JP2024021840A - Abnormality determination device, abnormality determination method, and machining system - Google Patents

Abstract

An object of the present invention is to determine whether a machining operation by a machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.
A communication interface (23) acquires a load curve that indicates temporal or positional changes in the load applied to a punch (13). The calculation device 21 calculates, from the load curve, a first section after the first point in time when the peak load is applied to the punch 13, and a second section after the first section. A second section in which a load larger than the load in the first section is applied to the punch 13 in at least a part of the section is extracted. The calculation device 21 determines whether the machining operation of the workpiece 3 by the machining device 1 is normal or abnormal based on the load applied to the punch 13 in the first section and the load applied to the punch 13 in the second section. Determine.
[Selection diagram] Figure 1

Description

The present disclosure relates to an abnormality determination device, an abnormality determination method, and a machining system.

BACKGROUND OF THE INVENTION The tools of machining equipment wear out due to repeated use, and as a result, the machining accuracy of a workpiece (object to be machined) deteriorates. When a tool can no longer process a workpiece with a predetermined accuracy, the tool's life ends. Techniques for estimating tool life are being considered in order to properly maintain machining equipment, such as replacing tools with new ones before the tool reaches the end of its life.

In order to estimate the life of the tool, for example, it may be evaluated whether the machining operation of the workpiece by the tool is normal or abnormal. For example, Patent Document 1 discloses a cutting process evaluation system that acquires physical quantities of cutting processes, performs machine learning on the physical quantities using a learning device, and derives evaluation results of cutting processes. The evaluation results include, for example, tool wear, chipping, burr height, etc. as causes of the abnormality.

JP2020-127968A

The cutting process evaluation system of Patent Document 1 requires machine learning in advance. It is required to determine whether a machining operation by a machining device is normal or abnormal with higher precision than before without requiring prior learning.

The present disclosure provides an abnormality determination device, an abnormality determination method, and a machining system that can determine whether a machining operation by a machining device is normal or abnormal with higher accuracy than before without requiring prior learning. do.

According to one aspect of the present disclosure,
An abnormality determination device for determining whether a repetitive machining operation of a target object by a machining device equipped with a tool is normal or abnormal,
a data input device that obtains a load curve indicating temporal or positional changes in the load applied to the tool;
From the load curve, a first section after the first point in time when a peak load is applied to the tool, and a second section after the first section, the second section A second section in which a load larger than the load in the first section is applied to the tool in at least a part of the load is extracted, and the load applied to the tool in the first section and the second section are extracted. and a calculation device that determines whether the machining operation of the object by the machining device is normal or abnormal based on the load applied to the tool in the section No. 2.

According to the present disclosure, it is possible to determine whether a machining operation by a machining device is normal or abnormal with higher accuracy than before without requiring prior learning.

1 is a block diagram showing the configuration of a machining system 100 including an abnormality determination device 2 according to an embodiment. 2 is a schematic diagram showing a first step in the process of machining a workpiece 3 by the machining device 1 of FIG. 1. FIG. 2 is a schematic diagram showing a second step in the process of machining a workpiece 3 by the machining device 1 of FIG. 1. FIG. 2 is a schematic diagram showing a third step in the process of machining a workpiece 3 by the machining device 1 of FIG. 1. FIG. 2 is a schematic diagram showing a fourth step in the process of machining a workpiece 3 by the machining device 1 of FIG. 1. FIG. 2 is a schematic diagram showing a fifth step in the process of machining a workpiece 3 by the machining device 1 of FIG. 1. FIG. 2 is a flowchart showing an abnormality determination process executed by the arithmetic device 21 of FIG. 1. FIG. 2 is a graph showing a load curve showing temporal changes in the load applied to the punch 13 of FIG. 1. FIG. 2 is a load curve showing temporal changes in the load applied to the punch 13 in FIG. 1, and is a schematic diagram showing a load curve when the machining operation of the workpiece 3 by the machining device 1 is normal. FIG. 2 is a load curve showing temporal changes in the load applied to the punch 13 in FIG. 1, and is a schematic diagram showing a load curve when there is an abnormality in the machining operation of the workpiece 3 by the machining device 1. FIG. A load curve showing temporal changes in the load applied to the punch 13 in FIG. 1, where the load of the 50,000th to 50,090th shots is shown when there is an abnormality in the machining operation of the workpiece 3 by the machining device 1. FIG. 3 is a schematic diagram showing a curve. 11 is a diagram for explaining calculation of a first characteristic value based on the load curve of FIG. 10. FIG. 11 is a diagram for explaining calculation of a second characteristic value based on the load curve of FIG. 10. FIG. 2 is a load curve showing a positional change in the load applied to the punch 13 in FIG. 1, and is a schematic diagram showing a load curve when there is an abnormality in the machining operation of the workpiece 3 by the machining device 1. FIG. 15 is a diagram for explaining calculation of a first characteristic value based on the load curve of FIG. 14. FIG. 15 is a diagram for explaining calculation of a second characteristic value based on the load curve of FIG. 14. FIG. 8 is a diagram for explaining calculation of the rate of change of characteristic values in step S4 of FIG. 7. FIG. It is a flowchart which shows the abnormality determination process performed by the abnormality determination apparatus 2 based on the 1st modification of embodiment. 19 is a diagram for explaining calculation of the rate of change of characteristic values in step S4 of FIG. 18. FIG. It is a flowchart which shows the abnormality determination process performed by the abnormality determination apparatus 2 based on the 2nd modification of embodiment. 21 is a diagram for explaining calculation of the rate of change of characteristic values in step S4 of FIG. 20. FIG. It is a flowchart which shows the abnormality determination process performed by the abnormality determination apparatus 2 based on the 3rd modification of embodiment. 23 is a diagram for explaining calculation of characteristic values in step S34 of FIG. 22. FIG. It is a flowchart which shows the abnormality determination process performed by the abnormality determination apparatus 2 based on the 4th modification of embodiment.

(The circumstances that led to this disclosure)
Tools used in machining equipment wear out due to repeated machining operations. Tool wear is estimated, for example, based on a load curve that shows temporal or positional changes in the load applied to the tool. The waveform of the load curve changes gradually as the tool wears.

Conventionally, the normality or abnormality of the machining operation of a machining device has been determined by performing machine learning using the waveform of a load curve as input data and extracting features mainly from the shape of the waveform. For example, according to the machining cutting process evaluation system of Patent Document 1, the physical quantities of the cutting process are acquired, the physical quantities are machine learned using a learning device, and the evaluation results of the cutting process are derived. It has been determined whether the operation is normal or abnormal.

However, in reality, as mentioned above, the waveform of the load curve gradually changes as the tool wears. Furthermore, in reality, the waveform of the load curve always has a predetermined variation due to the influence of various types of disturbances caused by machining equipment or molds. When determining whether the machining operation of a machining device is normal or abnormal using machine learning, it is necessary to learn the waveform of the load curve in consideration of changes and variations in the waveform of the load curve. Furthermore, when determining whether a machining operation of a machining device is normal or abnormal using machine learning, there is a problem in that only abnormalities that involve large changes in waveforms can be detected. Therefore, it is required to determine whether a machining operation by a machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

The present inventors have discovered that it is possible to determine the normality or abnormality of machining operations by machining equipment with higher accuracy than before without requiring prior learning, and also to detect small changes or gradual changes in the waveform of the load curve. The inventors have discovered that it is possible to detect an abnormality that causes a significant change, and that it is also possible to detect an abnormality from a load curve that includes changes or variations in waveform, leading to the following invention.

According to the abnormality determination device according to the first aspect of the present disclosure,
An abnormality determination device for determining whether a repetitive machining operation of a target object by a machining device equipped with a tool is normal or abnormal,
a data input device that obtains a load curve indicating temporal or positional changes in the load applied to the tool;
From the load curve, a first section after the first point in time when a peak load is applied to the tool, and a second section after the first section, the second section extracting a second section in which a load larger than the load in the first section is applied to the tool in at least a part of the section, and based on the load applied to the tool in the second section, and a calculation device that determines whether the machining operation of the object by the machining device is normal or abnormal.

According to this configuration, it is possible to determine whether the machining operation by the machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

According to the abnormality determination device according to the second aspect of the present disclosure, in the abnormality determination device according to the first aspect,
The first section starts at a second time point after the first time point when the load applied to the tool becomes a minimum value or zero or less, and a predetermined time has elapsed from the second time point. and then ending at a third point in time when the load applied to the tool begins to increase until it becomes greater than any load in the first section,
The second section starts at the third time point, and after the load applied to the tool becomes larger than any load in the first section, the load applied to the tool reaches a minimum value. Or it ends at the fourth point in time when it becomes less than or equal to zero.

According to this configuration, it is possible to extract the second section in which the load shows an abnormal increase from the load curve.

According to the abnormality determination device according to the third aspect of the present disclosure, in the abnormality determination device according to the first or second aspect,
The calculation device calculates, based on the load applied to the tool in the second section, an average value of the load with respect to time, an integral value of the load with respect to time, and an average value of the load with respect to displacement of the tool. and an integral value of the load with respect to the displacement of the tool is calculated as the characteristic value of the load.

According to this configuration, the characteristic value of the load can be calculated based on the load applied to the tool in the second section where the load shows an abnormal increase.

According to the abnormality determination device according to the fourth aspect of the present disclosure, in the abnormality determination device according to the third aspect,
The arithmetic device is
calculating a rate of change of the characteristic value with respect to the number of repetitions of the machining operation;
If the rate of change is larger than a first threshold, it is determined that there is an abnormality in the machining operation of the object by the machining device.

According to this configuration, it is possible to determine whether the machining operation by the machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

According to the abnormality determination device according to the fifth aspect of the present disclosure, in the abnormality determination device according to the fourth aspect,
When the number of times the rate of change changes between positive and negative is greater than a second threshold, the arithmetic device determines that there is an abnormality or a sign of an abnormality in the machining operation of the object by the machining device. It is determined that there is.

According to this configuration, a sign that an abnormality occurs in the machining operation of the machining device 1 can be detected.

According to the abnormality determination device according to the sixth aspect of the present disclosure, in the abnormality determination device according to the fourth or fifth aspect,
The arithmetic device reduces the first threshold value each time the rate of change changes between positive and negative values.

According to this configuration, the sensitivity for detecting abnormalities in machining operations can be improved.

According to the machining system according to the seventh aspect of the present disclosure,
machining equipment with tools;
and an abnormality determination device according to one of the first to sixth aspects.

According to this configuration, it is possible to determine whether the machining operation by the machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

According to the abnormality determination method according to the eighth aspect of the present disclosure,
An abnormality determination method for determining whether a repetitive machining operation of a target object by a machining device equipped with a tool is normal or abnormal, the method comprising:
Obtaining a load curve showing temporal or positional changes in the load applied to the tool;
From the load curve, a first section after the first point in time when a peak load is applied to the tool, and a second section after the first section, the second section a second section in which a load greater than the load in the first section is applied to the tool in at least a portion of the step;
and determining whether the machining operation of the object by the machining device is normal or abnormal based on the load applied to the tool in the second section.

According to this configuration, it is possible to determine whether the machining operation by the machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

According to the abnormality determination device according to the ninth aspect of the present disclosure,
An abnormality determination device for determining whether a repetitive machining operation of a target object by a machining device equipped with a tool is normal or abnormal,
a data input device that obtains a load curve indicating temporal or positional changes in the load applied to the tool;
From the load curve, a first section after the first point in time when a peak load is applied to the tool, and a second section after the first section, the second section A second section in which a load larger than the load in the first section is applied to the tool in at least a part of the load is extracted, and the load applied to the tool in the first section and the second section are extracted. and a calculation device that determines whether the machining operation of the object by the machining device is normal or abnormal based on the load applied to the tool in the section No. 2.

According to this configuration, it is possible to determine whether the machining operation by the machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

According to the abnormality determination device according to the tenth aspect of the present disclosure, in the abnormality determination device according to the ninth aspect,
The first section starts at a second time point after the first time point when the load applied to the tool becomes a minimum value or zero or less, and a predetermined time has elapsed from the second time point. and then ending at a third point in time when the load applied to the tool begins to increase until it becomes greater than any load in the first section,
The second section starts at the third time point, and after the load applied to the tool becomes larger than any load in the first section, the load applied to the tool reaches a minimum value. Or it ends at the fourth point in time when it becomes less than or equal to zero.

According to this configuration, it is possible to extract the second section in which the load shows an abnormal increase from the load curve.

According to the abnormality determination device according to the eleventh aspect of the present disclosure, in the abnormality determination device according to the ninth or tenth aspect,
The arithmetic device is
Based on the load applied to the tool in the first section, the average value of the load with respect to time, the integral value of the load with respect to time, the average value of the load with respect to the displacement of the tool, and the average value of the load with respect to time, Calculating one of the integral values of the load with respect to displacement as a first characteristic value of the load,
Based on the load applied to the tool in the second section, the average value of the load with respect to time, the integral value of the load with respect to time, the average value of the load with respect to the displacement of the tool, and the average value of the load with respect to time, One of the integral values of the load with respect to displacement is calculated as a second characteristic value of the load.

According to this configuration, the characteristic value of the load can be calculated based on the load applied to the tool in the first section and the second section.

According to the abnormality determination device according to the twelfth aspect of the present disclosure, in the abnormality determination device according to the eleventh aspect,
The arithmetic device is
calculating a first rate of change of the first characteristic value with respect to the number of repetitions of the machining operation;
calculating a second rate of change of the second characteristic value with respect to the number of repetitions of the machining operation;
If the second rate of change is larger than the first characteristic value, it is determined that there is an abnormality in the machining operation of the object by the machining device.

According to this configuration, it is possible to determine whether the machining operation by the machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

According to the abnormality determination device according to the thirteenth aspect of the present disclosure, in the abnormality determination device according to the eleventh aspect,
The arithmetic device is
calculating a first rate of change of the first characteristic value with respect to the number of repetitions of the machining operation;
calculating a second rate of change of the second characteristic value with respect to the number of repetitions of the machining operation;
If the second rate of change is greater than the first characteristic value, and if the second rate of change is greater than the first threshold, the machining operation of the object by the machining device It is determined that there is an abnormality.

According to this configuration, it is possible to determine with higher precision whether the machining operation by the machining device is normal or abnormal.

According to the machining system according to the fourteenth aspect of the present disclosure,
machining equipment with tools;
and an abnormality determination device according to one of the ninth to thirteenth aspects.

According to this configuration, it is possible to determine whether the machining operation by the machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

According to the abnormality determination method according to the fifteenth aspect of the present disclosure,
An abnormality determination method for determining whether a repetitive machining operation of a target object by a machining device equipped with a tool is normal or abnormal, the method comprising:
Obtaining a load curve showing temporal or positional changes in the load applied to the tool;
From the load curve, a first section after the first point in time when a peak load is applied to the tool, and a second section after the first section, the second section a second section in which a load greater than the load in the first section is applied to the tool in at least a portion of the step;
Determining whether the machining operation of the object by the machining device is normal or abnormal based on the load applied to the tool in the first section and the load applied to the tool in the second section. and a step of doing so.

According to this configuration, it is possible to determine whether the machining operation by the machining device is normal or abnormal with higher accuracy than before, without requiring prior learning.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings as appropriate. However, more detailed explanation than necessary may be omitted. For example, detailed explanations of well-known matters and redundant explanations of substantially the same configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. The inventors have provided the accompanying drawings and the following description in order for those skilled in the art to fully understand the present disclosure, and they are not intended to limit the subject matter recited in the claims. do not have.

[Embodiment]
[Configuration of embodiment]
FIG. 1 is a block diagram showing the configuration of a machining system 100 including an abnormality determination device 2 according to an embodiment. The machining system 100 includes a machining device 1 and an abnormality determination device 2. The machining device 1 performs a predetermined machining operation, for example, a punching process, on a workpiece 3 that is a workpiece. The abnormality determination device 2 determines whether the repetitive machining operation of the workpiece 3 by the machining device 1 is normal or abnormal.

The machining device 1 includes a control device 11, a drive device 12, a punch 13, a die 14, a load sensor 15, a position sensor 16, and a communication interface (I/F) 17. In this embodiment, a case will be described in which the machining apparatus 1 is a press working apparatus that repeatedly processes a plurality of workpieces 3 made of sheet metal using a punch 13 and a die 14.

The control device 11 controls the operation of the entire machining device 1 .

The punch 13 is provided to face the die 14. The workpiece 3 is placed on the die 14. The drive device 12 moves the punch 13 toward the die 14 under the control of the control device 11 . Thereby, the machining device 1 processes the workpiece 3 placed on the die 14 by applying a load from the punch 13 to the workpiece 3 .

Load sensor 15 detects the load applied to punch 13. The load sensor 15 has sufficiently high sensitivity to detect minute changes in the load on the punch 13. The load sensor 15 may be, for example, a crystal piezoelectric sensor. The position sensor 16 detects the moving distance of the punch 13 from a predetermined reference position. The position sensor 16 has a sufficiently high resolution to detect minute changes in position (movement distance) of the punch 13. The position sensor 16 may be, for example, an eddy current sensor or a capacitive sensor.

The control device 11 acquires the load and position of the punch 13 from the load sensor 15 and the position sensor 16, respectively, and generates a load curve showing temporal or positional changes in the load applied to the punch 13. The control device 11 transmits data including the load curve to the abnormality determination device 2 via the communication interface 17. Further, the control device 11 receives a control signal, for example, a stop command, from the abnormality determination device 2 via the communication interface 17, and controls the drive device 12 according to the control signal.

The abnormality determination device 2 includes a bus 20, a calculation device 21, a storage device 22, a communication interface (I/F) 23, a user input device 24, and a display device 25. The abnormality determination device 2 determines whether the machining operation of the workpiece 3 by the machining device 1 is normal or abnormal.

The arithmetic device 21 controls the operation of the entire abnormality determination device 2 . The arithmetic device 21 also performs an abnormality determination process, which will be described later, to determine whether the machining operation of the workpiece 3 by the machining device 1 is normal or abnormal. The arithmetic device 21 is, for example, a microcomputer, a CPU (Central Processing Unit), an MPU (Micro Processor Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), or an ASIC (Application Specific Integrated circuit).

The communication interface 23 receives data including a load curve from the machining device 1 . Communication interface 23 is an example of a data input device.

The storage device 22 stores current and past load curves received from the machining device 1 . Furthermore, the storage device 22 stores programs and other data necessary for the operation of the abnormality determination device 2.

The user input device 24 obtains user input that controls the operation of the abnormality determination device 2 . User input devices 24 include, for example, a keyboard and a pointing device.

The display device 25 displays information indicating the status of the machining device 1 and the abnormality determining device 2. For example, the display device 25 displays information indicating an error (abnormality) in the machining operation of the machining device 1 or a sign thereof.

Arithmetic device 21 , storage device 22 , communication interface 23 , user input device 24 , and display device 25 are connected to each other via bus 20 .

The abnormality determination device 2 may be configured using a general-purpose computer such as a personal computer or a workstation, or may be configured as another dedicated device. The functions of the abnormality determination device 2 may be realized only by hardware, or may be realized by a combination of hardware and software.

The machining device 1 and the abnormality determining device 2 are connected to each other by wire or wirelessly so that they can communicate with each other. The machining device 1 and the abnormality determination device 2 may be connected to each other via a public line such as the Internet and/or a private line.

The machining device 1 and the abnormality determination device 2 may be installed at the same location (for example, the same factory), or may be installed at mutually different locations.

[Operation of embodiment]
FIG. 2 is a schematic diagram showing the first step in the process of machining the workpiece 3 by the machining apparatus 1 of FIG. 1. As shown in FIG. FIG. 3 is a schematic diagram showing a second step in the process of machining the workpiece 3 by the machining apparatus 1 of FIG. 1. As shown in FIG. FIG. 4 is a schematic diagram showing the third step in the process of machining the workpiece 3 by the machining apparatus 1 of FIG. FIG. 5 is a schematic diagram showing the fourth step in the process of machining the workpiece 3 by the machining device 1 of FIG. 1. FIG. 6 is a schematic diagram showing the fifth step in the process of machining the workpiece 3 by the machining apparatus 1 of FIG.

In the examples of FIGS. 2-6, die 14 includes an upper portion 14a and a lower portion 14b. The workpiece 3 is sandwiched and fixed between the upper part 14a and the lower part 14b. The lower portion 14b has a hole 14c for accommodating or discharging unnecessary sections cut from the workpiece 3.

As shown in FIG. 2, when machining of the workpiece 3 is started, the punch 13 descends and comes into contact with the workpiece 3.

As shown in FIG. 3, when the load applied to the workpiece 3 from the punch 13 increases, the workpiece 3 deforms.

As shown in FIG. 4, when the load applied to the workpiece 3 from the punch 13 further increases, the workpiece 3 is cut into parts 3a and pieces 3b.

As shown in FIG. 5, the section 3b is accommodated in the hole 14c or ejected through the hole 14c.

However, as shown in FIG. 6, the section 3b may not be properly ejected and a plurality of sections 3b-1 to 3b-4 may remain in the hole 14c. In this case, the operation of the punch 13 is hindered, and there is a possibility that a problem may occur in the machining operation of the machining device 1. In this specification, the state in which the section 3b is clogged in the hole 14c as shown in FIG. 6 is also referred to as "clogged waste".

The abnormality determination device 2 according to the embodiment highly accurately determines whether the machining operation by the machining device is normal or abnormal by executing the abnormality determination process described below.

FIG. 7 is a flowchart showing the abnormality determination process executed by the arithmetic device 21 of FIG.

In step S1 of FIG. 7, the calculation device 21 acquires the load curve of the machining device 1. The calculation device 21 acquires the load curve related to the currently executed machining operation from the machining device 1 via the communication interface 23, and obtains the load curve (or its characteristic value) related to the past machining operation from the storage device 22. (described later)).

FIG. 8 is a graph showing a load curve showing temporal changes in the load applied to the punch 13 of FIG. FIG. 8 shows one machining operation on the workpiece 3 by the machining device 1. In this specification, one machining operation by the machining device 1 is also referred to as "one shot."

Referring to FIG. 8, at time 0, the punch 13 starts moving, and at time t1, the punch 13 contacts the workpiece 3 (see FIG. 2). As shown in FIG. 8, almost no load is applied to the punch 13 in a section d1 from when the punch 13 starts moving at time t0 (or time zero) until it contacts the workpiece 3.

When the punch 13 comes into contact with the workpiece 3, the load applied to the punch 13 increases rapidly, as shown in section d2. As the load applied to the punch 13 increases, the workpiece 3 deforms (see FIG. 3), and at time t2, the workpiece 3 is cut (see FIG. 4). can now move substantially unimpeded, and the load applied to the punch 13 is reduced to near zero.

As shown in section d3, for a while after cutting the workpiece 3, due to interference between the punch 13 and the die 14, or interference between the workpiece 3 and the punch 13 or the die 14, the punch 13 Loads may be applied. For example, when the punch 13 is inclined with respect to the die 14 and the punch 13 comes into contact with the die 14, a load may be applied to the punch 13. Alternatively, a load may be applied to the punch 13 by drawing the piece 4b between the punch 13 and the die 14 (see FIG. 6).

The calculation device 21 (or the control device 11 of the machining device 1) adjusts the load curve to eliminate short-term temporary load fluctuations (in the example of FIG. 8, for example, fluctuations of 0.1 milliseconds or less). May be smoothed.

FIG. 9 is a load curve showing temporal changes in the load applied to the punch 13 in FIG. be. If the operation of the punch 13 is not hindered as shown in FIG. 6, it is determined that the machining operation of the workpiece 3 by the machining device 1 is normal. However, if the punch 13 wears out due to repeated machining operations, the load applied to the punch 13 during execution of the machining operations increases. FIG. 9 shows a load curve when performing the 50,000th shot after starting to use the punch 13 and a load curve when performing the 1,000,000th shot after starting to use the punch 13. As shown in FIG. 9, when the machining operation is repeated, the peak load in section d2 increases. This is because the punch 13 wears out due to repeated machining operations, and it becomes necessary to apply a larger load to the punch 13 in order to cut the workpiece 3. Furthermore, when the machining operation is repeated, the load in the section d3 also increases. This is because when the punch 13 wears out, burrs increase and interfere with the punch 13, and the load applied to the punch 13 increases. Thus, it can be seen that the load curve and the progress of wear of the tool (punch 13) are closely related.

In step S2 of FIG. 7, the calculation device 21 extracts a first section d31 and a second section d32 from the load curve. For example, the first section d31 is a time section after the time when the peak load is applied to the punch 13. Further, the second section d32 is a time section after the first section d31, and in at least a part of the second section d32, a load larger than the load in the first section is applied to the punch 13. This is the time interval in which

FIG. 10 is a load curve showing temporal changes in the load applied to the punch 13 in FIG. be. For example, as shown in FIG. 6, if a plurality of pieces 3b-1 to 3b-4 remain in the hole 14c and the operation of the punch 13 is obstructed, an extra load is applied to the punch 13. By detecting such an extra load, it can be determined that there is an abnormality in the machining operation of the workpiece 3 by the machining device 1.

When the workpiece 3 is cut as shown in FIGS. 4 and 5, the punch 13 may interfere with the cut piece 3b. In principle, there is a possibility that a load due to interference will occur immediately after cutting. Further, when clogging occurs as shown in FIG. 6, an abnormal load is generated in a time period roughly corresponding to the time length during which the punch 13 moves across the thickness of the section 3b. Furthermore, the abnormal load further increases approximately in proportion to the number of pieces 3b that remain in the hole 14c. In this embodiment, the time interval immediately after cutting the workpiece 3 is extracted as the first interval d31, and the time interval in which the abnormal load is occurring is extracted as the second interval d32.

For example, the first section d31 starts at a second time t2 when the load applied to the punch 13 becomes a minimum value or zero or less, after the first time point when the peak load is applied to the punch 13, After a predetermined time d4 has elapsed from the second time t2, the load applied to the punch 13 starts to increase until it becomes larger than an arbitrary load in the first section d31, and the process is set to end at a third time t3. be done. Further, the second section d32 starts at the third time point t3, and after the load applied to the punch 13 becomes larger than the arbitrary load in the first section d31, the load applied to the punch 13 starts at the third time t3. It is set to end at the fourth time t4 when the value becomes the minimum value or zero or less.

In the example of FIG. 8, the time d4 may be set to any value between 0.1 and 1 millisecond, for example. The time d4 is set to distinguish between a load curve when the machining operation is normal, as shown in FIG. 9, and a load curve when the machining operation is abnormal, as shown in FIG.

FIG. 11 is a load curve showing temporal changes in the load applied to the punch 13 in FIG. FIG. 3 is a schematic diagram showing the load curve of a shot of When clogging occurs as shown in FIG. 6, as described above, the abnormal load further increases approximately in proportion to the number of pieces 3b that remain in the hole 14c. Therefore, in the second section d32, the load also increases as the number of shots increases.

As the wear of the punch 13 progresses, the phenomenon in which the piece 3b is drawn toward the punch 13 becomes more likely to occur. The increase in load due to interference does not vary greatly in the short term, but tends to change over a medium-term span of approximately several thousand to several hundred thousand shots, depending on the configuration of the punch 13 and die 14. .

Note that, as illustrated in FIGS. 10 and 11, the second section d32 has an approximately trapezoidal shape.

In step S3 of FIG. 7, the calculation device 21 calculates the characteristic value of the load in the second section d32 based on the load applied to the punch 13 in the second section.

FIG. 12 is a diagram for explaining calculation of the first characteristic value based on the load curve of FIG. 10. FIG. 13 is a diagram for explaining calculation of the second characteristic value based on the load curve of FIG. 10. As shown in FIG. 12, the calculation device 21 may calculate the average value A2 of the load with respect to time in the second section d32 as the characteristic value Q2 of the load in the second section d32. Furthermore, as shown in FIG. 13, the calculation device 21 may calculate the integral value B2 of the load with respect to time in the second section d32 as the characteristic value Q2 of the load in the second section d32.

In the examples of FIGS. 8 to 13, load curves showing temporal changes in the load applied to the punch 13 in FIG. 1 are referred to, but positional changes in the load applied to the punch 13 in FIG. , a load curve showing a change in load depending on the displacement of the punch 13 may be used.

FIG. 14 is a load curve showing a positional change in the load applied to the punch 13 in FIG. be. Displacements x0 to x4 in FIG. 14 indicate positions of the punch 13 corresponding to times t0 to t4 in FIG. 10, respectively. The first section d31 is a section in which the punch 13 is located after the time when the peak load is applied to the punch 13. The second section d32 is a section in which the punch 13 is located after the first section d31, and in at least a part of the second section d32, a load larger than the load in the first section d31 is applied to the punch 13. This is the interval in which it is applied. For example, the first section d31 starts at a second position x2 where the load applied to the punch 13 becomes a minimum value or zero or less after the first time point when the peak load is applied to the punch 13, After a predetermined time d4 has elapsed from the point corresponding to the second position x2, the load applied to the punch 13 begins to increase until it becomes larger than the arbitrary load in the first section d31, ending at the third position x3. is set to Further, the second section d32 starts at the third position x3, and after the load applied to the punch 13 becomes larger than the arbitrary load in the first section d31, the load applied to the punch 13 starts at the third position x3. It is set to end at the fourth position x4 where the value is the minimum value or less than zero.

FIG. 15 is a diagram for explaining calculation of the first characteristic value based on the load curve of FIG. 14. FIG. 16 is a diagram for explaining calculation of the second characteristic value based on the load curve of FIG. 14. As shown in FIG. 15, the calculation device 21 may calculate the average value A4 of the load related to the displacement of the punch 13 in the second section d32 as the characteristic value Q2 of the load in the second section d32. Further, as shown in FIG. 16, the calculation device 21 may calculate the integral value B4 of the load related to the displacement of the punch 13 in the second section d32 as the characteristic value Q2 of the load in the second section d32.

The calculation device 21 calculates, based on the load applied to the punch 13 in the second section d32, an average value A2 of the load related to time, an integral value B2 of the load related to time, and an average value A4 of the load related to the displacement of the punch 13. and the integral value B4 of the load related to the displacement of the punch 13 may be calculated as the second characteristic value Q2 of the load.

Further, as will be described later with reference to FIGS. 22 to 24, the calculation device 21 calculates an average value A1 of the time-related loads and a time-related load based on the loads applied to the punch 13 in the first section d31. Any one of the integral value B1 of the load related to the displacement of the punch 13, the average value A3 of the load related to the displacement of the punch 13, and the integral value B3 of the load related to the displacement of the punch 13 may be calculated as the first characteristic value Q1 of the load. .

In step S4 of FIG. 7, the calculation device 21 calculates the rate of change R2 of the characteristic value Q2 related to the second section d32, which is the rate of change R2 related to the number of repetitions of the machining operation. The rate of change R2 of the characteristic value Q2 is calculated, for example, by the following equation.

R2=R2(m, na)=(Q2(m)-Q2(m-na))/na (1)

Here, Q2(m) indicates a characteristic value related to the second section d32 calculated based on the currently executed machining operation, that is, the load curve related to the m-th shot. Q2(m−na) indicates a characteristic value related to the second section d32 calculated based on the load curve related to the past machining operation na times ago. The parameter na is set so as to reduce the influence of increased variation from shot to shot, and may be set to, for example, 5≦na. The parameter na may be set to a sufficiently large value so that the rate of change R2 does not become zero or a negative number due to variations.

FIG. 17 is a diagram for explaining the calculation of the rate of change of the characteristic value in step S4 of FIG. If the machining operation is normal, the load will hardly increase in the second section d32. On the other hand, if clogging occurs in the n1th shot, the characteristic values will vary to some extent depending on the number of shots. increases substantially monotonically. This rate of increase is calculated as the rate of change R2 of the characteristic value Q2. Since the processing time per shot may vary depending on the process, the rate of change per shot is calculated instead of simply calculating the rate of change per time.

In step S5 of FIG. 7, the arithmetic device 21 determines whether the rate of change R2 of the characteristic value Q2 is less than or equal to a predetermined threshold Th1, and if YES, returns to step S1, and if NO, returns to step S1. The process proceeds to step S6. The threshold value Th1 may be set, for example, by the following equation based on the currently executed machining operation, that is, the peak load P1(m) related to the m-th shot.

Th1=Th1(m)=P1(m)×0.01 (2)

When the rate of change R2 of the characteristic value Q2 is less than or equal to the threshold value Th1, the calculation device 21 determines that the current machining operation is normal. On the other hand, if the rate of change R2 of the characteristic value Q2 is larger than the threshold value Th1, the arithmetic device 21 determines that there is an abnormality in the current machining operation.

In step S6 in FIG. 7, the arithmetic device 21 notifies the user of the error using the display device 25, and also sends a control signal including a stop command to the machining device 1 via the communication interface 23 to machine the machine. The processing device 1 is stopped.

Note that even if no abnormality occurs in the machining operation, the rate of change R2 of the characteristic value Q2 may exceed the threshold Th1 temporarily, for example, only once, due to some disturbance element. be. Therefore, in order to more reliably detect an abnormality in the machining operation, the arithmetic unit 21 detects that there is an abnormality in the machining operation when the rate of change R2 of the characteristic value Q2 exceeds the threshold value Th1 over a plurality of consecutive shots. You may judge that. In actual machining operations, how much load must be accumulated on the tool to determine that an abnormality has occurred depends on the machining dimensions allowed in the machining operation or the status of the production plan. There are many different types.

If the second section d32 is not detected in step S2 of FIG. 7, that is, if a section indicating an abnormal increase in load is not detected, the calculation device 21 may determine that the current machining operation is normal. .

[Effects of embodiment]
According to the abnormality determination device 2 according to the present embodiment, by extracting the second section d32 indicating an abnormal increase in load from the load curve, the machining device 1 can It is possible to determine whether a machining operation is normal or abnormal with higher precision than before. The calculation device 21 determines whether the machining operation of the workpiece 3 by the machining device 1 is normal or abnormal based on the load applied to the punch 13 in the second section d32.

According to the abnormality determination device 2 according to the present embodiment, if the abnormality in the machining operation is not resolved, it may be determined that the life of the punch 13 or the machining device 1 has ended.

When the time length of the machining operation changes due to wear of the punch 13, it is better to use the integral value B2 of the time-related load as the characteristic value Q2 than to use the average value A2 of the time-related load as the characteristic value Q2. It is possible to determine with high accuracy whether a machining operation is normal or abnormal. Similarly, it is possible to more accurately determine whether the machining operation by the machining device 1 is normal or abnormal by using the integral value B4 of the load related to the position as the characteristic value Q2 than by using the average value A4 of the load related to the position as the characteristic value Q2. there is a possibility.

[First modification]
FIG. 18 is a flowchart showing an abnormality determination process executed by the abnormality determination device 2 according to the first modified example of the embodiment. The process in FIG. 18 includes steps S11 to S13 in addition to each step in FIG.

In step S5 of FIG. 18, if the rate of change R2 of the characteristic value Q2 is equal to or less than the threshold value Th1, the process proceeds to step S11 instead of step S1.

In step S11, the arithmetic device 21 counts the number k of times the rate of change R2 of the characteristic value Q2 transitions between positive and negative. The calculation device 21 may count the number of times the rate of change R2 of the characteristic value Q2 transitions from positive to negative as the number of transitions k, or may count the number of times the rate of change R2 of the characteristic value Q2 transitions from negative to positive. You can.

FIG. 19 is a diagram for explaining the calculation of the rate of change of characteristic values in step S4 of FIG. 18. According to the example of FIG. 19, the characteristic value Q2 increases periodically and decreases to near zero in the n11, n12, n13, and n14th shots. This indicates, for example, a phenomenon in which the clogging of debris is temporarily resolved and the hole 3c is clogged with the piece 3b again. As a result, the rate of change R2 of the characteristic value Q2 transitions between positive and negative.

In step S12, the arithmetic device 21 determines whether the number of transitions k is equal to or less than a predetermined threshold value Th2. If YES, the process returns to step S1; if NO, the process proceeds to step S13.

In step S13, the computing device 21 uses the display device 25 to notify the user of the sign of the error. If the number of transitions k exceeds another threshold that is larger than the threshold Th2, the arithmetic device 21 may notify the user of the error using the display device 25. After that, the process returns to step S1.

According to the first modification of the embodiment, a sign that an abnormality occurs in the machining operation of the machining device 1 is detected by counting the number of times k that the rate of change R2 of the characteristic value Q2 transitions between positive and negative. and notify the user.

[Second modification]
FIG. 20 is a flowchart showing an abnormality determination process executed by the abnormality determination device 2 according to the second modification of the embodiment. The process in FIG. 20 includes step S21 instead of steps S12 to S13 in FIG.

In step S21, the arithmetic device 21 reduces the threshold Th1 based on the number of times k that the rate of change R2 of the characteristic value Q2 transitions between positive and negative, for example, using the following equation.

Th1←Th1× ^0.95k (3)

After reducing the threshold Th1, the process returns to step S1.

FIG. 21 is a diagram for explaining the calculation of the rate of change of characteristic values in step S4 of FIG. 20. According to the example of FIG. 21, the threshold value is set to Th1a in the initial state, the threshold value is reduced to Th1b in the n21st shot, and the threshold value is reduced to Thic in the n22nd shot. Thereafter, in shots after the n23rd shot, the rate of change R2 of the characteristic value Q2 exceeds the threshold value Th1c, and it is determined that there is an abnormality in the machining operation. According to the example in FIG. 21, if the initial state threshold Th1a is set, it is not determined that there is an abnormality in the machining operation, but by reducing the threshold Th1a to Th1c, the machining operation is abnormal. The sensitivity to detect can be improved.

According to the second modification of the embodiment, by reducing the threshold Th1 based on the number of transitions k, it is possible to improve the sensitivity for detecting abnormalities in machining operations.

[Third modification]
FIG. 22 is a flowchart showing an abnormality determination process executed by the abnormality determination device 2 according to the third modification of the embodiment.

In step S31 of FIG. 22, the calculation device 21 acquires the load curve of the machining device 1. In step S22, the calculation device 21 extracts the first section d31 and the second section d32 from the load curve. Steps S31 to S32 in FIG. 22 are similar to steps S1 to S2 in FIG. 7.

In step S33, the calculation device 21 calculates the characteristic value Q1 of the load in the first section d31 based on the load applied to the punch 13 in the first section, and calculates the characteristic value Q1 of the load applied to the punch 13 in the second section. A characteristic value Q2 of the load in the second section d32 is calculated based on the load. As described above, the calculation device 21 calculates the average value A1 of the load related to time, the integral value B1 of the load related to time, and the value related to the displacement of the punch 13 based on the load applied to the punch 13 in the first section d31. Either the average value A3 of the load or the integral value B3 of the load related to the displacement of the punch 13 may be calculated as the first characteristic value Q1 of the load. Further, the calculation device 21 calculates an average value A2 of the load related to time, an integral value B2 of the load related to time, and an average value of the load related to the displacement of the punch 13 based on the load applied to the punch 13 in the second section d32. Either the value A4 or the integrated value B4 of the load related to the displacement of the punch 13 may be calculated as the second characteristic value Q2 of the load.

In step S34, the calculation device 21 calculates the rate of change R1 of the characteristic value Q1 related to the first section d31, which is related to the number of repetitions of the machining operation, and calculates the rate of change R1 of the characteristic value Q2 related to the second section d32. The rate of change R2 in relation to the number of repetitions of the machining operation is calculated. The rate of change R1 of the characteristic value Q1 and the rate of change R2 of the characteristic value Q2 are calculated, for example, by the following equation.

R1=R1(m,nb)=(Q1(m)-Q1(m-nb))/nb (4)
R2=R2(m,nb)=(Q2(m)-Q2(m-nb))/nb (5)

Here, Q1(m) indicates a characteristic value related to the first section d31 calculated based on the currently executed machining operation, that is, the load curve related to the m-th shot. Q1(m−nb) indicates a characteristic value related to the first section d31 calculated based on the load curve related to the past machining operation nb times ago. Further, Q2(m) indicates a characteristic value related to the second section d32 calculated based on the currently executed machining operation, that is, the load curve related to the m-th shot. Q2(m−nb) indicates a characteristic value related to the second section d32 calculated based on the load curve related to the past machining operation nb times ago. The parameter nb is set to reduce the influence of increased variation from shot to shot, and may be set to, for example, 10≦nb. The parameter nb may be set to a sufficiently large value so that the rates of change R1 and R2 do not become zero or a negative number due to variations.

In step S35, the arithmetic device 21 determines whether the rate of change R1 of the characteristic value Q1 is greater than or equal to the rate of change R2 of the characteristic value Q2. If YES, the process returns to step S31; if NO, the process proceeds to step S36. move on. In other words, when the rate of change R2 of the characteristic value Q2 is larger than the rate of change R1 of the characteristic value Q1, the arithmetic device 21 determines that there is an abnormality in the machining operation of the workpiece 3 by the machining device 1.

In step S36, the computing device 21 notifies the user of the error using the display device 25, and also sends a control signal including a stop command to the machining device 1 via the communication interface 23. stop.

FIG. 23 is a diagram for explaining the calculation of characteristic values in step S34 of FIG. 22. FIG. 23 shows the characteristic value ratio Q2/Q1. Since the load characteristic value Q1 in the first section d31 hardly changes in the short term, the characteristic value ratio Q2/Q1 increases or decreases in the same way as the graph of the characteristic value Q2 (see FIG. 17).

In order to more reliably detect an abnormality in the machining operation, the calculation device 21 detects an abnormality in the machining operation when the rate of change R2 of the characteristic value Q2 exceeds the rate of change R1 of the characteristic value Q1 over a plurality of consecutive shots. It may be determined that there is.

According to the third modification of the embodiment, by extracting the first section d31 and the second section d32 from the load curve, the machining operation by the machining device 1 can be adjusted without requiring prior learning. Normality or abnormality can be determined with higher accuracy than before. The calculation device 21 determines whether the machining operation of the workpiece 3 by the machining device 1 is normal based on the load applied to the punch 13 in the first section d31 and the load applied to the punch 13 in the second section d32. Or determine abnormality.

[Fourth modification]
FIG. 24 is a flowchart showing an abnormality determination process executed by the abnormality determination device 2 according to the fourth modification of the embodiment. The process in FIG. 24 includes step S37 in addition to each step in FIG. 22.

In step S35 of FIG. 24, if the rate of change R2 of the characteristic value Q2 exceeds the rate of change R1 of the characteristic value Q1, the process proceeds to step S37 instead of step S36.

In step S37, the arithmetic device 21 determines whether the rate of change R2 of the characteristic value Q2 is equal to or less than the threshold value Th1. If YES, the process returns to step S31; if NO, the process proceeds to step S36.

The process in FIG. 24 is a combination of the processes in FIGS. 7 and 22. By combining the processes in FIGS. 7 and 22, it is possible to determine with higher accuracy whether the machining operation by the machining device 1 is normal or abnormal.

In order to more reliably detect abnormalities in machining operations, the calculation device 21 detects when the rate of change R2 of the characteristic value Q2 exceeds the rate of change R1 of the characteristic value Q1 over consecutive multiple shots, and When the rate of change R2 of the characteristic value Q2 exceeds the threshold value Th1 over the number of shots, it may be determined that there is an abnormality in the machining operation.

According to the third modification of the embodiment, by combining the processes in FIGS. 7 and 22, it is possible to determine with higher accuracy whether the machining operation by the machining device 1 is normal or abnormal. The calculation device 21 controls the machining operation of the workpiece 3 by the machining device 1 when the second rate of change is larger than the first characteristic value and when the second rate of change is larger than the first threshold value. It is determined that there is an abnormality.

[Other variations]
The machining device 1 may use a removable storage medium instead of or in addition to the communication interface 23 as a data input device for acquiring the load curve.

The machining device 1 may use an audio alarm in place of or in addition to the display device 25 to notify the user of errors in machining operations.

The machining device 1 may include only the load sensor 15 instead of both the load sensor 15 and the position sensor 16.

The abnormality determination device 2 may be integrated into the machining device 1.

The abnormality determination device 2 is not limited to the machining device 1 which is a press processing device, but is, for example, a machining device that performs processing operations such as cutting (for example, shear cutting), bending, or drawing. This is applied to any machining device that can extract the second section d32 indicating an abnormal increase in load from the curve.

The abnormality determination device, abnormality determination method, and machining system according to the present disclosure are widely applicable to machining devices that perform machining operations such as cutting, bending, or drawing.

1 Machining device 2 Abnormality determination device 3 Work 11 Control device 12 Drive device 13 Punch 14 Die 15 Load sensor 16 Position sensor 17 Communication interface (I/F)
20 Bus 21 Arithmetic device 22 Storage device 23 Communication interface (I/F)
24 User input device 25 Display device 100 Machining system

Claims (7)

An abnormality determination device for determining whether a repetitive machining operation of a target object by a machining device equipped with a tool is normal or abnormal,
a data input device that obtains a load curve indicating temporal or positional changes in the load applied to the tool;
From the load curve, a first section after the first point in time when a peak load is applied to the tool, and a second section after the first section, the second section A second section in which a load larger than the load in the first section is applied to the tool in at least a part of the load is extracted, and the load applied to the tool in the first section and the second section are extracted. and a calculation device that determines whether the machining operation of the object by the machining device is normal or abnormal based on the load applied to the tool in the section No. 2.
Abnormality determination device. The first section starts at a second time point after the first time point when the load applied to the tool becomes a minimum value or zero or less, and a predetermined time has elapsed from the second time point. and then ending at a third point in time when the load applied to the tool begins to increase until it becomes greater than any load in the first section,
The second section starts at the third time point, and after the load applied to the tool becomes larger than any load in the first section, the load applied to the tool reaches a minimum value. or ends at a fourth point in time when the value is less than or equal to zero;
The abnormality determination device according to claim 1. The arithmetic device is
Based on the load applied to the tool in the first section, the average value of the load with respect to time, the integral value of the load with respect to time, the average value of the load with respect to the displacement of the tool, and the average value of the load with respect to time, Calculating one of the integral values of the load with respect to displacement as a first characteristic value of the load,
Based on the load applied to the tool in the second section, the average value of the load with respect to time, the integral value of the load with respect to time, the average value of the load with respect to the displacement of the tool, and the average value of the load with respect to time, calculating one of the integral values of the load with respect to displacement as a second characteristic value of the load;
The abnormality determination device according to claim 1 or 2. The arithmetic device is
calculating a first rate of change of the first characteristic value with respect to the number of repetitions of the machining operation;
calculating a second rate of change of the second characteristic value with respect to the number of repetitions of the machining operation;
If the second rate of change is larger than the first characteristic value, determining that there is an abnormality in the machining operation of the object by the machining device;
The abnormality determination device according to claim 3. The arithmetic device is
calculating a first rate of change of the first characteristic value with respect to the number of repetitions of the machining operation;
calculating a second rate of change of the second characteristic value with respect to the number of repetitions of the machining operation;
If the second rate of change is greater than the first characteristic value, and if the second rate of change is greater than the first threshold, the machining operation of the object by the machining device determine that there is an abnormality,
The abnormality determination device according to claim 3. machining equipment with tools;
and the abnormality determination device according to claim 1.
machining system. An abnormality determination method for determining whether a repetitive machining operation of a target object by a machining device equipped with a tool is normal or abnormal, the method comprising:
Obtaining a load curve showing temporal or positional changes in the load applied to the tool;
From the load curve, a first section after the first point in time when a peak load is applied to the tool, and a second section after the first section, the second section a second section in which a load greater than the load in the first section is applied to the tool in at least a portion of the step;
Determining whether the machining operation of the object by the machining device is normal or abnormal based on the load applied to the tool in the first section and the load applied to the tool in the second section. and a step of
Abnormality determination method.