JPH04348818A - Processing condition producing device for electric discharge - Google Patents

Abstract

PURPOSE:To provide a processing condition producing device for electric discharge which enables provision of a proper electrode approach amount according to a processing state, such as processing environment of an operator and a processing purpose. CONSTITUTION:A correction data part 45 to store a correction factor alpha variable according to a processing state is provided. During production of a processing condition B, an electrode approach amount bm is corrected based on the correction factor alpha.

Description

[Detailed description of the invention]

0001

[Industrial Application Field] This invention applies to a workpiece
Regarding a machining condition generation device for electrical discharge machining of a workpiece by a multi-stage rocking method with high voltage electrodes facing each other in close proximity to the
In particular, the present invention relates to a machining condition generation device for electric discharge machining that can generate an optimal electrode approach amount that is suitable for the environment and machining purpose.

0002

[Prior Art] Conventionally, electrical discharge machines that utilize electrical discharge phenomena have been used for workpieces with special shapes such as molds or that are difficult to machine, and the gap between the electrodes of the electrical discharge machine and the workpieces has been The surface of the workpiece is melted by the electric discharge. At this time, the electrode is oscillated along the workpiece machining surface to define the machining area, and the electrode is brought closer to the workpiece W in stages to form the final machining surface that meets the required specifications. It is necessary to set the commands, that is, the machining conditions.

[0003] Generally, the required specifications include the machining bottom area, machining depth, finished surface roughness, electrode reduction amount, electrode wear rate, machining time, etc., and are set for this required specification. The machining conditions include electrical conditions for electric discharge, electrode operating conditions, and, for example, the amount of electrode approach, which corresponds to the amount of feed of the electrode to the machining surface.

[0004] Electrical conditions include peak current, pulse width, rest time, polarity, power supply, etc., and it is known that reducing the peak current, pulse width, etc. will result in finer roughness of the machined surface. ing. Further, although the oscillating type is used here, electrode operating conditions generally include servo mode, jump mode, oscillating type, and the like. Furthermore, the amount of electrode approach is determined in stages while taking into account safety margins for the final machined surface.

By the way, in electrical discharge machining, it is necessary to spray machining fluid to remove machining powder on the surface of the workpiece generated by machining. At this time, depending on the machining shape, the jetting state of the machining fluid, the characteristics of the electrical discharge machine, etc., a rough machined surface from the intermediate machining stage may remain on the final machined surface.

For example, if there is a large amount of machining powder, the machining area will expand due to secondary discharge during electrical discharge machining, and the workpiece will be machined to a deeper position than usual from the surface. This difference becomes more pronounced as the electrical energy during discharge increases.
When a large amount of machining powder is accumulated, it is necessary to set a safety margin by moving the electrode of the electric discharge machine farther from the machining surface than usual to ensure the safety of the machining surface.

In multi-stage machining, normally, as the electrode approaches the surface of the workpiece, machining using higher electrical energy is sequentially shifted to machining using lower electrical energy. At this time, the greater the electrical energy, the rougher the machined surface and the shorter the processing time. Furthermore, as the safety margin increases, as a result, more machining using small electrical energy must be performed in subsequent machining stages, resulting in longer machining time.

Conventionally, machining conditions (
(including the electrical conditions and the amount of electrode gathering) are determined based on the required specifications for the workpiece and the knowledge and experience of the operator regarding electrical discharge machines. For example, the machining conditions were determined by the operator based on a comprehensive judgment based on the finished surface roughness and shape characteristics of the workpiece, and the machining conditions were input directly into the electric discharge machine.

However, in recent years, information processing technology has rapidly developed, and as described in, for example, Japanese Patent Laid-Open No. 62-130130 and Japanese Patent Laid-open No. 62-130131, machining electrodes and workpieces of electrical discharge machines have been rapidly developed. An apparatus has been proposed that automatically calculates and determines machining conditions by inputting relevant data. Also, for example, JP-A-64-11715
As described in the above publication, a machining condition generation device having a learning function that can change a machining condition database by feeding back evaluation results after actual machining has also been proposed.

FIG. 5 is a block diagram showing a general electric discharge machine including a machining condition generating device. In the figure, 1 is an electrical discharge machine having an electrode 1a facing the workpiece W, 2 is a power source that applies a predetermined voltage signal between the electrical discharge machine 1 and the machined surface of the workpiece W, and 3 is a power supply from the electrical discharge machine 1. 4 is a numerical control device that captures the feedback signal of and drives and controls the power source 2 according to machining conditions B, and 4 is a machining condition generating device that outputs machining conditions B according to required specifications A input from the outside to the numerical control device 3. It is. The processing condition generation device 4 may be included as a part of the numerical control device 3.

FIG. 6 is a block diagram showing the conventional configuration of the machining condition generation device 4 in FIG.
41 is an input section that takes in the required specifications A from the operator; 42 is a processing condition characteristic data section that stores in advance characteristic data D necessary for the generation of the characteristic data; 42 is an input section that takes in the required specifications A from the operator; A machining condition generation unit 43 that generates the condition B is an output unit that outputs the generated machining condition B to the numerical control device 3. The characteristic data D includes, for example, data corresponding to various machining conditions in addition to data such as surface roughness, machining speed, and electrode wear.

[0012] Requirement specification A includes, for example, n elements a1 to a.
n, and includes machining bottom area a1, machining depth a2, finished surface roughness a3, electrode reduction amount a4, electrode wear rate a5, machining time a6, etc. Further, the machining conditions B include, for example, m elements b1 to bm, and electrical conditions b1 to bj-1 such as peak current b1, pulse width b2, rest time b3, polarity b4, and power supply b5, and servo mode bj , electrode operating conditions bj to bk such as jump mode bj+1 and rocking type bj+2, and electrode approach amount bm.

FIG. 7 is a side view showing in detail the relationship between the electrode 1a and the machining surface of the workpiece W, and the broken line indicates the swinging position of the electrode 1a in one machining stage. L1 and L2
are the final finished dimensions in the side and bottom directions of the work W, respectively, E1 and E2 are the final machined surfaces of the side and bottom surfaces of the work W, respectively, and bm1 and bm2 are the final machined surfaces C1, respectively.
and the remaining amount for C2, that is, the electrode approach amount.

The electrode gathering amount bm is a numerical value corresponding to the feeding amount of the electrode 1a at each stage in the multi-stage oscillating machining method as shown in FIG.
The remaining margins for L2 and L2 are expressed as electrode approach amounts bm1 and bm2, respectively, but there are various ways to express them.

Next, referring to FIGS. 5 and 7, FIG.
The operation of the conventional machining condition generation device for electrical discharge machining shown in FIG. First, based on the specifications required for the workpiece W, the operator inputs various required specifications A consisting of a1 to an to the machining condition generating section 42 in the machining condition generating device 4 via the input section 41.

The machining condition generation device 42 generates at least one machining condition B corresponding to each machining stage, based on the input required specification A and the characteristic data D stored in advance, so as to conform to the required specification A. It is generated and output to the numerical control device 3 via the output unit 43 as b1 to bm. The machining conditions B generated in this way are output to the numerical control device 3 via the output section 43, and are also displayed on a CRT or recorded on a floppy disk as necessary.

Next, with reference to FIGS. 8 to 10, a detailed description will be given of the electric discharge machining operation in which the oscillating machining process is divided into multiple stages from the initial rough machining surface to the final finish machining surface. FIG. 8 shows each data flow in the machining condition generation section 42, and blocks shown with double lines indicate data searched or created within the machining condition generation section 42. Further, it is assumed that the characteristic data regarding the electrical conditions b1 to bj and the surface roughness are recorded in advance in the machining condition characteristic data section 40.

FIG. 9 shows the electrode approach amount bm in stages (from the first stage to
3) is an explanatory diagram showing a state in which the electrode 1a is brought closer to the final machining surface E, where F is the amount of electrode consumption, G is the clearance between the electrode 1a and the workpiece surface, and R is the clearance of the workpiece W. It is surface roughness. Further, FIG. 10 is an explanatory diagram showing the safety margin expected for the electrode approach amount bm, and H is the safety margin set according to the surface roughness R.

In FIG. 8, first, based on the characteristic data D, the starting electrical condition 11 and the starting surface roughness machined under this starting electrical condition 11 are determined from the machining bottom area a1 and the electrode reduction amount a4 included in the required specification A. The height 12 is determined, and the finishing electrical condition 13 is determined from the finished surface roughness a3. Furthermore, the finished surface roughness a3 becomes the finished surface roughness 14 as data in the machining condition generation section 42. Note that the starting electrical condition 11 and the finishing electrical condition 13 are output as machining condition B.

Subsequently, the surface roughness 15 at an intermediate stage is determined based on the starting surface roughness 12 and the finished surface roughness 14. The intermediate surface roughness 15 is the starting surface roughness 12 of 60 μmRmax.
If the finished surface roughness 14 is 4μmRmax, then 6
0μmRmax→30μmRmax→15μmRmax
The starting surface roughness 12 to the finished surface roughness 14 are sequentially set to values that are approximately halved, such as →8 μmRmax→4 μmRmax. Therefore, as shown in FIG. 9, the surface roughness R at each stage (represented by the maximum value Rmax in consideration of variations) is successively halved.

Next, the amount bm of the electrodes at each stage is determined based on the intermediate surface roughness 15, and the intermediate surface roughness 15
Appropriate electrical conditions b based on and other required specifications an, etc.
1 to bj-1 are extracted. At this time, if the machined surface of the workpiece W is uniform and flat, the electrode approach amount bm is the sum of the clearance G, surface roughness R, and electrode wear amount F (G+R+F), as is clear from FIG. It is expressed as

However, in reality, the machined surface is not uniform, and moreover, slight differences between the electrode 1a and the workpiece W affect the machined surface. It is necessary to set H. Therefore, the electrode approach amount bm is

[0023]

[Math 1]

It is given by: Furthermore, since the safety margin H is expressed as the product of the safety factor K and the surface roughness R, which are preset in the machining condition generation section 42, the formula (2) is as follows.

[0025]

[Math 2]

[0026] In this way, the electrical condition b
1 to bj-1 and the electrode approach amount bm are output as machining condition B. Note that machining condition B may be calculated in advance without being calculated every time at each step. In this case, machining condition B corrected by actual machining using standard electrodes is recorded in memory, and when requested Can be extracted based on specifications.

However, since the safety margin H is set to a general value, for example, when machining speed is more important than the finished state of the machining surface, or when machining a shape such as bag machining where machining powder is extremely likely to accumulate, When processing a workpiece,
It is not possible to set an appropriate electrode approach amount bm. Therefore, after machining condition B is generated, the operator must adjust the electrode approach amount bm little by little through trial and error based on the workpiece shape, how to apply machining fluid, machining purpose, actual machining results, etc. .

Next, a machining condition generation device equipped with a learning section will be explained. FIG. 11 is a block diagram showing a conventional machining condition generation device for electric discharge machining having a learning function. C is an evaluation result of the actual machining state of the workpiece W after machining, and is input into the machining condition generation device 4 by the operator. 44 is the processing condition B' before output based on the evaluation result C.
This is a learning part that changes the electrical conditions included in the process, and consists of a difference calculation part that calculates processing differences by comparing required specifications A and evaluation results C, and a change part that actually changes electrical conditions from the processing differences. , built into the processing condition generation device 4.

[0029] In this case, the operator, as described above,
The required specifications A are input to the machining condition generation device 4, and the electric discharge machine 1 is driven and controlled using the machining conditions B generated from the machining condition generation device 4. Then, actual electrical discharge machining results, i.e., i evaluation results C (c1 to ci) including surface roughness c1, machining time c2, accuracy c3, etc., are input to the learning section 44 via the input section 41.

The learning section 44 compares the input evaluation result C with the required specification A, and calculates the processing difference for each corresponding element of the required specification A and the evaluation result C. Then, "If the peak current for each single discharge is small, the finished surface roughness of the workpiece W will be fine", "If the discharge time (pulse width) for each single discharge is short, the finished surface roughness of the workpiece W will be fine", etc. According to the program created based on the facts, the machining condition generation unit 42
, processing conditions B, and characteristic data D are changed with respect to electrical conditions.

However, in order to realize a correct learning function, it is necessary to adjust the electrode approach amount bm instead of adjusting the electrical conditions. This is because the rough machined surface caused by the processing conditions in the middle stage often remains as the final machined surface, but this is actually due to the influence of processing powder rather than the roughness of the electrical conditions in the middle stage. This is because there are far more cases where the electrode approach amount bm is not appropriate due to fluctuations in the clearance G.

[0032]

[Problem to be Solved by the Invention] As described above, the conventional machining condition generation device for electrical discharge machining corrects the safety margin H used for calculation when setting the electrode approach amount bm for special machining situations. Therefore, there was a problem in that it was not possible to generate an appropriate electrode approach amount bm depending on the processing situation.

[0033] Furthermore, the machining condition generation device for electrical discharge machining equipped with the conventional learning section function is capable of generating electrical conditions b included in machining condition B.
Since the method only learns parameters related to 1 to bj-1, there is a problem that a correct learning function cannot be realized.

The present invention was made to solve the above-mentioned problems, and is a method for electric discharge machining that can generate an appropriate electrode approach amount according to the machining conditions such as the operator's machining environment and the machining purpose. The purpose is to obtain a processing condition generation device.

Another object of the present invention is to obtain a machining condition generation device for electrical discharge machining that realizes a correct learning function by learning and correcting parameters related to the amount of electrode approach included in the machining conditions.

[0036]

[Means for Solving the Problems] A machining condition generation device for electric discharge machining according to the present invention includes a correction data section for storing a correction coefficient that can be changed depending on the machining situation, and uses the amount of electrode approach as a correction coefficient. The correction is made based on this.

The machining condition generation device for electric discharge machining according to the present invention further includes a learning section that generates a correction coefficient based on the evaluation results after actual machining of the workpiece.

[0038]

[Operation] In the present invention, when generating machining conditions, an appropriate correction coefficient depending on the machining situation is used to correct the electrode approach amount included in the machining conditions.

Further, in the present invention, an appropriate correction coefficient to be reflected in the amount of electrode approach is generated based on the evaluation result for each actual process, and the correction coefficient is corrected by learning.

[0040]

DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing an embodiment of the present invention, and 4 and 42 correspond to the aforementioned machining condition generation device and machining condition generation section, respectively, and 40, 41, 43, 4
4, A and B are the same as above. Further, the overall configuration of the electric discharge machine is as shown in FIG.

α is a correction coefficient that contributes to the safety factor K used in calculating the electrode approach amount bm, and is set according to the machining situation such as the machining environment and the purpose of machining, and is input into the machining condition generation device 4 by the operator. Ru. Reference numeral 45 denotes a correction data section provided in the machining condition generation section 4, which stores the correction coefficient α input via the input section 41 as data and inputs it to the machining condition generation section 42.

Next, referring to the explanatory diagram of FIG.
The operation of the embodiment of the present invention shown in FIG. First, the operator sets the correction coefficient α according to the machining situation. For example, if you want to shorten the time even if some partially processed surfaces remain, set α<1. In addition, if you want to perform safe machining with fine details on the machining surface because the machining surface tends to remain during bag machining, etc., use α>1.
Set to .

When the correction coefficient α set in this way is input to the machining condition generation device 4 together with the required specification A, the required specification A is
is input to the machining condition generation unit 42 via the input unit 41,
After the correction coefficient α is stored in the correction data section 45, it is input to the processing condition generation section 42.

The machining condition generating section 42 generates machining conditions B as shown in FIG. 2 based on the required specifications A and various elements extracted from the required specifications. At this time, the surface roughness in the middle is 1
5 and the safety factor K, etc., but when calculating the safety margin H (see Figure 10), instead of considering only the safety factor K as in formula (■), the correction factor Also considering α,

[0045]

[Math 3]

The electrode approach amount bm is calculated from the following.

[0047] According to the formula (■), if the reduction of machining time is more important than the roughness of the remaining machining surface, the correction coefficient α is set to α<1, so the safety margin H is small, so the electrode approach amount is bm becomes smaller. Therefore, in machining with large electrical energy in the initial stage, the electrode 1a approaches the final machining surface E of the workpiece W for much machining, so the amount of machining with small electrical energy in the intermediate and final stages is small. Overall, machining can be completed in a short time.

[0048] In addition, when it is desired to make the roughness of the remaining machined surface finer with emphasis on the safety of the finished machined surface, the correction coefficient α is
Since α>1 is set, the safety margin H becomes large, so the electrode approach amount bm becomes large. Therefore, in machining with large electrical energy in the initial stage, since the electrode 1a is far from the final machining surface E of the workpiece W, the machining surface roughness does not affect deep positions from the final machining surface E.
There is less possibility that it will remain on the final machined surface.

In FIG. 1, the correction coefficient α is input directly from the outside, but for example, data representing "degree of emphasis on speed" etc. may be input to the machining condition generation device 4, and this input data may be developed into a correction coefficient α within the machining condition generation device 4.

Next, a second embodiment of the present invention having a learning function will be described with reference to FIG. In FIG. 3, 46 is a learning unit that generates a correction coefficient α based on the evaluation result C after actual processing, and stores the generated correction coefficient α in the correction data unit 45. The learning unit 46 may store, for example, a program written using fuzzy rules or a program including a neural network.

The learning unit 46 learns the fact that ``if there is a high possibility that a partially machined surface will remain, the correction coefficient α will be increased'' and that ``if the machining speed is slow, the correction coefficient α will be decreased''. Based on this fact, the correction coefficient α is updated every time the evaluation result C is obtained, and is stored in the correction data section 45 each time.

FIG. 4 is a flowchart showing the correction coefficient generation processing operation by the program installed in the learning section 46. It is assumed that the correction coefficient α is initially set to 1 in advance. First, based on the evaluation result C of the actual machining state, it is determined whether or not there remains a machining surface at an intermediate stage (step S
1), if any remain, change the correction coefficient α by 1 from the previous value.
．． It is increased by a factor of 1 (step S2).

[0053] If there is no intermediate machined surface remaining, it is determined whether the machining time is too long (step S3),
If it takes too long, the correction coefficient α is reduced to 0.9 times the previous value (step S4).

[0054] In this way, the machining electrode approach amount bm is automatically and optimally corrected. After learning and correcting the correction coefficient α in each step S2 or S3, the learning unit 46 waits for input of the next evaluation result C. Here, only the determination step using the machining surface and machining time of the evaluation result C has been shown, but it goes without saying that determinations using other evaluation results can be sequentially performed as necessary.

The learning section 46 may only learn and correct the correction coefficient α used for calculating the electrode approach amount bm, but as described above, it may correct the machining conditions B' etc. before being output. Good too. Further, the operation of inputting the evaluation result C can be performed by an operator by determining the actual machining state, or can be performed automatically by calculating the actual machining state by another computer.

Furthermore, although the correction coefficient α is automatically changed by the learning unit 46 in the machining condition generation device 4, for example, the judgment result in step S1 or S3 is displayed each time, and the actual correction Alternatively, the operator may decide whether or not to change the coefficient α. Further, at this time, a plurality of correction coefficients that are likely to have been changed may be displayed for the operator to select.

In the above embodiment, an appropriate surface roughness R was determined in advance, and when determining the safety margin H for each surface roughness R, a correction coefficient α was used as in equation (2). "Journal of Electrical Engineering Society" (Vol. 19, No. 37, pp. 39-4)
The correction coefficient α may be used when determining the amount of electrode gathering based on the simulation published by Masakazu Kishi, Junichi Yokota, and Yasuo Suzuki (p. 8).

Furthermore, although the correction coefficient α was set to a constant value for all safety margins H, the correction coefficient α may be set for each processing stage or for each surface roughness R, for example. In addition, although the safety factor K is corrected by the correction coefficient α, other elements such as the clearance G may be corrected as long as the electrode deviation amount bm is corrected as a result, or the electrode deviation amount bm itself may be corrected. may be subject to correction.

[0059]

As described above, according to the present invention, a correction data section is provided for storing a correction coefficient that can be changed depending on the machining situation, and when generating machining conditions, the electrode approach amount is adjusted based on the correction coefficient. Since the correction is made, it is possible to obtain a machining condition generation device for electrical discharge machining that can generate an appropriate electrode approach amount depending on the machining situation such as the machining environment of the operator and the machining purpose.

Further, according to the present invention, the learning section is further provided to generate a correction coefficient based on the evaluation result after actual machining of the workpiece, and the correction coefficient which is a parameter regarding the electrode approach amount is learned and corrected. This has the effect of providing a machining condition generation device for electrical discharge machining that realizes a correct learning function.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing a first embodiment of the invention.

FIG. 2 is an explanatory diagram showing the operation of the device in FIG. 1;

FIG. 3 is a block diagram showing a second embodiment of the invention.

FIG. 4 is a flowchart diagram showing the operation of the apparatus of FIG. 2;

FIG. 5 is a block diagram showing the overall configuration of a general electric discharge machine.

FIG. 6 is a block diagram showing a conventional machining condition generation device for electric discharge machining.

FIG. 7 is a side view showing in detail the relationship between the electrode and the processing surface of the workpiece.

FIG. 8 is an explanatory diagram showing the operation of the device in FIG. 6;

FIG. 9 is an explanatory diagram showing a stepwise change in the amount of electrode gathering.

FIG. 10 is an explanatory diagram showing a safety margin expected in the amount of electrode gathering.

FIG. 11 is a block diagram showing a conventional machining condition generation device for electric discharge machining including a learning function.

[Explanation of symbols]

1 Electric discharge machine 1a Electrode 4. Machining condition generation device 42 Machining condition generation section 45 Correction data section 46　　　　Learning Department W Work A Required specifications B Processing conditions bm Electrode close-up amount C Evaluation results α Correction coefficient E, E1, E2　　　　　Machine surface

Claims (2)

[Claims] 1. A machining condition generation device for electric discharge machining that generates machining conditions in stages according to required specifications of a workpiece for an electric discharge machine including a machining electrode, the machining conditions being The machining condition generation device for electrical discharge machining includes an electrode approach amount corresponding to the feed amount of the electrode with respect to the machining surface, further comprising a correction data section for storing a correction coefficient that can be changed according to the machining situation, A machining condition generation device for electric discharge machining, characterized in that the amount of deviation is corrected based on the correction coefficient. 2. The machining condition generation device for electrical discharge machining according to claim 1, further comprising a learning section that generates the correction coefficient based on an evaluation result after actual machining of the workpiece.