Patent ID: 12204299

DESCRIPTION OF EMBODIMENTS

First Embodiment

FIG.1is a block diagram illustrating the configuration of a machining condition searching device1according to the first embodiment. The machining condition searching device1searches for an optimal machining condition from a large number of machining conditions that can be set in a machining device2, and sets the machining condition resulting from the search in the machining device2. The optimal machining condition is, for example, a machining condition under which a machining result satisfying required machining specifications is obtained. In addition, a display unit3displays the machining condition searched by the machining condition searching device1, etc. For example, the display unit3displays a machining condition set in the machining device2and an evaluation value of machining performed by the machining device2in accordance with the machining condition.

The machining device2is an industrial device that performs machining in accordance with a machining condition, and machines a workpiece made of metal into a desired shape by, for example, cutting or polishing the workpiece or removing unnecessary portions using electricity or other energy. The workpiece is not limited to metal, and may be ceramics, glass, or wood. Examples of the machining device2include a laser beam machine, an electrical discharge machine, a cutting machine, a grinding machine, an electro-chemical machine, an ultrasonic machining device, and an electron beam machine. An example in which the machining device2is an electrical discharge machine, particularly a die sinking electrical discharge machine, will be described below.

The machining condition is constructed by a combination of a plurality of control parameters used to control the machining device2. Generally, different machining conditions result in different machining results. In addition, even if the same machining condition is set in the machining device2, different machining results may be obtained depending on a change in the machining state that indicates a state of the machining device2which is performing machining, a state of the workpiece that is being machined, and a state of an installation environment of the machining device2. That is, in order to obtain an optimal machining result satisfying required machining specifications, it is necessary to search for an appropriate machining condition based on the machining state.

For example, in a case where there are three control parameters that can be adjusted in machining by a die sinking electrical discharge machine, and the value of each control parameter can be selected on a scale of 1 to 10, there are 103=1000 machining conditions constituted by a combination of the control parameters. For this, the machining condition searching device1does not sequentially search for a large number of machining conditions that can be set in the machining device2, but narrows down the number of machining conditions to be searched depending on a change in the machining state, so that the number of attempts to search for machining conditions is reduced.

InFIG.1, the machining condition searching device1includes a machining condition generating unit11, a machining state collecting unit12, a machining result collecting unit13, a machining result evaluating unit14, a model construction unit15, and an evaluation value prediction model16. The machining condition searching device1also includes a machining state storage unit17A, a search result storage unit17B, a model storage unit17C, a prediction result storage unit17D, and an uncertainty storage unit17E. All or some of the storage units17A to17E may be provided in an external device provided separately from the machining condition searching device1.

The machining condition generating unit11generates a machining condition and sets the generated machining condition in the machining device2. The machining condition generating unit11includes a machining condition calculating unit11a, an actual machining instructing unit11b, and a search end determining unit11c.

The machining condition calculating unit11acalculates a machining condition to be set in the machining device2. For example, the machining condition calculating unit11aselects, from combinations of a plurality of control parameters of the machining device2and possible values of these control parameters, a combination corresponding to the machining detail and calculates the machining condition from the selected combination. The control parameters are, for example, laser output, cutting speed, beam magnification, focal position, and gas pressure.

The actual machining instructing unit11bcauses the machining device2to perform machining based on the machining condition calculated by the machining condition calculating unit11a. For example, the actual machining instructing unit11bgenerates an instruction for operating the machining device2on the basis of the machining condition calculated by the machining condition calculating unit11a, and outputs the generated instruction to the machining device2.

The search end determining unit11cdetermines whether or not to end the search for the machining condition on the basis of data stored in the prediction result storage unit17D or the uncertainty storage unit17E. When the search end determining unit11cdetermines that it is necessary to additionally search for machining conditions, the machining condition calculating unit11agenerates a machining condition under which machining is to be performed next. When it is determined that it is not necessary to additionally search for machining conditions, the machining condition calculating unit11adetermines the machining condition predicted to have the highest evaluation value as an optimal machining condition using the prediction result of the evaluation value stored in the prediction result storage unit17D.

The machining state collecting unit12collects data indicating a machining state of machining performed by the machining device2. The machining state includes a state of the machining device2, a state of the workpiece being machined, and a state of an installation environment of the machining device2. For example, the machining state indicates a state that affects the machining result but cannot be controlled by a user, such as the temperature of the machining device2, the temperature of the workpiece, the thickness of the workpiece, and the temperature or humidity in a room that is the installation environment of the machining device2. The data indicating the machining state indicates a state quantity obtained by quantifying each state indicated by the machining state. The data collected by the machining state collecting unit12is stored in the machining state storage unit17A.

The machining result collecting unit13collects, from the machining device2, a machining result of the machining performed in accordance with the machining condition. For example, the machining result collecting unit13collects information related to the machining obtained during and after the machining. Examples of the information related to the machining include detection data of sound or light observed during machining, the number of discharge pulses, and the surface state of the workpiece after machining.

The machining result evaluating unit14calculates an evaluation value of the machining performed by the machining device2in accordance with the machining condition on the basis of the machining result collected by the machining result collecting unit13. The evaluation value indicates the quality of machining, and is, for example, a value falls within the range from 0 to 1. The larger the evaluation value is, the better the machining result is. The evaluation value is 1 when the best machining is performed, and the evaluation value is 0 when the worst machining is performed. The machining result evaluating unit14stores a combination of the machining condition and the evaluation value in the search result storage unit17B as a search result. The search result storage unit17B stores the search result.

The model construction unit15constructs the evaluation value prediction model16on the basis of a degree of change in the relationship between the machining condition and the evaluation value, and performs weighting based on the machining state on the evaluation value prediction model16. For example, the model construction unit15calculates a degree of change in the relationship between the machining condition and the evaluation value on the basis of the information indicating the machining state stored in the machining state storage unit17A and the evaluation value stored in the search result storage unit17B. When the degree of change in the relationship between the machining condition and the evaluation value is equal to or greater than a threshold value, the model construction unit15determines that a change in the machining state for newly constructing the evaluation value prediction model has occurred, and constructs the evaluation value prediction model.

In addition, the model construction unit15performs weighting on the evaluation value prediction model16on the basis of the machining state. For example, the predictive values of the evaluation values respectively obtained by a plurality of evaluation value prediction models16are integrated using weights corresponding to the magnitudes of state quantities obtained by quantifying the machining states corresponding to the evaluation value prediction models16. In the integrated predictive value of the evaluation value, the prediction result of the evaluation value prediction model16having a larger weight is considered more important.

The evaluation value prediction model16predicts an evaluation value corresponding to an untried machining condition on the basis of the machining condition generated by the machining condition generating unit11, the machining state collected by the machining state collecting unit12, and the evaluation value calculated by the machining result evaluating unit14. InFIG.1, the evaluation value prediction model16includes an evaluation value predicting unit16aand an uncertainty evaluation unit16b.

The evaluation value predicting unit16apredicts an evaluation value corresponding to an untried machining condition (under which machining is not performed) on the basis of the machining condition stored in the search result storage unit17B and the evaluation value corresponding to this machining condition. In addition, the evaluation value predicting unit16astores the machining condition and a predictive value of the evaluation value corresponding to this machining condition in the prediction result storage unit17D. The prediction result storage unit17D stores an untried machining condition and a predictive value of an evaluation value corresponding to the untried machining condition in association with each other.

The uncertainty evaluation unit16bcalculates an index indicating uncertainty of the prediction of the evaluation value by the evaluation value predicting unit16a. For example, the uncertainty evaluation unit16bcalculates an index indicating uncertainty of the predictive value of the evaluation value, that is, the likelihood of deviation from the prediction, using the search result stored in the search result storage unit17B. The uncertainty evaluation unit16bstores uncertainty information including the calculated index value and the machining condition in the uncertainty storage unit17E. The uncertainty storage unit17E stores the untried machining condition and an index value indicating the uncertainty of prediction of the evaluation value corresponding to the untried machining condition in association with each other.

FIG.2is a flowchart illustrating the machining condition searching method according to the first embodiment, and illustrates a series of processing until an optimal machining condition is found by the machining condition searching device1. When the processing for searching for an optimal machining condition is started, the machining condition generating unit11generates an initial machining condition (step ST1). The machining condition calculating unit11aselects a certain number of combinations as initial machining conditions from among all combinations of control parameters that can be set in the machining device2. As a method for selecting the initial machining condition, an experimental design method, an optimal design method, and random sampling may be used. When the user has an idea of the optimal machining condition on the basis of the past usage record of the machining device2, the machining condition designated by the user may be used as the initial machining condition.

For example, when there are three control parameters constituting the machining condition, and a value to be set in the machining device2is selected from 10 values for each control parameter, the total number of combinations of the machining conditions is 1000. The machining condition calculating unit11aselects a certain number of machining conditions from 1000 machining conditions. In the following description, the certain number is, for example, five, and the machining condition calculating unit11aselects five machining conditions from 1000 machining conditions.

Subsequently, the machining condition generating unit11sets the initial machining conditions in the machining device2and causes the machining device2to perform machining in accordance with the initial machining conditions (step ST2). For example, the machining condition calculating unit11aselects one initial machining condition from among five initial machining conditions, and outputs the selected initial machining condition to the actual machining instructing unit11b. The actual machining instructing unit11bgenerates an instruction for operating the machining device2on the basis of the initial machining condition output from the machining condition calculating unit11a, and outputs the generated instruction to the machining device2. The machining device2performs machining in accordance with the initial machining condition. In the following description, machining in accordance with the initial machining condition is also referred to as “initial machining”.

The machining state collecting unit12collects data indicating a machining state of the machining performed by the machining device2in accordance with the initial machining condition (step ST3). The machining state collecting unit12stores the data indicating the machining state collected from the machining device2in the machining state storage unit17A. As a result, the machining state storage unit17A stores the initial machining condition and the machining state in association with each other.

The machining result collecting unit13collects data indicating a machining result of the machining performed by the machining device2in accordance with the initial machining condition (step ST4). The data collected by the machining result collecting unit13is output to the machining result evaluating unit14.

The machining result evaluating unit14calculates an evaluation value of the machining performed by the machining device2in accordance with the initial machining condition on the basis of the machining result collected by the machining result collecting unit13(step ST5). The machining result evaluating unit14calculates an evaluation value indicating the quality of machining by quantifying the machining result collected by the machining result collecting unit13. For example, the machining result evaluating unit14measures detection data of sound or light or the number of discharge pulses observed during machining in accordance with the initial machining condition, and converts the measured value into an evaluation value indicating the quality of machining, the evaluation value being a continuous value or a discrete value of a plurality of levels (for example, 10 levels). The search result storage unit17B stores a combination of the machining condition and the evaluation value.

The machining condition calculating unit11achecks whether or not initial machining has been completed for all the machining conditions selected as the initial machining conditions (step ST6). When there is an initial machining condition under which initial machining has not been completed (NO in step ST6), the processes from step ST1to step ST5are sequentially performed for the initial machining condition under which the initial machining has not been completed. As a result, all the initial machining conditions (for example, five initial machining conditions) and the data indicating machining states are stored in the machining state storage unit17A in association with each other. Further, the search result storage unit17B stores all the initial machining conditions and the evaluation values in association with each other.

When the initial machining under all the initial machining conditions has been completed (YES in step ST6), the machining condition calculating unit11achecks whether or not machining in accordance with the optimal machining condition resulting from the search has been completed (step ST7). When the machining device2completes machining in accordance with the optimal machining condition (YES in step ST7), the series of processes inFIG.2ends.

When machining in accordance with the optimal machining condition has not been completed (NO in step ST7), the model construction unit15determines whether or not the value of an index indicating a degree of change in the relationship between the machining condition and the evaluation value is equal to or greater than the threshold value using the data indicating the machining state stored in the machining state storage unit17A (step ST8). A method for obtaining the degree of change includes change point detection. The change point detection uses an abnormality degree as an index indicating the degree of change. The abnormality degree is, for example, a value obtained by squaring a difference between a predictive value of the machining state and an actual measurement value of the machining state stored in the machining state storage unit17A.

For example, an autoregressive model is used to calculate the predictive value of the machining state. In the autoregressive model, the predictive value hat y(t) of the machining state at a time t that is one step ahead is calculated using Equation (1) below. In Equation (1) below, y(t−1) is an actual measurement value of the machining state at a current time t−1, y(t−2) is an actual measurement value of the machining state at a past time t−2, and y(t−3) is an actual measurement value of the machining state at a past time t−3. The coefficient α1is a coefficient for the actual measurement value y(t−1), the coefficient α2is a coefficient for the actual measurement value y(t−2), and the coefficient α3is a coefficient for the actual measurement value y(t−3). Note that the model construction unit15may evaluate the degree of change in the relationship between the machining condition and the evaluation value using supervised learning such as a multiple regression model, a decision tree, or a neural network in place of the autoregressive model.
ŷ(t)=α1y(t−1)+α2y(t−2)+α3y(t−3)  (1)

When determining that the value of the index indicating the degree of change in the relationship between the machining condition and the evaluation value is equal to or greater than the threshold value (YES in step ST8), the model construction unit15determines that a change in the machining state for which the evaluation value prediction model16is to be newly constructed has occurred, and constructs the evaluation value prediction model16corresponding to the changed machining state (step ST9). The evaluation value prediction model16can be constructed using supervised learning such as an autoregressive model, a multiple regression model, a decision tree, or a neural network, for example.

When the model construction unit15determines that the value of the index indicating the degree of change in the relationship between the machining condition and the evaluation value is less than the threshold value (NO in step ST8) or when the process of ST9is completed, the model construction unit15performs weighting based on the machining state stored in the machining state storage unit17A on each of the already constructed evaluation value prediction models16(step ST10). The method for weighting includes linear interpolation and boosting. In addition, the model construction unit15may select one evaluation value prediction model16and perform weighting on the selected evaluation value prediction model16instead of performing weighting on the plurality of evaluation value prediction models16.

For example, the model construction unit15performs weighting on the evaluation value prediction model16using linear interpolation. The model construction unit15calculates, using a machining state s1at the past time t−2 and a machining state s2at the past time t−3 which are stored in the machining state storage unit17A, and a machining state s at the current time t−1 collected by the machining state collecting unit12, a weight γ1(s) for a function f1determined by the evaluation value prediction model16corresponding to the machining state s1and a weight β2(s) for a function f2determined by the evaluation value prediction model16corresponding to the machining state s2in accordance with Equation (2) below. An output f(x, s) of the evaluation value prediction model16for the machining condition x and the machining state s is calculated in accordance with Equation (3) below using the weights. Note that, although the weight values are determined for the two evaluation value prediction models16, the model construction unit15may calculate the weight values for three or more evaluation value prediction models16.
β1(s)=(s2−s)/(s2−s1)
β2(s)=(s−s1)/(s2−s1)  (2)
f(x,s)=β1(s)f1(x)+β2(s)f2(x)  (3)

When machining in accordance with, for example, the five initial machining conditions is completed, the evaluation value predicting unit16ain the evaluation value prediction model16predicts, using the machining conditions stored in the search result storage unit17B and the evaluation values corresponding thereto, evaluation values for all of the 1000 machining conditions described above (step ST11). The evaluation values predicted by the evaluation value predicting unit16aare stored in the prediction result storage unit17D (step ST12).

As a method for predicting the evaluation value by the evaluation value predicting unit16a, Gaussian process regression may be used, for example. In the Gaussian process regression, the evaluation value predicting unit16ais a probability model of the machining condition corresponding to the evaluation value constructed assuming that the evaluation value corresponding to the machining condition is a random variable. When a vector in which the machining conditions stored in the search result storage unit17B and kernel values corresponding thereto are arranged is k and a vector in which the evaluation values stored in the search result storage unit17B are arranged is t, the evaluation value predicting unit16acan calculate a predictive value m(x) of the evaluation value for the machining condition x in accordance with Equation (4) below. In Equation (4), CNis a gram matrix. Note that the evaluation value predicting unit16amay not use Gaussian process regression, and may predict the evaluation value using supervised learning such as a decision tree, linear regression, boosting, or a neural network.
m(x)=kT·(CN−1)·t(4)

When machining in accordance with, for example, the five initial machining conditions is completed, the uncertainty evaluation unit16bin the evaluation value prediction model16calculates indexes indicating uncertainty of prediction of the evaluation values for all of the 1000 machining conditions described above using the machining conditions stored in the search result storage unit17B and the evaluation values corresponding thereto (step ST13). The values of the indexes calculated by the uncertainty evaluation unit16bare stored in the uncertainty storage unit17E (step ST14).

As a method for calculating the indexes by the uncertainty evaluation unit16b, Gaussian process regression is used, for example. In the Gaussian process regression, when a vector in which the machining conditions stored in the search result storage unit17B and values of kernels corresponding thereto are arranged is k, and a scalar value obtained by adding the accuracy parameter of the evaluation value predicting unit16ato the value of the kernel between the machining conditions x is c, the uncertainty evaluation unit16bcan calculate an index σ2(x) indicating the uncertainty of the prediction of the evaluation value for the untried machining condition x in accordance with Equation (5) below. In Equation (5), CNis a gram matrix. The uncertainty evaluation unit16bmay not use the Gaussian process regression, and may calculate the index by regression using density estimation, a mixed density network, and Kullback-Leibler divergence.
σ2(x)=c−kT·(CN−1)k(5)

FIG.3is a graph illustrating a relationship among a machining condition, a predictive value of an evaluation value corresponding to the machining condition, and an index indicating uncertainty of prediction of the evaluation value. The evaluation value prediction model16performs prediction on the basis of the premise that, for example, Gaussian process regression is used and the evaluation value follows the Gaussian distribution. Black plots illustrated inFIG.3indicate machining conditions and evaluation values stored in the search result storage unit17B.FIG.3statistically indicates that, in a case where the predictive value of the evaluation value is set as an average m(x) of the Gaussian distribution and the index indicating the uncertainty of the prediction of the evaluation value is set as a standard deviation σ(x) of the Gaussian distribution, the black plots fall within a range of equal to or less than m(x)+2σ(x) and equal to or more than m(x)−2σ(x) with a probability of about 95%, even if the prediction of the evaluation value is wrong.

The search end determining unit11cincluded in the machining condition generating unit11determines whether or not to end the search for the machining condition using the predictive value of the evaluation value of the machining condition stored in the prediction result storage unit17D and the index indicating the uncertainty of the prediction of the evaluation value stored in the uncertainty storage unit17E (step ST15). For example, the search end determining unit11ccompares the values of the indexes, which are stored in the uncertainty storage unit17E and indicate the uncertainty of the prediction of the evaluation values of all the machining conditions searched so far, with a threshold value. In a case where the values of the indexes are equal to or less than the threshold value, the search end determining unit11cdetermines that the optimal machining condition has been searched, and ends the search for the machining condition.

In addition, when the number of machining conditions under which the prediction of the evaluation values may greatly deviates is equal to or less than a specified number as a result of comparison between the indexes indicating the uncertainty of the prediction of the evaluation values of all the machining conditions and the threshold value, the search end determining unit11cends the search for the machining condition. This is because, when there is a machining condition under which the actual measurement value of the evaluation value and the predictive value of the evaluation value are greatly different, the predictive value greatly changes in a wide range of the search space of the machining condition.

For example, the search end determining unit11cuses the machining condition x, the predictive value m(x) of the evaluation value for the machining condition x, and an index σ(x) (standard deviation) indicating the uncertainty of the prediction of the evaluation value, and can determine that, as the value of m(x)+κσ(x) increases, the significance of searching the machining condition is higher. Note that κ is a parameter determined before the search for the machining condition is started. As the value of κ is smaller, a machining condition having a higher predictive value of the evaluation value is selected, and as the value of κ is larger, a machining condition having a higher possibility of greatly 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 during the processing.

When determining to end the search for the machining condition (YES in step ST15), the search end determining unit11cextracts the machining condition predicted to have the highest evaluation value from the predictive values of the evaluation values of all the machining conditions stored in the prediction result storage unit17D, and outputs the extracted machining condition to the actual machining instructing unit11b. The actual machining instructing unit11boutputs an instruction including the machining condition output from the search end determining unit11cto the machining device2, and sets the machining condition in the machining device2(step ST16).

When determining that it is necessary to additionally search for a machining condition (NO in step ST15), the search end determining unit11coutputs continuation of the search to the machining condition calculating unit11a. When receiving an instruction to continue the search from the search end determining unit11c, the machining condition calculating unit11agenerates a machining condition to be tried next by using the predictive value of the evaluation value of the machining condition stored in the prediction result storage unit17D (step ST17). The machining condition to be tried next calculated by the machining condition calculating unit11ais output to the actual machining instructing unit11b. The actual machining instructing unit11boutputs an instruction including the machining condition to be tried next to the machining device2, and sets the machining condition in the machining device2.

When the optimal machining condition is set in step ST16or the machining condition to be tried next is set in step ST17, the machining device2performs machining (step ST18). During machining by the machining device2, the machining state collecting unit12collects data indicating the machining state and stores the machining condition and the machining state in association with each other in the machining state storage unit17A. The machining result collecting unit13collects data indicating the machining result and outputs the data to the machining result evaluating unit14. The machining result evaluating unit14calculates an evaluation value of the machining performed by the machining device2on the basis of the machining result collected by the machining result collecting unit13(step ST19). Next, the processing proceeds to the process of step ST7, and the above-described processing is executed.

The display unit3displays the machining condition and the evaluation value corresponding to the machining condition obtained during the search for the machining condition by the machining condition searching device1. In addition, the display unit3displays the machining condition and the predictive value of the evaluation value corresponding to the machining condition, or the optimal machining condition that is the search result. That is, the display unit3displays at least one of: the machining condition read from the search result storage unit17B and the evaluation value corresponding to this machining condition; the machining condition read from the prediction result storage unit17D and the predictive value of the evaluation value corresponding to this machining condition; or the optimal machining condition that is the search result output from the machining condition calculating unit11a. As a result, a machining worker can recognize the search situation and the search result of the machining condition by referring to the information displayed on the display unit3.

A hardware configuration for implementing the functions of the machining condition searching device1is as follows.

The functions of the machining condition generating unit11, the machining state collecting unit12, the machining result collecting unit13, the machining result evaluating unit14, the model construction unit15, and the evaluation value prediction model16in the machining condition searching device1are implemented by a processing circuit. That is, the machining condition searching device1includes a processing circuit that executes processes from step ST1to step ST19inFIG.2. The processing circuit may be dedicated hardware, or may be a central processing unit (CPU) that executes a program stored in a memory.

FIG.4Ais a block diagram illustrating a hardware configuration for implementing the function of the machining condition searching device1.FIG.4Bis a block diagram illustrating a hardware configuration for executing software that implements the function of the machining condition searching device1. InFIGS.4A and4B, an input interface100relays data indicating the machining state and the machining result output from the machining device2to the machining condition searching device1, and relays storage data output from each of the storage units17A to17E to the machining condition searching device1. An output interface101relays information output from the machining condition searching device1to the display unit3or data output from the machining condition searching device1to each of the storage units17A to17E.

When the processing circuit is a processing circuit102that is dedicated hardware illustrated inFIG.4A, the processing circuit102is, 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 of some of these circuits. The functions of the machining condition generating unit11, the machining state collecting unit12, the machining result collecting unit13, the machining result evaluating unit14, the model construction unit15, and the evaluation value prediction model16in the machining condition searching device1may be implemented by separate processing circuits, or these functions may be collectively implemented by a single processing circuit.

When the processing circuit is a processor103illustrated inFIG.4B, the functions of the machining condition generating unit11, the machining state collecting unit12, the machining result collecting unit13, the machining result evaluating unit14, the model construction unit15, and the evaluation value prediction model16in the machining condition searching device1are implemented by software, firmware, or a combination of software and firmware. Note that software or firmware is described as a program and stored in a memory104.

The processor103executes the functions of the machining condition generating unit11, the machining state collecting unit12, the machining result collecting unit13, the machining result evaluating unit14, the model construction unit15, and the evaluation value prediction model16in the machining condition searching device1by reading and executing the program stored in the memory104. For example, the machining condition searching device1includes the memory104for storing programs to eventually execute the processes from step ST1to step ST19in the flowchart illustrated inFIG.2when the processing in the flowchart is executed by the processor103. These programs cause a computer to execute procedures or methods performed by the machining condition generating unit11, the machining state collecting unit12, the machining result collecting unit13, the machining result evaluating unit14, the model construction unit15, and the evaluation value prediction model16. The memory104may be a computer-readable storage medium storing a program for causing a computer to function as the machining condition generating unit11, the machining state collecting unit12, the machining result collecting unit13, the machining result evaluating unit14, the model construction unit15, and the evaluation value prediction model16.

The memory104is, 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 an electrically-EPROM (EEPROM), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, or a digital versatile disk (DVD).

Some of the functions of the machining condition generating unit11, the machining state collecting unit12, the machining result collecting unit13, the machining result evaluating unit14, the model construction unit15, and the evaluation value prediction model16in the machining condition searching device1may be implemented by dedicated hardware, and some may be implemented by software or firmware. For example, the functions of the machining condition generating unit11, the machining state collecting unit12, the machining result collecting unit13, the machining result evaluating unit14, and the model construction unit15are implemented by the processing circuit102that is dedicated hardware, and the function of the evaluation value prediction model16is implemented by the processor103reading and executing the program stored in the memory104. As described above, the processing circuit can implement the above-mentioned functions by hardware, software, firmware, or a combination thereof.

As described above, in the machining condition searching device1according to the first embodiment, the evaluation value corresponding to the untried machining condition is predicted on the basis of the machining condition set in the machining device2, the machining state, and the evaluation value of machining, the evaluation value prediction model16is constructed on the basis of a degree of change in the relationship between the machining condition and the evaluation value, and weighting based on the machining state is performed on the evaluation value prediction model16. Even when the relationship between the machining condition and the evaluation value changes due to a change in the machining state, the evaluation value prediction model16is newly constructed on the basis of the degree of the change, and weighting based on the machining state is performed on the evaluation value prediction model16. Thus, the machining condition searching device1can search for the machining condition without constructing a learning model by collecting a large amount of data in advance, even when the relationship between the machining condition and the evaluation value thereof changes due to a change in the machining state. In addition, the machining condition searching device1does not sequentially search for a large number of machining conditions that can be set in the machining device2, but narrows down the number of machining conditions to be searched depending on a change in the machining state, so that the number of attempts to search for machining conditions is reduced.

It is to be noted that any components in the embodiment can be modified or omitted.

INDUSTRIAL APPLICABILITY

The machining condition searching device according to the present disclosure can be used to search for machining conditions of an electrical discharge machine, for example.

REFERENCE SIGNS LIST

1: machining condition searching device,2: machining device,3: display unit,11: machining condition generating unit,11a: machining condition calculating unit,11b: actual machining instructing unit,11c: search end determining unit,12: machining state collecting unit,13: machining result collecting unit,14: machining result evaluating unit,15: model construction unit,16: evaluation value prediction model,16a: evaluation value predicting unit,16b: uncertainty evaluation unit,17A: machining state storage unit,17B: search result storage unit,17C: model storage unit,17D: prediction result storage unit,17E: uncertainty storage unit,100: input interface,101: output interface,102: processing circuit,103: processor,104: memory