Prediction maintenance system for mold of press machine and method for prediction maintenance

Abstract

To accurately and easily diagnose deterioration of a mold.SOLUTION: A prediction maintenance system for a mold of a press machine includes a load sensor, a data collecting device, and a diagnosis device. The load sensor detects a press load applied to the mold. The data collecting device collects load data indicating time-series data of the press load. The diagnosis device diagnoses deterioration of the mold, on the basis of the waveform of the load data.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to a predictive maintenance system and method for predictive maintenance for press machine dies. [Background technology]

[0002] A press machine performs press working by pressing a die against a workpiece. For example, in the press machine of Patent Document 1, an upper die is attached to the underside of a slide. A bolster is disposed below the slide, and a lower die is attached to the upper surface of the bolster. The slide is raised and lowered by a drive mechanism such as a motor. The press machine performs press working by lowering the slide and pressing the upper die against a workpiece set on the lower die. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-167328 Summary of the Invention [Problem to be solved by the invention]

[0004] Dies deteriorate due to wear and tear caused by continued use. Therefore, maintenance of the dies is necessary to maintain good press processing quality. For example, it is possible to determine when to replace the dies by measuring their dimensions. In this case, the dies must be removed from the press machine and measured using dedicated measuring equipment, which takes a lot of time. In addition, removing the dies may affect the accuracy of the press processing. Alternatively, it is possible to determine when to replace the dies based on the number of press processing shots. In this case, it is difficult to accurately diagnose the deterioration of the dies. An object of the present disclosure is to accurately and easily diagnose the deterioration of the dies. [Means for solving the problem]

[0005] A system according to one aspect of the present disclosure is a predictive maintenance system for a die of a press machine. The system includes a load sensor, a data collection device, and a diagnostic device. The load sensor detects a press load applied to the die. The data collection device collects load data indicating time-series data of the press load. The diagnostic device diagnoses deterioration of the die based on the waveform of the load data.

[0006] A method according to another aspect of the present disclosure is a computer-implemented method for predictive maintenance of a die of a press machine, the method including: acquiring load data indicating time-series data of a press load applied to the die; and diagnosing deterioration of the die based on a waveform of the load data. [Effects of the Invention]

[0007] According to the present disclosure, time-series data of the press load applied to the die is collected as load data. Then, deterioration of the die is diagnosed based on the waveform of the load data. Therefore, deterioration of the die can be diagnosed accurately and easily. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram illustrating a predictive maintenance system according to an embodiment. [Figure 2] FIG. [Figure 3] 10 is a flowchart showing a process for diagnosing a mold. [Figure 4] FIG. 10 is a diagram illustrating an example of load data. [Figure 5] FIG. 10 is a diagram illustrating an example of offset value data. [Figure 6] FIG. 10 is a diagram showing an example of load data within an analysis range. [Figure 7] FIG. 10 is a diagram illustrating an example of a trend value graph. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, a predictive maintenance system for a die of a press machine according to an embodiment will be described with reference to the drawings. Fig. 1 is a schematic diagram showing a predictive maintenance system 1 according to an embodiment. As shown in Fig. 1, the predictive maintenance system 1 includes a press machine 2, a data collection device 3, a diagnostic device 4, an input device 5, and an output device 6.

[0010] FIG. 2 is a front view of the press machine 2. The press machine 2 includes a support frame 11, a slide 12, a slide drive device 13, a bolster 14, and a base 15. The support frame 11 is disposed on the base 15. The slide 12 is supported on the support frame 11 so as to be movable up and down. The slide drive device 13 operates the slide 12. The slide drive device 13 includes, for example, a motor and a transmission mechanism. The transmission mechanism converts rotation of the motor into linear motion and transmits it to the slide 12. The bolster 14 is disposed below the slide 12. The base 15 is disposed below the bolster 14 and supports the bolster 14.

[0011] A die 16 is attached to the press machine 2. The die 16 includes an upper die 17 and a lower die 18. The upper die 17 is attached to the slide 12. The lower die 18 is attached to the bolster 14. The press machine 2 performs press working by lowering the slide 12 and pressing the upper die 17 against the workpiece W1 set on the lower die 18.

[0012] The press machine 2 is equipped with a load sensor 19. The load sensor 19 detects the press load applied to the die 16. The load sensor 19 includes, for example, strain gauges 21 and 22. The strain gauges 21 and 22 are attached to the support frame 11. The load sensor 19 detects the press load applied to the die 16 via the strain of the support frame 11. The load sensor 19 outputs a load signal indicative of the press load.

[0013] As shown in FIG. 1, the data collection device 3 is communicatively connected to the load sensor 19. The data collection device 3 is realized by a computer including a processor and a storage device. The data collection device 3 receives a load signal from the load sensor 19. The data collection device 3 samples and collects the press load at a predetermined sampling period based on the load signal. The data collection device 3 generates and stores load data indicating time-series data of the press load based on the sampled press load.

[0014] The diagnostic device 4 is communicatively connected to the data collection device 3. The diagnostic device 4 is realized by a computer including a processor and a storage device. For example, the diagnostic device 4 may be a cloud server. In that case, the diagnostic device 4 communicates with the data collection device 3 via a communication network such as the Internet. Alternatively, the diagnostic device 4 may be a computer located in the same factory as the press machine 2 and the data collection device 3. The diagnostic device 4 receives load data from the data collection device 3. The diagnostic device 4 diagnoses deterioration of the die 16 based on the waveform of the load data. Diagnosis of the die 16 by the diagnostic device 4 will be described later.

[0015] The input device 5 can be operated by a user to set information for diagnosing the mold 16. The input device 5 is communicatively connected to the diagnostic device 4. The input device 5 transmits information input by the user to the diagnostic device 4. The output device 6 is communicatively connected to the diagnostic device 4. The output device 6 includes a display 23. The output device 6 may be connected to the diagnostic device 4 via the input device 5. The output device 6 displays a screen related to the diagnosis of the mold 16 based on an image signal from the diagnostic device 4.

[0016] The input device 5 and the output device 6 are realized by a computer including a processor and a storage device. The output device 6 may be a touch panel integrated with the input device 5. The input device 5 and the output device 6 may be a computer located in the same factory as the press machine 2 and the data collection device 3. Alternatively, the input device 5 and the output device 6 may be capable of communicating with the diagnostic device 4 via a communication network such as the Internet.

[0017] The predictive maintenance system 1 includes a reset switch 24. The reset switch 24 is communicatively connected to the data collection device 3. The reset switch 24 can be operated by a user to reset the diagnosis of the mold 16. When the reset switch 24 is operated, a reset signal for resetting the diagnosis of the mold 16 is sent to the data collection device 3. The reset signal may be sent from the input device 5 by operating the input device 5. When the data collection device 3 receives the reset signal, it sends a reset instruction for resetting the diagnosis of the mold 16 to the diagnosis device 4.

[0018] Next, the process for diagnosing the die 16 will be described. Fig. 3 is a flowchart showing the process for diagnosing the die 16 executed by the diagnostic device 4. As shown in Fig. 3, in step S101, the diagnostic device 4 acquires load data. The diagnostic device 4 acquires the load data from the data collecting device 3. Fig. 4 is a diagram showing an example of load data D1. As shown in Fig. 4, the load data D1 indicates a time-series waveform of the press load.

[0019] In step S102, the diagnostic device 4 acquires the analysis range. The user inputs the analysis range by operating the input device 5. The diagnostic device 4 sets the analysis range based on the user input received by the input device 5. The analysis range indicates the range of load data D1 used to diagnose deterioration of the mold 16. As shown in FIG. 4, the analysis range includes the number A1 of data of the load data D1 from the beginning to the end of the analysis range and the number A2 of data of the load data D1 from the beginning to the peak of the analysis range.

[0020] The analysis range also includes a first load threshold B1 and a second load threshold B2 for determining a peak in the load data D1. The first load threshold B1 is a threshold for the press load for determining a peak in the punching load. The punching load is the press load when punching the workpiece W1 from the material. The second load threshold B2 is a threshold for the press load for determining a peak in the forming load. The forming load is the press load when forming the workpiece W1.

[0021] In step S103, the diagnostic device 4 acquires the cumulative number of shots. The number of shots is the number of press processes performed by the die 16, and the cumulative number of shots is the number of shots since the previous reset command. The press machine 2 counts the number of shots, and the data collecting device 3 acquires the number of shots from the press machine 2 and transmits it to the diagnostic device 4. Alternatively, the diagnostic device 4 may count the number of forming loads in the waveform of the press load as the number of shots.

[0022] In step S104, the diagnostic device 4 determines an offset value. The diagnostic device 4 stores the offset value data shown in FIG. 5. The offset value data defines the relationship between the cumulative number of shots and the offset value. In the offset value data, the offset value increases as the cumulative number of shots increases. The diagnostic device 4 refers to the offset value data and determines the offset value from the cumulative offset value.

[0023] In step S105, the diagnostic device 4 calculates a trend value. The trend value indicates the trend of change in the load data D1. The diagnostic device 4 calculates the trend value based on the load data D1 within the analysis range. The diagnostic device 4 calculates the trend value using the following equation (1). Tv = Lmax - Lmin + Cn (1) Tv is a trend value. As shown in Fig. 6, Lmax is the maximum value of the load data D1 within the analysis range. Lmin is the minimum value after the maximum value Lmax within the analysis range. Cn is an offset value. That is, the diagnostic device 4 calculates the trend value based on the difference between the maximum and minimum values ​​of the load data D1 within the analysis range and the offset value.

[0024] In step S106, the diagnostic device 4 generates trend value data. The trend value data is time-series data of trend values. As shown in FIG. 7, the trend value data D2 indicates the time-series waveform of the trend value. In step S107, the diagnostic device 4 calculates a moving average value D3 of the trend value data D2. In step S108, the diagnostic device 4 displays a trend value graph 30 on the display 23. FIG. 7 shows an example of the trend value graph 30. As shown in FIG. 7, the trend value graph 30 includes the waveform of the trend value data D2 and the waveform of the moving average value D3 of the trend value data D2.

[0025] In step S109, the diagnostic device 4 determines whether the moving average value D3 of the trend value data D2 is greater than the first diagnostic threshold value TH1. The first diagnostic threshold value TH1 is a threshold value for diagnosing whether maintenance of the mold 16 is necessary. The diagnostic device 4 sets the first diagnostic threshold value TH1 based on a user input received by the input device 5. If the moving average value D3 of the trend value data D2 is greater than the first diagnostic threshold value TH1, the process proceeds to step S110.

[0026] In step S110, the maintenance information is displayed on the display 23. The maintenance information is information that requests maintenance such as replacement of the mold 16. The maintenance information is displayed as text or an image. The maintenance information may be displayed on an application screen for diagnosing abnormalities in the mold 16. Alternatively, the maintenance information may be sent and displayed as an email.

[0027] In step S111, the diagnostic device 4 determines whether the moving average value D3 of the trend value data D2 is greater than the second diagnostic threshold value TH2. The second diagnostic threshold value TH2 is a threshold value for determining an abnormality in the mold 16. The diagnostic device 4 determines the second diagnostic threshold value TH2 from the past load data D1. For example, the diagnostic device 4 determines the maximum value of the past trend values ​​as the second diagnostic threshold value TH2. If the moving average value D3 of the trend value data D2 is greater than the second diagnostic threshold value TH2, the process proceeds to step S112.

[0028] In step S112, the diagnostic device 4 displays the abnormality information on the display 23. The abnormality information is information that notifies the user of an abnormality in the mold 16. The abnormality information is displayed as text or an image. The abnormality information may be displayed on an application screen for diagnosing the abnormality in the mold 16. Alternatively, the abnormality information may be sent and displayed as an email.

[0029] In step S113, the diagnostic device 4 determines whether or not a reset command has been issued. The diagnostic device 4 receives a reset command when the above-described reset switch 24 is operated. For example, the user operates the reset switch 24 when replacing the die 16 to reset the diagnosis of the die 16. Alternatively, the user resets the diagnosis of the die 16 when the material of the workpiece W1 is changed.

[0030] If the diagnostic device 4 determines that a reset instruction has been issued, it resets the cumulative number of shots in step S114. As a result, the offset value is reset to the initial value C0, as shown in Fig. 5. Then, the diagnostic device 4 repeats the above-mentioned process. If a reset instruction has not been issued in step S113, the diagnostic device 4 repeats the above-mentioned process without resetting the cumulative number of shots.

[0031] In the predictive maintenance system 1 for the die 16 of the press machine 2 according to the present embodiment described above, time-series data of the press load applied to the die 16 is collected as load data D1. Then, deterioration of the die 16 is diagnosed based on the waveform of the load data D1. Therefore, deterioration of the die 16 can be diagnosed accurately and easily.

[0032] Although one embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications are possible without departing from the gist of the invention.

[0033] The configuration of the press machine 2 is not limited to that of the above embodiment and may be modified. For example, the load sensor 19 may be attached to the die 16. Alternatively, the load sensor 19 is not limited to a strain gauge and may be another sensor such as an AE (Acoustic Emission) sensor.

[0034] The process for diagnosing the mold 16 is not limited to that of the above embodiment and may be modified. For example, the offset value may be multiplied by the difference between the maximum and minimum trend values. The offset value is not limited to a value corresponding to the cumulative number of shots, but may also be a value corresponding to the temperature of the mold 16 or the thickness of the material of the workpiece W1.

[0035] The diagnostic device 4 may compare the trend value itself with the first and second diagnostic threshold values ​​TH1, TH2, instead of the moving average value D3 of the trend value. The trend value is not limited to that in the above embodiment and may be changed. For example, the trend value may be a Mahalanobis distance calculated by the MT method (Maharanobis-Taguchi System) using the load data D1 in the analysis range as a multidimensional vector. Alternatively, the trend value may be a Mahalanobis distance calculated by the MT method based on the average and standard deviation of the load data D1 in the analysis range. Alternatively, the trend value may be a Mahalanobis distance calculated by the MT method based on the results of FFT analysis of the load data D1 in the analysis range.

[0036] Alternatively, the trend value may be a value calculated by analyzing the load data D1 within the analysis range using multivariate analysis other than the MT method. Alternatively, the trend value may be an integrated value of the absolute values ​​of the load data D1 within the analysis range for each sampling. Alternatively, the trend value may be only the maximum value of the load data D1 within the analysis range. Alternatively, the trend value may be the difference between the press load at the start of the punching process for punching the workpiece W1 from the material and the maximum press load. [Industrial Applicability]

[0037] According to the present disclosure, deterioration of a mold can be diagnosed accurately and easily. [Explanation of symbols]

[0038] 1: Predictive maintenance system, 2: Press machine, 3: Data collection device, 4: Diagnostic device, 5: Input device, 6: Output device, 16: Mold, 19: Load sensor, 23: Display

Claims

1. A predictive maintenance system for a die of a press machine, comprising: a load sensor for detecting a press load applied to the die; a data collection device that collects load data indicating time-series data of the press load; a diagnostic device that diagnoses deterioration of the mold based on the waveform of the load data; A predictive maintenance system with

2. further comprising an input device for receiving an input of an analysis range of the load data; the diagnostic device diagnoses deterioration of the mold based on the load data within the analysis range. The predictive maintenance system of claim 1 .

3. The analysis range includes the number of pieces of load data from the beginning to the end of the analysis range. The predictive maintenance system of claim 2 .

4. The analysis range includes the number of data points of the load data from the beginning to the peak of the analysis range. The predictive maintenance system of claim 2 .

5. the analysis range includes a load threshold for determining a peak of the load data; The predictive maintenance system of claim 2 .

6. the diagnostic device calculates a trend value indicating a trend of change in the load data based on the load data within the analysis range; Diagnosing deterioration of the mold based on the trend value The predictive maintenance system of claim 2 .

7. the diagnostic device calculates the trend value based on a difference between a maximum value and a minimum value of the load data within the analysis range. The predictive maintenance system of claim 6.

8. The diagnostic device comprises: acquiring an offset value that increases in accordance with an increase in the number of shots of the press machine; calculating the trend value based on the difference between the maximum value and the minimum value of the load data within the analysis range and the offset value; The predictive maintenance system of claim 7.

9. It also has a display, the diagnostic device displays on the display a waveform of trend value data indicating time-series data of the trend value. The predictive maintenance system of claim 6.

10. further comprising an output device; The diagnostic device comprises: acquiring a first diagnostic threshold value set via the input device; By comparing the first diagnostic threshold value with the trend value, a diagnosis is made as to whether maintenance of the mold is necessary; When it is diagnosed that maintenance of the mold is necessary, information requesting maintenance of the mold is output to the output device. The predictive maintenance system of claim 6.

11. further comprising an output device; the diagnostic device diagnoses an abnormality in the mold by comparing a second diagnostic threshold value for determining an abnormality in the mold with the trend value; When the mold is diagnosed as being abnormal, information informing the abnormality of the mold is output to the output device. The predictive maintenance system of claim 6.

12. 1. A computer-implemented method for predictive maintenance for a press machine die, comprising: Obtaining load data indicating time-series data of the press load applied to the die; diagnosing deterioration of the mold based on the waveform of the load data; A method for providing

13. Obtaining an analysis range of the load data; diagnosing deterioration of the mold based on the load data within the analysis range; The method of claim 12 comprising:

14. Calculating a trend value indicating a trend of change in the load data based on the load data within the analysis range; diagnosing deterioration of the mold based on the trend value; The method of claim 13 comprising:

15. calculating the trend value based on a difference between a maximum value and a minimum value of the load data within the analysis range; 15. The method of claim 14.

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