Estimation model generation device and tool life estimation device

Abstract

An estimation model generation device according to the present invention is a device that generates an estimation model for estimating the life of a tool for repeatedly processing a plurality of workpieces by applying a load to a plate-shaped workpiece, the estimation being performed on the basis of a load curve indicating a temporal change or positional change of a load applied to the tool. The estimation model generation device is provided with: an information acquisition unit that acquires a load curve until the tool reaches the end of life due to the usage of the tool for repeated processing; an estimation model generation unit that generates an estimation model for predicting a tool life on the basis of load data in which the load curve is separated into a first load curve and a second load curve, and on the basis of a tool life from when the load data is acquired to when the tool reaches the end of life; and a storage unit that stores the estimation model. The first load curve is a load curve when a workpiece is deformed by being processed by the tool, and a second load curve is a load curve immediately after the workpiece is deformed by being processed by the tool.

Description

[Technical Field]

[0001] The present disclosure relates to an estimation model generation device and a tool life estimation device. [Background technology]

[0002] The tools used in machine tools wear out with repeated use, causing the machining accuracy of the workpiece to deteriorate. When the specified machining accuracy can no longer be maintained, the tool reaches the end of its life. Technology to estimate tool life is being investigated in order to understand the tool's life and take action, such as replacing the tool with a new one, before the tool reaches the end of its life.

[0003] Patent Document 1 discloses a tool life estimation device that constructs a learning model through unsupervised learning using machining information indicating the machining status as input data, and estimates the life of a tool using the learning model. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6404893 Summary of the Invention

[0005] An estimation model generation device according to one aspect of the present disclosure includes: 1. An apparatus for generating an estimation model for estimating the life of a tool that repeatedly processes a plurality of plate-shaped workpieces by applying a load to the workpieces, based on a load curve that indicates a time change or a position change of the load applied to the tool, an information acquisition unit that acquires the load curve until the tool reaches the end of its life by repeatedly performing machining using the tool; an estimation model generating unit that generates an estimation model for predicting the tool life based on load data obtained by separating the load curve into a first load curve and a second load curve and the tool life from the time when the load data is acquired until the tool life is reached; a storage unit that stores the estimation model; Equipped with the first load curve is a load curve when the workpiece is deformed by machining with the tool, The second load curve is a load curve immediately after the workpiece is deformed by machining with the tool. [Brief explanation of the drawings]

[0006] [Figure 1A] FIG. 1 is a block diagram illustrating an estimation model generating device according to a first embodiment; [Figure 1B] FIG. 1 is a block diagram showing a tool life estimation device according to a first embodiment. [Figure 1C] Block diagram showing the processing equipment [Figure 2A] Schematic diagram showing the process of punching a workpiece using a processing device [Figure 2B] Schematic diagram showing the process of punching a workpiece using a processing device [Figure 2C] Schematic diagram showing the process of punching a workpiece using a processing device [Figure 2D] Schematic diagram showing the process of punching a workpiece using a processing device [Figure 3] A graph showing the change in the load on the punch over time during punching on a processing device [Figure 4A] Graph showing the load curve for the 100th shot after starting to use the punch [Figure 4B] Graph showing the load curve for the 200,000th shot since starting to use the punch [Figure 5] FIG. 10 is a diagram showing a load curve acquired by an information acquisition unit of the estimation model generation device. [Figure 6] Graph showing the estimated model [Figure 7] FIG. 10 is a diagram showing a load curve acquired by an information acquisition unit of the tool life estimation device. [Figure 8] The graph plots the maximum load obtained during machining and the number of shots in the estimation model shown in Figure 6. [Figure 9] FIG. 10 is a diagram showing a load curve acquired by an information acquisition unit of the estimation model generating device according to the second embodiment; [Figure 10] A graph showing the tendency of the maximum load applied to the punch and the integral value (load energy) of the load curve [Figure 11] FIG. 11 is a diagram showing a load curve acquired by an information acquisition unit of the estimation model generating device according to the third embodiment. [Figure 12] A graph showing the integral value of the load energy for the entire load curve, and the tendency of the integral value of the load energy for each of the first and second load curves. [Figure 13] Graph showing the relationship between load curve and number of shots [Figure 14] Graph explaining estimation of tool life using the graph of FIG. 13 DETAILED DESCRIPTION OF THE INVENTION

[0007] (Background to this disclosure) The tools used in machine tools wear out with repeated machining and are no longer able to maintain the required machining accuracy. When a tool can no longer maintain the required machining accuracy, it is deemed to have reached the end of its tool life and is replaced with a new tool or polished.

[0008] Conventionally, tool life has been determined by the size of burrs that appear on the product shape obtained by machining. However, there is a problem in that defective products continue to be produced using tools that have reached the end of their life until the size of the burrs can be measured.

[0009] Therefore, methods have been considered in which a learning model is constructed using machining information indicating the machining status as input data, and the learning model is used to estimate the tool life from the machining information, as in the tool life estimation device described in Patent Document 1. However, the tool life estimation device described in Patent Document 1 still has room for improvement in terms of improving the accuracy of tool life prediction.

[0010] The present inventors discovered that tool life can be estimated with higher accuracy by constructing an estimation model using information on the load on the tool rather than information on machining, as described in Patent Document 1, and by using this estimation model, they arrived at the following invention. The present disclosure provides an estimation model generation device and a tool life estimation device with improved tool life prediction accuracy.

[0011] An estimation model generation device according to one aspect of the present disclosure includes: 1. An apparatus for generating an estimation model for estimating the life of a tool that repeatedly processes a plurality of plate-shaped workpieces by applying a load to the workpieces, based on a load curve that indicates a time change or a position change of the load applied to the tool, an information acquisition unit that acquires the load curve until the tool reaches the end of its life by repeatedly performing machining using the tool; an estimation model generating unit that generates an estimation model for predicting the tool life based on load data obtained by separating the load curve into a first load curve and a second load curve and the tool life from the time when the load data is acquired until the tool life is reached; a storage unit that stores the estimation model; Equipped with the first load curve is a load curve when the workpiece is deformed by machining with the tool, The second load curve is a load curve immediately after the workpiece is deformed by machining with the tool.

[0012] With this configuration, it is possible to provide an estimation model generating device that improves the accuracy of tool life prediction.

[0013] The load data may be generated based on an integral value of the first load curve and an integral value of the second load curve.

[0014] With this configuration, an estimation model can be generated using the load energy or impulse on the tool, and the accuracy of life prediction can be further improved.

[0015] The estimation model generation unit may generate the estimation model by performing machine learning using training data in which the load data is used as an explanatory variable and the tool life is used as a target variable.

[0016] With this configuration, the accuracy of life prediction can be further improved.

[0017] The estimation model generating unit may generate the estimation model using the load data weighted for the first load curve and the second load curve.

[0018] With this configuration, even if the tendency of the load energy on the tool differs depending on the material of the workpiece or the type of mold, the tool life can be predicted with high accuracy.

[0019] The first load curve and the second load curve may be weighted by multiplying the first load curve and the second load curve by a predetermined factor.

[0020] With this configuration, weighting is applied to the first load curve and the second load curve, and the life can be predicted with high accuracy.

[0021] The load curve may be a curve showing the relationship between the load applied to the tool and time.

[0022] With this configuration, an estimation model can be generated using the load energy applied to the tool, thereby improving prediction accuracy.

[0023] The load curve may be a curve that indicates the relationship between the load applied to the tool and the travel distance of the tool.

[0024] With this configuration, an estimation model can be generated using the impulse acting on the tool, thereby improving prediction accuracy.

[0025] A tool life estimation device according to one aspect of the present disclosure includes: 1. An apparatus for estimating the life of a tool that repeatedly processes a plurality of plate-shaped workpieces by applying a load to the workpieces, based on a load curve that indicates a time change or a position change of a load applied to the tool, a storage unit that stores an estimation model generated by any one of the estimation model generation devices described above; an information acquisition unit that acquires a load curve during machining by the tool; a load data generating unit that generates load data by separating the load curve during processing into a first load curve and a second load curve; an estimation unit that estimates the tool life from the load data based on the estimation model; Equipped with the first load curve is a load curve when the workpiece is deformed by machining with the tool, The second load curve is a load curve immediately after the workpiece is deformed by machining with the tool.

[0026] With this configuration, it is possible to provide a tool life estimation device with improved tool life prediction accuracy.

[0027] The load data may be generated based on an integral value of the first load curve and an integral value of the second load curve.

[0028] This configuration can further improve the prediction accuracy.

[0029] Hereinafter, embodiments of the present disclosure will be described in detail, with appropriate reference to the drawings. However, more detailed description than necessary may be omitted. For example, detailed description of already well-known matters or redundant description of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. Note that the inventors provide the accompanying drawings and the following description to enable those skilled in the art to fully understand the present disclosure, and do not intend for them to limit the subject matter described in the claims.

[0030] (Embodiment 1) [Overall configuration] FIG. 1A is a block diagram showing an estimation model generating device 100 according to the first embodiment. FIG. 1B is a block diagram showing a tool life estimation device 200 according to the first embodiment. FIG. 1C is a block diagram showing a machining device 300. Each of them may be installed in the same factory or in two or more locations on the premises. The estimation model generating device 100 and the tool life estimation device 200 may be integrated.

[0031] 1A to 1C, an estimation model generation device 100 and a tool life estimation device 200 according to this embodiment will be described. The estimation model generation device 100, the tool life estimation device 200, and the machining device 300 are connected to each other so that they can communicate with each other via wire or wirelessly. The communication can be performed using a public line such as the Internet and / or a dedicated line.

[0032] The estimation model generation device 100 shown in Fig. 1A is a device that generates an estimation model for predicting the life of a tool used in a machining device 300 shown in Fig. 1C, based on a load curve acquired during machining by the machining device 300. The estimation model generation device 100 can be constructed using a computer system such as a PC or a workstation. The estimation model generation device 100 includes an information acquisition unit 11, an estimation model generation unit 12, and a storage unit 13.

[0033] The information acquiring unit 11 acquires a load curve for the tool until the end of its life by repeatedly machining using the tool of the machining device. The load curve is determined based on the detection result by a sensor 34 of the machining device 300, which will be described later.

[0034] The estimation model generating unit 12 generates an estimation model for predicting the tool life based on the load curve and the tool life from the time when the load curve is acquired until the end of the tool life. The tool life will be described later.

[0035] The storage unit 13 stores the estimation model generated by the estimation model generation unit 12.

[0036] The tool life estimation device 200 shown in FIG. 1B is a device that estimates the life of a tool of a machining device 300 from the load curve of the machining device 300 based on the estimation model generated by the estimation model generation device 100 in FIG. 1A. The tool life estimation device 200 can be configured with, for example, a microcomputer, a CPU, an MPU, a GPU, a DSP, an FPGA, or an ASIC. The functions of the tool life estimation device 200 may be configured with hardware alone, or may be realized by combining hardware and software. The tool life estimation device 200 includes an information acquisition unit 21, an estimation unit 22, and a storage unit 23.

[0037] The information acquisition unit 21 acquires the load curve during processing by the processing device 300.

[0038] The storage unit 23 stores the estimation model generated by the estimation model generation device 100.

[0039] The estimation unit 22 estimates the tool life from the load curve during machining based on the estimation model.

[0040] 1C is a device that repeatedly processes a plurality of workpieces, which are plate-shaped metal workpieces, by applying a load to the workpieces. In this embodiment, a case will be described in which the processing device 300 is a press processing device that has a punch 31 and a die 32 and processes a workpiece 33 with the punch 31 and the die 32.

[0041] The processing device 300 has a die 32 and a punch 31 facing the die 32, and processes a workpiece 33 placed on the die 32 by the load of the punch 31.

[0042] The processing device 300 is provided with a sensor 34 for acquiring the load on the punch 31 and the movement distance of the punch 31. As the sensor 34, for example, a load sensor 35, a position sensor 36, etc. are used.

[0043] The load sensor 35 preferably has high sensitivity in order to detect minute changes in the load on the punch 31. For this reason, a quartz crystal piezoelectric sensor is suitable as the load sensor 35.

[0044] The position sensor 36 preferably has high resolution in order to detect minute changes in the position (movement distance) of the punch 31. For this reason, an eddy current sensor or a capacitance sensor is suitable as the position sensor 36.

[0045] <Estimation model generation device> The estimation model generation device 100 generates an estimation model for estimating the life of a tool that repeatedly processes a plate-shaped workpiece 33 by applying a load to the workpiece 33, based on a load curve that shows the change in time or position of the load on the tool.

[0046] The tool life refers to the wear or breakage of the tool that occurs when the tool (punch 31 and die 32) of the processing device 300 is used to repeatedly process multiple workpieces 33. When the tool wears out due to repeated processing and is no longer able to maintain the specified product shape, or when the tool is broken and no longer able to maintain the specified product shape, the tool is determined to have reached the end of its life, and is re-ground or replaced.

[0047] In this embodiment, the estimation model generating device 100 generates an estimation model based on a load curve that indicates the change over time in the load applied to the tool, particularly the load applied to the punch 31.

[0048] The load curve is a curve that indicates the time change or position change of the load applied to the punch acquired by the load sensor 35. Here, with reference to Figs. 2A to 3, a case where the load curve indicates the relationship between load and time will be described.

[0049] 2A to 2D are schematic diagrams showing the steps of punching a workpiece 33 using the processing device 300. Fig. 3 is a graph showing the change over time in the load applied to the punch 31 during punching using the processing device 300.

[0050] When machining starts, the punch 31 descends and comes into contact with the workpiece 33 (FIG. 2A). In the graph of FIG. 3, the point in time when the punch 31 comes into contact with the workpiece 33 is time T1. As shown in the graph of FIG. 3, almost no load is applied to the punch 31 until the punch 31 comes into contact with the workpiece 33 (section S1 in FIG. 3).

[0051] When the punch 31 starts punching the workpiece 33 (FIG. 2B), the load on the punch 31 increases rapidly, as shown in section S2 of the graph in FIG. 3. The point in time when the punch 31 cuts the workpiece 33 (FIG. 2C) is time T2 of the graph in FIG. 3. When the workpiece 33 is cut, the load on the punch 31 drops to near zero. This is because the workpiece 33 is punched and resistance to the punch 31 is eliminated. Note that even when the workpiece 33 is cut, the load on the punch 31 detected by the sensor 34 may not reach zero due to punch vibration or other external factors. In this case, it is desirable to determine the bottom dead center after the peak, which indicates a rapid increase in the load during punching, as the load after punching the workpiece 33. Furthermore, due to similar factors, the load on the punch 31 detected by the sensor 34 may be measured as zero multiple times. In this case, any time during which the load is zero can be determined as the load after punching the workpiece 33, and it is more desirable to determine the first time as the load after punching the workpiece 33.

[0052] For a while after punching the workpiece 33 (section S3 of the graph in FIG. 3), a load is applied to the punch 31 due to interference between the punch 31 and the die 32, or disturbances caused by the material. For example, the inclination of the punch 31 and the die 32 may cause the punch 31 and the die 32 to come into contact, applying a load to the punch 31. Alternatively, the workpiece 33 after cutting may be drawn between the punch 31 and the die 32 (FIG. 2D), causing a load to be applied to the punch 31.

[0053] As punch 31 wears due to repeated machining, the load applied to punch 31 during machining increases. FIG. 4A is a graph showing the load curve for the 100th shot since starting to use punch 31. FIG. 4B is a graph showing the load curve for the 200,000th shot since starting to use punch 31. As shown in FIGS. 4A and 4B, the maximum load during punching increases with repeated machining. This is because the punch 31 wears due to repeated machining, and a larger load is applied to punch 31. Furthermore, the load after punching also increases. This is because the wear of punch 31 increases burrs, which interfere with punch 31, thereby increasing the load applied to punch 31.

[0054] As described above, it can be seen that the load curve and the progress of wear of the tool (punch 31) are closely related. Therefore, in this embodiment, the estimation model generation unit 12 of the estimation model generation device 100 generates an estimation model for predicting the tool life based on the load curve and the tool life at that time.

[0055] The load curve is acquired by the information acquisition unit 11 of the estimation model generating device 100 based on the load acting on the punch 31 detected by the sensor 34 of the processing device 300.

[0056] FIG. 5 is a diagram showing load curves acquired by the information acquisition unit 11 of the estimation model generating device 100. The load curves in FIG. 5 are curves showing the relationship between the load applied to the punch 31 and time. (a) of FIG. 5 shows the load curve acquired at the 100,000th shot. (b) of FIG. 5 shows the load curve acquired at the 200,000th shot. (c) of FIG. 5 shows the load curve acquired at the 300,000th shot.

[0057] The information acquiring unit 11 acquires load curves such as those shown in Fig. 5(a) to Fig. 5(c) based on the detection values ​​of the sensor 34. The load curves may be acquired for all shots until the punch 31 reaches the end of its life, or may be acquired at predetermined time intervals.

[0058] The estimation model generation unit 12 generates an estimation model based on the load curve acquired by the information acquisition unit 11 and the tool life from the time of acquisition of the load curve until the end of life. For example, an estimation model can be generated based on the maximum load of the acquired load curves including (a) to (c) of FIG. 5.

[0059] In the load curve of (a) of FIG. 5, the maximum load is L11, and the load after punching converges to L12. Similarly, in the load curve of (b) of FIG. 5, the maximum load is L13, and the load after punching converges to L14. In the graph of (c) of FIG. 5, the maximum load is L15, and the load after punching converges to L16. Thus, the maximum load is calculated for all the acquired load curves and associated with the number of shots at the time the load curve was acquired. At this time, for example, load curves when abnormalities such as tool breakage occur may be excluded.

[0060] As shown in (a) to (c) of FIG. 5, it can be seen that as the number of shots increases, the maximum load increases. That is, the magnitudes of the maximum loads at each number of shots are in the relationship L11 < L13 < L15. Similarly, as the number of shots increases, the load after punching also increases. That is, the magnitudes of the loads after punching at each number of shots are in the relationship L12 < L14 < L16. This is because as the number of shots increases, the wear of the punch 31 progresses and the amount of material drawn in increases, so the amount of interference between the punch 31 and the workpiece 33 increases.

[0061] In the load curves of (a) to (c) of FIG. 5, the time t10 indicates the point when the punch 31 contacts the workpiece 33. In the load curve of (a) of FIG. 5, the maximum load L11 is shown at time t11, and the workpiece 33 is cut at time t12. Similarly, in the load curve of (b) of FIG. 5, the maximum load L13 is shown at time t13, and the workpiece 33 is cut at time t14. Further, in the load curve of (c) of FIG. 5, the maximum load L15 is shown at time t15, and the workpiece 33 is cut at time t16.

[0062] Here, when comparing the times indicating the maximum load at each shot number, the relationship is t11 < t13 < t15. This is because as the wear of the punch 31 progresses with an increase in the shot number, it takes more time for cracks to develop in the workpiece 33. Also, when comparing the times when the workpiece 33 is completely cut at each shot number, the relationship is t12 < t14 < t16. This is because as the wear of the punch 31 progresses with an increase in the shot number, it takes more time to completely cut the workpiece 33. This is because as the wear of the punch 31 progresses, it gradually shifts from a shear mode to a stretching mode. Also, when comparing the time from when the maximum load is indicated to when the workpiece 33 is cut, (t12 - t11) < (t14 - t13) < (t16 - t15). This is because the stretching mode takes more time to cut than the shear mode, and as the wear of the punch 31 progresses, the time to completely cut the material becomes longer.

[0063] Figure 6 is a graph showing the estimation model. When the tool life of the punch 31 is 500,000 shots, it shows the estimation model of the maximum load up to 500,000 shots, that is, until the punch 31 reaches its life.

[0064] As shown in (a) to (c) of Figure 5, for the data associating the maximum load and the shot number of the load curve until the punch 31 reaches its life, a graph like Figure 6 can be generated as a time-series trend graph. Also, for example, by applying a regression analysis method such as an ARIMA (AutoRegressive Integrated Moving Average) model or a SARIMA (Seasonal AutoRegressive Integrated Moving Average) model, a graph representing the time-series transition can be generated similar to Figure 6, and furthermore, a graph for estimating the time-series predicted value can be generated. In Figure 6, as the shot number increases, the variation becomes larger. This is because as the punch 31 approaches its tool life, the variation of the load curve becomes larger.

[0065] <Tool Life Estimation Device> The tool life estimation device 200 estimates the life of the tool (punch 31) of the processing device 300 based on the estimation model shown in FIG.

[0066] The storage unit 23 stores the estimation model generated by the estimation model generation device 100.

[0067] The information acquiring unit 21 acquires a load curve for the punch 31 during processing in the processing device 300. The load curve is acquired based on detection values ​​from the sensor 34 of the processing device 300.

[0068] Fig. 7 shows load curves acquired by the information acquisition unit 21 of the tool life estimation device 200. Fig. 7(a) shows the load curve acquired at the 100,000th shot. Fig. 7(b) shows the load curve acquired at the 200,000th shot. Fig. 7(c) shows the load curve acquired at the 300,000th shot.

[0069] In the load curve of Figure 7(a), the maximum load is L21, and the load after punching converges to L22. Similarly, in the load curve of Figure 7(b), the maximum load is L23, and the load after punching converges to L24. In the graph of Figure 7(c), the maximum load is L25, and the load after punching converges to L26.

[0070] 7(a) to 7(c), time t20 indicates the time when the punch 31 comes into contact with the workpiece 33. In the load curve of FIG. 7(a), the maximum load L21 is reached at time t21, and the workpiece 33 is cut at time t22. Similarly, in the load curve of FIG. 5(b), the maximum load L23 is reached at time t23, and the workpiece 33 is cut at time t24. Furthermore, in the load curve of FIG. 7(c), the maximum load L25 is reached at time t25, and the workpiece 33 is cut at time t26.

[0071] The estimation unit 22 estimates the tool life from the load curve for the punch 31 during machining, based on the estimation model generated by the estimation model generation device 100. Fig. 8 is a graph in which points indicating the maximum load obtained during machining and the number of shots are plotted on the estimation model of Fig. 6.

[0072] The estimation unit 22 predicts the number of shots until the punch 31 reaches the end of its life from the load and number of shots of the punch 31 during processing. For example, from the graph in FIG. 8, the maximum load is within the range of the estimation model at 100,000 shots and 200,000 shots. On the other hand, at 300,000 shots, it exceeds the maximum load indicated in the maximum load estimation model. Therefore, the estimation unit 22 estimates that the punch 31 currently being processed will reach the end of its tool life sooner than 500,000 shots, which was the tool life when the estimation model was generated.

[0073] [effect] According to the above-described embodiment, it is possible to provide an estimation model generating device and a tool life estimation device with improved tool life prediction accuracy.

[0074] In the above-described embodiment, the estimation model was generated using a load curve showing the relationship between the load on the tool (punch 31) and time, but the load curve may also be a curve showing the relationship between the load on the tool and the travel distance of the tool.

[0075] In the above-described embodiment, the processing device 300 is a press processing device that performs punching, but the processing device is not limited to such a press processing device. For example, the processing device may be a processing device that performs bending or drawing. Alternatively, the processing device may be a processing device that performs shear cutting.

[0076] (Embodiment 2) A second embodiment will be described with reference to Figures 9 and 10. In the second embodiment, the same or equivalent configurations as those in the first embodiment will be denoted by the same reference numerals. In the second embodiment, descriptions that overlap with those in the first embodiment will be omitted.

[0077] Fig. 9 is a diagram showing load curves acquired by the information acquisition unit 11 of the estimation model generating device 100 according to the second embodiment. (a) to (c) of Fig. 9 are the same load curves as (a) to (c) of Fig. 5 described in the first embodiment, but the second embodiment differs from the first embodiment in that an estimation model is generated based on the integral values ​​of these load curves.

[0078] In the load curve of Figure 9(a), the maximum load is L31, and the load after punching converges to L32. Similarly, in the load curve of Figure 9(b), the maximum load is L33, and the load after punching converges to L34. In the graph of Figure 9(c), the maximum load is L35, and the load after punching converges to L36.

[0079] In the load curves of Figures 9(a) to 9(c), time t30 indicates the time when the punch 31 comes into contact with the workpiece 33. In the load curve of Figure 9(a), the maximum load L31 is reached at time t31, and the workpiece 33 is cut at time t32. Similarly, in the load curve of Figure 9(b), the maximum load L33 is reached at time t33, and the workpiece 33 is cut at time t34. Furthermore, in the load curve of Figure 9(c), the maximum load L35 is reached at time t35, and the workpiece 33 is cut at time t36.

[0080] In this embodiment, an estimation model is generated using the integral values ​​of the load curves in FIGS. 9(a) and 9(b).

[0081] The shaded areas in Figures 9(a) to 9(c) are the areas of the load curves, which indicate the integral values ​​of the respective load curves. When the load curves indicate the relationship between load and time, the integral values ​​of the load curves indicate the impulse of the load applied to the tool (punch 31). When the load curves indicate the relationship between load and travel distance, the integral values ​​of the load curves indicate the energy of the load applied to the tool (punch 31).

[0082] The load impulse and the load energy have roughly the same sensitivity when generating an estimation model. For example, when the speed of the punch 31 decreases during processing, using the load impulse is more likely to improve prediction accuracy.

[0083] In this embodiment, a case will be described in which the load curve indicates the relationship between the load and the movement distance.

[0084] When punching the workpiece 33 with the processing device 300, the energy applied to the tool (punch 31) for each shot is converted into energy for cutting the workpiece 33 and into a load on the punch 31. Examples of energy converted into a load on the punch 31 include energy that wears the punch 31 and energy that accumulates distortion inside the punch 31. Such a load on the punch 31 accumulates in the punch 31 as processing is repeated with the processing device 300.

[0085] FIG. 10 is a graph showing the trends in the maximum load applied to the punch 31 and the integral value (load energy) of the load curve. As shown in the graph of FIG. 10, the integral value of the load curve tends to increase as the number of shots increases. This is also true when impulse is used as the integral value of the load curve. On the other hand, the maximum load does not necessarily increase as the number of shots increases.

[0086] Therefore, by generating an estimation model using the integral value of the load curve instead of the maximum load of the load curve, it is possible to further improve the prediction accuracy.

[0087] [effect] According to the above-described embodiment, by generating an estimation model using the integral value of the load curve, the load on the punch 31 can be captured with higher sensitivity, and therefore, an estimation model generation device and a tool life estimation device with improved prediction accuracy can be provided.

[0088] (Embodiment 3) Embodiment 3 will be described with reference to Figures 11 and 12. In Embodiment 3, the same or equivalent configurations as in Embodiment 1 will be denoted by the same reference numerals. In Embodiment 3, descriptions that overlap with Embodiment 1 will be omitted.

[0089] FIG. 11 is a diagram showing load curves acquired by the information acquisition unit 11 of the estimation model generating device 100 according to the third embodiment. (a) to (c) of FIG. 11 are the same load curves as those of (a) to (c) of FIG. 5 described in the first embodiment. The third embodiment differs from the first embodiment in that the estimation model generating unit 12 generates an estimation model based on load data in which these load curves are separated into a first load curve during deformation of the workpiece 33 and a second load curve immediately after deformation of the workpiece. "During deformation" refers to the period from the start of deformation to the completion of deformation. "Immediately after deformation" refers to a predetermined period from the completion of deformation.

[0090] In the load curve of Figure 11(a), the maximum load is L41, and the load after punching converges to L42. Similarly, in the load curve of Figure 11(b), the maximum load is L43, and the load after punching converges to L44. In the graph of Figure 11(c), the maximum load is L45, and the load after punching converges to L46.

[0091] In the load curves of Figures 11(a) to 11(c), time t40 indicates the time when the punch 31 comes into contact with the workpiece 33. In the load curve of Figure 11(a), a maximum load L41 is reached at time t41, and the workpiece 33 is cut at time t42. Similarly, in the load curve of Figure 11(b), a maximum load L43 is reached at time t43, and the workpiece 33 is cut at time t44. Furthermore, in the load curve of Figure 11(c), a maximum load L45 is reached at time t45, and the workpiece 33 is cut at time t46.

[0092] In this embodiment, an estimation model is generated using a first load curve and a second load curve obtained by dividing the load curve into two before and after cutting the workpiece 33 (before and after time t42, time t44, and time t46).

[0093] The first load curve is a curve extracted from the portions corresponding to sections S1 and S2 in Figure 3. That is, the first load curve is a curve from when punch 31 starts to descend until workpiece 33 is cut (Figure 2C). The second load curve is a curve extracted from the portion corresponding to section S3 in Figure 3. That is, the second load curve is a curve after workpiece 33 is cut (Figure 2D).

[0094] In this embodiment, the load data is generated based on the integral value of the first load curve and the integral value of the second load curve.

[0095] FIG. 12 is a graph showing the trends in the integral value of the load energy for the entire load curve and the integral value of the load energy for each of the first and second load curves. As shown in the graph of FIG. 12, the trends differ between the first and second load curves as the number of shots increases. For example, in the graph of FIG. 12, the integral values ​​of the first and second load curves are reversed at shot number C1. This indicates that the integral value of the first load curve is greater than the integral value of the second load curve up to shot number C1, so the energy during deformation of the workpiece 33 is greater. Similarly, after shot number C1, the integral value of the second load curve is greater than the integral value of the first load curve, so the energy immediately after deformation of the workpiece 33 is greater.

[0096] Therefore, the estimation model generating unit 12 may generate the estimation model using weighted load data for the first load curve and the second load curve. For example, the weighted load data can be generated by multiplying the first load curve and the second load curve by a first coefficient and a second coefficient, respectively.

[0097] As an example of the coefficient, if the workpiece 33 is made of a hard material, the coefficient for the first load curve should be 1.0, and the coefficient for the second load curve should be 0.1 or more and 1.0 or less. When the workpiece 33 is made of a hard material and is subjected to punching, the proportion of the energy applied to the punch 31 per shot that cuts the workpiece 33 is high. For this reason, it is advisable to increase the coefficient for the first load curve.

[0098] Furthermore, when the workpiece 33 is made of a material with high elongation such as Al or Cu, or when multiple layers are punched at once, it is advisable to set the coefficient for the first load curve to 0.1 or more and less than 1.0, and the coefficient for the second load curve to 1.0. In this case, the load energy on the punch 31 becomes greater than the energy required to cut the workpiece 33, because the workpiece 33 is pulled into the punch 31 after cutting, causing interference between the side surface of the punch 31 and the workpiece 33.

[0099] Furthermore, when the clearance between the punch 31 and the die 32 is small or the workpiece thickness is thin, the coefficient for the first load curve should be set to 0.1 or more but less than 1.0, and the coefficient for the second load curve should be set to 1.0. A small clearance between the punch 31 and the die 32 means that the clearance is approximately 10 μm or less. A thin workpiece 33 means that the thickness is approximately 150 μm or less. Generally, the thickness of the workpiece 33 and the clearance between the punch 31 and the die 32 are proportional to each other. In this case, too, the coefficient for the first load curve should be set to 0.1 or more but less than 1.0, and the coefficient for the second load curve should be set to 1.0. This is because when the clearance between the punch 31 and the die 32 is small, the cumulative tolerances of the machining accuracy of the punch 31 and the die 32 or the assembly accuracy of the punch 31 and the die 32 approach the clearance, making it more likely that the side of the punch 31 will interfere with the material.

[0100] [effect] According to the above-described embodiment, by generating an estimation model based on load data in which the load curve is separated into a first load curve and a second load curve, it is possible to provide an estimation model generation device and a tool life estimation device with higher prediction accuracy.

[0101] Whether the load on the punch 31 is greater during or immediately after the workpiece deformation depends on the tool or processing conditions of the processing device. Therefore, by separating the load curves for when the workpiece is deforming and immediately after the workpiece deformation, fine tuning for each tool of the processing device or each processing condition becomes possible. This further improves prediction accuracy.

[0102] (Fourth embodiment) A fourth embodiment will be described with reference to Figs. 13 and 14. In the fourth embodiment, the same or equivalent configurations as those in the first embodiment will be described with the same reference numerals. In the fourth embodiment, descriptions that overlap with those in the first embodiment will be omitted. Fig. 13 is a graph showing the relationship between the load curve and the number of shots. Fig. 14 is a graph explaining the estimation of tool life using the graph of Fig. 13.

[0103] In the fourth embodiment, the estimation model generation unit 12 differs from the first embodiment in that it generates an estimation model by performing machine learning using training data in which the load curve is used as an explanatory variable and the tool life is used as a target variable.

[0104] For example, data on a processing device 300 equipped with a punch 31 that reaches the end of its tool life after 500,000 shots is used as training data. In this case, the explanatory variables are the load curves shown in Figures 5(a) to 5(c), and the objective variable is the number of shots from when the load curve is acquired until the end of the tool life (500,000 shots).

[0105] The estimation model generation unit 12 of the estimation model generation device 100 performs machine learning using training data that associates load curves as explanatory variables with the number of shots until the tool life ends as a target variable. The relationship between the load curves and the number of shots as a result of machine learning is shown in the graph of FIG. 13. In the graph of FIG. 13, for example, the characteristics of each load curve are extracted as numerical values ​​and associated with the number of shots at the time each load curve was acquired. A life prediction line can be derived from the graph of FIG. 13. Note that the amplitude W1 shown in FIG. 13 may be set depending on the load curve variation or learning frequency.

[0106] The load curve used for machine learning is preferably a load curve in a state where abnormal phenomena are few. That is, it is preferable to use, for machine learning, a load curve for a series of machining operations that are repeated without causing abnormalities as much as possible from the start of use of the punch 31 until the end of its life. Alternatively, the series of load curves that are repeated from the start of machining of the punch 31 until the end of its life may be learned multiple times. In this case, the absolute number of load curves to be learned can be increased, and the influence of load curves when abnormalities occur on the learning results can be reduced.

[0107] As a machine learning algorithm, for example, a neural network can be used. By using a neural network, it is possible to process the load curve as an image, extract the characteristics of the load curve, and generate an estimation model that predicts the relationship between the waveform of the load curve and the tool life.

[0108] When the training data is time-series data as in this embodiment, prediction accuracy can be further improved by using an RNN (Recurrent Neural Network).

[0109] The estimation unit 22 of the tool life estimation device estimates tool life based on the load curve during actual machining, for example, during mass production. For example, if a load curve having the same characteristics as a load curve that appeared after 300,000 shots during learning appears after 200,000 shots during mass production, it can be estimated that the punch 31 during mass production will have a shorter life than the punch 31 during learning. As shown in FIG. 14, the estimation unit 22 estimates how much longer the life of the punch 31 currently being machined (how many shots remaining) will be, based on the load curve acquired during mass production.

[0110] [effect] According to the above-described embodiment, it is possible to provide an estimation model generating device and a tool life estimation device with improved tool life prediction accuracy.

[0111] In the above-described embodiment, an example has been described in which the estimation model generation unit 12 generates an estimation model by performing machine learning using a load curve in a state where there are few abnormal phenomena, but the data used for machine learning is not limited to this. For example, machine learning may be repeated using a reference load curve in a state where there are few abnormal phenomena and a load curve for a punch 31 with a short lifespan. This makes it possible to generate an estimation model with higher prediction accuracy.

[0112] In addition to the training data associating the load curve with the tool life, the input data may include data including information on the workpiece material, machining conditions, or tool conditions. Material information indicates, for example, the workpiece material, workpiece thickness, workpiece height, workpiece elongation, and number of workpieces. Machining conditions indicate, for example, the number of shots, the travel distance of the punch 31, the operation time of the punch 31, and the operation speed of the punch 31. Tool conditions indicate, for example, the clearance between the punch 31 and the die 32, the material of the punch 31 and the die 32, the perimeter of the punch 31, the shape of the punch 31, and the coating material of the punch 31.

[0113] (Embodiment 5) A fifth embodiment will be described. In the fifth embodiment, the same or equivalent configurations as those in the fourth embodiment will be denoted by the same reference numerals. In the fifth embodiment, descriptions that overlap with those in the fourth embodiment will be omitted.

[0114] The fifth embodiment differs from the fourth embodiment in that teacher data is used in which the integral value of the load curve is used as an explanatory variable and the tool life is associated with it as a response variable.

[0115] In this embodiment, the estimation model generation unit 12 generates an estimation model by performing machine learning using training data in which the integral values ​​of the load curves as shown in Figures 9(a) to 9(c) are used as explanatory variables and the tool life is used as a target variable.

[0116] During machine learning, by using the integral value of the load curve instead of the load curve, the load on the punch 31 can be captured with higher sensitivity.

[0117] [effect] According to the above-described embodiment, it is possible to provide an estimation model generating device and a tool life estimating device with improved prediction accuracy.

[0118] (Embodiment 6) A sixth embodiment will now be described. In the sixth embodiment, the same or equivalent configurations as those in the fourth embodiment will be denoted by the same reference numerals. In the sixth embodiment, descriptions that overlap with those in the fourth embodiment will be omitted.

[0119] In this embodiment, the estimation model generation unit 12 differs from the fourth embodiment in that it uses load data generated based on the integral value of the first load curve and the integral value of the second load curve as explanatory variables, as shown in (a) of FIG. 11 to (c) of FIG. 11.

[0120] By dividing the load curve into a first load curve and a second load curve and performing machine learning using the integral values ​​of each, the load on the punch 31 can be captured with greater sensitivity.

[0121] [effect] According to the above-described embodiment, it is possible to provide an estimation model generating device and a tool life estimating device with improved prediction accuracy. [Industrial Applicability]

[0122] The estimation model generation device and tool life estimation device according to the present disclosure are widely applicable to tool life prediction in processing devices that perform processes such as cutting, bending, or drawing. [Explanation of symbols]

[0123] 11 Information acquisition department 12 Estimation model generation unit 13 Storage section 21 Information Acquisition Department 22 Estimation part 23 Memory section 31 Punch 32 Die 33 Work 34 Sensors 100 Estimation model generation device 200 Tool life estimation device 300 Processing equipment

Claims

1. An apparatus for generating an estimation model for estimating the life of a tool that repeatedly processes a plurality of plate-shaped workpieces by applying a load to the workpieces, based on a load curve including a time load curve that indicates a time change in the load applied to the tool in one machining operation with the tool, or a travel distance load curve that indicates a relationship between the load applied to the tool and a position change of the tool in one machining operation with the tool, an information acquisition unit that acquires the load curve until the tool reaches the end of its life by repeatedly performing machining using the tool; an estimation model generation unit that separates the load curve in one machining operation acquired by the information acquisition unit into a first load curve that indicates a load change when the workpiece is deformed during machining by the tool and a second load curve that indicates a load change immediately after the workpiece is deformed during machining by the tool, and generates an estimation model for predicting the tool life based on load data including feature amounts of the separated first load curve and second load curve and the tool life from the time of acquisition of the load data until the tool life is reached; a storage unit that stores the estimation model; Equipped with Estimation model generator.

2. the load data includes an integral value of the first load curve and an integral value of the second load curve as feature quantities; The estimation model generating device according to claim 1 .

3. the estimation model generation unit generates the estimation model by performing machine learning using teacher data in which the load data is used as an explanatory variable and the tool life is used as a target variable. The estimation model generating device according to claim 1 or 2.

4. the estimation model generation unit generates the estimation model using the load data weighted by multiplying the feature amounts of the first load curve and the second load curve by a predetermined coefficient. The estimation model generating device according to claim 1 .

5. weighting the first load curve and the second load curve by multiplying the first load curve and the second load curve by a first factor and a second factor, respectively; The estimation model generating device according to claim 4 .

6. The load curve is a time-load curve showing the relationship between the load applied to the tool and time. The estimation model generating device according to claim 1 .

7. the load curve is a travel distance load curve showing the relationship between the load applied to the tool and the travel distance of the tool; The estimation model generating device according to claim 1 .

8. An apparatus for estimating the life of a tool that repeatedly processes a plurality of plate-shaped workpieces by applying a load to the workpieces, based on a load curve including a time load curve that indicates a time change in the load applied to the tool in one processing run by the tool, or a travel distance load curve that indicates a relationship between the load applied to the tool and a position change of the tool in one processing run by the tool, a storage unit that stores an estimation model generated by the estimation model generation device according to any one of claims 1 to 7; an information acquisition unit that acquires a load curve during machining by the tool; a load data generating unit that separates the load curve during machining in one machining operation acquired by the information acquiring unit into a first load curve that indicates a load change when the workpiece is deformed during machining by the tool and a second load curve that indicates a load change immediately after the workpiece is deformed during machining by the tool, and generates load data during machining including feature quantities of the separated first load curve and second load curve; an estimation unit that estimates the tool life from the load data during machining based on the estimation model; Equipped with Tool life estimation device.

9. the load data includes an integral value of the first load curve and an integral value of the second load curve as feature quantities; The tool life estimation device according to claim 8.

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