FORM 2
THE PATENTS ACT, 1970
[39 OF 1970]
&
The Patents Rules, 20035
10
COMPLETE SPECIFICATION
[See Section 10 and Rule 13]
15
20
TITLE: MACHINING CONDITION SEARCH DEVICE AND
MACHINING CONDITION SEARCH METHOD
25
NAME AND ADDRESS OF THE APPLICANT:30
MITSUBISHI ELECTRIC CORPORATION of 7-3, Marunouchi 2-chome, Chiyoda-
ku, Tokyo 1008310, Japan
35
Nationality: Japanese
The following specification particularly describes the invention and the manner in which it40
is to be performed.
2
DESCRIPTION5
TITLE OF INVENTION: MACHINING CONDITION SEARCH DEVICE AND
MACHINING CONDITION SEARCH METHOD
TECHNICAL FIELD
[0001] The present disclosure relates to a machining condition search device and a10
machining condition search method for searching for a machining condition.
BACKGROUND ART
[0002] Generally, a plurality of control parameters can be set for a machining apparatus
used in industrial applications. The machining result of the machining apparatus15
depends on a machining condition that is a combination of parameter values of the
plurality of control parameters. That is, in order to obtain a desired machining result, it
is necessary to set an appropriate machining condition for the machining apparatus.
[0003] However, there are more than one control parameter, and the parameter value of
each control parameter is a continuous value or can be set in multiple levels. Thus, if a20
person selects a machining condition that actually causes the machining apparatus to
perform machining and obtains a desired machining result, it takes a huge amount of time.
For example, in the case of a sheet metal laser machining apparatus, five parameters of
laser output, cutting speed, beam diameter, focal position, and gas pressure are
exemplified as main control parameters having a large influence on the machining result.25
One control parameter is selected from values in multiple levels. Here, for example, if
each of the five control parameters can be selected from values in 10 levels, the total
number of combinations is 105. At this time, if it takes five minutes to try one machining
condition, it takes about 347 days to try 105 machining conditions.
Thus, conventionally, there is known a technique of calculating an evaluation30
3
value corresponding to a machining condition on the basis of machining results obtained5
by causing the machining apparatus to perform machining under several machining
conditions to be tried generated from among machining conditions of combinations of
assumed control parameters, predicting an evaluation value corresponding to an untried
machining condition using Gaussian process regression on the basis of the calculated
evaluation value and the machining condition corresponding to the evaluation value, and10
searching for an optimal machining condition from among a huge number of
combinations of machining conditions on the basis of the calculated evaluation value and
the predicted evaluation value (for example, Patent Literature 1). Examples of a method
of using the Gaussian process regression to predict the evaluation value corresponding to
the untried machining condition include a method of using a probability model generated15
on the assumption that the evaluation value for the machining condition is a random
variable following a specific distribution.
CITATION LIST
PATENT LITERATURE20
[0004] Patent Literature 1: WO 2020/261572
SUMMARY OF INVENTION
TECHNICAL PROBLEM
[0005] A machining result obtained when a machining apparatus is caused to perform25
machining under certain machining conditions may change in a vibratory manner in the
course of progress of machining. For example, the machining speed obtained as a
machining result changes in a vibratory manner when viewed in a short time even if the
machining speed appears to be proceeding at a constant speed when viewed in a long time.
When the machining result changes in a vibratory manner, the evaluation value30
4
corresponding to the machining result also changes in a vibratory manner.5
In the optimal machining condition search technique represented by the
technique disclosed in Patent Literature 1, machining is continuously performed by a
machining apparatus for a certain period of time until a vibrational change in a machining
result settles for each of all the machining conditions to be tried, and an evaluation value
corresponding to the machining condition is calculated after the vibrational change in the10
machining result settles.
Thus, in the above-described search technique, there is a problem that it takes
time to calculate an evaluation value corresponding to a machining condition tried, and
as a result, it takes time to find an optimal machining condition.
[0006] The present disclosure solves the above problems, and an object thereof is to15
provide a machining condition search device and a machining condition search method
capable of shortening the time until an optimal machining condition can be found, as
compared with the conventional technique in which a machining apparatus is caused to
perform machining under all machining conditions to be tried is performed until a
vibrational change in a machining result settles.20
SOLUTION TO PROBLEM
[0007] A machining condition search device according to the present disclosure includes
a machining condition calculating unit to generate a machining condition including a
plurality of control parameters settable in a machining apparatus, an actual machining25
commanding unit to cause the machining apparatus to perform machining in accordance
with the machining condition generated by the machining condition calculating unit, a
machining result collecting unit to collect machining result information indicating a
machining result of the machining performed by the machining apparatus by the actual
machining commanding unit, an evaluation value acquiring unit to calculate a provisional30
5
evaluation value for the performed machining on the basis of the machining result5
information collected by the machining result collecting unit, a convergence determining
unit to determine whether or not the provisional evaluation value has converged on the
basis of the provisional evaluation values in time series calculated by the evaluation value
acquiring unit, and estimate an estimated convergence value to be a convergence
destination of the provisional evaluation value when it is determined that the provisional10
evaluation value has not converged, a stop determining unit to determine whether or not
to terminate the machining under the machining condition being tried before the
provisional evaluation value converges when the convergence determining unit
determines that the provisional evaluation value has not converged, an evaluation
determining unit, when the stop determining unit determines to terminate the machining15
under the machining condition being tried, to cause the actual machining commanding
unit to end the machining in accordance with the machining condition for the machining
apparatus and determine the estimated convergence value estimated by the convergence
determining unit as an evaluation value of the machining performed in accordance with
the machining condition, and determine, when the stop determining unit determines not20
to terminate the machining under the machining condition being tried, a convergence
value of the provisional evaluation value as the evaluation value after the convergence
determining unit determines that the provisional evaluation value has converged, a
prediction unit to predict a prediction value of the evaluation value corresponding to the
machining condition untried on the basis of the evaluation value determined by the25
evaluation determining unit and the machining condition corresponding to the evaluation
value, and a search end determining unit to determine whether or not to end a search for
the machining condition, determine the machining condition that is optimum on the basis
of the evaluation value determined by the evaluation determining unit when ending the
search, and cause the machining condition calculating unit to generate the machining30
6
condition to be tried next on the basis of the prediction value predicted by the prediction5
unit when not ending the search, in which until the search end determining unit determines
to end the search, each of processes by the machining condition calculating unit, the actual
machining commanding unit, the machining result collecting unit, the evaluation value
acquiring unit, the convergence determining unit, the stop determining unit, the
evaluation determining unit, the prediction unit, and the search end determining unit is10
repeatedly performed.
ADVANTAGEOUS EFFECTS OF INVENTION
[0008] According to the present disclosure, when searching for an optimal machining
condition, it is possible to shorten the time until an optimal machining condition can be15
found as compared with the conventional technique in which machining under the
machining condition is performed until a vibrational change in the machining result
settles in the machining apparatus for all the machining conditions to be tried.
BRIEF DESCRIPTION OF DRAWINGS20
[0009] FIG. 1 is a diagram illustrating a configuration example of a machining condition
search device according to a first embodiment.
FIG. 2 is a flowchart for describing an operation of the machining condition
search device according to the first embodiment.
FIG. 3 is a concept diagram of a method example in which a stop determining25
unit determines whether or not to terminate machining under a machining condition being
tried by comparing the largest provisional evaluation value among provisional evaluation
values within a quartile range with a termination threshold in the first embodiment.
FIG. 4 is a concept diagram of a method example in which the stop determining
unit determines whether or not to terminate machining under a machining condition being30
7
tried by comparing a provisional evaluation value included in a section of an average5
value ± κσ of provisional evaluation values with the termination threshold in the first
embodiment.
FIG. 5 is a graph conceptually illustrating a relationship between a prediction
value of an evaluation value and an index indicating uncertainty in the first embodiment.
FIGS. 6A and 6B are graphs illustrating an example of a result of comparing a10
time until an optimal machining condition is found in a conventional optimal machining
condition search technique with a time until an optimal machining condition is found by
the machining condition search device according to the first embodiment.
FIG. 7 is a diagram for describing an example of a method in which the stop
determining unit sets a variable termination threshold on the basis of a tried machining15
condition and an evaluation value corresponding to the machining condition in the first
embodiment.
FIGS. 8A and 8B are diagrams illustrating an example of a hardware
configuration of the machining condition search device according to the first embodiment.
20
DESCRIPTION OF EMBODIMENTS
[0010] First Embodiment
FIG. 1 is a diagram illustrating a configuration example of a machining condition
search device 1 according to a first embodiment.
The machining condition search device 1 according to the first embodiment is25
connected to a machining apparatus 2 and a display unit 3. The machining condition
search device 1 searches for an optimal machining condition (hereinafter referred to as
“optimal machining conditions”) from a large number of machining conditions that can
be set in the machining apparatus 2. The optimal machining condition is, for example,
a machining condition under which a machining result satisfying requested specifications30
8
of machining is obtained. Further, the display unit 3 displays the machining conditions5
and the like found by the machining condition search device 1 in accordance with a
request from a user such as a machining worker. For example, the display unit 3 displays
machining conditions set in the machining apparatus 2 and evaluation values of
machining performed by the machining apparatus 2 in accordance with the machining
conditions. Further, for example, the display unit 3 displays a machining condition that10
is not performed by the machining apparatus 2 and a prediction value of an evaluation
value of machining when it is assumed that the machining apparatus 2 performs
machining in accordance with the machining condition. In addition, for example, an
optimal machining condition that is a search result of a search by the machining condition
search device 1 is displayed. Note that, in FIG. 1, the display unit 3 is provided outside15
the machining condition search device 1 and the machining apparatus 2, but this is merely
an example. The display unit 3 may be provided in, for example, the machining
condition search device 1 or the machining apparatus 2.
[0011] The machining apparatus 2 is an industrial device that performs machining in
accordance with machining conditions. For example, the machining apparatus 2 forms20
a manufactured article as a workpiece into a desired shape by removing unnecessary
portions. The machining apparatus 2 can also perform, for example, additive machining.
Hereinafter, the manufactured article is referred to as a workpiece. The material of the
workpiece is, for example, metal. Note that this is merely an example, and the material
of the workpiece is not limited to metal. The material of the workpiece may be, for25
example, ceramic, glass, or wood.
Examples of the machining apparatus 2 include a laser machining apparatus, an
electrical discharge machining apparatus, a cutting machining apparatus, a grinding
machining apparatus, an electrolytic machining apparatus, an ultrasonic machining
apparatus, an electron beam machining apparatus, and an additional machining apparatus.30
9
In the following first embodiment, as an example, the machining apparatus 2 is assumed5
to be a laser machining apparatus. Note that this is merely an example, and in the first
embodiment, the machining apparatus 2 may be a machining apparatus other than the
laser machining apparatus.
[0012] The machining apparatus 2 can perform normal machining for forming the
workpiece into a desired shape, and can perform experimental machining on the10
workpiece.
In the experimental machining, the machining condition search device 1
according to the first embodiment generates a trial machining condition, and causes the
machining apparatus 2 to perform the experimental machining in accordance with the
machining condition. The machining apparatus 2 performs preset experimental15
machining on the workpiece in accordance with the machining conditions described
above.
Here, the machining conditions are configured by a combination of a plurality of
control parameters used for controlling the machining apparatus 2. The control
parameters are, for example, laser power, cutting speed, beam diameter, focal position,20
and gas pressure. Each control parameter included in the machining conditions can be
adjusted. For example, when there are five control parameters that can be adjusted in
machining of the laser machining apparatus, and the value of each control parameter can
be selected in 10 levels, there are 105 = 100000 machining conditions configured by a
combination of each control parameter.25
[0013] The machining condition search device 1 generates a trial machining condition
for search from such a huge number of combinations of machining conditions, and causes
the machining apparatus 2 to perform experimental machining. When the machining
apparatus 2 performs the experimental machining in accordance with the machining
conditions, the machining condition search device 1 collects information indicating30
10
machining results (hereinafter referred to as “machining result information”) from the5
machining apparatus 2. The machining result information is, for example, information
indicating the state of the machining apparatus 2 during machining, information
indicating the state of the workpiece during machining, or information indicating the state
of the workpiece after machining. The machining result information also includes
information on machining conditions according to which the machining apparatus 2 has10
performed machining.
For example, the machining apparatus 2 includes a sensor that detects sound,
light, or a machining speed generated during machining, and the machining condition
search device 1 collects machining result information from the sensor. For example, the
sensor may be an imaging device that acquires an image obtained by imaging the15
workpiece after machining, or a measuring instrument that measures unevenness of a cut
surface of the workpiece. Further, the sensor may be provided at a location different
from the machining apparatus 2. The machining condition search device 1 only needs
to be able to collect the machining result information.
[0014] The machining condition search device 1 determines an evaluation value of20
machining performed in accordance with a machining condition on the basis of machining
result information collected by performing machining in accordance with the machining
condition. Then, the machining condition search device 1 searches for an optimal
machining condition while predicting an evaluation value corresponding to an untried
machining condition on the basis of a combination of the machining condition and the25
evaluation value. Details of a method by which the machining condition search device
1 searches for the optimal machining condition will be described later.
[0015] Here, as described above, the machining result obtained when the machining
apparatus 2 is caused to perform machining under a certain machining condition may
change in a vibratory manner as the machining proceeds. When the machining result30
11
changes in a vibratory manner, the evaluation value corresponding to the machining result5
calculated on the basis of the machining result also changes in a vibratory manner. If
the machining condition search device 1 causes the machining apparatus 2 to perform
machining for a certain amount of time until the vibrational change in the machining
result in accordance with each machining condition settles for all the machining
conditions to be tried, and waits for the vibrational change in the machining result to settle,10
it takes time to calculate the evaluation value corresponding to each machining condition.
Accordingly, the machining condition search device 1 according to the first
embodiment employs the evaluation value calculated in the process until the vibrational
change of the machining result settles for the search for the optimal machining condition
even if the evaluation value is an evaluation value before the vibrational change settles as15
long as the evaluation value is assumed to have no influence on the search for the optimal
machining condition, terminates the machining in the experiment in accordance with the
machining condition being tried, and switches the machining condition for the search.
Thus, the machining condition search device 1 according to the first embodiment shortens
the time until an optimal machining condition can be found.20
[0016] A detailed configuration example of the machining condition search device 1
according to the first embodiment will be described.
The machining condition search device 1 includes a search machining condition
generating unit 11, a machining result collecting unit 12, an evaluation value acquiring
unit 13, a convergence determining unit 14, a stop determining unit 15, an evaluation25
determining unit 16, and a machine learning unit 17. Further, the machining condition
search device 1 also includes a machining result storage unit 18A, an evaluation value
storage unit 18B, a convergence result storage unit 18C, a stop determination storage unit
18D, a search result storage unit 18E, a prediction result storage unit 18F, and an
uncertainty storage unit 18G. Note that all or some of the storage units 18A to 18G may30
12
be provided by an external device provided separately from the machining condition5
search device 1.
[0017] The search machining condition generating unit 11 generates machining
conditions to be used in experimental actual machining, and causes the machining
apparatus 2 to perform machining in accordance with the generated machining conditions.
That is, the search machining condition generating unit 11 generates a machining10
condition to be searched for by actual machining in a multidimensional space having
control parameters constituting the machining condition as dimensions. As illustrated
in FIG. 1, the search machining condition generating unit 11 includes a machining
condition calculating unit 111, an actual machining commanding unit 112, and a search
end determining unit 113.15
[0018] The machining condition calculating unit 111 of the search machining condition
generating unit 11 generates a machining condition including a plurality of control
parameters that can be set in the machining apparatus 2. Specifically, the machining
condition calculating unit 111 generates machining conditions to be used in the
experimental machining. For example, the machining condition calculating unit 11120
selects a combination corresponding to the machining content from combinations of a
plurality of control parameters of the machining apparatus 2 and a range of values that
can be taken by these control parameters, and generates the machining condition from the
selected combination. The control parameters are, for example, laser power, cutting
speed, beam diameter, focal position, and gas pressure.25
The machining condition calculating unit 111 outputs the generated machining
condition to the actual machining commanding unit 112.
[0019] The actual machining commanding unit 112 causes the machining apparatus 2
to perform machining in accordance with the machining conditions generated by the
machining condition calculating unit 111. Note that the actual machining commanding30
13
unit 112 causes the machining apparatus 2 to continuously perform machining in5
accordance with the machining conditions generated by the machining condition
calculating unit 111. Specifically, the actual machining commanding unit 112 generates
a command for operating the machining apparatus 2 in accordance with the machining
conditions output from the machining condition calculating unit 111, and outputs the
generated command to the machining apparatus 2. The machining apparatus 2 performs10
machining in accordance with the machining conditions on the basis of the command
output from the actual machining commanding unit 112.
Further, when the evaluation determining unit 16 outputs an instruction to end
machining under the machining condition being tried (hereinafter referred to as a
“machining end instruction”), the actual machining commanding unit 112 ends15
experimental machining that is currently being performed on the machining apparatus 2.
Details of the evaluation determining unit 16 will be described later.
[0020] The search end determining unit 113 determines whether or not to end the search
for the machining condition on the basis of the information stored in the prediction result
storage unit 18F or the uncertainty storage unit 18G.20
When it is determined that it is not necessary to additionally search for a
machining condition, the search end determining unit 113 determines an optimal
machining condition on the basis of the evaluation value determined by the evaluation
determining unit 16. Specifically, the search end determining unit 113 sets the
machining condition corresponding to the highest evaluation value among the evaluation25
values stored in the search result storage unit 18E as the optimal machining condition.
Details of the evaluation determining unit 16 will be described later.
Further, when it is determined that it is necessary to additionally search for a
machining condition, the search end determining unit 113 causes the machining condition
calculating unit 111 to generate a machining condition for search to be tried next.30
14
[0021] The machining result collecting unit 12 collects, from the machining apparatus5
2, machining result information indicating a machining result of machining performed in
accordance with the machining conditions.
The machining result collecting unit 12 collects a machining result every time
the actual machining commanding unit 112 causes machining to be performed. As
described above, the actual machining commanding unit 112 causes the machining to be10
performed continuously in accordance with the machining conditions. While the
machining apparatus 2 performs the machining, machining is performed in a plurality of
steps. Therefore, when the machining apparatus 2 performs experimental machining in
accordance with certain machining conditions, a plurality of pieces of machining result
information is collected.15
The machining result collecting unit 12 causes the machining result storage unit
18A to store the collected machining result information. The machining result
collecting unit 12 causes the machining result storage unit 18A to store the machining
result information in association with the acquisition time of the machining result
information, for example.20
The machining result storage unit 18A stores the machining result information
in time series.
[0022] The evaluation value acquiring unit 13 calculates an evaluation value for
machining performed by the machining apparatus 2 on the basis of the machining result
information collected by the machining result collecting unit 12. In the first25
embodiment, the evaluation value calculated by the evaluation value acquiring unit 13 on
the basis of the machining result information is also referred to as a “provisional
evaluation value”. The evaluation value acquiring unit 13 calculates a provisional
evaluation value for each piece of the machining result information. That is, the
evaluation value acquiring unit 13 calculates a provisional evaluation value for each30
15
machining step. Note that the evaluation value acquiring unit 13 acquires the machining5
result information collected by the machining result collecting unit 12 from the machining
result storage unit 18A.
In the first embodiment, the evaluation value is a value indicating the quality of
machining, and is defined as a value indicating that the larger the value, the better the
machining. The evaluation value is indicated by, for example, a value from 0 to 1. In10
this case, the evaluation value is 1 when the best machining is performed, and the
evaluation value is 0 when the worst machining is performed.
The evaluation value acquiring unit 13 causes the evaluation value storage unit
18B to store information (hereinafter referred to as “provisional evaluation value
information”) in which the acquisition time of the machining result information, the15
machining condition, and the calculated provisional evaluation value are associated with
each other. Note that, here, in the provisional evaluation value information, the
acquisition time of the machining result information is associated with the machining
condition and the provisional evaluation value, but this is merely an example. For
example, in the provisional evaluation value information, the calculation time of the20
provisional evaluation value may be associated with the machining condition and the
provisional value.
The evaluation value storage unit 18B stores the provisional evaluation value
information in time series.
[0023] The convergence determining unit 14 determines whether or not the provisional25
evaluation value has converged on the basis of provisional evaluation values in time series
calculated by the evaluation value acquiring unit 13. In the first embodiment,
“convergence” means that there is no vibrational change in value. The convergence
determining unit 14 determines whether the provisional evaluation value has converged
for each machining condition. Note that the convergence determining unit 14 acquires30
16
the provisional evaluation values in time series calculated by the evaluation value5
acquiring unit 13 from the provisional evaluation value information stored in the
evaluation value storage unit 18B.
When it is determined that the provisional evaluation value has converged, the
convergence determining unit 14 causes the convergence result storage unit 18C to store,
as post-convergence determination information, information in which the acquisition time10
of the machining result information, information indicating that the provisional
evaluation value has converged, the machining condition, the provisional evaluation
value, and a convergence value of the provisional evaluation value are associated with
each other. Instead of the acquisition time of the machining result information, the
calculation time of the provisional evaluation values may be associated. For example,15
the convergence determining unit 14 sets the latest provisional evaluation value as a
convergence value of the provisional evaluation value. Note that this is merely an
example, and for example, information (hereinafter referred to as “convergence value
calculation information”) defining how to calculate the convergence value of the
provisional evaluation value on the basis of the provisional evaluation values in time20
series is determined in advance, and the convergence determining unit 14 may calculate
the convergence value of the provisional evaluation value on the basis of the convergence
value calculation information.
On the other hand, when it is determined that the provisional evaluation value
has not converged, the convergence determining unit 14 estimates a value (hereinafter25
referred to as an “estimated convergence value”) to be a convergence destination of the
provisional evaluation value. Then, the convergence determining unit 14 causes the
convergence result storage unit 18C to store, as post-convergence determination
information, information in which the acquisition time of the machining result
information, information indicating that the provisional evaluation value has not30
17
converged, the machining condition, the provisional evaluation value, and the estimated5
convergence value are associated with one another. Instead of the acquisition time of
the machining result information, the calculation time of the provisional evaluation values
may be associated.
[0024] When the convergence determining unit 14 determines that the provisional
evaluation value has not converged, the stop determining unit 15 determines whether or10
not to terminate the machining under the machining condition being tried before the
provisional evaluation value converges. The stop determining unit 15 determines
whether or not to terminate machining under the machining condition being tried for each
machining condition. Note that the stop determining unit 15 only needs to determine
that the convergence determining unit 14 has determined that the provisional evaluation15
value has not converged from the post-convergence determination information stored in
the convergence result storage unit 18C. The stop determining unit 15 may directly
acquire information indicating that it is determined that the provisional evaluation value
has not converged from the convergence determining unit 14. Note that, in FIG. 1,
arrows from the convergence determining unit 14 to the stop determining unit 15 are20
omitted.
[0025] The stop determining unit 15 causes the stop determination storage unit 18D to
store information (hereinafter referred to as “post-termination determination
information”) in which the determination result (hereinafter referred to as a “termination
determination result”) as to whether or not to terminate machining under the machining25
condition being tried is associated with the latest post-convergence determination
information output from the convergence determining unit 14.
The stop determination storage unit 18D stores the post-termination
determination information.
[0026] When the stop determining unit 15 determines to terminate the machining under30
18
the machining condition being tried, the evaluation determining unit 16 causes the actual5
machining commanding unit 112 to terminate the machining in accordance with the
machining condition for the machining apparatus 2, and determines the estimated
convergence value estimated by the convergence determining unit 14 as the evaluation
value of the machining performed in accordance with the machining condition. When
the stop determining unit 15 determines not to terminate the machining under the10
machining condition being tried, the convergence determining unit 14 determines that the
provisional evaluation value has converged, and thereafter the evaluation determining
unit 16 determines the convergence value of the provisional evaluation value as the
evaluation value of the machining performed in accordance with the machining condition.
Note that the evaluation determining unit 16 determines, for each machining condition,15
an evaluation value for machining performed in accordance with the machining condition.
The evaluation determining unit 16 only needs to specify whether or not the stop
determining unit 15 has determined to terminate machining under the machining
condition being tried, the estimated convergence value estimated by the convergence
determining unit 14, or the convergence value of the provisional evaluation value, from20
the post-termination determination information stored in the stop determination storage
unit 18D. For example, the evaluation determining unit 16 may directly acquire the
post-termination determination information from the stop determining unit 15. Note
that, in FIG. 1, arrows from the stop determining unit 15 to the evaluation determining
unit 16 are omitted.25
[0027] The evaluation determining unit 16 causes the search result storage unit 18E to
store the combination of the machining condition and the evaluation value as a search
result.
The search result storage unit 18E stores the search result.
[0028] The machine learning unit 17 predicts an evaluation value of machining30
19
corresponding to an untried machining condition (machining is not performed) using the5
search result stored in the search result storage unit 18E. Further, the machine learning
unit 17 calculates uncertainty with respect to the prediction value of the evaluation value,
that is, the likelihood of deviation of the prediction.
[0029] The machine learning unit 17 includes a prediction unit 171 and an uncertainty
evaluating unit 172.10
The prediction unit 171 predicts an evaluation value corresponding to an untried
machining condition on the basis of the evaluation value determined by the evaluation
determining unit 16 and the machining condition corresponding to the evaluation value.
The prediction unit 171 only needs to acquire the evaluation value determined by the
evaluation determining unit 16 and the machining condition corresponding to the15
evaluation value from the search result stored in the search result storage unit 18E.
The prediction unit 171 causes the prediction result storage unit 18F to store
information (hereinafter referred to as “prediction result information”) in which the
prediction value of the evaluation value obtained by the prediction is associated with the
machining condition. The prediction result information is information in which an20
untried machining condition is associated with a prediction value of an evaluation value
corresponding thereto.
The prediction result storage unit 18F stores prediction result information.
[0030] The uncertainty evaluating unit 172 calculates an index indicating the
uncertainty of the prediction of the evaluation value by the prediction unit 171. The25
uncertainty evaluating unit 172 calculates an index indicating uncertainty of the
evaluation value with respect to the prediction value, that is, a likelihood of deviation of
the prediction by using the search result stored in the search result storage unit 18E. The
uncertainty evaluating unit 172 causes the uncertainty storage unit 18G to store
information (hereinafter referred to as “uncertainty information”) in which the calculated30
20
value of the index is associated with the machining condition. The uncertainty5
information is information in which an untried machining condition is associated with an
index value indicating uncertainty of prediction of an evaluation value corresponding to
the untried machining condition.
The uncertainty storage unit 18G stores uncertainty information.
[0031] Next, the operation of the machining condition search device 1 according to the10
first embodiment will be described.
FIG. 2 is a flowchart for describing the operation of the machining condition
search device 1 according to the first embodiment.
When the machining condition search process is started, first, the machining
condition calculating unit 111 of the search machining condition generating unit 1115
generates an initial machining condition (step ST1). The machining condition
calculating unit 111 generates the initial machining condition by selecting a
predetermined number of machining conditions as initial machining conditions from
among all combinations that can be set as machining conditions. Examples of a method
of selecting the initial machining condition by the machining condition calculating unit20
111 include an experimental planning method, an optimal planning method, and random
sampling. In addition, in a case where the user has an idea of a machining condition that
is considered to be optimal from past use results or the like, the machining condition
calculating unit 111 may use the machining condition input from the user as the initial
machining condition. Note that these methods are merely examples, and the machining25
condition calculating unit 111 may use any method to generate the initial machining
conditions.
[0032] For example, when the number of control parameters constituting the machining
conditions is five, and a value to be set in the machining apparatus 2 can be selected from
values in 10 levels for each control parameter, the total number of combinations of the30
21
machining conditions is 105 = 100000. The machining condition calculating unit 1115
selects, for example, 10 machining conditions as initial machining conditions from these
combinations. Note that the number of control parameters constituting the machining
conditions, the number of levels that can be set for each control parameter, or the number
of machining conditions selected as the initial machining conditions is not limited thereto.
The number of levels that can be set may be different depending on the control parameter.10
[0033] Next, the machining condition search device 1 selects one initial machining
condition from the initial machining conditions generated by the machining condition
calculating unit 111, and causes the machining apparatus 2 to perform machining under
the selected initial machining condition (step ST2). Specifically, the machining
condition calculating unit 111 selects one of the initial machining conditions and outputs15
the selected initial machining condition to the actual machining commanding unit 112 of
the search machining condition generating unit 11. The actual machining commanding
unit 112 generates a command for operating the machining apparatus 2 on the basis of the
initial machining condition output from the machining condition calculating unit 111, and
outputs the generated command to the machining apparatus 2. Thus, the machining20
apparatus 2 performs machining based on the initial machining condition selected by the
machining condition calculating unit 111. As described above, the machining condition
search device 1 according to the first embodiment first causes the machining apparatus 2
to perform machining based on the initial machining condition. Hereinafter, the
machining based on the initial machining condition is also referred to as “initial25
machining”.
[0034] The machining result collecting unit 12 collects, from the machining apparatus
2, machining result information indicating the machining result of the initial machining
performed according to the initial machining condition (step ST3).
The machining result collecting unit 12 causes the machining result storage unit30
22
18A to store the collected machining result information.5
[0035] The evaluation value acquiring unit 13 calculates a provisional evaluation value
for machining performed by the machining apparatus 2 in accordance with the initial
machining conditions in step ST2 on the basis of the machining result information
collected by the machining result collecting unit 12 (step ST4).
The evaluation value acquiring unit 13 causes the evaluation value storage unit10
18B to store the provisional evaluation value information in which the acquisition time
of the machining result information, the machining condition, here, the initial machining
condition, and the calculated provisional evaluation value are associated with each other.
[0036] The convergence determining unit 14 determines whether or not the provisional
evaluation value has converged on the basis of the provisional evaluation values in time15
series calculated by the evaluation value acquiring unit 13. When it is determined that
the provisional evaluation value has converged, the convergence determining unit 14
causes the convergence result storage unit 18C to store the post-convergence
determination information in which the acquisition time of the machining result
information, the information indicating that the provisional evaluation value has20
converged, the machining condition, here, the initial machining condition, the provisional
evaluation value, and the convergence value of the provisional evaluation value are
associated with one another. On the other hand, when it is determined that the
provisional evaluation value has not converged, the convergence determining unit 14
estimates the estimated convergence value, and causes the convergence result storage unit25
18C to store the post-convergence determination information in which the acquisition
time of the machining result information, the information indicating that the provisional
evaluation value has not converged, the machining condition, here, the initial machining
condition, the provisional evaluation value, and the estimated convergence value are
associated with each other (step ST5).30
23
[0037] Here, a method for determining, by the convergence determining unit 14 in step5
ST5, whether or not the provisional evaluation value has converged based on the
provisional evaluation values in time series, and a method for estimating the estimated
convergence value in a case where it is determined that the provisional evaluation value
has not converged will be described with specific examples.
[0038] The convergence determining unit 14 determines whether the provisional10
evaluation value has converged and estimates a determination estimated convergence
value on the basis of, for example, the degree of variation in the provisional evaluation
values in time series.
As a specific example, for example, the convergence determining unit 14 obtains
a quartile range of the provisional evaluation values from the provisional evaluation15
values in time series. Then, the convergence determining unit 14 determines whether or
not the provisional evaluation value has converged on the basis of the value range of the
quartile range of the provisional evaluation values. For example, a value range
(hereinafter referred to as a “first convergence determination range”) in a case where it is
determined that the provisional evaluation value has converged is determined in advance.20
If the quartile range of the provisional evaluation values falls within the first convergence
determination range, the convergence determining unit 14 determines that the provisional
evaluation value has converged. If the quartile range of the provisional evaluation
values is not within the first convergence determination range, the convergence
determining unit 14 determines that the provisional evaluation value has not converged.25
When it is determined that the provisional evaluation value has not converged,
the convergence determining unit 14 then estimates an estimated convergence value from
the quartile range of the provisional evaluation values obtained from the provisional
evaluation values in time series. For example, the convergence determining unit 14
estimates the median of the quartile range of the provisional evaluation values as the30
24
estimated convergence value.5
[0039] As another specific example, for example, the convergence determining unit 14
may estimate a distribution by regarding the provisional evaluation values in time series
as a specific distribution, and determine whether or not the provisional evaluation value
has converged on the basis of how much a value in a section of the average value ± κσ of
the provisional evaluation value is in the distribution of the provisional evaluation value.10
For example, a value range (hereinafter referred to as a “second convergence
determination range”) in a case where it is determined that the provisional evaluation
value has converged is determined in advance. If the value in the section of the average
value ± κσ of the provisional evaluation value in the distribution of the provisional
evaluation value falls within the second convergence determination range, the15
convergence determining unit 14 determines that the provisional evaluation value has
converged. If the value in the section of the average value ± κσ of the provisional
evaluation value in the distribution of the provisional evaluation value is not within the
second convergence determination range, the convergence determining unit 14
determines that the provisional evaluation value has not converged.20
When it is determined that the provisional evaluation value has not converged,
the convergence determining unit 14 then estimates an estimated convergence value from
the distribution estimated from the provisional evaluation values in time series. For
example, the convergence determining unit 14 estimates the average value of the
provisional evaluation value as the estimated convergence value.25
[0040] In addition, for example, the convergence determining unit 14 may estimate the
estimated convergence value on the basis of a learned model (hereinafter referred to as a
“first machine learning model”) that receives the evaluation values in time series as inputs
and outputs the estimated convergence value. The convergence determining unit 14
inputs the provisional evaluation values in time series to the first machine learning model30
25
to obtain the estimated convergence value.5
In addition, for example, the first machine learning model may be a model that
outputs information regarding the degree of variation of the provisional evaluation values
in addition to the estimated convergence value. The convergence determining unit 14
may determine whether or not the provisional evaluation value has converged on the basis
of information regarding the degree of variation of the provisional evaluation values10
obtained by inputting the provisional evaluation values in time series to the first machine
learning model.
[0041] When the convergence determining unit 14 determines that the provisional
evaluation value has not converged, the stop determining unit 15 determines whether or
not to terminate machining under the initial machining condition being tried before the15
provisional evaluation value converges (step ST6).
[0042] Here, a method of determining, by the stop determining unit 15, whether or not
to terminate machining under the machining condition being tried before the provisional
evaluation value converges will be described with a specific example.
[0043] The stop determining unit 15 determines whether or not to terminate machining20
under the machining condition being tried before the provisional evaluation value
converges, for example, by comparing the degree of variation in the provisional
evaluation values in time series calculated by the evaluation value acquiring unit 13 and
stored in the evaluation value storage unit 18B with a threshold (hereinafter referred to as
a “termination threshold”).25
The termination threshold is specified in advance by the user, for example, and
is stored in the stop determining unit 15. For example, the user designates in advance,
as the termination threshold, an evaluation value (hereinafter referred to as a “reference
evaluation value”) serving as a reference for terminating the machining under the
machining condition being tried in a case where the evaluation value does not exceed the30
26
evaluation value. For example, the user sets the reference evaluation value depending5
on requested performance desired for the machining apparatus 2.
[0044] As a specific example, for example, when the convergence determining unit 14
obtains the quartile range of the provisional evaluation values from the provisional
evaluation values in time series, the stop determining unit 15 determines whether or not
to terminate the machining under the machining condition being tried by comparing the10
largest provisional evaluation value among the provisional evaluation values within the
quartile range with the termination threshold. In this case, the stop determining unit 15
determines to terminate the machining under the machining condition being tried if the
largest provisional evaluation value among the provisional evaluation values within the
quartile range is less than the termination threshold. On the other hand, when the largest15
provisional evaluation value among the provisional evaluation values within the quartile
range is equal to or more than the termination threshold, the stop determining unit 15
determines to continue the machining under the machining condition being tried.
[0045] FIG. 3 is a concept diagram of a method example in which the stop determining
unit 15 determines whether or not to terminate machining under a machining condition20
being tried by comparing the largest provisional evaluation value among the provisional
evaluation values within the quartile range with a termination threshold in the first
embodiment.
The horizontal axis in FIG. 3 represents a time width in which machining is
performed in accordance with a certain machining condition, and the vertical axis in FIG.25
3 represents an evaluation value (provisional evaluation value). Points indicated by
black circles in FIG. 3 indicate provisional evaluation values calculated on the basis of
machining results of machining performed in accordance with the machining conditions.
Note that FIG. 3 illustrates a state in which the provisional evaluation value converges
for ease of understanding. In FIG. 3, reference numerals 201a, 201b, and 201c denote30
27
the quartile range of the provisional evaluation values.5
The quartile range of the provisional evaluation values is the range indicated by
201a at the time point when t1 hours have elapsed, and the quartile range of the provisional
evaluation values is the range indicated by 201b at the time point when t2 hours have
elapsed. For the quartile ranges illustrated in 201a and 201b, the largest provisional
evaluation value among the provisional evaluation values within the quartile range is10
equal to or more than the termination threshold. Therefore, in this case, the stop
determining unit 15 determines to continue machining under the machining condition
being tried.
When t3 hours have elapsed, the quartile range of the provisional evaluation
values is a range indicated by 201c, and the largest provisional evaluation value among15
the provisional evaluation values within the quartile range is less than the termination
threshold. In this case, the stop determining unit 15 determines to terminate the
machining under the machining condition being tried.
[0046] As another specific example, for example, when the convergence determining
unit 14 estimates the distribution of the provisional evaluation values in time series, the20
stop determining unit 15 may determine whether or not to terminate machining under the
machining condition being tried by comparing the provisional evaluation value included
in the section of the average value ± κσ of the provisional evaluation values with the
termination threshold. In this case, when all the provisional evaluation values included
in the section of the average value ± κσ of the provisional evaluation values are less than25
the termination threshold, the stop determining unit 15 determines to terminate the
machining under the machining condition being tried. On the other hand, when all the
provisional evaluation values included in the section of the average value ± κσ of the
provisional evaluation values are not less than the termination threshold, the stop
determining unit 15 determines to continue the machining under the machining condition30
28
being tried.5
[0047] FIG. 4 is a concept diagram of a method example in which the stop determining
unit 15 determines whether or not to terminate machining under a machining condition
being tried by comparing a provisional evaluation value included in a section of the
average value ± κσ of provisional evaluation values with the termination threshold in the
first embodiment.10
The horizontal axis in FIG. 4 represents a time width in which machining is
performed in accordance with a certain machining condition, and the vertical axis in FIG.
4 represents an evaluation value (provisional evaluation value). Points indicated by
black circles in FIG. 4 indicate provisional evaluation values calculated on the basis of
machining results of machining performed in accordance with a machining condition.15
Note that FIG. 4 illustrates a state in which the provisional evaluation value converges
for ease of understanding. In FIG. 4, 301a, 301b, and 301c represent the largest
provisional evaluation values among the provisional evaluation values included in the
interval of the average value ± κσ of the provisional evaluation values.
The largest provisional evaluation value among the provisional evaluation values20
included in the section of the average value ± κσ of the provisional evaluation values at
the time point after t4 hours is a value indicated by 301a, and the largest provisional
evaluation value among the provisional evaluation values included in the section of the
average value ± κσ of the provisional evaluation values at the time point t5 hours is a value
indicated by 301b. Both the value indicated by 301a and the value indicated by 301b25
are equal to or more than the termination threshold. That is, all the provisional
evaluation values included in the section of the average value ± κσ of the provisional
evaluation values including the value indicated by 301a are not less than the termination
threshold. Further, all the provisional evaluation values included in the section of the
average value ± κσ of the provisional evaluation values including the value indicated by30
29
301b are not less than the termination threshold. Therefore, in this case, the stop5
determining unit 15 determines to continue machining under the machining condition
being tried.
When t6 hours have elapsed, the largest provisional evaluation value among the
provisional evaluation values included in the section of the average value ± κσ of the
provisional evaluation values is a value indicated by 301c. A value indicated by 301c is10
less than the termination threshold. That is, all the provisional evaluation values within
the section of the average value ± κσ of the provisional evaluation values including the
value indicated by 301c are less than the termination threshold. In this case, the stop
determining unit 15 determines to terminate the machining under the machining condition
being tried.15
[0048] Furthermore, for example, the stop determining unit 15 can also determine
whether or not to terminate machining under the machining condition being tried before
the provisional evaluation value converges on the basis of a learned model (hereinafter
referred to as a “second machine learning model”) that receives the evaluation values in
time series as an input and outputs information indicating whether or not to stop20
machining. The stop determining unit 15 inputs the provisional evaluation values in
time series calculated by the evaluation value acquiring unit 13 to the second machine
learning model to obtain information indicating whether or not to stop machining. Note
that the stop determining unit 15 only needs to acquire the provisional evaluation values
in time series calculated by the evaluation value acquiring unit 13 from, for example,25
post-convergence determination information stored in the convergence result storage unit
18C.
[0049] Even if the provisional evaluation value does not converge, if the provisional
evaluation value is substantially within a low value range when the degree of variation of
the provisional evaluation values in time series is observed, it is assumed that a high30
30
provisional evaluation value cannot be obtained even when the machining apparatus 25
continues the machining, in other words, the obtained evaluation value is low.
Accordingly, in a case where the stop determining unit 15 determines that the provisional
evaluation value substantially fall within the low value range from the degree of variation
of the provisional evaluation values in time series by, for example, the method as
described above, the stop determining unit determines to terminate the machining under10
the machining condition being tried even before the provisional evaluation value
converges.
[0050] The stop determining unit 15 causes the stop determination storage unit 18D to
store the post-termination determination information in which the termination
determination result is associated with the latest post-convergence determination15
information output from the convergence determining unit 14.
[0051] When the stop determining unit 15 determines to terminate the machining under
the initial machining condition being tried before the provisional evaluation value
converges (“YES” in step ST6), the evaluation determining unit 16 causes the actual
machining commanding unit 112 to end the machining in accordance with the initial20
machining condition for the machining apparatus 2. Specifically, the evaluation
determining unit 16 outputs a machining end instruction to the actual machining
commanding unit 112. When the machining end instruction is output from the
evaluation determining unit 16, the actual machining commanding unit 112 ends
machining in accordance with the initial machining condition generated in step ST1,25
which is currently performed on the machining apparatus 2. Further, the evaluation
determining unit 16 determines the estimated convergence value estimated by the
convergence determining unit 14 as the evaluation value of the machining performed in
accordance with the initial machining condition. Then, the evaluation determining unit
16 causes the search result storage unit 18E to store the combination of the machining30
31
condition and the evaluation value as a search result (step ST8). Specifically, the5
evaluation determining unit 16 causes the search result storage unit 18E to store a
combination of the initial machining condition and the evaluation value, here, the
estimated convergence value as a search result.
When the stop determining unit 15 determines not to terminate the machining
under the initial machining condition being tried (“NO” in step ST6), the evaluation10
determining unit 16 determines whether or not the convergence determining unit 14
determines that the provisional evaluation value has converged (step ST7). When the
convergence determining unit 14 determines that the provisional evaluation value has not
converged (“NO” in step ST7), the operation of the machining condition search device 1
returns to the processing of step ST2. When the convergence determining unit 1415
determines that the provisional evaluation value has converged (“YES” in step ST7), the
evaluation determining unit 16 determines the convergence value of the provisional
evaluation value as the evaluation value. Then, the evaluation determining unit 16
causes the search result storage unit 18E to store the combination of the machining
condition and the evaluation value as a search result (step ST8). Specifically, the20
evaluation determining unit 16 causes the search result storage unit 18E to store a
combination of the initial machining condition and the evaluation value, here, the
convergence value of the provisional evaluation value, as a search result.
[0052] The machining condition calculating unit 111 checks whether or not the initial
machining has been completed for all the machining conditions selected as the initial25
machining conditions (step ST9).
When there is an initial machining condition for which the initial machining has
not been completed (“NO” in step ST9), the processing from step ST1 to step ST8 is
sequentially performed for the initial machining condition for which the initial machining
has not been completed. In the second and subsequent steps ST1, the machining30
32
condition calculating unit 111 selects an initial machining condition that has not been5
selected in the previous step ST1. Thus, the search result storage unit 18E stores a
search result in which all initial machining conditions (for example, 10 initial machining
conditions) and combinations of evaluation values are associated with each other.
[0053] For example, when the initial machining in accordance with 10 initial machining
conditions is completed, the prediction unit 171 of the machine learning unit 17 predicts10
an evaluation value corresponding to an untried machining condition by using the search
result (a machining condition and an evaluation value corresponding thereto) stored in
the search result storage unit 18E, in other words, on the basis of the evaluation value
determined by the evaluation determining unit 16 and the machining condition
corresponding to the evaluation value (step ST10). With respect to the tried machining15
condition on which machining has been performed, an evaluation value is determined in
step ST8 described above. On the other hand, the machining conditions under which
machining is performed are a part of the combinations of all machining conditions. For
example, when there are 100000 combinations of machining conditions and 10 initial
machining conditions are generated, there are 99990 untried machining conditions after20
the end of the initial machining. Therefore, in this case, the prediction unit 171
calculates 99990 prediction values of the evaluation values. Note that, as described later,
also in steps ST15 to ST22, selection of a machining condition, execution of machining,
collection of a machining result, calculation of a provisional evaluation value, prediction
of a convergence value of the provisional evaluation value, determination of whether or25
not to terminate machining before convergence of the provisional evaluation value, and
determination of the evaluation value are performed, and processing in step ST10 is
performed after processing in step ST22. When step ST10 is performed via the
processing of steps ST15 to ST22, the machining conditions set in step ST15 are excluded
from the untried machining conditions.30
33
[0054] As a method by which the prediction unit 171 calculates a prediction value of an5
evaluation value corresponding to an untried machining condition, that is, as an example
of a method of predicting an evaluation value corresponding to an untried machining
condition, there is a method using Gaussian process regression. When the prediction
unit 171 predicts the evaluation value corresponding to an untried machining condition
using the Gaussian process regression, the following calculation is performed. The10
method using the Gaussian process regression is an example of a method using a
probability model for the machining condition of the evaluation value, which is generated
on the assumption that the evaluation value for the machining condition is a random
variable following a specific distribution. Assuming that the number of observation
values, that is, the number of machining conditions under which machining is performed15
and evaluation values are calculated is N, the gram matrix is CN, and values of the control
parameters in the machining conditions stored in the search result storage unit 18E are x1
to xN, a prediction value m(xN+1) of the evaluation value for an untried machining
condition xN+1 can be calculated by Expression (1) below. As illustrated in Expression
(2) below, k is a vector in which values of kernel functions when each of the found20
machining conditions x1, ..., and xN and xN+1 are used as arguments are arranged. Note
that a superscript T represents transposition, and a superscript -1 represents an inverse
matrix.
m(xN+1) = kT·(CN-1)·t ...(1)
25
[0055] Note that, here, an example has been described in which the prediction unit 171
performs prediction using the Gaussian process regression, but the method of predicting
the evaluation value used by the prediction unit 171 is not limited thereto. For example,
34
the prediction unit 171 may predict the evaluation value using supervised learning such5
as a decision tree, linear regression, boosting, or a neural network.
[0056] When predicting an evaluation value corresponding to an untried machining
condition, the prediction unit 171 stores a prediction value of the evaluation value (step
ST11). Specifically, the prediction unit 171 causes the prediction result storage unit 18F
to store prediction result information in which the prediction value of the evaluation value10
predicted in step ST10 and the machining condition are associated with each other.
[0057] Further, the uncertainty evaluating unit 172 of the machine learning unit 17
calculates an index indicating uncertainty with respect to prediction of the evaluation
value corresponding to the untried machining condition using the search result stored in
the search result storage unit 18E (step ST12). An example of the index indicating the15
uncertainty is a standard deviation calculated using the Gaussian process regression
which is an example of a probability model. In a case where the uncertainty evaluating
unit 172 derives an index indicating the uncertainty by using the Gaussian process
regression, for example, the following calculation is performed. The number of
observation values, that is, the number of machining conditions under which machining20
has been performed and evaluation values have been calculated is denoted by N, the gram
matrix is denoted by CN, a vector obtained by arranging the machining conditions stored
in the search result storage unit 18E is denoted by k, and a scalar value obtained by adding
an accuracy parameter of a prediction model to values of the kernels of the untried
machining conditions xN+1 is denoted by c. At this time, when one of the control25
parameters constituting the machining conditions is xi (i is a natural number) and the
values of the control parameters in the machining conditions stored in the search result
storage unit 18E are x1 to xN, a standard deviation σ(xN+1), which is an index indicating
uncertainty with respect to prediction of an evaluation value for an untried machining
condition xN+1, can be calculated by Expression (3) below. Note that, in Expression (3),30
35
a variance σ2(xN+1) is obtained, but a standard deviation σ(xN+1) can be obtained by5
calculating the square root of the variance.
σ2(xN+1) = c - kT·(CN-1)·k ...(3)
[0058] Note that, here, an example has been described in which the uncertainty
evaluating unit 172 calculates the index indicating the uncertainty with respect to the
prediction using the Gaussian process regression, but the method of calculating the index10
indicating the uncertainty is not limited thereto. For example, the uncertainty evaluating
unit 172 may calculate the index using a method such as density estimation or a mixed
density network.
[0059] Here, the prediction value of the evaluation value and the uncertainty of the
prediction value in the first embodiment will be described.15
FIG. 5 is a graph conceptually illustrating a relationship between a prediction
value of an evaluation value and an index indicating uncertainty in the first embodiment.
FIG. 5 illustrates an example in which a prediction value and an index indicating
uncertainty are calculated using the Gaussian process regression. The horizontal axis in
FIG. 5 represents the value x of the control parameter that is the machining condition, and20
the vertical axis in FIG. 5 represents the evaluation value. Points indicated by black
circles in FIG. 5 indicate evaluation values (hereinafter also referred to as an evaluation
value of actual machining) calculated based on actual machining using the initial
machining conditions. In the prediction using the Gaussian process regression, the
evaluation value is predicted assuming that the evaluation value follows a Gaussian25
distribution. Thus, when the prediction value of the evaluation value is an average m(x)
of the Gaussian distribution and the index indicating the uncertainty of the prediction is a
standard deviation σ(x) of the Gaussian distribution, it is statistically indicated that the
actual evaluation value falls within a range of m(x) - 2σ(x) or more and m(x) + 2σ(x) or
less with a probability of about 95%. In FIG. 5, a curve indicated by a solid line30
36
indicates m(x) which is a prediction value of the evaluation value. Further, in FIG. 5,5
curves indicated by broken lines indicate a curve of m(x) - 2σ(x) and a curve of m(x) +
2σ(x).
As illustrated in FIG. 5, the index indicating the uncertainty decreases at a
position close to the evaluation value of the actual machining, and the index indicating
the uncertainty increases at a position away from the evaluation value of the actual10
machining.
[0060] The description returns to the operation of the machining condition search device
1 illustrated in the flowchart of FIG. 2.
The uncertainty evaluating unit 172 stores an index indicating the uncertainty of
the prediction value (step ST13). Specifically, the uncertainty evaluating unit 17215
causes the uncertainty storage unit 18G to store uncertainty information in which the
calculated value of the index is associated with the machining condition.
[0061] The search end determining unit 113 of the search machining condition
generating unit 11 determines whether or not to end the search for the machining
condition using the prediction value of the evaluation value of the machining condition20
stored in the prediction result storage unit 18F and the index indicating the uncertainty of
the prediction value of the evaluation value stored in the uncertainty storage unit 18G
(step ST14). For example, the search end determining unit 113 compares a value of an
index indicating the uncertainty of the prediction of the evaluation values of all the
machining conditions found so far stored in the uncertainty storage unit 18G with a25
threshold, and determines that the optimal machining condition has been found when the
value of the index is equal to or less than the threshold, and ends the search for machining
condition.
[0062] For example, by using a machining condition x, the prediction value m(x) of the
evaluation value for the machining condition x, and the index (standard deviation) σ(x)30
37
indicating the uncertainty of the prediction of the evaluation value, the search end5
determining unit 113 can determine that the larger the value of m(x) + κσ(x), the higher
the value of the machining condition is to be searched for. Note that κ is a parameter
determined before searching for a machining condition. As the value of κ is smaller, a
machining condition having a higher prediction value of the evaluation value is selected,
and as the value of κ is larger, a machining condition having a higher possibility of greatly10
deviating from the prediction of the evaluation value is selected. The same value may
be continuously used as the value of κ, or the value may be changed in the middle.
[0063] When it is determined to end the search for machining condition (“YES” in step
ST14), the search end determining unit 113 determines, as the optimal machining
condition, the machining condition associated with the highest evaluation value among15
the evaluation values of all the machining conditions stored in the search result storage
unit 18E. For example, the search end determining unit 113 extracts an optimal
machining condition and outputs the extracted machining condition to the actual
machining commanding unit 112. The actual machining commanding unit 112 outputs
a command including the machining conditions output from the search end determining20
unit 113 to the machining apparatus 2, and sets the machining conditions in the machining
apparatus 2. Thus, the actual machining commanding unit 112 causes the machining
apparatus 2 to perform normal machining in accordance with the set machining conditions.
Note that this is merely an example, and for example, the search end determining unit 113
may store the determined optimal machining condition in a storage unit (not illustrated).25
[0064] When it is determined that the search for the machining condition is not to be
ended, in other words, when it is determined that it is necessary to additionally search for
the machining condition (“NO” in step ST14), the search end determining unit 113
instructs the machining condition calculating unit 111 to generate a machining condition
to be tried next.30
38
[0065] When instructed by the search end determining unit 113 to generate a machining5
condition to be tried next, the machining condition calculating unit 111 generates a
machining condition to be tried next by using the prediction value of the evaluation value
of the machining condition stored in the prediction result storage unit 18F (step ST15).
Specifically, the machining condition calculating unit 111 selects a machining condition
to be tried next, that is, a new machining condition, from among all machining conditions.10
The machining condition to be tried next generated by the machining condition
calculating unit 111 is output to the actual machining commanding unit 112.
[0066] The actual machining commanding unit 112 outputs the command including the
machining condition to be tried next generated by the machining condition calculating
unit 111 in step ST15 to the machining apparatus 2, and causes the machining apparatus15
2 to perform machining under the machining condition (step ST16). During machining
by the machining apparatus 2, the machining result collecting unit 12 collects machining
result information (step ST17). The evaluation value acquiring unit 13 calculates a
provisional evaluation value for the machining performed in step ST16 (step ST18).
The convergence determining unit 14 determines whether the provisional evaluation20
value has converged and estimates the estimated convergence value on the basis of the
degree of variation in the provisional evaluation values in time series (step ST19). The
stop determining unit 15 determines whether or not to terminate machining under the
machining condition being tried (step ST20). The evaluation determining unit 16
determines the estimated convergence value as the evaluation value when the stop25
determining unit 15 determines to terminate machining under the machining condition
being tried, and determines the convergence value of the provisional evaluation value as
the evaluation value after the convergence determining unit 14 determines that the
provisional evaluation value has converged when the stop determining unit 15 determines
not to terminate machining under the machining condition being tried (step ST21). Then,30
39
the evaluation determining unit 16 causes a search result to be stored (step ST22). Next,5
the machining proceeds to the processing of steps ST10 and ST12, and the above-
described processing is executed.
[0067] The display unit 3 displays information obtained in the course of the above-
described processing, optimal machining conditions obtained as results of the processing,
and the like. For example, the display unit 3 displays the machining condition and the10
evaluation value corresponding to the machining condition obtained during the search for
the machining condition by the machining condition search device 1. Further, the
display unit 3 displays a machining condition and a prediction value of an evaluation
value corresponding to the machining condition. Furthermore, the display unit 3
displays the optimal machining condition as a search result. That is, the display unit 315
displays at least one of the machining condition read from the search result storage unit
18E and the evaluation value corresponding to the machining condition, the machining
condition read from the prediction result storage unit 18F and the prediction value of the
evaluation value corresponding to the machining condition, or the optimal machining
condition of the search result output from the machining condition calculating unit 111.20
Thus, the user can recognize the search situation and the search result of the machining
condition by referring to the information displayed on the display unit 3.
[0068] As described above, the machining condition search device 1 calculates a
provisional evaluation value for the performed machining on the basis of the machining
result information collected by causing the machining apparatus 2 to perform machining25
in accordance with the generated machining condition. The machining condition search
device 1 determines whether or not the provisional evaluation value has converged on the
basis of the calculated provisional evaluation values in time series, and when it is
determined that the provisional evaluation value has not converged, it is determined
whether or not to terminate machining under the machining condition being tried before30
40
the provisional evaluation value converges. For example, when it is determined that a5
high evaluation value cannot be obtained even when machining is continued as it is, in
other words, when it is determined that the obtained evaluation value is low, the
machining condition search device 1 determines to terminate the machining under the
machining condition being tried before the provisional evaluation value converges, by
comparing the degree of variation (for example, a quartile range of the provisional10
evaluation values or a distribution of the provisional evaluation values) of the provisional
evaluation values in time series with the termination threshold. When the obtained
evaluation value is low, the evaluation value is assumed to be an evaluation value having
no influence on the search for the optimal machining condition. When the machining
under the machining condition being tried is terminated before the provisional evaluation15
value converges, the machining condition search device 1 sets the estimated convergence
value as the evaluation value corresponding to the machining condition being tried.
When predicting the prediction value of the evaluation value, the machining condition
search device 1 determines whether or not to end the search for the machining condition,
and when ending the search for the machining condition, the machining condition search20
device 1 determines an optimal machining condition on the basis of the determined
evaluation value and the prediction value of the evaluation value, and when not ending
the search for the machining condition, the machining condition search device 1 generates
a machining condition to be tried next. The machining condition search device 1 repeats
the above-described processing until it is determined to end the search for the machining25
condition. Thus, the machining condition search device 1 determines the optimal
machining condition.
[0069] In the conventional optimal machining condition search technique, the
machining apparatus 2 is caused to perform machining for a certain period of time until
the vibrational change in the machining result is settled for each of all the machining30
41
conditions to be tried, and the evaluation value corresponding to the machining condition5
is calculated after the vibrational change in the machining result is settled. Therefore,
the conventional search technique for the optimal machining condition has poor time
efficiency until the optimal machining condition can be found.
On the other hand, as described above, in the machining condition search device
1 according to the first embodiment, when it is determined that a high evaluation value10
cannot be obtained even when machining is continued as it is in calculating the evaluation
value, machining under the machining condition being tried is terminated before the
evaluation value (provisional evaluation value) converges, and the estimated convergence
value is set as the evaluation value corresponding to the machining condition being tried.
Thus, as to the machining under a certain machining condition for which it is determined15
that a high evaluation value cannot be obtained, the machining condition search device 1
can omit the time from the time point at which the machining is terminated until the
machining result converges from the time period during which the machining result for
the machining converges. That is, the machining condition search device 1 can shorten
the total time necessary to search for the optimal machining condition by the omitted time.20
[0070] FIGS. 6A and 6B are graphs illustrating an example of a result of comparing the
time until the optimal machining condition is found in the conventional search technique
for the optimal machining condition with the time until the optimal machining condition
is found by the machining condition search device 1 according to the first embodiment.
FIG. 6A is a graph illustrating evaluation values until an optimal machining25
condition is found in the conventional search technique for the optimal machining
condition, and FIG. 6B is a graph illustrating evaluation values until an optimal machining
condition is found by the machining condition search device 1 according to the first
embodiment.
In FIGS. 6A and 6B, black circles indicate evaluation values calculated on the30
42
basis of machining results of actual machining performed until the machining results5
converge. In FIG. 6B, points indicated by white circles indicate estimated convergence
values calculated on the basis of machining results of actual machining that is terminated
before the machining results converge.
Note that FIGS. 6A and 6B are results obtained by searching the same machining
apparatus 2 for optimal machining conditions under which the same desired machining10
result can be obtained.
[0071] In the conventional search technique for the optimal machining condition, as
illustrated in FIG. 6A, machining is continued until the machining result, in other words,
the evaluation value converges regardless of whether the evaluation value is good or bad,
and thus it takes time until the optimal machining condition is found. In the example15
illustrated in FIG. 6A, it takes 21 minutes to find the optimal machining condition.
On the other hand, in the machining condition search device 1 according to the
first embodiment, as illustrated in FIG. 6B, the machining is terminated when the
machining result, in other words, the evaluation value is expected to be low, so that the
optimal machining condition can be found in a short time. In the example illustrated in20
FIG. 6B, the optimal machining condition is found in 14 minutes. The time needed until
the optimal machining condition is found by the machining condition search device 1
according to the first embodiment is shortened by 7 minutes as compared with the time
needed until the optimal machining condition is found by the conventional search
technique for the optimal machining condition illustrated in FIG. 6A.25
[0072] Note that, in the first embodiment described above, in the machining condition
search device 1, the termination threshold used when the stop determining unit 15
determines whether or not to terminate machining under the machining condition being
tried before the provisional evaluation value converges is the reference evaluation value
specified by the user in advance. That is, the termination threshold is a fixed value.30
43
Then, the stop determining unit 15 determines whether or not to terminate the machining5
under the machining condition being tried before the provisional evaluation value
converges by comparing the degree of variation of the provisional evaluation values in
time series with the termination threshold. However, this is merely an example.
For example, the stop determining unit 15 can also set the termination threshold
on the basis of a tried machining condition and the evaluation value corresponding to the10
machining condition. The tried machining condition and the evaluation value
corresponding to the machining condition are stored in the search result storage unit 18E
by the evaluation determining unit 16 as search results. The termination threshold set
on the basis of the evaluation value determined by the stop determining unit 15 is also
referred to as a “variable termination threshold”. Note that, in this case, when the15
variable termination threshold is set, the stop determining unit 15 determines whether or
not to terminate the machining under the machining condition being tried before the
provisional evaluation value converges, for example, by comparing the estimated
convergence value estimated by the convergence determining unit 14 with the variable
termination threshold. The estimated convergence value estimated by the convergence20
determining unit 14 is an estimated convergence value in the latest post-convergence
determination information stored in the convergence result storage unit 18C.
Specifically, the stop determining unit 15 sets the variable termination threshold
in accordance with a preset condition (hereinafter referred to as a “variable termination
threshold setting condition”) on the basis of, for example, a tried machining condition and25
an evaluation value corresponding to the machining condition.
[0073] As the variable termination threshold setting condition, for example, a condition
such as , , or below is set.

When the number of times of trial is less than X times, a value for not terminating30
44
machining is set as a variable termination threshold, and when the number of times of5
trial is equal to or more than X times, an X-th evaluation value among the evaluation
values corresponding to all the tried machining conditions is set as a variable termination
threshold.

An evaluation value of higher y order among evaluation values corresponding to10
all tried machining conditions is set as a variable termination threshold

The lowest evaluation value among the evaluation values of higher Z% among
evaluation values corresponding to all tried machining conditions is set as a variable
termination threshold.15
[0074] Note that the value of X, Y, or Z in , , or
can be set as appropriate.
Further, in , the “value for not terminating machining” is, for
example, “0”. Note that this is merely an example, and it is only necessary that a value
that does not exceed an estimated convergence value that can be assumed is set as the20
“value for not terminating machining”.
[0075] Here, FIG. 7 is a diagram for describing an example of a method in which the
stop determining unit 15 sets a variable termination threshold based on a tried machining
condition and an evaluation value corresponding to the machining condition in the first
embodiment.25
FIG. 7 is a diagram for describing an example of a method of setting a variable
termination threshold when the stop determining unit 15 sets the variable termination
threshold in accordance with the variable termination threshold setting condition of
described above on the basis of a tried machining condition and an
evaluation value corresponding to the machining condition. In FIG. 7, as an example,30
45
X in is set to “5”.5
The horizontal axis in FIG. 7 indicates the number of times of trial of the
machining condition. The number of trials is, that is, the number of machining
conditions that have been tried. In FIG. 7, the vertical axis represents the evaluation
value corresponding to each machining condition. Note that, when the machining
condition is being tried, the evaluation value on the vertical axis in FIG. 7 is an estimated10
convergence value. In FIG. 7, points indicated by black circles are an evaluation value
or an estimated convergence value each corresponding to the machining conditions.
[0076] For convenience of explanation, in FIG. 7, it is assumed that the machining
condition is tried nine times, but for example, it is assumed that the sixth machining
condition is currently being tried. That is, in this case, in FIG. 7, the evaluation value15
corresponding to the sixth trial is the estimated convergence value.
In this case, according to FIG. 7, when the five trials are ended, the fifth
evaluation value among the evaluation values corresponding to the machining conditions
that have been tried five times is the evaluation value corresponding to the machining
condition that has been tried for the third time. Accordingly, the stop determining unit20
15 sets the evaluation value corresponding to the machining condition tried for the third
time as the variable termination threshold. Note that, since the machining condition
being tried, in other words, the estimated convergence value for the machining condition
being tried for the sixth time is less than the variable termination threshold, the stop
determining unit 15 determines to terminate the machining under the machining condition25
being tried.
[0077] Further, for example, it is assumed that the ninth machining condition is currently
being tried. That is, in this case, in FIG. 7, the evaluation value corresponding to the
ninth trial is the estimated convergence value.
In this case, according to FIG. 7, at the end of eight trials, the fifth evaluation30
46
value among the evaluation values corresponding to the machining conditions that have5
been tried eight times is the evaluation value corresponding to the machining condition
that has been tried for the fourth time. Therefore, the stop determining unit 15 sets the
evaluation value corresponding to the machining condition tried for the fourth time as the
variable termination threshold. Note that, since the machining condition being tried, in
other words, the estimated convergence value for the machining condition being tried for10
the ninth time is less than the variable termination threshold, the stop determining unit 15
determines to terminate the machining under the machining condition being tried.
[0078] In this manner, the stop determining unit 15 can change the criterion used when
it is determined whether or not to terminate machining under the machining condition
being tried before the provisional evaluation value converges, in other words, the15
termination threshold.
For example, when the termination threshold is too high, the machining
condition search device 1 may terminate the machining condition of the machining that
needs to wait for convergence of the machining result in the middle, and a deviation of
the prediction value of the predicted evaluation value may increase. Consequently, there20
is a possibility that the machining condition search device 1 cannot search for the optimal
machining condition. Conversely, for example, in a case where the termination
threshold is too low, the machining condition search device 1 may take time to determine
that machining under the machining condition corresponding to the evaluation value that
is not high is terminated before the provisional evaluation value converges, or may wait25
without terminating the machining until the provisional evaluation value converges.
Consequently, the machining condition search device 1 may take time to find the optimal
machining condition.
In the machining condition search device 1, the stop determining unit 15 can
change the termination threshold, so that the machining condition search device 1 can30
47
shorten the time until the optimal machining condition can be found while maintaining5
the possibility of being able to find the optimal machining condition.
Note that, in this case, for the operation of the machining condition search device
1 described with reference to the flowchart of FIG. 2, a step in which the stop determining
unit 15 performs processing of setting the variable termination threshold is added between
step ST5 and step ST6 and between step ST19 and step ST20.10
[0079] A hardware configuration for implementing the functions of the machining
condition search device 1 is as follows.
The functions of the search machining condition generating unit 11, the
machining result collecting unit 12, the evaluation value acquiring unit 13, the
convergence determining unit 14, the stop determining unit 15, the evaluation15
determining unit 16, and the machine learning unit 17 in the machining condition search
device 1 are implemented by a processing circuit. That is, the machining condition
search device 1 includes a processing circuit that executes processing from step ST1 to
step ST22 in FIG. 2. The processing circuit may be dedicated hardware or a central
processing unit (CPU) that executes a program stored in a memory.20
[0080] FIG. 8A is a block diagram illustrating a hardware configuration that implements
the functions of the machining condition search device 1. Further, FIG. 8B is a block
diagram illustrating a hardware configuration for executing software for implementing
the functions of the machining condition search device 1. In FIGS. 8A and 8B, an input
interface device 102 relays the machining result information output from the machining25
apparatus 2 to the machining condition search device 1, and relays the stored information
output from the storage units 18A to 18G to the machining condition search device 1.
An output interface device 103 relays information output from the machining condition
search device 1 to the display unit 3 or information output from the machining condition
search device 1 to the storage units 18A to 18G.30
48
[0081] In a case where the processing circuit is the processing circuit 101 of dedicated5
hardware illustrated in FIG. 8A, the processing circuit 101 corresponds to, for example,
a single circuit, a composite circuit, a programmed processor, a parallel programmed
processor, an application specific integrated circuit (ASIC), a field-programmable gate
array (FPGA), or a combination thereof. The functions of the search machining
condition generating unit 11, the machining result collecting unit 12, the evaluation value10
acquiring unit 13, the convergence determining unit 14, the stop determining unit 15, the
evaluation determining unit 16, and the machine learning unit 17 in the machining
condition search device 1 may be implemented by separate processing circuits, or these
functions may be collectively implemented by one processing circuit.
[0082] When the processing circuit is a processor 104 illustrated in FIG. 4B, the15
functions of the search machining condition generating unit 11, the machining result
collecting unit 12, the evaluation value acquiring unit 13, the convergence determining
unit 14, the stop determining unit 15, the evaluation determining unit 16, and the machine
learning unit 17 in the machining condition search device 1 are implemented by software,
firmware, or a combination of software and firmware. Note that software or firmware20
is described as a program and stored in a memory 105.
[0083] The processor 104 reads and executes the program stored in the memory 105 to
implement the functions of the search machining condition generating unit 11, the
machining result collecting unit 12, the evaluation value acquiring unit 13, the
convergence determining unit 14, the stop determining unit 15, the evaluation25
determining unit 16, and the machine learning unit 17 in the machining condition search
device 1. For example, the machining condition search device 1 includes the memory
105 for storing a program that results in execution of the processing from step ST1 to step
ST22 in the flowchart illustrated in FIG. 2 when executed by the processor 104. These
programs cause a computer to execute a processing procedure or method performed by30
49
the search machining condition generating unit 11, the machining result collecting unit5
12, the evaluation value acquiring unit 13, the convergence determining unit 14, the stop
determining unit 15, the evaluation determining unit 16, and the machine learning unit 17.
The memory 105 may be a computer-readable storage medium storing a program for
causing a computer to function as the search machining condition generating unit 11, the
machining result collecting unit 12, the evaluation value acquiring unit 13, the10
convergence determining unit 14, the stop determining unit 15, the evaluation
determining unit 16, and the machine learning unit 17.
[0084] The memory 105 corresponds to, for example, a nonvolatile or volatile
semiconductor memory such as a random access memory (RAM), a read only memory
(ROM), a flash memory, an erasable programmable read only memory (EPROM), or an15
electrically-EPROM (EEPROM), a magnetic disk, a flexible disk, an optical disk, a
compact disk, a mini disk, or a DVD.
[0085] Some of the functions of the search machining condition generating unit 11, the
machining result collecting unit 12, the evaluation value acquiring unit 13, the
convergence determining unit 14, the stop determining unit 15, the evaluation20
determining unit 16, and the machine learning unit 17 in the machining condition search
device 1 may be implemented by dedicated hardware, and some of the functions may be
implemented by software or firmware. For example, the functions of the search
machining condition generating unit 11, the machining result collecting unit 12, the
evaluation value acquiring unit 13, the convergence determining unit 14, the stop25
determining unit 15, and the evaluation determining unit 16 are implemented by the
processing circuit 101 that is dedicated hardware, and the functions of the machine
learning unit 17 are implemented by the processor 104 reading and executing a program
stored in the memory 105. As described above, the processing circuit can implement
the above-described functions by hardware, software, firmware, or a combination thereof.30
50
[0086] Further, in the first embodiment described above, the machining condition search5
device 1 may be mounted on the machining apparatus 2, or may be provided in a server
connected to the machining apparatus 2 via a network, for example. For example, some
of the search machining condition generating unit 11, the machining result collecting unit
12, the evaluation value acquiring unit 13, the convergence determining unit 14, the stop
determining unit 15, the evaluation determining unit 16, and the machine learning unit 1710
may be mounted on the machining apparatus 2, and the others may be provided in the
server.
[0087] As described above, the machining condition search device 1 according to the
first embodiment includes the machining condition calculating unit 111 to generate a
machining condition including a plurality of control parameters settable in the machining15
apparatus 2, the actual machining commanding unit 112 to cause the machining apparatus
2 to perform machining in accordance with the machining condition generated by the
machining condition calculating unit 111, the machining result collecting unit 12 to
collect machining result information indicating a machining result of the machining
performed by the machining apparatus 2 by the actual machining commanding unit 112,20
the evaluation value acquiring unit 13 to calculate a provisional evaluation value for the
performed machining on the basis of the machining result information collected by the
machining result collecting unit 12, and the convergence determining unit 14 to determine
whether or not the provisional evaluation value has converged on the basis of the
provisional evaluation values in time series calculated by the evaluation value acquiring25
unit 13, and estimate an estimated convergence value to be a convergence destination of
the provisional evaluation value when it is determined that the provisional evaluation
value has not converged, the stop determining unit 15 to determine whether or not to
terminate the machining under the machining condition being tried before the provisional
evaluation value converges when the convergence determining unit 14 determines that30
51
the provisional evaluation value has not converged, the evaluation determining unit 16,5
when the stop determining unit 15 determines to terminate the machining under the
machining condition being tried, to cause the actual machining commanding unit 112 to
end the machining in accordance with the machining condition for the machining
apparatus 2 and determine the estimated convergence value estimated by the convergence
determining unit 14 as an evaluation value of the machining performed in accordance10
with the machining condition, and determine, when the stop determining unit 15
determines not to terminate the machining under the machining condition being tried, a
convergence value of the provisional evaluation value as the evaluation value after the
convergence determining unit 14 determines that the provisional evaluation value has
converged, the search end determining unit 113 to determine whether or not to end a15
search for the machining condition, determine the machining condition that is optimum
on the basis of the evaluation value determined by the evaluation determining unit 16
when ending the search, and cause the machining condition calculating unit 111 to
generate the machining condition to be tried next on the basis of the prediction value
predicted by the prediction unit 171 when not ending the search, in which until the search20
end determining unit 113 determines to end the search, each of processes by the
machining condition calculating unit 111, the actual machining commanding unit 112, the
machining result collecting unit 12, the evaluation value acquiring unit 13, the
convergence determining unit 14, the stop determining unit 15, the evaluation
determining unit 16, the prediction unit 171, and the search end determining unit 113 is25
repeatedly performed. Thus, when searching for the optimal machining conditions, the
machining condition search device 1 can shorten the time until the optimal machining
conditions can be found, as compared with the conventional technology in which
machining under the machining conditions is performed until the vibratory change in the
machining result is settled for the machining apparatus 2 for all the machining conditions30
52
to be tried.5
[0088] Note that any component of the embodiment can be modified or any component
of the embodiment can be omitted.
INDUSTRIAL APPLICABILITY
[0089] A machining condition search device according to the present disclosure can be10
used to search for machining conditions of a laser machining apparatus, for example.
REFERENCE SIGNS LIST
[0090] 1: machining condition search device, 2: machining apparatus, 3: display unit,
11: search machining condition generating unit, 111: machining condition calculating unit,15
112: actual machining commanding unit, 113: search end determining unit, 12: machining
result collecting unit, 13: evaluation value acquiring unit, 14: convergence determining
unit, 15: stop determining unit, 16: evaluation determining unit, 17: machine learning unit,
171: prediction unit, 172: uncertainty evaluating unit, 18A: machining result storage unit,
18B: evaluation value storage unit, 18C: convergence result storage unit, 18D: stop20
determination storage unit, 18E: search result storage unit, 18F: prediction result storage
unit, 18G: uncertainty storage unit, 101: processing circuit, 102: input interface device,
103: output interface device, 104: processor, 105: memory
25
30
53
We Claim:5
1. A machining condition search device, comprising:
a machining condition calculating unit to generate a machining condition
including a plurality of control parameters settable in a machining apparatus;
an actual machining commanding unit to cause the machining apparatus to
perform machining in accordance with the machining condition generated by the10
machining condition calculating unit;
a machining result collecting unit to collect machining result information
indicating a machining result of the machining performed by the machining apparatus by
the actual machining commanding unit;
an evaluation value acquiring unit to calculate a provisional evaluation value for15
the performed machining on a basis of the machining result information collected by the
machining result collecting unit;
a convergence determining unit to determine whether or not the provisional
evaluation value has converged on a basis of the provisional evaluation values in time
series calculated by the evaluation value acquiring unit, and estimate an estimated20
convergence value to be a convergence destination of the provisional evaluation value
when it is determined that the provisional evaluation value has not converged;
a stop determining unit to determine whether or not to terminate the machining
under the machining condition being tried before the provisional evaluation value
converges when the convergence determining unit determines that the provisional25
evaluation value has not converged;
an evaluation determining unit, when the stop determining unit determines to
terminate the machining under the machining condition being tried, to cause the actual
machining commanding unit to end the machining in accordance with the machining
condition for the machining apparatus and determine the estimated convergence value30
54
estimated by the convergence determining unit as an evaluation value of the machining5
performed in accordance with the machining condition, and determine, when the stop
determining unit determines not to terminate the machining under the machining
condition being tried, a convergence value of the provisional evaluation value as the
evaluation value after the convergence determining unit determines that the provisional
evaluation value has converged;10
a prediction unit to predict a prediction value of the evaluation value
corresponding to the machining condition untried on a basis of the evaluation value
determined by the evaluation determining unit and the machining condition
corresponding to the evaluation value; and
a search end determining unit to determine whether or not to end a search for the15
machining condition, determine the machining condition that is optimum on a basis of
the evaluation value determined by the evaluation determining unit and the evaluation
value predicted by the prediction unit when ending the search, and cause the machining
condition calculating unit to generate the machining condition to be tried next on a basis
of the prediction value predicted by the prediction unit when not ending the search,20
wherein until the search end determining unit determines to end the search, each
of processes by the machining condition calculating unit, the actual machining
commanding unit, the machining result collecting unit, the evaluation value acquiring
unit, the convergence determining unit, the stop determining unit, the evaluation
determining unit, the prediction unit, and the search end determining unit is repeatedly25
performed.
2. The machining condition search device according to claim 1, wherein
the convergence determining unit estimates the estimated convergence value on
a basis of a degree of variation in the provisional evaluation values in time series30
55
calculated by the evaluation value acquiring unit.5
3. The machining condition search device according to claim 1, wherein
the convergence determining unit estimates the estimated convergence value on
a basis of the provisional evaluation values in time series calculated by the evaluation
value acquiring unit and a first machine learning model that receives the evaluation values10
in time series as an input and outputs the estimated convergence value.
4. The machining condition search device according to claim 1, wherein
the stop determining unit determines whether or not to terminate the machining
under the machining condition being tried before the provisional evaluation value15
converges by comparing a degree of variation of the provisional evaluation values in time
series calculated by the evaluation value acquiring unit with a termination threshold.
5. The machining condition search device according to claim 4, wherein
the stop determining unit sets a variable termination threshold on a basis of the20
machining condition that has been tried and the evaluation value corresponding to the
machining condition, and determines whether or not to terminate the machining under the
machining condition that is being tried before the provisional evaluation value converges
by comparing the estimated convergence value estimated by the convergence determining
unit with the set variable termination threshold.25
6. The machining condition search device according to claim 1, wherein
the stop determining unit determines whether or not to terminate the machining
under the machining condition being tried before the provisional evaluation value
converges on a basis of the provisional evaluation values in time series calculated by the30
56
evaluation value acquiring unit and a second machine learning model that receives the5
evaluation values in time series as an input and outputs information indicating whether or
not to stop machining.
7. The machining condition search device according to claim 1, further comprising:
an uncertainty evaluating unit to calculate an index indicating uncertainty of10
prediction by the prediction unit, wherein
the machining condition calculating unit generates the machining condition to
be tried next on a basis of the prediction value of the evaluation value predicted by the
prediction unit and the index indicating prediction uncertainty.
15
8. The machining condition search device according to claim 7, wherein
the search end determining unit determines whether or not to end the search for
the machining condition using the prediction value of the evaluation value and the index
indicating uncertainty of the evaluation value, and sets the machining condition
corresponding to the evaluation value that is highest among the evaluation values20
determined by the evaluation determining unit as the machining condition that is optimal
when it is determined to end the search for the machining condition.
9. The machining condition search device according to claim 7, wherein
the prediction unit predicts the prediction value using a probability model for the25
machining condition of an evaluation value generated on an assumption that the
evaluation value for the machining condition is a random variable following a specific
distribution, and
the uncertainty evaluating unit calculates the index indicating uncertainty of the
prediction using the probability model.30
57
5
10. The machining condition search device according to claim 1, further comprising:
a display unit to display at least one of the machining condition and the
evaluation value corresponding to the machining condition, the machining condition and
the prediction value of the evaluation value corresponding to the machining condition, or
the machining condition of a search result.10
11. A machining condition search method, comprising:
generating, by a machining condition calculating unit, a machining condition
including a plurality of control parameters settable in a machining apparatus;
causing, by an actual machining commanding unit, the machining apparatus to15
perform machining in accordance with the machining condition generated by the
machining condition calculating unit;
collecting, by a machining result collecting unit, machining result information
indicating a machining result of the machining performed by the machining apparatus by
the actual machining commanding unit;20
calculating, by an evaluation value acquiring unit, a provisional evaluation value
for the performed machining on a basis of the machining result information collected by
the machining result collecting unit;
determining, by a convergence determining unit, whether or not the provisional
evaluation value has converged on a basis of the provisional evaluation values in time25
series calculated by the evaluation value acquiring unit, and estimating, by the
convergence determining unit, an estimated convergence value to be a convergence
destination of the provisional evaluation value when it is determined that the provisional
evaluation value has not converged;
determining, by a stop determining unit, whether or not to terminate the30
58
machining under the machining condition being tried before the provisional evaluation5
value converges when the convergence determining unit determines that the provisional
evaluation value has not converged;
causing, by an evaluation determining unit, when the stop determining unit
determines to terminate the machining under the machining condition being tried, the
actual machining commanding unit to end the machining in accordance with the10
machining condition for the machining apparatus and determining, by the evaluation
determining unit, the estimated convergence value estimated by the convergence
determining unit as an evaluation value of the machining performed in accordance with
the machining condition, and determining, when the stop determining unit determines not
to terminate the machining under the machining condition being tried, a convergence15
value of the provisional evaluation value as the evaluation value after the convergence
determining unit determines that the provisional evaluation value has converged;
predicting, by a prediction unit, a prediction value of the evaluation value
corresponding to the machining condition untried on a basis of the evaluation value
determined by the evaluation determining unit and the machining condition20
corresponding to the evaluation value; and
determining, by a search end determining unit, whether or not to end a search for
the machining condition, determines the machining condition that is optimum on a basis
of the evaluation value determined by the evaluation determining unit when ending the
search, and causing the machining condition calculating unit to generate the machining25
condition to be tried next on a basis of the prediction value predicted by the prediction
unit when not ending the search; and
repeatedly performing each of processes by the machining condition calculating
unit, the actual machining commanding unit, the machining result collecting unit, the
evaluation value acquiring unit, the convergence determining unit, the stop determining30
59
unit, the evaluation determining unit, the prediction unit, and the search end determining5
unit until the search end determining unit determines to end the search.