JP-7854607-B2 - Learning device and cutting process evaluation system

Inventors

• 藤井 慶太郎
• 高橋 正行
• 和田 紀彦

Assignees

• パナソニックＩＰマネジメント株式会社

Dates

Publication Date

20260507

Application Date

20200108

Priority Date

20190207

Claims (4)

1. Input processing unit, A learning processing unit is provided, The input processing unit is, Physical quantities related to the cutting process are acquired with a sampling period of 1/100th or less of the time required for the cutting process . The state variables based on the aforementioned physical quantities are input to the evaluation model of the learning processing unit. From the evaluation model, the cutting evaluation results are output, which are the results of evaluating whether or not there is an abnormality in the cutting process when the physical quantity is measured, and if there is an abnormality , the type of cause and the stepwise degree of the abnormality . The aforementioned learning processing unit, A dataset is accumulated for each processing step, in which the state variables based on the physical quantities measured during a single processing step are used as input data, and the actual processing results when those state variables were measured are used as output data. The error between the truncation evaluation result output from the evaluation model and the truncation result included in the dataset is calculated using a loss function. A learning device that updates the weight coefficients of the evaluation model using an optimization algorithm based on the calculated error.
2. The aforementioned cutting process is a punching process in which the workpiece is punched out. The input processing unit uses the following as the physical quantity: The learning device according to claim 1, which acquires at least one of the following: the load acting on the workpiece during punching, the shear rate during punching, the clearance between the punch and the die, and the temperature of the workpiece during punching.
3. A sensor that measures the aforementioned physical quantity, A learning device according to claim 1 or 2, A cutting process evaluation system comprising: an output processing unit that derives the cutting evaluation result using the evaluation model updated by the learning device; and
4. The aforementioned cutting process is a punching process in which the workpiece is punched out. As the aforementioned sensor, The workpiece is equipped with at least one load sensor for measuring the load applied during the punching process , a position sensor for measuring the position of the punch, and a sound sensor for measuring the sound generated by the punching process . The cutting process evaluation system according to claim 3.

Description

This disclosure relates to a learning device used in an evaluation system for workpieces manufactured by cutting processes, and to a cutting process evaluation system using the same. In cutting processes, a workpiece is typically placed on a die and held in place by a stripper while a punch is used to press and punch out the workpiece, thereby obtaining the desired shape. Cutting processes are widely used in various manufacturing fields, including the production of home appliances, precision equipment, and automotive parts. In cutting processes using such dies, it is common practice to adjust the die position or die shape through trial and error according to each individual die. However, there are cases where such trial and error adjustments are insufficient, and in such cases, processed products of the desired quality cannot be obtained. Therefore, evaluation methods have been proposed, such as the evaluation method disclosed in Patent Document 1, which measure physical quantities generated by the cutting process and diagnose abnormalities by comparing the measured values of these physical quantities with reference values. Furthermore, as a method for determining the quality of processed products in general processing equipment, an evaluation method has been proposed, such as the one disclosed in Patent Document 2, which determines quality by comparing measured values of the processing equipment's internal information with a threshold value set in a provisional judgment unit, and updates the threshold value of the provisional judgment unit by feeding back the actual quality of the processed product. Japanese Patent Application Publication No. 6-304800Japanese Patent Publication No. 2017-174236 A block diagram illustrating the outline of the cutting process evaluation system used in the embodiments of this disclosure.Block diagram showing an overview of the learning process in the evaluation system used in the embodiments of this disclosure.A general cutting load-punch stroke diagram illustrating the correlation between load and processing quality used in the embodiments of this disclosure.A general sound-time diagram illustrating the correlation between sound and processing quality used in the embodiments of this disclosure.A general temperature-time diagram illustrating the correlation between temperature and processing quality used in the embodiments of this disclosure.Diagram of the convolutional neural network used in the embodiments of this disclosureAn overall diagram showing the layout of a cutting apparatus to which the cutting process evaluation system used in the embodiments of this disclosure is applied.Functional block diagram relating to the control unit of the embodiment of the present disclosureA flowchart illustrating the learning steps performed by the learning device according to the embodiment of this disclosure.A flowchart illustrating the evaluation steps performed by the cutting process evaluation system according to the embodiment of this disclosure.A diagram showing an example of the cutting result. The embodiments of this disclosure will be described below with reference to Figures 1 to 10. Figure 1 shows a block diagram illustrating the outline of the cutting process evaluation system 1 according to the embodiment of this disclosure. In Figure 1, the cutting process evaluation system 1 of this disclosure includes, functionally, a learning device 21 and an output processing unit 4 that executes an output step. The learning device 21 includes, functionally, an input processing unit 2 that executes an input step and a learning processing unit 3 that executes a learning step. The input step involves acquiring a physical quantity 15 measured by a sensor 150 and generating a state variable 12 based on the physical quantity 15. The output step involves outputting a cutting evaluation result 13 obtained by inputting the state variable 12 into the evaluation model 11. Furthermore, the cutting process evaluation system 1 may also include a sensor 150 that measures the physical quantity 15 related to the cutting process. The sensor 150 includes at least one of the following: a load sensor 151, a sound sensor 152, a position sensor 153, and a temperature sensor 154. The input processing unit 2 acquires the physical quantity 15 measured during the cutting process as a state variable 12 and inputs it into the evaluation model 11 (described later) of the learning processing unit 3. The learning processing unit 3 is included in the learning device 21 and comprises a pre-trained evaluation model 11 and a dataset group 14. The output processing unit 4 outputs the cutting evaluation result 13. With this configuration, the cutting process evaluation system 1 is configured to input state variables 12 to a pre-trained evaluation model 11 and output a cutting evaluation result 13. The evaluation model 11 is optimized through a training step performed by the learning processing unit 3 using a dataset gro