,
the material for the gear 20 is performed by a rotating die.
Further, when the strain rate of the steel material is set, the
rotation condition of the die for forming a predetermined part of
the gear is set.
[0076] Specifically, the tooth surface 21, the tooth base 22, the
central part 23, and the like of the gear 20 are processed under
different processing conditions so that the martensitic
transformation rates of the tooth surface 21 and the tooth base 22
become larger than that of the central part 23. In this process,
the rotation speed and the like of the die are set so that each
part of the gear 20 will have its desired martensitic
transformation rate. Therefore, it is possible to change the
martensitic transformation rate of each part of the gear 20
according to the place of that part and thereby to make each part
of the gear 20 have a different strength.
[0077] One of examples of the processing methods is component
rolling. The component rolling is also one of the processing
methods in which a material is deformed by applying a strong force
in order to improve its strength. However, in the component
rolling, the strength of a material is improved by using work
hardening by a plastic strain. Therefore, the component rolling
differs from the method in which the strength of a material is
improved by causing a martensitic transformation in the material
which is carried out by performing deformation processing according
to this embodiment.
[0078] Although the thermo-mechanical heat treatment in which
deformation processing is performed as well as heat treatment is a
technical field that has been studied for many years, its mechanism
is complicated and there are a myriad of forming patterns.
Therefore, it is difficult to find conditions for good products. In
the method for setting processing conditions using a method for
predicting a martensitic transformation rate according to this
embodiment, it is possible to elucidate the mechanism of a phase
transformation in dynamic heat treatment and thereby to accurately
control the strain and the structure. Therefore, it is important
for the control of the strength in the deformation processing, such
as control for making each part of the gear 20 have a different
strength.
[0079] Embodiments of the present disclosure have been explained
above. However, the present disclosure is not limited to the
above-described configurations, and they can be modified without
departing from the technical idea of the present disclosure. For
example, the following manufacturing method is also included in the
scope of technical idea of the predetermined: i.e., a method for
manufacturing a component such as a gear by subjecting a steel
material to deformation processing by using a method for predicting
a martensitic transformation rate according to an embodiment so
that the resultant steel material has a predetermined martensitic
transformation rate, in which the deformation processing is
performed at a temperature and a strain rate set by the
above-described method for setting a processing condition.
[0080] From the disclosure thus described, it will be obvious that
the embodiments of the disclosure may be varied in many ways. Such
variations are not to be regarded as a departure from the spirit
and scope of the disclosure, and all such modifications as would be
obvious to one skilled in the art are intended for inclusion within
the scope of the following claims.
* * * * *