Electrical discharge machining evaluation system and electrical discharge machining evaluation method

Abstract

The present application provides a kind of discharge machining evaluation system and discharge machining evaluation method.Discharge machining evaluation system includes storage device and processor.Storage device is used to store characteristic calculation module, coefficient of variation calculation module and divergence analysis module.Processor is electrically connected storage device, and it is used to execute characteristic calculation module, coefficient of variation calculation module and divergence analysis module, and receives multiple sets of discharge signals from discharge machining machine.Characteristic calculation module is used to calculate multiple sets of characteristic parameters according to multiple sets of discharge signals.Coefficient of variation calculation module is used to calculate multiple sets of coefficient of variation according to multiple sets of characteristic parameters.Divergence analysis module is used to carry out divergence calculation according to multiple sets of characteristic parameters, to generate divergence analysis result.

Description

TECHNICAL FIELD

[0001] The present application relates to a workpiece processing evaluation mechanism, and more particularly to a discharge machining evaluation system and a discharge machining evaluation method. BACKGROUND

[0002] Generally, a conventional discharge machining is observed by a user through human eyes or by a sound to judge a processing result, so that a processing error is easily generated. Moreover, if a discharge machining time is long, an influence of the processing error is increased, and finally a workpiece processing failure is caused. SUMMARY

[0003] According to an embodiment of the present application, a discharge machining evaluation system includes a storage device and a processor. The storage device is used to store a feature calculation module, a coefficient of variation calculation module, and a divergence analysis module. The processor is electrically connected to the storage device, and is used to execute the feature calculation module, the coefficient of variation calculation module, and the divergence analysis module, and receives a plurality of discharge signals from a discharge machining machine. The feature calculation module is used to calculate a plurality of feature parameters according to the plurality of discharge signals, and the coefficient of variation calculation module is used to calculate a plurality of coefficients of variation according to the plurality of feature parameters. The divergence analysis module is used to perform divergence calculation according to the plurality of feature parameters to generate a divergence analysis result.

[0004] According to an embodiment of the present application, a discharge machining evaluation method includes the following steps: receiving a plurality of discharge signals from a discharge machining machine; calculating a plurality of feature parameters according to the plurality of discharge signals; calculating a plurality of coefficients of variation according to the plurality of feature parameters; and performing divergence calculation according to the plurality of feature parameters to generate a divergence analysis result.

[0005] Based on the above, the discharge machining evaluation system and the discharge machining evaluation method of the present application can effectively evaluate a discharge machining result.

[0006] In order to make the above features and advantages of the present application more apparent, the following embodiments are specifically described below, and the detailed description is made below with reference to the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 FIG. 1 is a schematic diagram of a discharge machining evaluation system according to an embodiment of the present application;

[0008] Figure 2 FIG. 2 is a flowchart of a discharge machining evaluation method according to an embodiment of the present application;

[0009] Figure 3 FIG. 3 is a schematic diagram of a plurality of modules according to an embodiment of the present application;

[0010] Figure 4 FIG. 4 is a schematic diagram of a workpiece according to an embodiment of the present application;

[0011] Figure 5 is a graph of discharge voltage and discharge current of an embodiment of the present application;

[0012] Figure 6A is a graph of parameter comparison of an embodiment of the present application;

[0013] Figure 6B is a graph of parameter comparison of another embodiment of the present application;

[0014] Figure 7 is a graph of divergence analysis result of another embodiment of the present application.

[0015] BRIEF DESCRIPTION OF DRAWINGS

[0016] 100: discharge machining evaluation system;

[0017] 110: processor;

[0018] 120: storage device;

[0019] 201, 400: workpiece;

[0020] 202: discharge machining machine;

[0021] 310: feature calculation module;

[0022] 320: parameter comparison module;

[0023] 330: coefficient of variation calculation module;

[0024] 340: divergence analysis module;

[0025] 350: evaluation module;

[0026] 401: first contact surface;

[0027] 402: second contact surface;

[0028] 501: discharge voltage signal;

[0029] 502: discharge current signal;

[0030] D1, D2, D3: direction;

[0031] S210 to S240: step. DETAILED DESCRIPTION

[0032] Reference will now be made in detail to exemplary embodiments of the present application, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used in the different drawings and the description to refer to the same or like parts.

[0033] Figure 1is a schematic diagram of an electrical discharge machining evaluation system of an embodiment of the present application. Referring to Figure 1 The electrical discharge machining evaluation system 100 includes a processor 110 and a storage device 120. The processor 110 is electrically connected to the storage device 120. The storage device 120 can store algorithms and programs of a plurality of modules for the processor 110 to read and execute. The storage device 120 can also store relevant data, data and analysis results generated during the electrical discharge machining evaluation process. In this embodiment, the processor 110 is electrically connected to an electrical discharge machining machine 202, and the electrical discharge machining machine 202 is used to perform electrical discharge machining on a workpiece 201, which can be a metal material.

[0034] In this embodiment, the processor 110 can include, for example, a central processing unit (CPU), a graphics processing unit (GPU), or other programmable general-purpose or special-purpose microprocessors, digital signal processors (DSPs), programmable controllers, application specific integrated circuits (ASICs), programmable logic devices (PLDs), other similar processing devices, or combinations of these devices.

[0035] In this embodiment, the storage device 120 can be, for example, a dynamic random access memory (DRAM), a flash memory, or a non-volatile random access memory (NVRAM), etc.

[0036] Figure 2 is a flowchart of an electrical discharge machining evaluation method of an embodiment of the present application. Referring to Figure 1 and Figure 2, the discharge machining evaluation system 100 can perform steps S210-S240 as follows. In the present embodiment, a user can operate the discharge machining machine 202 to perform discharge machining on the workpiece 201, wherein the user can set different sets of machining setting parameters to the discharge machining machine 202 to perform multiple times of discharge machining on the workpiece 201. In step S210, the processor 110 can receive multiple sets of discharge signals from the discharge machining machine 202. In the present embodiment, the multiple sets of discharge signals can respectively include a discharge current signal and a discharge voltage signal, and the discharge current signal and the discharge voltage signal are respectively high frequency full domain signals. The discharge machining machine 202 can provide the discharge signals to the processor 110 respectively during the multiple times of discharge machining.

[0037] In step S220, the processor 110 can calculate multiple sets of characteristic parameters according to the multiple sets of discharge signals. In the present embodiment, the multiple sets of characteristic parameters can respectively include an average speak frequency (ASF), an average discharge current pulse duration (ADCPD), an average peak discharge current (APDC), an average discharge energy (ADE), an average ignition delay time (AIDT), an average gap voltage (AGV), and an open circuit ratio (OCR).

[0038] The average speak frequency (ASF) can satisfy the following formula (1), wherein Nt is the total number of sparks during one machining, and Tt is the time length of the one machining.

[0039]

[0040] The average discharge current pulse duration (ADCPD) can satisfy the following formula (2), wherein Ati is the duration length of the i-th spark, wherein i is a positive integer.

[0041]

[0042] The average peak discharge current (APDC) can satisfy the following formula (3), wherein Ii(max) is the maximum current value of the i-th spark.

[0043]

[0044] The average discharge energy (ADE) can satisfy the following equation (4), where Ei is the discharge energy of the i-th spark. Ei can satisfy the following equation (5), where Vt is the discharge voltage, and It is the discharge current.

[0045]

[0046]

[0047] The average ignition delay time (AIDT) can satisfy the following equation (6), where td,i is the open circuit voltage time of the i-th spark, and te,i is the ignition delay time of the i-th spark.

[0048]

[0049] The average gap voltage (AGV) can satisfy the following equation (7), where Vi(max) is the maximum gap voltage generated during the i-th spark.

[0050]

[0051] The open circuit ratio (OCR) can satisfy the following equation (8), where Ot can be the total number of open circuits. An open circuit is defined as when the voltage peak Vi(max) ends to discharge, but the current peak Ii(max) has not risen.

[0052]

[0053] The processor 110 can execute the above equations (1)-(8) to obtain the corresponding characteristic parameters. At step S230, the processor 110 can calculate a plurality of coefficients of variation (CVs) according to the plurality of sets of characteristic parameters. In the present embodiment, the coefficient of variation can be used as an evaluation index for evaluating the dispersion degree of a plurality of discharge machining results corresponding to different machining setting parameters. The system or the user can effectively evaluate whether a specific machining setting parameter is suitable for the discharge machining of the workpiece 201 by the plurality of coefficients of variation. The coefficient of variation is defined as the value obtained by dividing the standard deviation (σ) in a set of data by the average (μ) (CV = σ / μ). The coefficient of variation is a relative difference quantity, and can be used to compare the data dispersion situation with another set of data.

[0054] At step S240, the processor 110 can perform divergence calculation according to the plurality of sets of characteristic parameters to generate divergence analysis results. In the present embodiment, the divergence analysis results can be used to provide distribution relationships between different evaluation indexes of a plurality of electrical discharge machining results corresponding to different sets of machining setup parameters, so that the system or the user can effectively evaluate whether a specific set of machining setup parameters is suitable for electrical discharge machining of the workpiece 201. Therefore, the electrical discharge machining evaluation system 100 and the electrical discharge machining evaluation method of the present embodiment can effectively provide a plurality of evaluation indexes for the system or the user to effectively evaluate whether a specific set of machining setup parameters is suitable for electrical discharge machining of the workpiece 201. However, the specific implementation of each of the above steps will be illustrated by the following embodiments.

[0055] Figure 3 is a schematic diagram of a plurality of modules of an embodiment of the present application. Figure 4 is a schematic diagram of a workpiece of an embodiment of the present application. Reference is made to Figure 1 and Figure 3 The storage device 120 can store, for example, the characteristic calculation module 310, the parameter comparison module 320, the coefficient of variation calculation module 330, the divergence analysis module 340, and the evaluation module 350. The processor 110 can execute the characteristic calculation module 310, the parameter comparison module 320, the coefficient of variation calculation module 330, the divergence analysis module 340, and the evaluation module 350 to implement the electrical discharge machining evaluation method of the present application.

[0056] For example, reference is made to Figure 4 The workpiece 201 can implement a workpiece 400 as shown in Figure 4 . Figure 4 is a side perspective view of the workpiece 400. The opening surface of the workpiece 400 can be parallel to a plane (e.g., a horizontal plane) formed by the direction D1 and the direction D2 extending, respectively. The electrode of the electrical discharge machining machine 202 can enter the opening of the workpiece 400 along the direction D3 (e.g., a vertical direction) and perform electrical discharge machining on the interior of the workpiece 400 to shape the internal structure of the workpiece 400. The direction D1, the direction D2, and the direction D3 can be perpendicular to each other. The electrical discharge machining machine 202 can perform electrical discharge machining on, for example, the first contact surface 401 and the second contact surface 402 of the interior of the workpiece 400 according to a plurality of sets of electrical discharge machining setup parameters, and the processor 110 can receive a plurality of sets of electrical discharge signals from the electrical discharge machining machine 202.

[0057] in combination with Figure 5 , Figure 5 is a schematic diagram of an electrical discharge voltage and an electrical discharge current of an embodiment of the present application. A set of electrical discharge signals can include, for example, Figure 5The discharge voltage signal 501 and the discharge current signal 502 are shown. In this regard, during the charging period T1, the discharge voltage signal 501 can be pulled up to generate a charging waveform (the EDM machine 202 charges the electrode), and during the discharge period T2, the discharge voltage signal 501 will be pulled down due to the spark generated between the electrode and the workpiece 400. Conversely, during the discharge period T2, the discharge current signal 502 will be pulled up to generate a discharge waveform. However, Figure 5 For example embodiments, the signal waveforms of the discharge voltage signal and the discharge current signal for each discharge can not be the same.

[0058] The processing feature calculation module 310 can obtain, for example, the spark duration length Δti, the maximum current value Ii(max) of the spark, the gap voltage value Vi(max) of the spark, the open circuit voltage time td,i, the ignition delay time te,i, and other related parameters from the discharge voltage signal 501 and the discharge current signal 502. Then, the processor 110 can perform the calculations of the above equations (1) to (8) according to the aforementioned parameters to obtain a plurality of sets of feature parameters.

[0059] With reference to Figure 6A And Figure 6B . Figure 6A is a schematic diagram of a parameter comparison of an embodiment of the present application. Figure 6B is a schematic diagram of a parameter comparison of another embodiment of the present application. The parameter comparison module 320 can generate a plurality of sets of parameter comparison results from the plurality of sets of feature parameters, wherein each set of the plurality of sets of parameter comparison results is a percentage distribution result of a plurality of different feature parameters corresponding to the same parameter type. For example, the EDM machine 202 can perform the EDM process on the workpiece 400 according to three different sets of processing setting parameters (e.g. parameter A, parameter B, and parameter C, wherein parameter A, parameter B, and parameter C are combinations of machine settings such as voltage, current, and discharge time), and during the EDM process, the processor 110 can obtain three sets of parameter comparison results corresponding to the plurality of feature parameters, respectively.

[0060] Figure 6A And Figure 6B The percentages in the bar charts are used to represent the time proportion occupied by different parameters during the EDM process. First of all, Figure 6A And Figure 6B Each bar chart shown in the bar chart represents a different feature parameter. In this regard, different feature parameters represent different meanings of the percentage of occupation. For example, the bar chart of the discharge duration length Δti represents the percentage of the discharge duration length Δti in the total discharge time, the bar chart of the maximum current value Ii(max) represents the percentage of the maximum current value Ii(max) in the total current value, and the bar chart of the gap voltage value Vi(max) represents the percentage of the gap voltage value Vi(max) in the total voltage value. Figure 6BFor example, assume that the characteristic 1 is the average spark frequency, then the parameter B has the highest percentage of about 75% of the total number of sparks measured in a set time, and the total number of sparks is the sum of the parameters A, B and C in the set time. That is, it can be expressed that in the characteristic of the average spark frequency, the effect of the parameter B is excellent. Or, assume that the characteristic 2 is the average energy, then the parameter C has the highest percentage of about 65% of the average energy measured in a set time, and the percentage basis is the total average energy of the parameters A, B and C. In other words, the performances of the parameters A, B and C in each characteristic can be comprehensively considered to select the most suitable parameter combination.

[0061] For this purpose, as shown in Figure 6A , Figure 6A is the discharge machining result of the workpiece 400 by the discharge machining machine 202 based on three different sets of machining setting parameters for the first contact surface 401. As shown in Figure 6B , Figure 6B is the discharge machining result of the workpiece 400 by the discharge machining machine 202 based on three different sets of machining setting parameters for the second contact surface 402. For Figure 6A , by comparing the percentages and according to different machining characteristics and calculation results, the user or the system can observe that the difference in the influence of the relevant characteristic parameters generated by the discharge machining with different parameters on the first contact surface 401 (smaller machining area) is less, but the user or the system can still observe the more suitable parameters. And, for Figure 6B , the user or the system can observe that the difference in the influence of the relevant characteristic parameters generated by the discharge machining with different parameters on the second contact surface 402 (larger machining area) is larger, and the user or the system can more easily observe the more suitable parameters.

[0062] Regarding how to evaluate the more suitable parameters, in this embodiment, the higher the average spark frequency, the better the machining efficiency. The more consistent the average discharge current pulse duration with the original machine setting (the original machine setting will be affected by the machining environment, so that the actual machining performance deviates from the original machine setting), the better. The larger the average energy, the better the machining efficiency, but too large may cause damage to the workpiece. The smaller the ignition delay time, the better, but too small may cause short circuit. The less the average gap voltage deviates from the original machine setting, the better. The smaller the open circuit ratio, the better.

[0063] For example, corresponding to the EDM result of the second contact surface 402 of the workpiece 400 (the variation coefficient calculation and the divergence analysis of the EDM result of the first contact surface 401 can be analogously applied), the variation coefficient calculation module 330 can calculate a plurality of sets of variation coefficients according to the aforementioned plurality of sets of characteristic parameters. The divergence analysis module 340 can perform divergence calculation according to the plurality of sets of characteristic parameters to generate a divergence analysis result. For this purpose, the divergence analysis module 340 can perform KL (Kullback-Leibler) divergence calculation. In reference to Figure 7 , Figure 7 is a schematic diagram of a divergence analysis result of another embodiment of the present application. The divergence analysis module 340 can generate a divergence analysis result as shown in Figure 7 . Figure 7 The horizontal axis of the divergence analysis result of Figure 7 may correspond to the machining stability index, and the vertical axis can correspond to the machining efficiency index, but the present application is not limited thereto. Figure 7 The distance between each pair of adjacent points on the horizontal axis and the vertical axis of Figure 7 is expressed in Euclidean distance to represent the similarity between them (the unit of the horizontal axis and the vertical axis of Figure 7 is used to represent an interval of Euclidean distance). The horizontal axis and the vertical axis of the divergence analysis result can also correspond to other indexes, not limited to shown in

[0064] . For this purpose, for example, a plurality of EDM results corresponding to six different sets of machining setup parameters can have different distribution trends in the distribution relationship of machining efficiency and machining stability. For example, the plurality of EDM results corresponding to the first set of machining setup parameters (the darkest color) can have clear machining stability and machining efficiency, with moderate machining stability and generally poor machining efficiency. For example, the plurality of EDM results corresponding to the sixth set of machining setup parameters (the lightest color) can have clear machining stability, but have non-fixed machining efficiency, with moderate machining stability and possibly high or ordinary machining efficiency. In other words, compared to the first set of machining setup parameters (the darkest color), the plurality of EDM results of this set of machining setup parameters (the lightest color) have a higher probability of achieving high machining efficiency characteristics.

[0065] Therefore, the electrical discharge machining evaluation system 100 can establish the divergence analysis result as described above via different machining setting parameters, and during the actual machining process of the workpiece 201 by the electrical discharge machining machine 202, the electrical discharge machining evaluation system 100 can obtain the real-time electrical discharge signal. In this regard, the real-time electrical discharge signal can be operated via the above-mentioned modules to generate corresponding characteristic parameters and parameter comparison results, and the falling points in the divergence analysis result can be displayed to enable the user or the system to judge the current machining characteristics in real time, and to actively or automatically adjust the machining setting parameters to achieve efficient and good electrical discharge machining for the workpiece 201.

[0066] In summary, the electrical discharge machining evaluation system and the electrical discharge machining evaluation method of the present application can collect multiple sets of electrical discharge signals generated during multiple electrical discharge machining processes, and perform signal analysis to generate a divergence analysis result. In this way, the divergence analysis result can be used to evaluate whether the machining setting parameters used in the multiple electrical discharge machining processes are appropriate, thereby effectively assisting the user in making decisions.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not limiting; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A discharge machining evaluation system, characterized in that, include: Storage device for storing the feature calculation module, the coefficient of variation calculation module, and the divergence analysis module; as well as The processor, electrically connected to the storage device, is used to execute the feature calculation module, the coefficient of variation calculation module, and the divergence analysis module, and to receive multiple sets of discharge signals from the electrical discharge machining (EDM) machine. The feature calculation module is used to calculate multiple sets of feature parameters based on the multiple sets of discharge signals, and the coefficient of variation calculation module is used to calculate multiple sets of coefficients of variation based on the multiple sets of feature parameters. The divergence analysis module is used to perform divergence calculations based on the multiple sets of feature parameters to generate divergence analysis results.

2. The electrical discharge machining evaluation system according to claim 1, characterized in that, The multiple sets of discharge signals include discharge current signals and discharge voltage signals, respectively.

3. The electrical discharge machining evaluation system according to claim 2, characterized in that, The discharge current signal and the discharge voltage signal are both high-frequency global signals.

4. The electrical discharge machining evaluation system according to claim 2, characterized in that, During charging, the discharge voltage signal is pulled up to generate a charging waveform, and during discharging, the discharge voltage signal is pulled down.

5. The electrical discharge machining evaluation system according to claim 4, characterized in that, The multiple sets of discharge signals also include discharge time.

6. The electrical discharge machining evaluation system according to claim 1, characterized in that, The storage device also stores a parameter comparison module, and the processor executes the parameter comparison module. The parameter comparison module generates multiple sets of parameter comparison results based on the multiple sets of feature parameters, wherein each set of parameter comparison results is a percentage distribution result of multiple different feature parameters corresponding to the same parameter type.

7. The electrical discharge machining evaluation system according to claim 1, characterized in that, The divergence analysis module is used to calculate KL divergence.

8. The electrical discharge machining evaluation system according to claim 1, characterized in that, The storage device also stores an evaluation module, and the processor executes the evaluation module. The evaluation module is used to calculate scoring parameters based on the divergence analysis results.

9. The electrical discharge machining evaluation system according to claim 8, characterized in that, The evaluation module includes a non-linear machine learning model.

10. The electrical discharge machining evaluation system according to claim 1, characterized in that, The multiple sets of characteristic parameters include average spark frequency, average discharge current pulse duration, average discharge peak current, average discharge energy, average ignition delay time, average gap voltage, and open circuit ratio.

11. A method for evaluating electrical discharge machining, characterized in that, include: Receive multiple sets of discharge signals from the electrical discharge machining (EDM) machine; Calculate multiple sets of characteristic parameters based on the multiple sets of discharge signals; Calculate multiple sets of variation coefficients based on the aforementioned multiple sets of characteristic parameters; as well as Divergence is calculated based on the multiple sets of characteristic parameters to generate divergence analysis results.

12. The electrical discharge machining evaluation method according to claim 11, characterized in that, The multiple sets of discharge signals include discharge current signals and discharge voltage signals, respectively.

13. The electrical discharge machining evaluation method according to claim 12, characterized in that, The discharge current signal and the discharge voltage signal are both high-frequency global signals.

14. The electrical discharge machining evaluation method according to claim 12, characterized in that, During charging, the discharge voltage signal is pulled up to generate a charging waveform, and during discharging, the discharge voltage signal is pulled down.

15. The electrical discharge machining evaluation method according to claim 14, characterized in that, The multiple sets of discharge signals also include discharge time.

16. The electrical discharge machining evaluation method according to claim 11, characterized in that, Also includes: Multiple sets of parameter comparison results are generated based on the multiple sets of feature parameters, wherein each set of the multiple sets of parameter comparison results is a percentage distribution result of multiple different feature parameters corresponding to the same parameter type.

17. The electrical discharge machining evaluation method according to claim 11, characterized in that, The divergence calculation is the KL divergence calculation.

18. The electrical discharge machining evaluation method according to claim 11, characterized in that, Also includes: The evaluation module calculates the scoring parameters based on the divergence analysis results.

19. The electrical discharge machining evaluation method according to claim 18, characterized in that, The evaluation module includes a non-linear machine learning model.

20. The electrical discharge machining evaluation method according to claim 11, characterized in that, The multiple sets of characteristic parameters include average spark frequency, average discharge current pulse duration, average discharge peak current, average discharge energy, average ignition delay time, average gap voltage, and open circuit ratio.

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