CN116438028B - Machining condition setting device, machining condition setting method, and electric discharge machining device - Google Patents

Abstract

The processing condition setting device (30) comprises: an input device (31) for receiving the electrode consumption rate, which is the consumption rate of the tool electrode, the machining speed, the machining surface roughness, which is the roughness of the machined surface of the workpiece, and the surface area ratio of the actual machined surface to the area of a specific region in the machined surface of the workpiece as the machining result expected for the shape carving electric discharge machining; a storage device (32) for storing basic data representing theoretical correlation of machining results for a combination of a discharge current peak value and a discharge current pulse width of a previously acquired form-engraving electric discharge machining; and a calculation device (33) for calculating the machining conditions from the 1 st stage to the final stage when the machining process from the 1 st stage to the final stage is performed as a plurality of times of the engraving and discharging machining, based on the machining result received by the input device and the basic data stored in the storage device.

Description

Machining condition setting device, machining condition setting method, and electric discharge machining device

Technical Field

The present invention relates to a machining condition setting device, a machining condition setting method, and an electric discharge machine that set machining conditions for shape carving electric discharge machining.

Background

In the form-engraving electric discharge machining, a voltage pulse is applied between a tool electrode and a workpiece disposed to face each other, and electric discharge is repeatedly generated in a machining gap, and the shape of the tool electrode is transferred to the workpiece by arc heat of the generated electric discharge, thereby forming a machined hole.

In order to obtain a desired machined shape in the shape-engraving electric discharge machining, it is necessary to perform a plurality of times of machining from rough machining, in which a large amount of the workpiece is removed, to finish machining, in which the machined surface is finished to a desired precision. Therefore, the operator needs to set the optimum processing conditions for obtaining the desired processing result for a plurality of times of processing. A series of processing conditions in the multiple processing will be hereinafter referred to as a multi-stage processing condition series.

In the engraving electric discharge machining, machining results such as a machining shape and a machining speed are determined by a discharge current pulse of one electric discharge. Therefore, in order to obtain a desired machining result, the operator needs to set a machining condition that affects the machining result among machining conditions of the shape-engraving electric discharge machining for a plurality of stages of machining condition rows. For example, the machining conditions that affect the machining result include a combination of a discharge current peak value and a discharge current pulse width. The combination is not only a machining result, but also a number of combinations are available because the tool electrode and the workpiece are different in material, shape, size, and the like, and the appropriate set values are different. Therefore, it is difficult for the operator to manually perform appropriate settings from among numerous combinations, and development of techniques for automatically setting combinations has been advanced.

The machining condition setting device described in patent document 1 automatically sets a multi-stage machining condition series corresponding to a machining request based on basic data indicating a theoretical correlation between a plurality of types of parameters including the machining condition and the machining request.

Patent document 1: japanese patent laid-open No. 2009-279335

Disclosure of Invention

However, in the technique of patent document 1, the discharge current pulse width for the discharge current peak value is uniquely determined in accordance with the machining request such as the electrode consumption rate inputted by the operator, and therefore the machining surface quality is also uniquely determined. The quality of the machined surface as referred to herein is defined as a quality of the machined surface which is different from the machined surface roughness expressed by Ra or Rz which is standard in ISO (International Organization for Standardization ), and is defined as a surface area ratio of an actual machined surface to an area of a certain region in the machined surface.

The larger the machined surface roughness, the larger the surface area of the concave-convex portion of the machined surface becomes, and thus the larger the surface area ratio becomes, but the surface area ratio may be different even with the same machined surface roughness. For example, the longer the discharge current pulse width becomes, the smaller the surface area for the same surface roughness becomes, and thus the smaller the surface area ratio also becomes. On the other hand, the shorter the discharge current pulse width becomes, the larger the electrode consumption rate becomes. That is, if an attempt is made to increase the surface area ratio, the discharge current pulse width needs to be shortened, and thus the electrode consumption rate becomes large. Accordingly, in the technique of patent document 1, there is a problem that a proper combination of a discharge current peak value and a discharge current pulse width considering a surface area ratio cannot be determined by determining a discharge current pulse width for the discharge current peak value by an electrode consumption rate inputted by an operator.

The present invention has been made in view of the above circumstances, and an object thereof is to provide a processing condition setting device capable of generating appropriate processing conditions corresponding to the surface area ratio of an input workpiece.

In order to solve the above problems and achieve the object, the machining condition setting device of the present invention includes an input device that receives, as machining results desired for the contour discharge machining, an electrode consumption rate, which is a consumption rate of a tool electrode, a machining speed, a machining surface roughness, which is a roughness of a machined surface of a workpiece, and a surface area ratio of an actual machined surface to an area of a specific region in the machined surface of the workpiece. The machining condition setting device of the present invention further includes a storage device that stores basic data indicating a theoretical correlation of a machining result for a combination of a discharge current peak value and a discharge current pulse width of the shape-engraved electric discharge machining that is acquired in advance. The machining condition setting device of the present invention further includes an arithmetic device for calculating the machining conditions from the 1 st stage to the final stage when the machining process from the 1 st stage to the final stage is performed as the plurality of times of the shape-carving electric discharge machining, based on the machining result received by the input device and the basic data stored in the storage device.

ADVANTAGEOUS EFFECTS OF INVENTION

The processing condition setting device according to the present invention has an effect of being able to generate appropriate processing conditions corresponding to the surface area ratio of the input workpiece.

Drawings

Fig. 1 is a block diagram showing a configuration of an electric discharge machine including a machining condition setting device according to embodiment 1.

Fig. 2 is a diagram for explaining a relationship between a discharge current waveform and a machined surface roughness.

Fig. 3 is a diagram for explaining a relationship between a discharge current pulse width, a machined surface roughness Ra, and a surface area ratio Sdr.

Fig. 4 is a diagram showing an example of surface roughness data used in the processing condition setting device according to embodiment 1.

Fig. 5 is a diagram showing an example of electrode consumption data used in the processing condition setting device according to embodiment 1.

Fig. 6 is a flowchart showing a procedure of calculation processing of a machining condition sequence by the machining condition setting device according to embodiment 1.

Fig. 7 is a diagram for explaining a calculation process of a discharge current peak value and a discharge current pulse width performed by the machining condition setting device according to embodiment 1.

Fig. 8 is a flowchart showing a procedure of calculation processing of a processing condition sequence by the processing condition setting device according to embodiment 2.

Fig. 9 is a block diagram showing a configuration example of a learning device according to embodiment 4.

Fig. 10 is a flowchart showing a processing procedure of learning processing performed by the learning device according to embodiment 4.

Fig. 11 is a block diagram showing a configuration example of the estimation device according to embodiment 4.

Fig. 12 is a flowchart showing a processing procedure of the estimation process performed by the estimation device according to embodiment 4.

Fig. 13 is a diagram showing an example of a hardware configuration of a learning device according to embodiment 4.

Detailed Description

Next, a machining condition setting device, a machining condition setting method, and an electric discharge machine according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

Embodiment 1.

Fig. 1 is a block diagram showing a configuration of an electric discharge machine including a machining condition setting device according to embodiment 1. The electric discharge machine 1 is a pattern discharge machine that performs pattern discharge machining on a workpiece. The electric discharge machine 1 performs machining a plurality of times from rough machining to finish machining in order to obtain a machining result required by an operator.

The electric discharge machine 1 includes a machining condition setting device 30, a machining power source 34, and a machining mechanism 35. The machining condition setting device 30 is a computer that sets a machining condition series of a plurality of stages for obtaining a machining result inputted by an operator. That is, the machining condition setting device 30 is a computer that sets a combination of machining conditions from rough machining to finish machining for obtaining a machining result desired by an operator. In embodiment 1, the processing surface area of the workpiece is included in the processing result desired by the operator. Therefore, the machining condition setting device 30 sets an appropriate multi-stage machining condition series corresponding to the machining surface area of the workpiece. The multi-stage processing condition row is a combination of processing conditions from the 1 st stage to the final stage. The machining condition of the 1 st stage among the multi-stage machining condition series is the roughest machining condition, and the machining of the final stage is the smoothest machining condition.

The machining condition setting device 30 includes an input device 31, a storage device 32, and an arithmetic device 33. If the machining result desired by the operator is input to the machining condition setting device 30, the input device 31 receives the machining result. The processing result input to the input device 31 by the operator is, for example, a physical quantity indicating the processing result. By inputting specific physical quantities, the processing conditions can be set in detail for the desired processing result. The machining result received by the input device 31 is a machining result desired by the operator, and thus can be said to be a machining request of the operator.

The processing result in embodiment 1 is processing surface roughness, processing surface area, electrode consumption rate, processing speed, reduction, processing area, and the like. Examples of the machined surface roughness include Ra and Rz defined by ISO, and the quality of the machined surface defined by german society of technical standards (VDI: verein Deutscher Ingenieure), and the like, which are expressed by numerical values in standards desired by operators. Ra is the arithmetic average roughness of the machined surface, and Rz is the maximum height of the crest lines and the bottom lines of the machined surface. Next, a case where the machined surface roughness Ra is applied as the machined surface roughness will be described.

The machining surface area is a surface area ratio (hereinafter, referred to as a surface area ratio Sdr) of an actual machining surface to an area of a specific region in the machining surface. The area of the specific region in the working plane is an area in the case where the specific region is observed from the upper surface. The actual surface area of the machined surface is an area that also takes into consideration the shape of the specific region in the height direction. The surface area of the actual working surface corresponds to the area in the case where the actual working surface is spread out in a plane parallel to the specific region. As the surface area ratio Sdr, a value in a range corresponding to the machined surface roughness input to the input device 31 can be input. That is, the machining surface roughness and the surface area ratio Sdr in the range corresponding to the machining surface roughness are input to the input device 31.

The electrode consumption rate is a consumption rate of an electrode used in the engraving electric discharge machining. Specifically, the electrode consumption rate is a ratio of the amount of processing the object to be processed to the consumption amount of the electrode. The machining speed is a speed of the engraving electric discharge machining and corresponds to a forming speed of a machining hole formed in the workpiece.

Since the electrode consumption rate and the processing speed cannot be simultaneously achieved, the operator selects a desired degree of importance from information indicating what degree of importance is stepwise and inputs the selected degree of importance to the input device 31. The machining condition setting device 30 according to embodiment 1 will be described in the case where the machining condition is set so as to satisfy the electrode consumption rate desired by the operator and to achieve the maximum machining speed.

The storage device 32 stores basic data acquired in advance through experiments. The basic data is a data table showing the machining result for the combination of the discharge current peak value and the discharge current pulse width. Specifically, the basic data is a data table composed of combinations of experimental data of machining surface roughness, machining surface area, electrode consumption rate, machining speed, and reduction for combinations of all or a part of discharge current peaks and discharge current pulse widths that can be output by the electric discharge machining apparatus 1. That is, the basic data includes a data table of the machining surface roughness, the machining surface area, the electrode consumption rate, the machining speed, the reduction amount, and the like. The details of the basic data will be described later.

The computing device 33 calculates a plurality of processing condition sequences based on the processing result input to the input device 31 by the operator and the basic data stored in the storage device 32. The arithmetic device 33 outputs instructions corresponding to the respective machining conditions included in the multi-stage machining condition series to the machining power source 34.

The machining conditions for the engraving electric discharge machining include electrical conditions of the tool electrode and the workpiece, moving conditions of the tool electrode, control conditions related to specific control, environmental conditions, and the like. The electrical conditions are discharge current peak, discharge current pulse width, rest time, servo reference voltage, polarity, no-load voltage, etc. The moving conditions are the jump speed, jump time, the amount of swing, etc. of the tool electrode. The control conditions include a condition for controlling the waveform of the discharge current pulse, a condition for controlling the waveform of the voltage pulse applied to the machining gap, and the like. The environmental conditions include the presence or absence of a jet of the working fluid, a jet method of the working fluid, and the like.

The discharge current peak value and the discharge current pulse width among the machining conditions of the engraving electric discharge machining have a great influence on machining results such as machining surface roughness, machining surface quality, electrode consumption rate and machining speed. Therefore, the machining condition setting device 30 sets an optimal combination of the discharge current peak value and the discharge current pulse width for the multi-stage machining condition series so as to obtain an optimal machining result corresponding to the desired machining result.

The machining power source 34 applies a voltage pulse to the machining means 35 in accordance with the multi-stage machining condition sequence calculated by the arithmetic unit 33. The tool electrode and the workpiece are disposed in the processing means 35. In the machining means 35, a voltage pulse from the machining power source 34 is applied to the machining gap 36 between the tool electrode and the workpiece. Thus, discharge is repeatedly generated in the machining gap between the tool electrode and the workpiece, and the electric discharge machining is performed on the workpiece.

Here, a relationship between a combination of a discharge current peak value and a discharge current pulse width and a machined surface shape, that is, a machined surface roughness of a workpiece will be described. Fig. 2 is a diagram for explaining a relationship between a discharge current waveform and a machined surface roughness. The machining power source 34 can output various discharge current pulses.

Fig. 2 shows a discharge current waveform 11 as an example of the 1 st waveform and a discharge current waveform 12 as an example of the 2 nd waveform. Fig. 2 shows a machined surface 13 of the workpiece in the case where electric discharge machining is performed by the electric discharge current waveform 11, and a machined surface 14 of the workpiece in the case where electric discharge machining is performed by the electric discharge current waveform 12.

Fig. 2 shows that the machined surfaces 13 and 14 obtained by the 2 types of discharge current pulses such as the discharge current waveforms 11 and 12 have the same surface roughness such as the machined surface roughness Ra or the maximum height Rz, but have different surface area ratios Sdr. When the discharge current waveforms 11 and 12 are compared, the discharge current waveform 11 has a lower discharge current peak value and a longer discharge current pulse width than the discharge current waveform 12, but has the same machining surface roughness. However, since the radial size of the discharge trace formed by the primary discharge is larger in the machined surface 13 than in the machined surface 14, the surface area ratio Sdr of the machined surface 13 is smaller than that of the machined surface 14.

Fig. 3 is a diagram for explaining a relationship between a discharge current pulse width, a machined surface roughness Ra, and a surface area ratio Sdr. The horizontal axis of the graph shown in fig. 3 represents the machined surface roughness Ra, and the vertical axis represents the surface area ratio Sdr. Fig. 3 shows a machined surface quality (surface texture) 21 in the case of the electric discharge current pulse width machining according to example 1 and a machined surface quality 22 in the case of the electric discharge current pulse width machining according to example 2. The discharge current pulse width of example 1 corresponding to the machining surface mass 21 is smaller than the discharge current pulse width of example 2 corresponding to the machining surface mass 22.

As shown in fig. 2 and 3, the longer the discharge current pulse width is, the smaller the surface area for the same machining surface roughness Ra becomes, and therefore the smaller the surface area ratio Sdr becomes. The surface area of the machined surface may have a large influence on the performance of the machined object after the engraving and discharge machining, and therefore the surface area ratio Sdr needs to be appropriately controlled. The machining condition setting device 30 shows appropriate machining conditions (discharge current peak value and discharge current pulse width) corresponding to the inputted machining surface area (surface area ratio Sdr) of the workpiece.

Next, an example of the basic data will be described. Fig. 4 is a diagram showing an example of surface roughness data used in the processing condition setting device according to embodiment 1. Fig. 5 is a diagram showing an example of electrode consumption data used in the processing condition setting device according to embodiment 1. The surface roughness data 41, which is data of the machining surface roughness Ra, and the electrode consumption data 42, which is data of the electrode consumption rate, are all examples of the basic data. The basic data is data indicating a theoretical correlation of machining results of a combination of a discharge current peak value and a discharge current pulse width for the shape-engraved electric discharge machining obtained in advance.

As shown in fig. 4, the surface roughness data 41 is data representing the machined surface roughness corresponding to the combination of the discharge current peak value IP and the discharge current pulse width ON. As shown in fig. 5, the electrode consumption data 42 is data indicating the electrode consumption rate corresponding to the combination of the discharge current peak value IP and the discharge current pulse width ON.

In fig. 4 and 5, the minimum discharge current peak value IP is passed through I ₁ The discharge current peak IP with the m-th (m is a natural number) small is shown to pass through I _m And (3) representing. In addition, the minimum discharge current pulse width ON is passed through t ₁ The n-th (n is a natural number) small discharge current pulse width ON is represented by t _n And (3) representing. I _m Is the maximum discharge current peak IP, t _n Is the maximum discharge current pulse width ON.

For example, by the mth small discharge current peak I _m And the nth smallest discharge current pulse width t _n The machined surface roughness Ra in the case of performing electric discharge machining in combination of (a) is Ra _mn The electrode consumption rate was EW _mn 。

The larger the discharge current peak IP, the rougher the machined surface roughness Ra. In addition, the smaller the discharge current pulse width ON, the rougher the machined surface roughness Ra. In addition, the larger the discharge current peak IP, the larger the electrode consumption rate becomes. In addition, the smaller the discharge current pulse width ON, the larger the electrode consumption rate becomes.

The storage device 32 stores, as basic data, a data table of a machining surface area (surface area ratio Sdr), a machining speed, a reduction amount, and the like. The data table of the processing surface area, the processing speed, the reduction amount, and the like has the same structure as the surface roughness data 41 and the electrode consumption data 42. For example, in the data table of the machining surface area, the surface area ratio Sdr corresponding to the combination of the discharge current peak value IP and the discharge current pulse width ON is stored. Similarly, in the processing speed data table, processing speeds corresponding to combinations of the discharge current peak value IP and the discharge current pulse width ON are stored. In the reduced amount data table, a reduced amount corresponding to a combination of the discharge current peak value IP and the discharge current pulse width ON is stored.

Fig. 6 is a flowchart showing a procedure of calculation processing of a machining condition sequence by the machining condition setting device according to embodiment 1. If the machining result desired by the operator, such as the reduction amount and the machining area, is input to the machining condition setting device 30, the input device 31 of the machining condition setting device 30 receives the desired machining result (step S10). The input device 31 sends the processing result to the arithmetic device 33. The processing result input to the input device 31 includes, for example, data such as a desired reduction amount, a processing area, an electrode consumption rate, a processing surface roughness, a processing surface area, a processing speed, and the like.

The computing device 33 calculates the allowable maximum discharge current peak value ip_1 based on the input reduction amount, the processing area, and the like (step S20). IP_1 is the maximum discharge current peak value used in the electric discharge machining of paragraph 1.

Next, the computing device 33 calculates a discharge current pulse width on_1 corresponding to the maximum discharge current peak value ip_1 based ON the electrode consumption data 42, which is the basic data (step S30). In this case, the arithmetic device 33 determines the maximum pulse width as the discharge current pulse width on_1 from the selectable discharge current pulse widths ON among the electrode consumption data 42. ON_1 is a discharge current pulse width used in the electric discharge machining of paragraph 1.

The computing device 33 calculates the maximum machining surface roughness, that is, the machining surface roughness ra_1, based ON the maximum discharge current peak value ip_1, the discharge current pulse width on_1, and the surface roughness data 41 (step S40). Ra_1 is the machined surface roughness at the completion of the electric discharge machining in the 1 st stage.

The computing device 33 calculates the number N of steps in the machining condition row (N is a natural number) and the machining surface roughness ra_k (k is a natural number from 2 to (N-1)) in the middle of the machining condition row based on the determined maximum machining surface roughness ra_1 and the machining surface roughness input as the machining result (step S50). Specifically, the arithmetic device 33 determines Ra_1 (N-1) from the number N of segments and the machined surface roughness Ra_1 from the 2 nd to the (N-1) th segments.

The computing device 33 determines that the machining surface roughness of each stage is smaller than the machining surface roughness of the machining condition of the preceding stage. The arithmetic device 33 calculates the machined surface roughness in accordance with the logic previously added to the arithmetic device 33.

For example, the arithmetic device 33 continuously reduces the machining surface roughness by a factor of n=log from the machining surface roughness ra_1 of the 1 st stage to the machining surface roughness ra_n of the final stage at a predetermined ratio p _p The number of stages N of the processing condition row and the processing surface roughness Ra_k of the kth stage are calculated by the formula (Ra_N/Ra_1) and Ra_k=p×Ra_k (k-1).

The computing device 33 calculates the discharge current peak value ip_k and the discharge current pulse width on_k of each processing condition other than the first final stage based ON the processing surface roughness ra_k, the surface roughness data 41, and the electrode consumption data 42 of each processing condition calculated in the above-described order (step S60).

Fig. 7 is a diagram for explaining the calculation processing of the discharge current peak value and the discharge current pulse width by the machining condition setting device according to embodiment 1. The horizontal axis of the graph shown in the upper part of fig. 7 is the discharge current pulse width, and the vertical axis is the machined surface roughness. The horizontal axis of the graph shown in the lower part of fig. 7 is the discharge current pulse width, and the vertical axis is the electrode consumption rate.

In the graph shown in the upper part of fig. 7, the correspondence 23, 24 between the discharge current pulse width and the machined surface roughness is shown. The correspondence relation 23 is a correspondence relation between the discharge current pulse width and the machining surface roughness in the case where the discharge current peak value is the k-th stage discharge current peak value ip_k. The correspondence relation 24 is a correspondence relation between the discharge current pulse width and the machining surface roughness in the case where the discharge current peak value is the discharge current peak value ip_k+1 of the (k+1) -th segment.

In the graph shown in the lower part of fig. 7, the correspondence relationship 25 and 26 between the discharge current pulse width and the electrode consumption rate are shown. The correspondence relation 25 is a correspondence relation between the discharge current pulse width and the electrode consumption rate in the case where the discharge current peak value is the discharge current peak value ip_k of the kth segment. The correspondence relation 26 is a correspondence relation between the discharge current pulse width and the electrode consumption rate in the case where the discharge current peak value is the discharge current peak value ip_k+1 of the (k+1) -th segment. Here, k is, for example, k=1.

The machining surface roughness in the machining condition of the kth stage is ra_k, the discharge current peak is ip_k, and the discharge current pulse width is on_k. When ip_k is applied to the (k+1) -th stage, the computing device 33 generates a discharge current pulse width t from the surface roughness data 41 for the machined surface roughness ra_k (k+1) of the (k+1) -th stage _k+1 And searching.

Next, the computing device 33 refers to the electrode consumption data 42 and compares the detected discharge current pulse width t with the electrode consumption data 42 _k+1 Electrode consumption rate EW1 corresponding to the combination of the discharge current peaks IP_k is calculated.The arithmetic device 33 determines whether the electrode consumption rate EW1 is less than or equal to the desired electrode consumption rate EW.

If EW is greater than or equal to EW1, the computing device 33 will operate on IP_k, t _k+1 The discharge current peak IP_ (k+1) and the discharge current pulse width ON_ (k+1) of the (k+1) th stage are set. That is, if EW.gtoreq.EW1, the operation device 33 applies the discharge current peak value IP_k of the kth segment to the discharge current peak value IP_1 (k+1) of the (k+1) th segment.

On the other hand, if EW1 > EW, the arithmetic device 33 sets the discharge current pulse width t, which is Ra (k+1) from the surface roughness data 41, to the discharge current peak value ip_k (k-1) lower than the ip_k set in the electric discharge machining device 1 by 1 stage _k+1 And searching.

The computing device 33 refers to the electrode consumption data 42 and compares the detected discharge current pulse width t with the current pulse width t _k+1 Electrode consumption rate EW2 corresponding to the combination of the discharge current peaks IP_k-1 is calculated. The operation device 33 determines whether the electrode consumption rate EW2 is less than or equal to the desired electrode consumption rate EW.

The arithmetic device 33 repeats the process of decreasing the discharge current pulse width and the process of decreasing the discharge current peak until the combination of the discharge current pulse width and the discharge current peak is found to satisfy the desired electrode consumption rate EW. Thus, the arithmetic device 33 can determine the combination of the discharge current peak value and the discharge current pulse width in all machining conditions except the first final stage.

The computing device 33 calculates a combination of a discharge current peak value and a discharge current pulse width in the machining condition of the final stage based on the machining surface roughness ra_n of the final stage, the surface roughness data 41, and the basic data of the machining surface area.

The basic data of the machining surface area is data representing the machining surface area corresponding to the combination of the discharge current peak value and the discharge current pulse width, as described above. The basic data of the processing surface area will be referred to as surface area data hereinafter.

The computing device 33 searches for a combination of a discharge current peak value and a discharge current pulse width, which can obtain the machining surface roughness ra_n of the final stage, from the surface roughness data 41. Next, the arithmetic device 33 reads out the machining surface area corresponding to the combination of the retrieved discharge current peak value and the discharge current pulse width based on the surface area data. The arithmetic device 33 determines whether the read processing surface area satisfies a desired processing surface area.

When the read machining surface area does not satisfy the desired machining surface area, the computing device 33 searches for a combination of the discharge current peak value and the discharge current pulse width that satisfy the desired machining surface area. That is, the computing device 33 can obtain the machining surface roughness ra_n of the final stage, and search for a combination of the discharge current peak value and the discharge current pulse width that satisfies the desired machining surface area. The computing device 33 can obtain the machining surface roughness ra_n of the final stage based on the surface roughness data 41 and the surface area data, and extract the combination of the discharge current peak value and the discharge current pulse width that all satisfy the desired machining surface area.

The arithmetic device 33 determines, as the machining condition of the final stage, a combination that satisfies the longest discharge current pulse width among the combinations of the desired machining surface roughness ra_n and the machining surface area. That is, the computing device 33 calculates the discharge current peak value ip_n and the discharge current pulse width on_n as the machining conditions of the final stage (step S70). As described above, the arithmetic unit 33 can reduce the electrode consumption rate as much as possible by selecting the machining conditions in which the discharge current pulse width is long.

The arithmetic device 33 determines the processing conditions obtained by the processing in steps S20 to S70 as a multi-stage processing condition series (step S80). The machining power source 34 generates a discharge current pulse according to the multi-stage machining condition sequence calculated by the computing device 33, and applies discharge energy to the machining gap 36.

By the procedure described in fig. 6, the arithmetic unit 33 can automatically calculate the machining conditions satisfying the machining surface roughness, electrode consumption rate, and machining surface area desired by the operator. The arithmetic unit 33 can set the fastest machining condition row satisfying the desired machining surface roughness, electrode consumption rate, and machining surface area by selecting the combination of the discharge current peak value and the discharge current pulse width in the above order. That is, the arithmetic unit 33 can set the machining speed to the maximum speed by selecting a combination of the maximum discharge current peak value and the minimum discharge current pulse width within a range satisfying the machining result desired by the operator.

The arithmetic unit 33 may set the machining conditions so as to satisfy the machining speed desired by the operator and to minimize the electrode consumption rate. In this case, the arithmetic device 33 uses basic data of the machining speed for a combination of the discharge current peak value and the discharge current pulse width instead of the electrode consumption data 42. In addition, the arithmetic device 33 uses the information of the machining speed instead of the information of the electrode consumption rate in the calculation process of the machining condition sequence, and executes the same process as the above-described process.

As described above, in embodiment 1, the computing device 33 calculates the machining conditions at the time of performing the machining process from the 1 st stage to the final stage as the shape-engraved electric discharge machining performed a plurality of times, based on the machining result received by the input device 31 and the basic data stored in the storage device 32. The processing result received by the input device 31 includes a processing surface area. Therefore, the machining condition setting device 30 can generate appropriate machining conditions corresponding to the machining surface area of the input workpiece.

Embodiment 2.

Next, embodiment 2 will be described with reference to fig. 8. The processing condition setting device 30 according to embodiment 2 has the same configuration as the processing condition setting device 30 according to embodiment 1, and therefore, the description thereof is omitted.

The machining condition setting device 30 according to embodiment 1 determines a combination of a discharge current peak value and a discharge current pulse width based on the machining surface area only in the machining condition of the final stage. Therefore, the electrode consumption rate does not satisfy the desired processing conditions only in the processing conditions of the final stage, but the electrode consumption in the finishing conditions is small, and thus can be neglected in some cases.

In addition, when an operator desires extremely high precision machining, it is necessary to suppress the electrode consumption rate. Therefore, the processing condition setting device 30 according to embodiment 2 can set desired processing results for each of the processing condition rows of the plurality of stages. Thus, even when the electrode consumption rate exceeds the desired electrode consumption rate in the processing conditions of the first final stage, the processing condition setting device 30 can set the total electrode consumption amount of the entire processing conditions to a desired value by setting the electrode consumption rate of the processing condition row of the other stage to be smaller than the desired value. More preferably, the operator does not manually set all electrode consumption rates of the multi-stage machining condition series, but the machining condition setting device 30 calculates the machining conditions in the procedure described in embodiment 1. Then, the processing condition setting device 30 calculates the electrode consumption again. Logic for calculating the electrode consumption amount again after calculating the machining conditions in the procedure described in embodiment 1 is added to the arithmetic unit 33 of the machining condition setting unit 30. In embodiment 2, the desired processing result includes the sum of electrode consumption amounts of the entire processing conditions.

Fig. 8 is a flowchart showing a procedure of calculation processing of a processing condition sequence by the processing condition setting device according to embodiment 2. Among the processes shown in fig. 8, the process similar to the process executed by the process condition setting device 30 of embodiment 1 described in fig. 6 is omitted from the description.

The processing of steps S10 to S70 performed by the processing condition setting device 30 of embodiment 2 is the same processing as the processing of steps S10 to S70 performed by the processing condition setting device 30 of embodiment 1.

After the calculation of the machining conditions of the entire segment, that is, after the processing of step S70, the arithmetic device 33 of embodiment 2 calculates the total electrode consumption in the whole of the electric discharge machining based on the electrode consumption data 42.

The arithmetic device 33 determines whether or not the sum of the electrode consumption amounts is larger than the desired electrode consumption amount (step S75). When the sum of the electrode consumption amounts is equal to or smaller than the desired electrode consumption amount (step S75, no), the arithmetic device 33 directly sets the calculated processing conditions as a multi-stage processing condition row (step S80).

On the other hand, when the sum of the electrode consumptions is larger than the desired electrode consumption (Yes in step S75), the arithmetic device 33 resets the reference value of the electrode consumption rate (the desired electrode consumption rate EW) to a value smaller than the desired value in all the processing conditions except for the final stage. That is, the arithmetic device 33 sets a value lower than the input electrode consumption rate as the desired electrode consumption rate (step S76). The computing device 33 determines the discharge current pulse width for the maximum discharge current peak described in embodiment 1 using the electrode consumption rate set again. Then, the arithmetic device 33 again executes the processing of steps S30 to S70 in fig. 8, and determines a combination of the discharge current peak value and the discharge current pulse width in all the machining conditions. By repeating the processing in steps S30 to S70 described above, the computing device 33 can automatically calculate a series of processing conditions for a plurality of stages that satisfy a desired electrode consumption amount as a whole even if the processing conditions for only the final stage do not satisfy the desired electrode consumption rate.

As described above, according to embodiment 2, the machining condition setting device 30 can set the desired machining result for each of the plurality of stages of machining condition rows, and thus can calculate the plurality of stages of machining condition rows satisfying the desired machining result as a whole.

Embodiment 3.

Next, embodiment 3 will be described. In embodiment 3, the basic data generated based on the actual measurement values actually measured by the specific electric discharge machine 1 is stored in all the electric discharge machines 1, and the basic data different for each electric discharge machine 1 is not stored.

When there are a plurality of electric discharge machines 1, the basic data stored in the storage device 32 of each electric discharge machine 1 is preferably basic data acquired by a specific 1 electric discharge machine 1. That is, the data of the machining results corresponding to the actual measurement values of the discharge current peak value and the discharge current pulse width obtained by one 1 electric discharge machine 1 are preferably basic data used by all electric discharge machines 1. In other words, the basic data used by each electric discharge machine 1 is preferably basic data obtained from a specific electric discharge machine 1.

When the basic data based on the set value set in the specific electric discharge machine 1 is stored in another electric discharge machine 1, a difference occurs between the peak value of the electric discharge current and the pulse width of the electric discharge current in the other electric discharge machine 1 due to the fluctuation of the electric circuit for each electric discharge machine 1. That is, the discharge current peak value and the discharge current pulse width have errors for each of the electric discharge machining apparatuses 1. Therefore, in each electric discharge machine 1, there is a possibility that an error may occur between the basic data and the actual machining result.

In embodiment 3, for each of the electric discharge machining apparatuses 1, basic data generated based on actual measurement values actually measured by the specific electric discharge machining apparatus 1 is stored. Thus, even if a difference occurs between the discharge current peak value and the discharge current pulse width for each of the electric discharge machining devices 1, the basic data can be converted into the basic data suitable for each of the electric discharge machining devices 1 by performing the numerical correction on the basic data.

As described above, according to embodiment 3, since the basic data generated based on the actual measurement value actually measured by the specific electric discharge machine 1 is stored in the other electric discharge machine 1, the other electric discharge machine 1 can convert the basic data into the basic data suitable for the present apparatus by performing the numerical correction on the basic data.

Embodiment 4.

Next, embodiment 4 will be described with reference to fig. 9 to 13. The electric discharge machine 1 according to embodiments 1 to 3 can automatically set the machining condition under which the theoretical machining speed becomes the fastest, which satisfies the desired machining result based on the basic data. Further, there is a possibility that the fastest machining conditions may change depending on the material actually used for the tool electrode, the state of machining chips between machining poles 36, and the like. Therefore, in embodiment 4, the machining speed during machining for the machining conditions is learned. That is, the electric discharge machine 1 searches for a combination in which the machining speed is the fastest from among combinations of the discharge current peak value and the discharge current pulse width in which the machining result satisfies the value desired by the operator, based on the input basic data. In embodiment 4, the machining results desired by the operator are the electrode consumption rate, the machining surface roughness, and the machining surface area.

< learning phase >)

Fig. 9 is a block diagram showing a configuration example of a learning device according to embodiment 4. The learning device 50 is a computer that learns a trained model 71, and the trained model 71 is used to provide the electric discharge machine 1 with an electric discharge current peak value and an electric discharge current pulse width at which the machining speed is maximized within a range that satisfies a desired machining result. The learning device 50 includes a data acquisition unit 51 and a model generation unit 52.

The data acquisition unit 51 acquires, as learning data, the position of the machining axis, the discharge current peak value corresponding to the position of the machining axis, and the discharge current pulse width corresponding to the position of the machining axis. The position of the machining shaft is information corresponding to the machining speed. The data acquired by the data acquisition unit 51 as learning data is data actually used at the time of machining. The data acquisition unit 51 is a 1 st data acquisition unit.

The model generating unit 52 learns the machining speed of the discharge current peak value and the discharge current pulse width set for the learning based on learning data including the discharge current peak value, the discharge current pulse width, and the position of the machining axis. That is, the model generating unit 52 generates a trained model 71 for estimating the machining speed of the discharge current peak value and the discharge current pulse width set for the learning, based on the position of the machining axis of the electric discharge machining apparatus 1.

The model generation unit 52 may use a known learning algorithm such as teacher learning, non-teacher learning, reinforcement learning, or the like. As an example, a case will be described in which reinforcement learning (Reinforcement Learning) is applied to the model generating unit 52. In reinforcement learning, an agent (action subject) in an environment observes a current state (parameter of the environment) and determines an action to be taken. The environment is dynamically changed by the actions of the agent, and the agent is given a return in response to the change in the environment. The agent repeatedly proceeds to learn the course of action that is most rewarded by a series of actions. As typical methods of reinforcement learning, Q learning (Q-learning) and TD learning (TD-learning) are known. For example, in the case of Q learning, a general update formula of the action cost function Q (s, a) is represented by the following formula (1).

[ 1 ]

In formula (1), s _t A represents the state of the environment at time t, a _t The action at time t is shown. By action a _t The state becomes s _t+1 。r _t+1 The return by the change in state is represented by γ representing the discount rate and α representing the learning coefficient. In addition, γ is in the range of 0 < γ.ltoreq.1, and α is in the range of 0 < α.ltoreq.1. The discharge current peak value and the discharge current pulse width corresponding to the position of the machining axis become action a _t The position of the machining shaft becomes the state s _t The model generating unit 52 generates a model of the state s at time t _t Best action a of (a) _t Learning is performed.

The update represented by the formula (1) is to increase the action value Q if the action value Q of the action a having the highest Q value at the time t+1 is larger than the action value Q of the action a executed at the time t, and to decrease the action value Q in the opposite case. In other words, the action cost function Q (s, a) is updated so that the action cost Q of the action a at the time t approaches the best action cost at the time t+1. Thus, the best action value in an environment is propagated in turn to the action value in its previous environment.

As described above, when the trained model 71 is generated by reinforcement learning, the model generating unit 52 includes the return calculating unit 53 and the function updating unit 54.

The return calculation unit 53 calculates a return based on the discharge current peak value, the discharge current pulse width, and the position of the machining axis. The return calculation unit 53 calculates the return r based on the process speed reference. The return calculation unit 53 increases the return r (for example, gives a return of "1") when the processing speed increases (when the return increase criterion is satisfied), and decreases the return r (for example, gives a return of "-1") when the processing speed decreases (when the return decrease criterion is satisfied).

The function updating unit 54 updates the function for determining the processing speed in accordance with the return calculated by the return calculating unit 53, and outputs the updated function to the trained model storage unit 70 disposed outside the learning device 50. For example, in the case of Q learning, the function updating unit 54 updates the action cost function Q(s) represented by the expression (1) _t ，a _t ) As a function for calculating the machining speed of the discharge current peak value and the discharge current pulse width set for learning.

The function updating unit 54 repeatedly performs the above learning. The trained model storage unit 70 updates the action cost function Q(s) updated by the function updating unit 54 _t ，a _t ) I.e. the trained model 71 is stored.

Next, a processing sequence of the learning processing performed by the learning device 50 will be described with reference to fig. 10. Fig. 10 is a flowchart showing a processing procedure of learning processing performed by the learning device according to embodiment 4. The data acquisition unit 51 acquires the discharge current peak value, the discharge current pulse width, and the machining axis position as learning data (step S110).

The model generating unit 52 calculates a return based on the discharge current peak value, the discharge current pulse width, and the position of the machining axis (step S120). Specifically, the report calculating unit 53 obtains the discharge current peak value, the discharge current pulse width, and the position of the machining axis, calculates the machining speed based on the position of the machining axis, and determines whether to increase the report or decrease the report based on a predetermined machining speed reference (report reference). The machining speed reference is that the machining speed becomes larger or smaller.

The return calculation unit 53 increases the return when it determines to increase the return, and decreases the return when it determines to decrease the return. That is, the return calculation unit 53 increases the return when the machining speed calculated from the position of the machining axis increases (step S130). On the other hand, the return calculation unit 53 reduces the return when the machining speed calculated from the position of the machining axis is reduced (step S140). The return calculation unit 53 may not increase or decrease the return when the processing speed is not changed.

The function updating unit 54 calculates, based on the return calculated by the return calculating unit 53, the action cost function Q(s) represented by the formula (1) stored in the trained pattern storage unit 70 _t ，a _t ) An update is performed (step S150). Thus, the machining speed corresponding to the discharge current peak value and the discharge current pulse width is learned.

The learning device 50 repeatedly executes the steps from step S110 to step S150, and generates the action cost function Q (S _t ，a _t ) Is stored in the trained model storage unit 70 as a trained model 71.

The learning device 50 according to embodiment 4 is configured to store the trained model 71 in the trained model storage unit 70 provided outside the learning device 50, but the trained model storage unit 70 may be provided inside the learning device 50.

< valid use phase >

Fig. 11 is a block diagram showing a configuration example of the estimation device according to embodiment 4. The estimation device 60 includes a data acquisition unit 61 and an estimation unit 62. The data acquisition unit 61 is a 2 nd data acquisition unit.

The data acquisition unit 61 acquires a discharge current peak value and a discharge current pulse width as data for estimating the machining speed. The data acquisition unit 61 acquires, for example, the discharge current peak value and the discharge current pulse width described in fig. 4 and 5.

The estimating unit 62 estimates the machining speed corresponding to the discharge current peak value and the discharge current pulse width by using the trained model 71 stored in the trained model storage unit 70. That is, the estimating unit 62 inputs the discharge current peak value and the discharge current pulse width acquired by the data acquiring unit 61 to the trained model 71, thereby estimating the machining speed suitable for the discharge current peak value and the discharge current pulse width. The estimating unit 62 outputs the obtained machining speed to the storage device 32 of the electric discharge machine 1.

In the present embodiment, the case where the training model 71 learned by the model generating unit 52 of the learning device 50 is used by the estimating device 60 has been described, but the estimating device 60 may use the training model 71 acquired from another learning device 50. In this case, the estimating device 60 outputs the machining speed based on the trained model 71 acquired from the other learning device 50. The other learning device 50 learns the trained model 71 from another electric discharge machining device different from the electric discharge machining device 1. That is, the estimating device 60 may estimate the machining speed suitable for the discharge current peak value and the discharge current pulse width using the trained model 71 learned by the other electric discharge machining device.

Next, a processing procedure of the estimation processing performed by the estimation device 60 will be described with reference to fig. 12. Fig. 12 is a flowchart showing a processing procedure of the estimation process performed by the estimation device according to embodiment 4. The data acquisition unit 61 acquires estimation data, which is data for estimating the machining speed (step S210). Specifically, the data acquisition unit 61 acquires a discharge current peak value and a discharge current pulse width set for estimation.

The estimating unit 62 inputs the discharge current peak value and the discharge current pulse width to the trained model 71 stored in the trained model storage unit 70 (step S220), and obtains the machining speed for the discharge current peak value and the discharge current pulse width.

The estimating unit 62 outputs the obtained data, that is, the machining speed for the discharge current peak value and the discharge current pulse width, to the electric discharge machining apparatus 1 (step S230).

The memory device 32 of the electric discharge machine 1 stores the machining speed for the output discharge current peak value and the discharge current pulse width as a learning result (step S240). The estimating unit 62 determines whether or not there is a combination of the discharge current peak value and the discharge current pulse width that satisfies the desired machining result, the combination not being acquired (step S250).

If there is a combination in which no data is acquired (Yes in step S250), the estimating unit 62 changes the combination of the discharge current peak value and the discharge current pulse width used for estimating the machining speed (step S260). That is, the estimating unit 62 changes the combination of the discharge current peak value and the discharge current pulse width to the combination of the discharge current peak value and the discharge current pulse width for which no data is obtained, and sets the combination as the estimation data.

Until no combination of data is obtained, the estimation device 60 repeats the processing from step S210 to step S260. If the combination of the non-acquired data is lost (No in step S250), the estimating unit 62 searches for a combination of the discharge current peak value and the discharge current pulse width at which the machining speed is the maximum, from among the machining speeds stored as the learning result. That is, the estimating unit 62 searches for a combination of the discharge current peak value and the discharge current pulse width at which the machining speed is the maximum within a range that satisfies the desired machining result.

The estimating unit 62 transmits the combination of the retrieved discharge current peak value and the discharge current pulse width to the memory device 32 of the electric discharge machining apparatus 1. The storage device 32 stores a combination of a discharge current peak value and a discharge current pulse width at which the machining speed is maximized within a range that satisfies a desired machining result. Thus, the electric discharge machining apparatus 1 sets the combination of the discharge current peak value and the discharge current pulse width transmitted from the estimating apparatus 60 as the parameter used in the electric discharge machining (step S270). The electric discharge machine 1 performs electric discharge machining using a combination of an electric discharge current peak value and an electric discharge current pulse width at which the machining speed stored in the storage device 32 is maximized.

In embodiment 4, the case where reinforcement learning is applied to the learning algorithm used by the estimating unit 62 has been described, but the present invention is not limited to this. As for the learning algorithm, besides reinforcement learning, teacher learning, non-teacher learning, half-teacher learning, or the like can be applied.

As a Learning algorithm used in the model generating unit 52, deep Learning (Deep Learning) may be used, which learns the extraction of the feature quantity itself. The model generation section 52 may perform machine learning by other well-known methods, such as neural network, genetic programming, functional logic programming, support vector machine, and the like.

The learning device 50 and the estimating device 60 may be devices connected to the electric discharge machine 1 via a network, for example, and separate from the electric discharge machine 1. At least one of the learning device 50 and the estimating device 60 may be incorporated in the electric discharge machine 1. The learning device 50 and the estimating device 60 may be present on a cloud server.

The model generating unit 52 may learn the machining speed for the combination of the discharge current peak value and the discharge current pulse width using the learning data acquired from the plurality of electric discharge machining apparatuses 1. The model generating unit 52 may acquire learning data from a plurality of electric discharge machining devices 1 used in the same region, or may learn the machining speed for the discharge current peak value and the discharge current pulse width using learning data collected from a plurality of electric discharge machining devices 1 operating independently in different regions. Further, the electric discharge machine 1 that collects learning data may be added to or removed from the object in the middle of the process. Further, a learning device that learns the machining speed for the peak value of the discharge current and the pulse width of the discharge current may be applied to another electric discharge machine 1, and the machining speed for the peak value of the discharge current and the pulse width of the discharge current may be relearned and updated in the other electric discharge machine 1.

The hardware configuration of the learning device 50 and the estimating device 60 will be described here. Since the learning device 50 and the estimation device 60 have the same hardware configuration, the hardware configuration of the learning device 50 will be described below.

Fig. 13 is a diagram showing an example of a hardware configuration of a learning device according to embodiment 4. The learning device 50 can be realized by a processor 91, a memory 92, an output device 93, and an input device 94.

Examples of the processor 91 are a CPU (also referred to as Central Processing Unit, central processing unit, arithmetic unit, microprocessor, microcomputer, DSP (Digital Signal Processor)) or a system LSI (Large Scale Integration). Examples of memory 92 are RAM (Random Access Memory), ROM (Read Only Memory).

The learning device 50 is realized by the processor 91 reading and executing a learning program stored in the memory 92 and executable by a computer for executing the operation of the learning device 50. The learning program for executing the actions of the learning device 50 can be said to be a sequence or a method for causing a computer to execute the learning device 50. The learning program for executing the operation of the learning device 50 includes a program for learning the machining speed, and the like.

The learning program executed by the learning device 50 has a module structure including a model generating unit 52, and is downloaded to the main storage device, and is generated on the main storage device.

The input device 94 receives the discharge current peak value, the discharge current pulse width, the position of the machining axis, and the like, and transmits the received signals to the processor 91. The memory 92 stores basic data such as the surface roughness data 41 and the electrode consumption data 42. In addition, the memory 92 is used as a temporary memory when various processes are executed by the processor 91. The output device 93 outputs the machining speed generated by the processor 91 to the machining power source 34.

The learning program may be provided as a computer program product by storing a file in an installable form or an executable form in a computer-readable storage medium. The learning program may be provided to the learning device 50 via a network such as the internet. The function of the learning device 50 may be partly implemented by dedicated hardware such as a dedicated circuit, and partly implemented by software or firmware. The machining condition setting device 30 also has the same hardware configuration as the learning device 50.

As described above, in embodiment 4, the learning device 50 generates the trained model 71 for estimating the machining speed based on the position of the machining axis of the electric discharge machine 1, the set electric discharge current peak value, and the electric discharge current pulse width. The estimating device 60 estimates the machining speed from the position of the machining axis of the electric discharge machining device 1, the set electric discharge current peak value, and the set electric discharge current pulse width, using the trained model 71. This makes it possible to obtain a machining speed for the set discharge current peak value and discharge current pulse width corresponding to the position of the machining axis of the electric discharge machine 1. The estimating device 60 can perform electric discharge machining in a short time while satisfying a desired machining result by applying a combination of an electric discharge current peak value and an electric discharge current pulse width at which the machining speed is maximized to electric discharge machining.

The configuration shown in the above embodiment is an example, and other known techniques may be combined, or the embodiments may be combined with each other, and a part of the configuration may be omitted or changed without departing from the scope of the present invention.

Description of the reference numerals

The electric discharge machining apparatus comprises an electric discharge machining device, 11, 12 electric discharge current waveforms, 13, 14 machined surfaces, 21, 22 machined surface quality, 23-26 correspondence, 30 machining condition setting means, 31, 94 input means, 32 storage means, 33 calculation means, 34 machining power supply, 35 machining means, 36 machining interelectrode, 41 surface roughness data, 42 electrode consumption data, 50 learning means, 51, 61 data acquisition means, 52 model generation means, 53 report calculation means, 54 function update means, 60 estimation means, 62 estimation means, 70 trained model storage means, 71 trained model, 91 processor, 92 memory, 93 output means.

Claims (10)

1. A processing condition setting device, characterized by comprising:

an input device that receives, as a machining result desired for the shape-engraved electric discharge machining, an electrode consumption rate, which is a consumption rate of a tool electrode, a machining speed, a machining surface roughness, which is a roughness of a machined surface of a workpiece, and a surface area ratio of an actual machined surface to an area of a specific region within the machined surface of the workpiece;

A storage device that stores basic data representing a theoretical correlation of the machining result with respect to a combination of a discharge current peak value and a discharge current pulse width of the form-engraving electric discharge machining, which are acquired in advance; and

and a computing device that calculates a machining condition from the 1 st stage to the final stage when machining processing from the 1 st stage to the final stage is performed as the shape carving electric discharge machining performed a plurality of times, based on the machining result received by the input device and the basic data stored in the storage device.

2. The processing condition setting device according to claim 1, wherein,

the processing result is information indicating the physical quantity.

3. The processing condition setting device according to claim 1 or 2, wherein,

the input device receives the machining result for each of the shape carving electric discharge machining.

4. The processing condition setting device according to claim 3, wherein,

the arithmetic device calculates the machining conditions from the 1 st stage to the final stage, with respect to the machining process at the stage preceding the final stage, when the electrode consumption rate does not satisfy the machining result in the machining process at the final stage in the shape carving electric discharge machining performed a plurality of times.

5. The processing condition setting device according to claim 4, wherein,

the machining result includes a sum of consumption amounts of the tool electrodes in the engraving electric discharge machining,

the arithmetic device calculates the processing condition that the sum of the electrode consumption amounts from the 1 st stage to the final stage satisfies the processing result.

6. The processing condition setting device according to claim 1 or 2, wherein,

the basic data is data generated in advance based on an actual measurement value at the time of executing the engraving discharge processing.

7. The processing condition setting device according to claim 1 or 2, wherein,

also comprises a learning device for learning the machining speed of the shape carving electric discharge machining,

the learning device includes:

a 1 st data acquisition unit that acquires learning data including a position of a machining axis of a contour discharge machining apparatus that performs the contour discharge machining using the machining conditions, and the discharge current peak value and the discharge current pulse width at the position of the machining axis; and

and a model generation unit that generates a trained model for estimating a machining speed corresponding to the discharge current peak value and the discharge current pulse width from the discharge current peak value and the discharge current pulse width using the learning data.

8. The processing condition setting device according to claim 7, wherein,

and an estimating means for estimating the machining speed using the trained model,

the estimation device comprises:

a 2 nd data acquisition unit that acquires the discharge current peak value and the discharge current pulse width at the position of the machining shaft; and

and an estimating unit that estimates and outputs the machining speed corresponding to the discharge current peak value and the discharge current pulse width acquired by the 2 nd data acquiring unit, using the trained model.

9. A processing condition setting method, comprising:

a storage step of storing basic data representing a theoretical correlation of a machining result of the engraving electric discharge machining with respect to a combination of an electric discharge current peak value and an electric discharge current pulse width obtained in advance;

an input step in which an input device receives, as a machining result desired for the shape-engraved electric discharge machining, an electrode consumption rate, which is a consumption rate of a tool electrode, a machining speed, a machining surface roughness, which is a roughness of a machined surface of a workpiece, and a surface area ratio of an actual machined surface to an area of a specific region within the machined surface of the workpiece; and

And a calculation step of calculating, by the calculation device, machining conditions from the 1 st stage to the final stage when machining processing from the 1 st stage to the final stage is performed as the shape carving electric discharge machining performed a plurality of times, based on the machining result received by the input device and the basic data stored in the storage device.

10. An electric discharge machine, comprising:

a processing mechanism for performing shape carving electric discharge processing; and

a processing condition setting device for setting processing conditions used by the processing mechanism,

the processing condition setting device comprises:

an input device that receives, as a machining result desired for the shape-engraved electric discharge machining, an electrode consumption rate, which is a consumption rate of a tool electrode, a machining speed, a machining surface roughness, which is a roughness of a machined surface of a workpiece, and a surface area ratio of an actual machined surface to an area of a specific region within the machined surface of the workpiece;

a storage device that stores basic data representing a theoretical correlation of the machining result with respect to a combination of a discharge current peak value and a discharge current pulse width of the form-engraving electric discharge machining, which are acquired in advance; and

And a computing device that calculates a machining condition from the 1 st stage to the final stage when machining processing from the 1 st stage to the final stage is performed as the shape carving electric discharge machining performed a plurality of times, based on the machining result received by the input device and the basic data stored in the storage device.