JPH04135204A - Numerical controller with automatic machining condition producing function - Google Patents

Abstract

PURPOSE:To obtain accurate machining condition data by inputting selectively the arithmetic programs and the coupling coefficient data on a neural network set according to the actual machining type out of a storage medium. CONSTITUTION:The arithmetic programs and their coupling coefficient data of two selected neural networks are stored in the prescribed areas of a neural network area 334 and a coupling coefficient areas 335 of a RAM 33 from a flexible disk FD 37. Then the area where the coupling coefficient data of the 1st and 2nd neural networks stored in the area 335 is rewritten in the FD 37, and the latest learned coupling coefficient data is stored. As a result, the accurate machining condition data can be quickly obtained for the next machining of the same type.

Description

[Detailed description of the invention] [Industrial application field]

The present invention relates to a numerical control device having a function of automatically creating processing condition data.

[Prior art]

Conventionally, for example, in grinding using a numerically controlled grinder, the rotation of the workpiece for each grinding mode such as rough grinding, fine grinding, fine grinding, etc. There is known an apparatus that uses a computer to automatically determine machining conditions such as grinding speed, grinding start position, grinding feed rate, and post-grinding feed stop time.

[Problem to be solved by the invention]

However, in reality, it is known from experience that the automatically determined machining condition data is not optimal for various fixed condition data. In such cases, when the operator looks at the automatically determined machining condition data and finds that it is not appropriate, the operator may correct the value based on the operator's experience or intuition, or perform the actual machining using the machining condition data. The operator judged the resulting machining results and corrected the machining condition data based on experience and intuition. How to correct which machining condition data from fixed condition data, or how to correct which machining condition data from the machining error obtained from the machining result when machining a workpiece with certain machining condition data. The correct correction depends on the experience and intuition of the operator, and the correct correction is not always performed. For this reason, it was necessary to machine the workpiece using the corrected machining condition data and to correct the machining condition data again according to the machining results. Furthermore, even if the operator corrects the machining condition data correctly, there is a problem in that the causal relationship between the machining condition data at that time and the fixed condition data or machining error data is not reflected in the subsequent correction of the machining condition data. Ta. Therefore, there is a problem that it takes time to create appropriate processing condition data. Furthermore, how the machining condition data is corrected using fixed condition data and machining error data differs depending on the type of workpiece and the type of machining. For this reason, it is necessary to provide the numerical control device with an automatic creation means that corresponds to various types of machining, and there is a problem that the capacity of the storage device becomes enormous. The present invention has been made to solve the above problems, and its purpose is to enable automatic creation of machining condition data for various types of machining, and to improve the efficiency of storage capacity. is to aim for

[Means to solve the problem]

As shown in FIG. 12, the configuration of the invention for solving the above problem is that a neural network calculation program having a structure corresponding to the type of machining and learned coupling coefficient data are stored in correspondence with each type of machining. Input means for selectively inputting only the neural network calculation program necessary for processing and the coupling coefficient data of the neural network from the storage medium, the neural network storage means for storing the calculation program, and the coupling coefficient data. The present invention includes a coupling coefficient data storage means, an input condition data storage means for storing input condition data, and a control means for inputting force condition data to a neural network to start the neural network and obtain processing condition data. be.

[Effect]

Neural network arithmetic programs and coupling coefficient data corresponding to actual processing types are selectively input from a storage medium that stores them according to a large number of processing types. Then, machining condition data is determined from the input condition data by the neural network of the input structure. The input condition data input to the neural network are required values or fixed data (fixed condition data) such as the finished diameter of the workpiece, machining allowance, tool rotation speed, etc., and the machining condition data are used to actually calculate the workpiece. This includes the error between the processing result and the required value (processing error data) when processing. The neural network coupling coefficients that have already been learned are stored in the storage medium. Therefore, the user can stop using the neural network without having to perform new learning. Further, when the user uses the neural network to correct the processing condition data, if the data is not appropriate, the connection coefficient of the neural network can be corrected using the data. It is also possible to output the learned coupling coefficient to a storage medium to update the stored coupling coefficient data.

【Example】

The present invention will be described below based on specific examples. (1) Structure of Grinding Machine FIG. 1 is a block diagram showing the entire mechanical structure of a numerically controlled grinding machine having an automatic processing condition determination device according to the present invention. 50 is a grinding machine, and a table 52 that slides on the bed 51 of the grinding machine 50 is provided. The table 52 is moved in the left-right direction in the drawing by driving the table feed motor 53. Also, on the table 52 are a headstock 54 and a Shinshindai 56.
are arranged, the headstock 54 has a main shaft 55, and the Shinshindai 56 has a psychological axis 57. The workpiece W is supported by a main shaft 55 and a psychological shaft 57,
It is rotated by the rotation of the main shaft 55. This rotation of the main shaft 55 is performed by a main shaft motor 59 disposed on the head stock 54. On the other hand, a grinding wheel 60 for grinding the workpiece W is pivotally supported by a drive shaft of a grinding wheel drive motor 62 provided on a grinding wheel head 61. Further, the grindstone head 61 is controlled to move in the vertical direction of the drawing by a grindstone head feed motor 63. Table feed motor 53, grindstone feed motor 63,
A numerical control device 30 is provided to drive and control the main shaft motor 59, grinding wheel drive motor 62, and the like. (2) Configuration of numerical control device The numerical control device 30 mainly consists of
It is composed of a CPU 31, a ROM 32, a RAM 33, and an IP (interface) 34. The RAM 33 has an NC data area 331 for storing NC programs, a fixed condition data area 332 for storing fixed condition data such as finishing diameter, machining allowance, rigidity coefficient, part type, rough finishing division, operator name, etc., and workpiece rotation speed, A machining condition data area 333 that stores machining condition data such as a grinding start position, a grinding feed rate, and a feed stop time after grinding, a neural network area 334 that stores a neural network calculation program, and a coupling coefficient that stores a neural network coupling coefficient. A region 335 is provided. In addition, the RAM 33 includes a required value area 336 that stores required values of machining results determined from required machining specifications, a reference value area 337 that stores automatically determined standard values of machining condition data, and calculations using a neural network. A correction amount area 338 for storing the correction amount of the processed machining condition data, a machining result data area 339 for storing machining result data, and a machining error data area 340 for storing machining error data are formed. Note that the RAM 33 is backed up by a battery, and the learned coupling coefficients are stored therein. Further, the ROM 32 includes a control program area 321 storing a control program for operating the numerically controlled grinding machine according to NC data, an automatic determination program area 322 storing a main automatic determination program for determining machining condition data, and a neural network. A learning program area 323 storing a program for learning coupling coefficients is formed. In addition, the numerical control device 30 is connected to the operation panel 20 via the IF 54.
is installed. Operation panel 2 of the operation panel 20
1, there are provided a keyboard 22 for inputting data and a CRT display device 23 for displaying data. Further, an FD drive device 36 is connected to the numerical control device 30 via an IF 55, and an FD (flexible disk) 37, which is a storage medium, is read and written via the FD drive device 36. (3) Operation Next, the processing procedure of the CPU 31 used in the device of this embodiment will be explained based on a flowchart. 1. General operation of the entire apparatus The general operation of the apparatus will be explained with reference to FIG. 11. In step Sl, a first neural network calculation program used to actually obtain machining condition data is created from the FD37a that stores neural network calculation programs corresponding to many types of machining and their learned coupling coefficient data. and its coupling coefficient data are stored in RAM3.
Hereinafter, the calculation program of the first neural network loaded into the computer 3 will also be referred to as a condition matching engine. In step S2, fixed condition data is input to the RAM 33. In step S3, the reference value of the machining condition data is automatically determined from the fixed condition data. In step S4, the correction amount of the machining condition data is calculated by the calculation program (condition adaptation engine) of the first neural network that receives the fixed condition data as input. In step S5, the processing condition data corrected by the corrected quantum reference value is displayed on the CRT 23. In step S6, the machining condition data is judged based on the operator's experience and intuition to determine whether it is appropriate. If it is not appropriate, the processing condition data is corrected in step S7. Further, in step S8, the coupling coefficient of the first neural network is learned using the corrected correction amount as a teacher signal. By repeatedly performing learning and calculation of the machining condition data, the machining condition data is determined to be appropriate in step S6. Incidentally, since the first neural network is supplied in a trained form, in most cases, appropriate machining condition data can be obtained by calculating -degrees. In step S9, the workpiece is actually machined using the machining condition data. In step SIO, machining results such as finished dimensions of the workpiece are measured, and machining error data indicating an error from the required value is calculated. In step Sll, the machining error data is evaluated, and if appropriate, machining is completed in step S12. Furthermore, if the machining results are not appropriate, it is necessary to correct the machining condition data. In step S13, the above machining error data is stored in RAM3.
3 is input. In step S14, the second neural network calculation program corresponding to the processing type and its learned coupling coefficient are input to the RAM 33 from the FD 37b. The calculation program of the second neural network is also called a machining diagnosis engine. In step S15, a second
The neural network is activated to calculate the correction amount of the machining condition data. In step S16, new machining condition data is calculated using concave machining condition data + correction amount, and the influence of the new machining condition data on the machining result is predicted. In step S17, the modified machining condition data is evaluated based on the predicted machining results. If the processing condition data is not appropriate, step 318
Then, the coupling coefficient of the second neural network is learned using the appropriate correction amount as a teacher signal. By repeatedly performing learning and calculation of the machining condition data, the machining condition data is determined to be appropriate in step S17. Incidentally, since the second neural network is supplied in a trained form, in most cases, appropriate machining condition data can be obtained by calculating -degrees. Then, the process returns to step S6, and if the correction amount by the second neural network is large in the corrected machining condition data, in step S8, the deviation of the machining condition data from the reference value is used as a teacher signal, and the first The coupling coefficients of the neural network are also learned. Therefore, as learning progresses, the correction amount by the second neural network is gradually added to the coupling coefficient of the first neural network, so that when learning progresses, only the first neural network
It becomes possible to obtain optimal processing condition data. That is, the machining error data gradually becomes smaller, and the amount of correction by the second neural network also gradually decreases. In addition, the coupling coefficient data of the first and second neural networks learned as described above during actual processing is F.
The data is output to D37a, 37b, and the corresponding coupling coefficient data stored in FD37a, 37b is updated. As described above, in this embodiment, the machining condition data is corrected by the condition matching engine and the machining diagnosis engine. FIG. 3 shows a flowchart of a program that inputs a predetermined neural network calculation program and coupling coefficient data from the FD 37 of the storage medium and stores them in the RAM 33. The FD 37 stores a large number of first and second neural network calculation programs and learned coupling coefficients corresponding to the type of processing (for example, grinding of an engine camshaft, etc.). In step 700, a list of all neural network names is displayed on the CRT 23. In step 702, the operator looks at the list and selects the first and second neural networks (condition matching engine and processing diagnosis engine) corresponding to the type of processing to be performed. Next, in step 704, the calculation programs of the two selected neural networks and their coupling coefficient data are transferred from the FD 37 to the neural network area 334 and the coupling coefficient area 335 of the RAM 33, respectively.
0 is stored in a predetermined area. 3. Input of fixed condition data FIG. 4 is a flowchart of the main program that automatically generates machining condition data from fixed condition data. At step 100, fixed condition data input from the keyboard 22 is read and stored in the fixed condition data area 332 of the RAM 33. In this embodiment, the fixed condition data includes finished diameter data DI, machining allowance data D7.
Rigidity coefficient data 19 Part type data D4. Rough finishing division data Ds, A worker name data D, E3 worker name data D, C worker name data 0. 4. Calculation of reference value of machining condition data In the next step 102, the above fixed condition data (D to D
, ) and other fixed condition data, the reference values V, ~Vl+ of the machining condition data are calculated. In this embodiment, the machining condition data includes coarse grinding rotation speed data of 1. 3. Seiken rotation speed data. 3. to the equipment rotation speed data. 4. Coarse grinding start diameter data. Seiken starting diameter data Ks, equipment starting diameter data 6. 6. Coarse grinding feed speed data Kt, fine grinding feed speed data. Equipment feed speed data Ks, feed stop time data after rough grinding, , equipment rearward feed stop time data K
It consists of 11. The calculation of this reference value is performed as follows. Regarding the rotation speed data for each grinding mode, the circumferential speed of the workpiece is determined in advance as a function of the required surface roughness of the workpiece with respect to the circumferential speed of the grinding wheel during a certain grinding. The circumferential speed of the workpiece is calculated from the commanded surface roughness of the workpiece, and the rotation speed data of the workpiece is calculated from the circumferential speed of the workpiece and the diameter of the workpiece. Regarding the feed rate data for each grinding mode, the amount of cut per rotation of the workpiece of the grinding wheel is determined in advance as a function of dimensional tolerance. The depth of cut is calculated from the commanded dimensional tolerance of each grinding mode, and the grinding feed rate data is calculated from the rotation speed data. The grinding start diameter data for each grinding mode has a standard feed amount set for each grinding mode, and is calculated based on the relationship between the commanded finishing diameter and this feed amount. Regarding the post-grinding feed stop time data, the rotational speed of the workpiece at which the feed is stopped is determined depending on whether fixed-size grinding is performed and whether grinding is performed in divided steps. The feed stop rotation speed is determined from the fixed condition data, and the feed stop time is calculated using the rotation speed data. Next, in step 104 of FIG. 4, the first neural network is activated to generate the fixed condition data D1 to D.
, and input each correction amount δ, ~δ1 . of each processing condition data (K, ~KI+). is calculated. The first neural network has the configuration shown in FIG. In this example, the neural network is
It has a three-layer structure: an input layer, an intermediate layer, and an output layer. As is well known in the neural network, each element of the second intermediate layer and the third output layer is defined as an element that performs the following calculation. The output of the jth element of the first layer is 0. is calculated using the following formula. However, l≧2. 0, 2f(1,)-(1) 1, = ΣW, I, JJk +Vt
−(2)f (x)=1/ (1+ex
p(-x)) -(3)
However, V is the bias of the j-th arithmetic element in the second layer,
L, , is the coupling coefficient between the th element of the first layer and the jth element of the first layer, 0. represents the output value of the jth element of the first layer. In other words, since it is the first layer, it outputs the input as it is without performing any calculations, so the input layer (
It is also the input value of the first element of the first layer). Therefore, oj=nj-(4) where D is fixed condition data input to the third element of the input layer. The specific calculations of the neural network are executed according to the procedure shown in FIG. In step 200, each element of the intermediate layer (second layer) has output values D1 to D from each element of the input layer (first layer),
As for the first element of the second layer, which is inputted and the product-sum calculation of the following equation is performed, the calculation is performed by the following equation. In this embodiment, the bias is zero. Next, in step 202, the output of each element of the intermediate layer (second layer) is calculated using the sigmoid function of the product-sum function value of the input values of equation (5) using the following equation. 2nd layer
The output value of the th element is calculated using the following equation. This output value DJ becomes the input value of each element of the output layer (third layer). Next, in step 204, a product-sum operation of the input values of each element of the output layer (third layer) is performed. Next, in step 206, the output value of each element in the output layer is calculated using a sigmoid function, similar to equation (6). This output value becomes the correction amount δ of the machining condition data. That is, the correction amount δ is obtained by the following equation. Returning to step 106 in FIG. 4, the reference values v1 to V of the machining condition data obtained in step 102 and step 1
By the sum of the correction amounts 61 to δ11 determined in 04, ~K11 is determined in the machining condition data. Among the machining condition data, ~K11 is stored in the machining condition data area 333 of the RAM 33. Next, in step 108, ~Kl is added to the machining condition data.
l is displayed on the CRT 23, and the operator looks at the displayed result and, if necessary, inputs a correction value from the keyboard 22.
The value is stored in the machining condition data area 333. Next, in step 110, NC data is calculated using -, , , , , etc. in the processing condition data, and the calculated NC data is stored in the NC data area 331 of the RAM 33. 7. Generation of machining error data Next, the workpiece is trial-machined according to this NC data. The machining results of the workpiece are measured and machining result data is obtained. Machining result data includes, for example, total grinding time, finished surface roughness, and finished dimensional accuracy. Finish roundness, degree of cracking, degree of chatter, etc. Next, in step 500 of FIG. 7, which describes a procedure for modifying machining condition data from these measured machining result data, the above-mentioned measured machining result data is inputted from the keyboard 22 by the operator, and the input The value is stored in the processing result data area 339 of the RAM 33. Next, in step 502, the required value of the machining result is input in the same way, and the value is stored in the required value area 336 of the RAM 33.
is memorized. Next, in step 504, machining error data, which is the deviation of the machining result data from the required value, is calculated, and these data are stored in the machining error data area 340 of the RAM 33. The machining error data includes a total grinding time error HI, a finished surface roughness error H2, a finished finish error H8, a finished roundness error H4, a degree error of scorching or cracking H2, a degree error of chatter H6, and the like. 8 Second neural network (machining diagnosis engine) Next, in step 506, machining error data (H, ~ H,
) to start the second 221 network. Then, in step 508, the correction amount (61
~δ11) is stored in the correction amount area 338 of the RAM 33. This second neural network has the configuration shown in Figure 9J8. In this embodiment, the second neural network has a three-layer structure including an input layer, a middle layer, and an output layer. The arithmetic function of each element of this second neural network is completely the same as the arithmetic function of the first neural network described above. Next, in step 510, each concave machining condition data (
The corrected new machining condition data (K, ~kz) is obtained by adding the correction amount (61 to δ1.) to K, ~11). These data are stored by rewriting the contents of the machining condition data area 333 of the RAM 33. As described above, in this embodiment, the reference value of the machining condition data is corrected by the first neural network, and further, the second neural network is used to perform correction taking into account machining errors. Therefore, more accurate machining condition data is required. Furthermore, by performing learning of the first neural network even when the amount of correction by the second neural network is large, as the learning progresses, the first neural network alone can obtain suitable processing condition data. The amount of correction can be determined. That is, as the learning of the first neural network progresses, the amount of correction by the second neural network decreases, making it possible to obtain machining condition data without the need for trial machining or the like. 9. Learning of the first neural network Next, a procedure for learning the coupling coefficients of the first neural network will be explained. Next, a method for learning the coupling coefficients of this neural network will be explained. The coupling coefficients are determined using the well-known backpropagation method for the neural network shown in FIG. This learning is performed when it becomes necessary to further learn the already learned coupling coefficients at the time of use. That is,
When the correction amount output by the first neural network is not appropriate, and when the processing error becomes large when processing using the processing condition data corrected by the first neural network, in other words, when the correction amount output by the first neural network is This is a case where the amount of correction to be output is large. At step 60[) in FIG. 9, a learning signal for each element of the output layer is calculated using the following equation. However, T is a teacher signal for the output correction amount δ4, and f' (x) is a derivative of the sigmoid function. Next, in step 602, the learning signal of the intermediate layer is calculated using the following equation. Next, in step 604, the coupling coefficients of each element of the output layer are corrected. The amount of correction is determined by the following formula. is the amount of change in the coupling coefficient between the child and the first element of the intermediate layer. This is the amount of correction. P and Q are proportionality constants. Therefore, the coupling coefficient is w+, ''+Δω1. j(1)→19.
The corrected coupling coefficient is determined by ・−■. Next, the process moves to step 606, and the coupling coefficients of the elements in the intermediate layer are corrected. The amount of correction of the coupling coefficient is determined by the following equation, as in the case of the output layer. Δω1. J (t)=P 4J-f (I Meng)+Q・
Δω ring, J (t-t) α3 Therefore, the coupling coefficient is L, J+Δω+, j(t)→W+,. The corrected coupling coefficient is determined by α. Next, in step 60B, it is determined whether the correction amount of the coupling coefficient has become less than or equal to a predetermined value, and it is determined whether the coupling coefficient has converged. If the coupling coefficient has not converged, the process returns to step 600, similar calculations are repeated using the newly corrected coupling coefficient, and the coupling coefficient is again corrected. By repeating such calculations, learning for a certain teacher signal is completed. 10. Learning of the second neural network Regarding the second neural network that receives machining error data as input, if the correction amount of the output machining condition data is not appropriate, the teacher signal for the correction amount is used to perform the necessary correction. Learning takes place accordingly. 11. Saving the learned coupling coefficient data As shown in step 800 in FIG.
The coupling coefficient data of the first and second neural networks stored in the FD 35 are rewritten in the area where the coupling coefficient data was stored, and the most recently learned coupling coefficient data is saved. As a result, when performing the same type of machining next time, accurate machining condition data will be generated quickly, and the neural network will be more convenient to use. In the above embodiment, the neural network calculation program and its coupling coefficient data necessary for processing are stored on the FD37.
Although the number is input from the main computer via a communication line, it may also be input from the main computer to the number 4 LIII control device. That is, it can also be applied to machining centers and numerical control devices connected to a parent computer via a communication line.

【Effect of the invention】

The present invention selectively inputs a neural network calculation program and coupling coefficient data corresponding to the actual processing type from a storage medium storing them according to a large number of processing types, and uses the neural network. , to obtain processing condition data. Therefore, it is possible to obtain accurate machining condition data for various types of machining without increasing the storage capacity of the numerical control device. Furthermore, since the neural network can be designed to have an optimal structure corresponding to the type of machining, the machining condition data can be corrected more accurately and quickly. In addition, in the present invention, since many already trained neural network libraries according to the type of processing are supplied by the storage medium, the user does not need to perform initial training.
The learning results can be saved on a storage medium, making the system extremely easy to use.

[Brief explanation of drawings]

FIG. 1 is a configuration diagram showing the overall mechanical configuration of a numerically controlled grinding machine having an automatic processing condition determination device according to a specific embodiment of the present invention. FIG. 2 is a block diagram showing the electrical configuration of the numerical control device and operation panel according to the same embodiment. FIG. 3 is a flowchart showing a procedure for inputting a neural network calculation program and its coupling coefficient data by the CPU used in the device of the embodiment. FIG. 4 is a flowchart showing the calculation procedure of machining condition data by the CPU used in the apparatus of the embodiment. Fifth
The figure is a structural diagram showing the structure of the first neural network according to the same embodiment. FIG. 6 is a flowchart showing the calculation procedure by the first neural network. 7th
The figure is a flowchart showing a procedure for correcting machining condition data based on machining error data by the CPU used in the apparatus of the embodiment. FIG. 8 is a structural diagram showing the structure of the second neural network. FIG. 9 is a flowchart showing the learning procedure of the first neural network. FIG. 10 is a flowchart showing a procedure for saving coupling coefficient data learned by the CPU used in the device of the embodiment in a storage medium. FIG. 11 is a flowchart showing the overall operational concept of this device. FIG. 12 is a block diagram showing the concept of the present invention. 20 Operation panel 21 Operation panel 22 Keyboard 23-CRT display device 30 Numerical control device 31-CPU 32-ROM 33-RAM 50 Grinding machine 51
Bed 52 Table Table feed motor 54 Headstock spindle 56゛Tailstock 57゛-Tailstock spindle motor 60゛Grinding wheel 61 Grinding wheel head Grinding wheel drive motor Grinding wheel head motor W-Workpiece

Claims (1)

[Scope of Claim] A numerical control device having a function of automatically creating machining condition data for controlling machining conditions, comprising a calculation program for a neural network structured according to the type of machining and coupling coefficient data learned by the neural network. an input means for selectively inputting only a neural network calculation program necessary for processing and coupling coefficient data of the neural network from a storage medium stored corresponding to each processing type; and an input neural network calculation program. a neural network storage means for storing input coupling coefficient data; an input condition data storage means for storing input condition data that is an input to the neural network; and the input condition data storage means inputting the input condition data stored in the neural network into the neural network;
A numerical control device having a processing condition automatic creation function, comprising: a control means for activating a neural network and determining the processing condition data according to the calculation program of the neural network and the coupling coefficient data.