Control parameter generation method, program, recording medium, and control parameter generation device

Abstract

A control parameter generation method used in a production device acquires measurement data indicating a position from a sensor that measures the position of a driven object, and generates evaluation index data indicating vibration of the driven object after the arrival time on the basis of the measurement data. When the vibration indicated by the evaluation index data is equal to or less than a predetermined threshold value, the control parameter is updated using a machine learning model that learns the relationship between the evaluation index data and the control parameter on the basis of the evaluation index data, and when the vibration indicated by the evaluation index data exceeds the predetermined threshold value, the control parameter is updated using a machine learning model that learns the relationship between the evaluation index data and the control parameter. And interrupting updating of the control parameters.

Description

Technical Field

[0001] The present disclosure relates to a method for generating a control parameter, and in particular to a method for generating a control parameter used in a production device. Background Art

[0002] Conventionally, a method for generating a control parameter, which is a parameter for controlling a device, has been proposed (for example, refer to Patent Document 1).

[0003] Prior Art Literature

[0004] Patent Literature

[0005] Patent Document 1: International Publication No. 2018 / 151215 Summary of the invention

[0006] In the past, it was desired to generate appropriate control parameters.

[0007] Therefore, an object of the present disclosure is to provide a control parameter generation method and the like capable of generating appropriate control parameters.

[0008] A control parameter generation method according to one embodiment of the present disclosure generates control parameters, the control parameters being generated by a control parameter generation device and used by the production device while a sensor confirms the state of the production device. The production device comprises: a servo motor; a control circuit for controlling the servo motor; a memory for storing the control parameters used when the control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor measures the position of the driven object after the driven object reaches an allowable position based on an instruction for making the position of the driven object a given target position, and outputs measurement data representing the position, wherein the allowable position is a position that can be evaluated as reaching the given target position. The control parameter generation method has the following method. The measurement data representing the position output from the sensor is acquired. Based on the measurement data, evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position is generated. Based on the evaluation index data, when the vibration represented by the evaluation index data is below a given threshold, the control parameter is updated using a machine learning model that learns the relationship between the evaluation index data and the control parameter. The updated control parameter is output to the production device to be stored in the memory, and when the vibration indicated by the evaluation index data exceeds the given threshold value, the updating of the control parameter is interrupted.

[0009] A program according to another aspect of the present disclosure is a program for causing a computer of an information processing device connected to the production device to execute the control parameter generation method described above.

[0010] A recording medium according to another aspect of the present disclosure is a recording medium having a program recorded thereon for causing a computer of an information processing device connected to the production device to execute the control parameter generation method.

[0011] Another aspect of the present disclosure relates to a control parameter generation device that generates control parameters used by the production device while a sensor confirms the state of the production device. The production device includes: a servo motor; a first control circuit that controls the servo motor; a memory that stores control parameters used when the first control circuit controls the servo motor; and a driven object that is driven by the servo motor. The sensor measures the position of the driven object after the driven object reaches an allowable position based on an instruction for making the position of the driven object a given target position, and outputs measurement data representing the position, wherein the allowable position is a position that can be evaluated as reaching the given target position. The control parameter generation device includes an input unit, a second control circuit, and an output unit. The input unit acquires the measurement data representing the position from the sensor. Based on the measurement data, the second control circuit generates evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position, and based on the evaluation index data, when the vibration represented by the evaluation index data is less than a given threshold value, updates the control parameter using a machine learning model that learns the relationship between the evaluation index data and the control parameter. The output unit outputs the updated control parameter to the production device to store it in the memory. The second control circuit further interrupts the updating of the control parameter when the vibration indicated by the evaluation index data exceeds the given threshold value.

[0012] Another aspect of the present disclosure relates to a method for generating control parameters, which generates control parameters generated by a control parameter generating device and used by the production device while a sensor with a processing device confirms the state of the production device. The production device includes: a servo motor; a control circuit that controls the servo motor; a memory that stores control parameters used when the control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor with a processing device measures the position of the driven object after the driven object reaches an allowable position based on an instruction for making the position of the driven object a given target position, and outputs evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position, wherein the allowable position is a position that can be evaluated as reaching the given target position. The control parameter generation method has the following method. The evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position output from the sensor with a processing device is obtained. Based on the evaluation index data, when the vibration represented by the evaluation index data is less than a given threshold value, the control parameter is updated using a machine learning model that learns the relationship between the evaluation index data and the control parameter. The updated control parameter is output to the production device to be stored in the memory. When the vibration indicated by the evaluation index data exceeds the given threshold, the updating of the control parameter is interrupted.

[0013] A program according to another aspect of the present disclosure is a program for causing a computer of an information processing device connected to the production device to execute the control parameter generation method described above.

[0014] A recording medium according to another aspect of the present disclosure is a recording medium having a program recorded thereon for causing a computer of an information processing device connected to the production device to execute the control parameter generation method.

[0015] Another aspect of the present disclosure relates to a control parameter generation device that generates control parameters used by the production device while a sensor with a processing device confirms the state of the production device. The production device includes: a servo motor; a first control circuit that controls the servo motor; a memory that stores control parameters used when the first control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor with a processing device measures the position of the driven object after the driven object reaches an allowable position based on an instruction for making the position of the driven object a given target position, and outputs evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position, and the allowable position is a position that can be evaluated as reaching the given target position. The control parameter generation device includes an input unit, a second control circuit, and an output unit. The input unit acquires the evaluation index data output from the sensor with a processing device that represents the vibration of the driven object after the driven object reaches the allowable position. The second control circuit updates the control parameter based on the evaluation index data using a machine learning model that learns the relationship between the evaluation index data and the control parameter when the vibration represented by the evaluation index data is below a given threshold value. The output unit outputs the updated control parameter to the production device to be stored in the memory. The second control circuit further interrupts the update of the control parameter when the vibration represented by the evaluation index data exceeds the given threshold value.

[0016] According to the control parameter generation method, program, recording medium, and control parameter generation device according to the above-mentioned one aspect of the present disclosure, appropriate control parameters can be generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram showing an overview of the control parameter generation system according to the first embodiment.

[0018] Figure 2 This is a block diagram showing the structure of the control parameter generation system according to the first embodiment.

[0019] Figure 3 This is a schematic diagram showing an example of control parameters according to the first embodiment.

[0020] Figure 4 1 is a schematic diagram showing an example of transition of a position deviation of a driven object relative to a target position.

[0021] Figure 5 This is a data structure diagram showing an example of measurement data according to the first embodiment.

[0022] Figure 6 This is a schematic diagram showing a situation in which the machine learning model involved in Implementation Example 1 outputs a control parameter based on the result obtained by learning the relationship between evaluation index data and the control parameter.

[0023] Figure 7 This is a diagram showing an example of an image generated by the image generating unit according to the first embodiment.

[0024] Figure 8 This is a diagram showing an example of an image generated by the image generating unit according to the first embodiment.

[0025] Fig. 9 This is a sequence diagram of the first control parameter adjustment process involved in the first embodiment.

[0026] Fig.10 This is a flowchart of the first control parameter adjustment process according to the first embodiment.

[0027] Fig.11 This is a schematic diagram showing an overview of a control parameter generation system according to the second embodiment.

[0028] Fig.12 This is a block diagram showing the structure of the control parameter generation system involved in the second embodiment.

[0029] Fig.13 This is a sequence diagram of the second control parameter adjustment process involved in Implementation Example 2.

[0030] Fig.14 This is a flowchart of the second control parameter adjustment process involved in Implementation Example 2.

[0031] Fig.15 This is a schematic diagram showing an overview of a control parameter generation system according to the third embodiment.

[0032] Fig.16 This is a block diagram showing the structure of a control parameter generation system according to the third embodiment.

[0033] Fig.17 This is a data structure diagram showing an example of the data structure of the adjustment history involved in the third embodiment.

[0034] Fig.18 This is a diagram showing an example of an image displayed by the display device according to the third embodiment.

[0035] Fig.19 This is a diagram showing an example of an adjustment restart time point designation image displayed on the display device according to the third embodiment.

[0036] Fig. 20This is a flowchart of the third control parameter adjustment process involved in Implementation Example 3.

[0037] Fig.21 This is a schematic diagram showing an overview of a control parameter generation system according to a fourth embodiment.

[0038] Fig. 22 This is a block diagram showing the structure of a control parameter generation system according to a fourth embodiment.

[0039] Fig.23 This is a flowchart of the fourth control parameter adjustment process involved in the fourth implementation mode.

[0040] Fig.24 It is a block diagram showing the structure of a generating device according to a modification example.

[0041] Fig.25 : is a block diagram showing the structure of a control parameter generation system according to a modification example.

[0042] Fig.26 It is a diagram showing a vibration waveform of a production device. DETAILED DESCRIPTION

[0043] (How One Method of the Present Disclosure Was Acquired)

[0044] Conventionally, the control parameters used in production equipment equipped with a servo motor that drives a driven object are adjusted manually. However, the number of control parameters used in such production equipment may reach 80, and the number of parameters that actually need to be adjusted may reach 30 to 60. In addition, the parameters to be adjusted vary depending on the driven object installed in the production equipment, the purpose, etc.

[0045] Due to such circumstances, conventionally, the adjustment of control parameters has been handled by a skilled person alone. However, even so, the adjustment of control parameters may require a period of several days.

[0046] Such adjustment of control parameters is necessary to suppress the generation of abnormal noise due to vibration, or to suppress the adverse effect of vibration of the previous operation on the next operation when the operation is temporarily stopped and then the next operation is started.

[0047] For example, Patent Document 1 proposes using a machine learning model to adjust such control parameters. However, skilled technicians adjust control parameters by including the five senses of human beings, such as confirming abnormal sounds through hearing and confirming vibrations through vision and touch. Therefore, it is necessary to conduct additional research on what kind of data should be input into the machine learning model to effectively adjust the control parameters.

[0048] In this context, the inventors have repeatedly conducted research and experiments on what kind of data should be input into the machine learning model to effectively adjust the control parameters. As a result, the inventors found that there is a correlation between the evaluation index data representing the vibration of the driven object after the driven object reaches the allowed position that can be evaluated as reaching a given target position and the control parameters. In addition, the inventors have obtained the knowledge that if the above evaluation index data is input into the machine learning model, the control parameters can be effectively adjusted.

[0049] Based on this knowledge, the inventors have further conducted repeated studies and experiments, and have come up with a control parameter generation method that can generate appropriate control parameters in a relatively short time without relying on skilled workers.

[0050] However, the inventors noticed that even after adjusting the control parameters using the control parameter generation method, there are still cases where the control parameters need to be readjusted again. That is, the inventors noticed that even after adjusting the control parameters using the control parameter generation method, there are still cases where abnormal noise caused by vibration occurs. In such a case, it is necessary to readjust the control parameters using the control parameter generation method from the beginning. Moreover, it was confirmed that by reading again, the generation of abnormal noise caused by vibration is suppressed.

[0051] The inventors investigated the cause of such a phenomenon and obtained the following knowledge.

[0052] That is, when the control parameters are adjusted using the machine learning model, sometimes the production device etc. that is the object of adjustment is not always monitored by a person. Therefore, it is found that during the period of adjusting the control parameters, the vibration of the production device etc. that is the object of adjustment caused by a person walking around the production device etc. that is the object of adjustment, a large truck etc. driving on the road around the factory etc. where the production device etc. that is the object of adjustment is installed, or the door of the entrance and exit of the room etc. where the production device etc. that is the object of adjustment is installed is opened and closed, etc., sometimes has an adverse effect on the adjustment of the control parameters.

[0053] Fig.26 An example of measured data that served as the basis for the inventors to obtain the above-mentioned knowledge is the vibration waveform of the production device, which is measured data of the displacement of the production device measured by a laser displacement meter when a large truck is traveling on the roads around the factory where the production device is installed during the period when the control parameters are adjusted, and measured data of the displacement of the production device measured by the laser displacement meter when no large truck is traveling.

[0054] exist Fig.26In the figure, the horizontal axis is the elapsed time, and the vertical axis is the amplitude of the displacement of the production device.

[0055] like Fig.26 As shown, the inventors noticed the fact that even when the amplitude of the production device is sufficiently within the range of the threshold when no large truck is running on the surrounding roads, the vibration of the production device does not converge within the range of the threshold when a large truck is running on the surrounding roads. Furthermore, the inventors noticed the fact that when the above phenomenon that the vibration of the production device does not converge within the range of the threshold due to the running of a large truck on the surrounding roads occurs during the period of adjusting the control parameters, even after the control parameters are adjusted, the generation of abnormal noise caused by vibration may still occur.

[0056] The inventors further acquired and analyzed the measured data and confirmed that the vibration of the production device did not converge to the threshold range due to people walking around the production device, and the vibration of the production device did not converge to the threshold range due to the opening and closing of the door of the entrance and exit of the room where the production device is installed. In addition, the following facts were found: when such a phenomenon occurred during the period of adjusting the control parameters, even after the control parameters were adjusted, the generation of abnormal noise caused by vibration sometimes still occurred.

[0057] Therefore, the inventors further repeated their studies and experiments based on the newly acquired knowledge, and thus conceived of the control parameter generation method, program, recording medium, and control parameter generation device according to the present disclosure described below.

[0058] A control parameter generation method according to one embodiment of the present disclosure generates control parameters, the control parameters being generated by a control parameter generation device and used by the production device while a sensor confirms the state of the production device. The production device comprises: a servo motor; a control circuit for controlling the servo motor; a memory for storing the control parameters used when the control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor measures the position of the driven object after the driven object reaches an allowable position based on an instruction for making the position of the driven object a given target position, and outputs measurement data representing the position, wherein the allowable position is a position that can be evaluated as reaching the given target position. The control parameter generation method has the following method. The measurement data representing the position output from the sensor is acquired. Based on the measurement data, evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position is generated. Based on the evaluation index data, when the vibration represented by the evaluation index data is below a given threshold, the control parameter is updated using a machine learning model that learns the relationship between the evaluation index data and the control parameter. The updated control parameter is output to the production device to be stored in the memory. When the vibration indicated by the evaluation index data exceeds the given threshold, the updating of the control parameter is interrupted.

[0059] According to the control parameter generation method described above, by updating the control parameter based on the evaluation index data indicating the vibration after reaching the allowable position, it is possible to generate appropriate control parameters in a relatively short time without relying on skilled workers.

[0060] Furthermore, according to the above-mentioned control parameter generation method, when the vibration represented by the evaluation index data exceeds a given threshold, the update of the control parameters will be interrupted, thereby suppressing the adverse effect of the vibration of the production equipment, etc. that is the adjustment object during the period of adjusting the control parameters on the adjustment of the control parameters.

[0061] Therefore, according to the above-mentioned method of generating control parameters, it is possible to suppress the frequency with which the control parameters need to be readjusted.

[0062] Furthermore, when the vibration indicated by the evaluation index data exceeds the given threshold value, a message to interrupt the updating of the control parameter may be output to the display device.

[0063] Thus, when the vibration indicated by the evaluation index data exceeds a given threshold value, a message to the effect of suspending the updating of the parameters can be displayed on the display device.

[0064] In addition, it can also be set that after interrupting the updating of the control parameters, when the vibration represented by the evaluation index data becomes below the given threshold value, the interrupted updating of the control parameters is started again based on the evaluation index data generated based on the measurement data acquired after the moment when the vibration represented by the evaluation index data becomes below the given threshold value.

[0065] As a result, when the vibration indicated by the evaluation index data becomes equal to or less than the predetermined threshold again, the interrupted updating of the control parameter can be restarted.

[0066] Furthermore, the production device may include any one of a mounting device, a processing device, or a working device.

[0067] Thereby, appropriate control parameters can be generated for a production device such as a mounting device, a processing device, or a working device.

[0068] A program according to another aspect of the present disclosure is a program for causing a computer of an information processing device connected to the production device to execute the control parameter generation method described above.

[0069] According to the above program, by updating the control parameters based on the evaluation index data indicating the vibration after the time when the permissible position is reached, it is possible to generate appropriate control parameters in a relatively short time without relying on a skilled worker.

[0070] Furthermore, according to the above-mentioned procedure, when the vibration represented by the evaluation index data exceeds a given threshold, the updating of the control parameters is interrupted, thereby being able to suppress the adverse effects of the vibration of the production equipment, etc., which is the adjustment object, on the adjustment of the control parameters during the period of adjusting the control parameters.

[0071] Therefore, according to the above-mentioned program, it is possible to suppress the frequency with which the control parameters need to be readjusted.

[0072] A recording medium according to another aspect of the present disclosure is a recording medium having a program recorded thereon for causing a computer of an information processing device connected to the production device to execute the control parameter generation method.

[0073] According to the above-described recording medium, by updating the control parameters based on the evaluation index data indicating the vibration after reaching the allowable position, it is possible to generate appropriate control parameters in a relatively short time without relying on a skilled worker.

[0074] Furthermore, according to the above-mentioned recording medium, when the vibration represented by the evaluation index data exceeds a given threshold, the updating of the control parameters is interrupted, thereby being able to suppress the adverse effects of the vibration of the production equipment, etc., which is the adjustment object, on the adjustment of the control parameters during the period of adjusting the control parameters.

[0075] Therefore, according to the above-mentioned recording medium, it is possible to suppress the frequency with which the control parameters need to be readjusted.

[0076] Another aspect of the present disclosure relates to a control parameter generation device that generates control parameters used by the production device while a sensor confirms the state of the production device. The production device includes: a servo motor; a first control circuit that controls the servo motor; a memory that stores control parameters used when the first control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor measures the position of the driven object after the driven object reaches an allowable position based on an instruction for making the position of the driven object a given target position, and outputs measurement data representing the position, wherein the allowable position is a position that can be evaluated as reaching the given target position. The control parameter generation device includes an input unit, a second control circuit, and an output unit. The input unit acquires the measurement data representing the position output from the sensor. The second control circuit generates evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position based on the measurement data, and updates the control parameter based on the evaluation index data, when the vibration represented by the evaluation index data is less than a given threshold value, using a machine learning model that learns the relationship between the evaluation index data and the control parameter. The output unit outputs the updated control parameter to the production device to store it in the memory. The second control circuit further interrupts the updating of the control parameter when the vibration indicated by the evaluation index data exceeds the given threshold value.

[0077] According to the control parameter generation device, by updating the control parameters based on the evaluation index data indicating the vibration after the driven object reaches the permitted position, appropriate control parameters can be generated in a relatively short time without relying on skilled workers.

[0078] Furthermore, according to the above-mentioned control parameter generating device, when the vibration represented by the evaluation index data exceeds a given threshold value, the updating of the control parameters is interrupted, thereby being able to suppress the adverse effect of the vibration of the production equipment, etc. that is the adjustment object during the period of adjusting the control parameters on the adjustment of the control parameters.

[0079] Therefore, according to the control parameter generation device described above, it is possible to suppress the frequency with which the control parameter needs to be readjusted.

[0080] Another aspect of the present disclosure relates to a method for generating control parameters, which generates control parameters used by a production device while a sensor with a processing device confirms the state of the production device. The production device includes: a servo motor; a control circuit that controls the servo motor; a memory that stores control parameters used when the control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor with a processing device measures the position of the driven object after the driven object reaches an allowed position based on an instruction for making the position of the driven object a given target position, and outputs evaluation index data representing the vibration of the driven object after the driven object reaches the allowed position, wherein the allowed position is a position that can be evaluated as reaching the given target position. The method for generating control parameters has the following method. Evaluation index data representing the vibration of the driven object after the driven object reaches the allowed position output from the sensor with a processing device is obtained. Based on the evaluation index data, when the vibration represented by the evaluation index data is below a given threshold, the control parameter is updated using a machine learning model that learns the relationship between the evaluation index data and the control parameter. The updated control parameter is output to the production device to be stored in the memory. When the vibration indicated by the evaluation index data exceeds the given threshold, the updating of the control parameter is interrupted.

[0081] According to the control parameter generation method, by updating the control parameter based on the evaluation index data indicating the vibration after the driven object reaches the permitted position, appropriate control parameters can be generated in a relatively short time without relying on skilled workers.

[0082] Furthermore, according to the above-mentioned control parameter generation method, when the vibration represented by the evaluation index data exceeds a given threshold, the update of the control parameters will be interrupted, thereby suppressing the adverse effect of the vibration of the production equipment, etc. that is the adjustment object during the period of adjusting the control parameters on the adjustment of the control parameters.

[0083] Therefore, according to the above-mentioned method of generating control parameters, it is possible to suppress the frequency with which the control parameters need to be readjusted.

[0084] Furthermore, when the vibration indicated by the evaluation index data exceeds the given threshold value, a message to interrupt the updating of the control parameter may be output to the display device.

[0085] Thus, when the vibration indicated by the evaluation index data exceeds a given threshold value, a message to the effect of suspending the updating of the parameters can be displayed on the display device.

[0086] In addition, it can also be set that after interrupting the update of the control parameter, when the vibration represented by the evaluation index data becomes below the given threshold value, the interrupted update of the control parameter is started again based on the evaluation index data acquired after the moment when the vibration represented by the evaluation index data becomes below the given threshold value.

[0087] As a result, when the vibration indicated by the evaluation index data becomes equal to or less than the predetermined threshold again, the interrupted updating of the control parameter can be restarted.

[0088] Furthermore, in the sensor with a processing device, the evaluation index data may be generated based on the position of the driven object measured by the sensor with a processing device.

[0089] Thus, it is possible to generate evaluation index data based on the position of the driven object measured by the sensor provided with the processing device.

[0090] Furthermore, the sensor with a processing device may include an image processing device.

[0091] In this way, evaluation index data can be generated based on image processing.

[0092] Furthermore, the production device may include any one of a mounting device, a processing device, or a working device.

[0093] Thereby, appropriate control parameters can be generated for a production device such as a mounting device, a processing device, or a working device.

[0094] A program according to another aspect of the present disclosure is a program for causing a computer of an information processing device connected to the production device to execute the control parameter generation method described above.

[0095] According to the above program, by updating the control parameters based on the evaluation index data indicating the vibration after the time when the permissible position is reached, it is possible to generate appropriate control parameters in a relatively short time without relying on a skilled worker.

[0096] Furthermore, according to the above-mentioned procedure, when the vibration represented by the evaluation index data exceeds a given threshold, the updating of the control parameters is interrupted, thereby being able to suppress the adverse effects of the vibration of the production equipment, etc., which is the adjustment object, on the adjustment of the control parameters during the period of adjusting the control parameters.

[0097] Therefore, according to the above-mentioned program, it is possible to suppress the frequency with which the control parameters need to be readjusted.

[0098] A recording medium according to another aspect of the present disclosure is a recording medium having a program recorded thereon for causing a computer of an information processing device connected to the production device to execute the control parameter generation method.

[0099] According to the above-described recording medium, by updating the control parameters based on the evaluation index data indicating the vibration after reaching the allowable position, it is possible to generate appropriate control parameters in a relatively short time without relying on a skilled worker.

[0100] Furthermore, according to the above-mentioned recording medium, when the vibration represented by the evaluation index data exceeds a given threshold, the updating of the control parameters is interrupted, thereby being able to suppress the adverse effects of the vibration of the production equipment, etc., which is the adjustment object, on the adjustment of the control parameters during the period of adjusting the control parameters.

[0101] Therefore, according to the above-mentioned recording medium, it is possible to suppress the frequency with which the control parameters need to be readjusted.

[0102] A control parameter generation device according to one embodiment of the present disclosure generates control parameters used by a production device while a sensor with a processing device confirms the state of the production device. The production device includes: a servo motor; a first control circuit that controls the servo motor; a memory that stores control parameters used when the first control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor with a processing device measures the position of the driven object after the driven object reaches an allowable position based on an instruction for making the position of the driven object a given target position, and outputs evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position, and the allowable position is a position that can be evaluated as reaching the given target position. The control parameter generation device includes an input unit, a second control circuit, and an output unit. The input unit acquires the evaluation index data representing the vibration of the driven object after the driven object reaches the allowable position, which is output from the sensor with a processing device. The second control circuit updates the control parameter based on the evaluation index data using a machine learning model that learns the relationship between the evaluation index data and the control parameter when the vibration represented by the evaluation index data is below a given threshold value. The output unit outputs the updated control parameter to the production device to be stored in the memory. The second control circuit further interrupts the update of the control parameter when the vibration represented by the evaluation index data exceeds the given threshold value.

[0103] According to the control parameter generation device, by updating the control parameter based on the evaluation index data indicating the vibration after the time when the permissible position is reached, it is possible to generate appropriate control parameters in a relatively short time without relying on skilled workers.

[0104] Furthermore, according to the above-mentioned control parameter generating device, when the vibration represented by the evaluation index data exceeds a given threshold value, the updating of the control parameters is interrupted, thereby being able to suppress the adverse effect of the vibration of the production equipment, etc. that is the adjustment object during the period of adjusting the control parameters on the adjustment of the control parameters.

[0105] Therefore, according to the control parameter generation device described above, it is possible to suppress the frequency with which the control parameter needs to be readjusted.

[0106] Hereinafter, a specific example of a control parameter generation system involved in one embodiment of the present disclosure will be described with reference to the accompanying drawings. The embodiments shown herein all represent a specific example of the present disclosure. Therefore, the numerical values, shapes, components, configurations and connection methods of the components, steps (processes), and the order of steps shown in the following embodiments are examples, and their purpose is not to limit the present disclosure. In addition, each figure is a schematic diagram and may not be a rigorous illustration. In each figure, the same symbols are marked for substantially the same structure, and repeated descriptions are omitted or simplified.

[0107] (Implementation method 1)

[0108] Hereinafter, a description will be given of a control parameter generation system according to Embodiment 1. The control parameter generation system is a system for generating control parameters to be used in a production device including a servo motor that drives a driven object.

[0109] <Structure>

[0110] Figure 1 This is a schematic diagram showing an overview of the control parameter generation system 1 according to the first embodiment.

[0111] Figure 2 is a block diagram showing the structure of the control parameter generation system 1 .

[0112] like Figure 1 As shown, the control parameter generation system 1 includes a generation device 10, a production device 20, and a sensor 30. The sensor 30 may be provided separately from the production device 20, or may be a component of the production device 20. In addition, the sensor 30 may be an inspection device independent of the production device 20 or a part thereof.

[0113] The production device 20 is a device for producing equipment, and performs processing, installation, and transportation of equipment. The production device 20 is, for example, set up in a production line of a factory. Specifically, the production device 20 is, for example, an LED (light emitting diode) bonding machine, an installation machine, a processing machine, a removal robot, and the like.

[0114] like Figure 2 As shown, the production device 20 includes a memory 21 , a control circuit 22 , a servo motor 23 , and a driven object 24 .

[0115] The servo motor 23 is controlled in operation by the control circuit 22 to drive the driven object 24. The servo motor 23 may be, for example, a rotary motor or a linear motor.

[0116] The driven object 24 is an object driven by the servo motor 23. The driven object 24 is, for example, a processing object to be processed in the production device 20, an installation object to be installed, a transport object to be transported, etc. In addition, the driven object 24 may be, for example, an arm that holds objects to be processed in the production device 20, or a tray that transports these objects.

[0117] The control circuit 22 controls the servo motor 23 by outputting a command for setting the position of the driven object 24 to a given target position to the servo motor 23. The command output by the control circuit 22 to the servo motor 23 may be, for example, a position command indicating the position of the servo motor 23 or the driven object 24, or a torque command indicating the torque of the servo motor 23.

[0118] The control circuit 22 controls the servo motor 23 based on the control parameters stored in the memory 21. In other words, the control circuit 22 uses the control parameters stored in the memory 21 when controlling the servo motor 23.

[0119] The memory 21 stores control parameters used when the control circuit 22 controls the servo motor 23. The control parameters stored in the memory 21 are control parameters output from the generating device 10.

[0120] Figure 3 2 is a schematic diagram showing an example of the control parameters stored in the memory 21 .

[0121] like Figure 3 As shown, the control parameters stored in the memory 21 include, for example, parameters a1 and a2 for adjusting the vibration frequency of the driven object 24, parameters b1 and b2 for adjusting the high speed of the driven object 24, parameters c1 and c2 for adjusting the depth of the singular point in the vibration characteristics of the driven object 24, parameters d1 and d2 for adjusting the vibration amplitude of the driven object 24, etc.

[0122] Generally, control parameters include parameters having a trade-off relationship with each other, such as parameters b1 and b2 for adjusting speed, parameters c1 and c2 for adjusting the depth of a singular point in vibration characteristics, and parameters d1 and d2 for adjusting vibration amplitude.

[0123] Back again Figure 1 as well as Figure 2 , continue with the description of the control parameter generating system 1.

[0124] The sensor 30 measures the position of the driven object 24 . The sensor 30 outputs measurement data indicating the position of the driven object 24 after the time when the driven object 24 reaches the permitted position that can be evaluated as reaching a given target position to the generating device 10 .

[0125] Figure 4 1 is a schematic diagram showing an example of transition of the position deviation of the driven object 24 relative to the target position when the production device 20 drives the driven object 24 to the target position.

[0126] exist Figure 4 In FIG. 1 , the horizontal axis represents time, and the vertical axis represents the position deviation of the driven object 24 relative to the target position.

[0127] like Figure 4 As shown in FIG. 1 , in this specification, the allowable position refers to a position where the position deviation from the target position is within the required accuracy. Figure 4 As shown, in this specification, the time when the driven object 24 reaches the permitted position that can be evaluated as reaching the target position (hereinafter also referred to as "stable time") refers to the time when the driven object 24 finally reaches the permitted position when it does not deviate from the permitted position after reaching the permitted position ( Figure 4 moment in time (sett_t).

[0128] Figure 5 This is a data structure diagram showing an example of the measurement data output by the sensor 30 .

[0129] like Figure 5 As shown, as an example, the measurement data is data in which the elapsed time [ms] after the time of reaching the permitted position and the deviation amount [mm] from the target position are associated one-to-one.

[0130] Back again Figure 1 as well as Figure 2 , continue with the description of the control parameter generating system 1.

[0131] The generation device 10 generates control parameters (hereinafter also referred to as "update control parameters") for updating control parameters (hereinafter also referred to as "storage control parameters") stored in the memory 21. More specifically, the generation device 10 sequentially acquires measurement data output from the sensor 30 as a result of the production device 20 driving the drive object 24 to the target position using the storage control parameters, sequentially generates update control parameters based on the acquired measurement data, and sequentially outputs the generated update control parameters to the production device 20.

[0132] like Figure 2As shown, the generating device 10 includes an input unit 11 , an output unit 12 , a control circuit 13 , a machine learning model 14 , an operation accepting unit 15 , an image generating unit 16 , and a display unit 17 .

[0133] The generating device 10 is realized, for example, by executing a program stored in a memory in a computer device having a processor, a memory, and an input / output interface. Such a computer device is, for example, a personal computer (PC).

[0134] The input unit 11 acquires measurement data output from the sensor 30. The input unit 11 may be, for example, an electronic component or an electronic circuit that inputs a signal.

[0135] The control circuit 13 generates evaluation index data indicating the vibration of the driven object 24 after the stabilization time based on the measurement data acquired by the input unit 11, and updates the control parameters based on the generated evaluation index data using the machine learning model 14 that learns the relationship between the evaluation index data and the control parameters.

[0136] The evaluation index data generated by the control circuit 13 is any one of the following (1) to (3) or (4). That is,

[0137] (1) The sum of the areas enclosed by the deviation waveform indicating the vibration of the driven object 24 after the stabilization time and the reference axis (hereinafter also referred to as “sum area evaluation index data”).

[0138] (2) Indicative of the degree of fluctuation of the deviation waveform of the vibration of the driven object 24 after the stabilization time (hereinafter also referred to as “fluctuation degree evaluation index data”).

[0139] (3) An effective value indicating a deviation waveform of the vibration of the driven object 24 after the stabilization time (hereinafter also referred to as “effective value evaluation index data”).

[0140] Any of these, or

[0141] (4) The time from the time when the driven object 24 starts to be driven to the time when it stabilizes (hereinafter also referred to as “stabilization time evaluation index data”).

[0142] The evaluation index data generated by the control circuit 13 is specified by a user who uses the control parameter generation system 1 .

[0143] In the following, the total area evaluation index data, the fluctuation degree evaluation index data, and the effective value evaluation index data generated by the control circuit 13 are referred to as follows. Figure 4 Let's explain in more detail.

[0144] like Figure 4As shown, more specifically, the total area data generated by the control circuit 13 is calculated by the mathematical formula shown in (Formula 1), (Formula 2) or (Formula 3) when the position deviation f of the driven object relative to the target position is represented by f=E(t) as a variable at time t.

[0145] [Mathematical formula 1]

[0146]

[0147] [Mathematical formula 2]

[0148]

[0149] [Mathematical formula 3]

[0150]

[0151] Here, the starting point sett_t of the integration interval is the stable time, and the end point cal_e of the integration interval is the time set by the user. In addition, here, it is assumed that the starting point sett_t of the integration interval is the stable time for explanation, but the starting point sett_t of the integration interval can be either the stable time or any time after the stable time.

[0152] For example, when the position deviation of the driven object 24 relative to the target position reaches within 1 / 10 of the required accuracy and the position deviation no longer deviates from within 1 / 10 of the required accuracy, cal_e may be set to the time when the position deviation finally reaches within 1 / 10 of the required accuracy. In addition, cal_e may be set to the time when the number of data included in the measurement data acquired by the input unit 11 reaches a given number, or may be set to the time after a specified time has passed from the stable time.

[0153] More specifically, when def=cal_e-sett_t is assumed, the fluctuation degree evaluation index data generated by the control circuit 13 is calculated by the mathematical formula shown in (Formula 4).

[0154] [Formula 4]

[0155]

[0156] More specifically, the effective value evaluation index data generated by the control circuit 13 is calculated by the mathematical formula shown in (Formula 5).

[0157] [Formula 5]

[0158]

[0159] Back again Figure 1 as well as Figure 2, continue with the description of the control parameter generating system 1.

[0160] The machine learning model 14 is a machine learning model that learns the relationship between evaluation index data and control parameters. Whenever evaluation index data is input from the control circuit 13 in sequence, the relationship between the evaluation index data and the control parameters is learned, and the control parameters are output to the control circuit 13 in sequence based on the learning results.

[0161] More specifically, the machine learning model 14 utilizes a Bayesian optimization algorithm to learn the relationship between the evaluation index data and the control parameters corresponding to the evaluation index data (hereinafter also referred to as "corresponding control parameters") whenever evaluation index data is sequentially input from the control circuit 13, and outputs the control parameters predicted to be the minimum of the evaluation index data to the control circuit 13 based on the learning result.

[0162] Figure 6 It is a schematic diagram showing a situation in which the machine learning model 14 learns the relationship between the evaluation index data and the corresponding control parameter, and outputs the control parameter predicted to minimize the evaluation index data based on the learning result.

[0163] exist Figure 6 In , the horizontal axis is the value of the control parameter, and the vertical axis is the value of the evaluation index data. Figure 6 In FIG. 1 , the points drawn with circles are points representing the values ​​of the corresponding control parameters of the evaluation index data for each value of the evaluation index data input to the machine learning model 14 in the past.

[0164] like Figure 6 As shown, the machine learning model 14 uses the Bayesian optimization algorithm to predict the relationship between the evaluation index data and the corresponding control parameters to a certain extent, and uses the predicted relationship to output the control parameters predicted to minimize the evaluation index data.

[0165] Back again Figure 1 as well as Figure 2 , continue with the description of the control parameter generating system 1.

[0166] The output unit 12 outputs the control parameters updated by the control circuit 13 to the production device 20 to store them in the memory 21. The output unit 12 may be, for example, an electronic component or an electronic circuit that outputs a signal.

[0167] The operation accepting unit 15 accepts an input operation to the generating device 10 by a user who uses the control parameter generating system 1. The operation accepting unit 15 may be an input device such as a button or a keyboard.

[0168] The display unit 17 displays an image provided to a user utilizing the control parameter generating system 1. The display unit 17 may be, for example, a liquid crystal monitor, an organic EL (Electro Luminescence) monitor, or a CRT (Cathode Ray Tube) monitor.

[0169] The image generation unit 16 generates an image to be displayed by the display unit 17. The image generation unit 16 may be, for example, a graphic tablet.

[0170] Figure 7 This is an example of an image generated by the image generating unit 16 . Figure 7 This is an example of an image (hereinafter also referred to as an "evaluation index data designation image") that urges a user using the control parameter generation system 1 to specify which evaluation index data to set as the evaluation index data generated by the control circuit 13 when the control parameter generation system 1 starts the first control parameter adjustment process described later.

[0171] like Figure 7 As shown, the generating device 10 prompts the user to specify the following evaluation index data as the evaluation index data generated by the control circuit 13 by displaying the evaluation index data designation image on the display unit 17: specification of stabilization time evaluation index data, or specification of any one of the total area evaluation index data, fluctuation degree evaluation index data, and effective value evaluation index data (hereinafter, these three evaluation index data are also referred to as "vibration evaluation index data").

[0172] For example, the user specifies the stabilization time evaluation index data as the evaluation index data by checking the "stabilization time" for the evaluation index data designation image. For example, the user specifies the area sum evaluation index data as the evaluation index data by checking the "vibration" for the evaluation index data designation image and putting a dot next to the "area". For example, the user specifies the fluctuation degree evaluation index data as the evaluation index data by checking the "vibration" for the evaluation index data designation image and putting a dot next to the "variance". For example, the user specifies the effective value evaluation index data as the evaluation index data by checking the "vibration" for the evaluation index data designation image and putting a dot next to the "RMS (effective value)".

[0173] In addition, the user specifies the stabilization time evaluation index data and the vibration evaluation index data as the evaluation index data by, for example, checking both the "stabilization time" and the "vibration" for the evaluation index data designation image. In this case, the control circuit 13 may use the evaluation index data in which the sum of the value of the stabilization time evaluation index data and the value of the vibration evaluation index data is the value of the evaluation index data, or may use the evaluation index data in which the sum of the values ​​obtained by multiplying the value of the stabilization time evaluation index data and the value of the vibration evaluation index data by a predetermined weighting coefficient is the value of the evaluation index data.

[0174] Figure 8 This is an example of an image generated by the image generating unit 16 . Figure 8 This is an example of an image (hereinafter also referred to as a “status prompt image”) that prompts a user using the control parameter generation system 1 of the status of the value of the evaluation index data in the first control parameter adjustment process while the control parameter generation system 1 is executing the first control parameter adjustment process described later.

[0175] exist Figure 8 In the graph in FIG. 1 , the horizontal axis is the number of control parameters sequentially output by the generator 10, and the vertical axis is the value of the evaluation index data corresponding to the control parameters sequentially output by the generator 10. Figure 8 In the graph in , the solid line represents the transition of the minimum value of the evaluation index data corresponding to the control parameter output by the generation device 10.

[0176] like Figure 8 As shown, an icon labeled “interrupt” (hereinafter also referred to as “interrupt icon”) and an icon labeled “resume” (hereinafter also referred to as “resume icon”) are arranged at the upper right of the status presentation image.

[0177] The interrupt icon is an icon as follows: for example, when the user visually confirms the status prompt image and finds that the value of the evaluation index data corresponding to the control parameter output by the generating device 10 has become sufficiently small, and it is judged that it is no longer necessary to continue the first control parameter adjustment process, the first control parameter adjustment process is interrupted by clicking the interrupt icon.

[0178] The restart icon is an icon that continues the first control parameter adjustment process by clicking the restart icon, for example, when the user visually confirms the status prompt image and finds that the value of the evaluation index data corresponding to the control parameter output by the generating device 10 has not become sufficiently small, and therefore it is determined that the first control parameter adjustment process needs to be continued.

[0179] <Action>

[0180] Next, the operation of the control parameter generation system 1 having the above configuration will be described.

[0181] The control parameter generation system 1 performs a first control parameter adjustment process to adjust the control parameters stored in the memory 21 to appropriate values. For example, a user using the control parameter generation system 1 performs an operation on the generation device 10 to start the first control parameter adjustment process, thereby starting the first control parameter adjustment process.

[0182] Fig. 9 is a sequence diagram of the first control parameter adjustment process. Fig.10 This is a flowchart of the first control parameter adjustment process.

[0183] like Fig. 9 , Fig.10 As shown, when the first control parameter adjustment process starts, the generating device 10 starts a predetermined program for executing the first control parameter adjustment process (step S10 ).

[0184] When the given program is started, the output unit 12 outputs the initial value of the control parameter to the memory 21 (step S15). At this time, the output unit 12 may output the initial value of the control parameter consisting of a predetermined value, the initial value of the control parameter consisting of a value specified by the user, or the initial value of the control parameter consisting of a value calculated by a calculation method specified by the user.

[0185] When the control parameter is output from the output unit 12, the memory 21 stores the control parameter output from the output unit 12 (step S20). Then, the control circuit 22 generates a command for making the position of the driven object 24 a given target position based on the control parameter stored in the memory 21, and outputs the command to the servo motor 23. Thus, the control circuit 22 controls the servo motor 23 (step S25). Then, the servo motor 23 drives the driven object 24 based on the command output from the control circuit 22 (step S30).

[0186] When the driven object 24 is driven by the servo motor 23, the sensor 30 measures the position of the driven object 24 (step S35), and outputs the measurement data indicating the position of the driven object 24 after the driven object 24 reaches the allowable position that can be evaluated as reaching the given target position to the input unit 11. Then, the input unit 11 acquires the measurement data output from the sensor 30 (step S40).

[0187] If the input unit 11 acquires the measurement data, the control circuit 13 generates evaluation index data representing the vibration of the driven object 24 after the stabilization moment based on the measurement data (step S45). In addition, the control circuit 13 investigates whether the generation of the evaluation index data satisfies a given condition (step S50). Here, the given condition refers to, for example, a condition that the number of updates (i.e., the number of generation) of the evaluation index data has reached a given number of times, a condition that a given time has passed since the adjustment of the control parameter began, and the like. Here, the given number of times and the given time may be predetermined or may be specified by a user using the control parameter generation system 1.

[0188] When the generation of the evaluation index data does not satisfy the given condition in the process of step S50 (step S50: No), the control circuit 13 outputs the generated evaluation index data to the machine learning model 14. Then, the machine learning model 14 learns the relationship between the evaluation index data and the corresponding control parameter (step S55), and outputs the control parameter predicted to be the minimum value of the evaluation index data. Then, the control circuit 13 updates the control parameter previously output by the output unit 12 by the control parameter newly output from the machine learning model 14 (step S60). Then, the output unit 12 sends the control parameter newly updated by the control circuit 13 to the memory 21 (step S65).

[0189] When the process of step S65 is completed, the control parameter generation system 1 proceeds to the process of step S20 and repeats the processes from step S20 onwards.

[0190] When the evaluation index data generated in the process of step S50 satisfies the given condition (step S50 : Yes), the control parameter generation system 1 ends the first control parameter adjustment process.

[0191] <Investigation>

[0192] As described above, the control parameter generation system 1 having the above configuration repeatedly generates control parameters by executing the first control parameter adjustment process until the generation of evaluation index value data satisfies a given condition, thereby adjusting the control parameters.

[0193] Therefore, according to the control parameter generation system 1, appropriate control parameters can be generated in a relatively short time without relying on skilled workers.

[0194] (Implementation method 2)

[0195] Hereinafter, a control parameter generation system according to a second embodiment will be described. The second embodiment is configured by changing a part of the structure of the control parameter generation system 1 according to the first embodiment.

[0196] The control parameter generation system 1 is an example of a system in which the sensor 30 measures the position of the driven object 24 and outputs the measurement data, and the generation device 10 acquires the measurement data output from the sensor 30 .

[0197] In contrast, the control parameter generating system involved in Implementation 2 is an example of a system in which the sensor with a processing device involved in Implementation 2 measures the position of the driven object 24 and outputs evaluation index data, and the generating device involved in Implementation 2 obtains the evaluation index data output from the sensor with a processing device involved in Implementation 2.

[0198] Here, regarding the control parameter generation system involved in embodiment 2, the same components as those of control parameter generation system 1 are assumed to have been described and marked with the same reference numerals, and their detailed description is omitted, and the description focuses on the differences from control parameter generation system 1.

[0199] Fig.11 This is a schematic diagram showing an overview of a control parameter generating system 1A according to the second embodiment.

[0200] Fig.12 is a block diagram showing the structure of the control parameter generating system 1A.

[0201] like Fig.11 As shown, the control parameter generation system 1 according to the first embodiment is configured by changing the generation device 10 to a generation device 10A, and changing the sensor 30 to a sensor 30A with a processing device. Fig.12 As shown, the input unit 11 of the generating device 10 is changed to an input unit 11A, and the control circuit 13 is changed to a control circuit 13A, so as to configure a generating device 10A.

[0202] The sensor 30A with a processing device measures the position of the driven object 24. The sensor 30A with a processing device generates measurement data indicating the position of the driven object 24 after the time when the driven object 24 reaches the allowable position that can be evaluated as reaching the given target position. Furthermore, the sensor 30A with a processing device generates evaluation index data indicating the vibration of the driven object 24 after the stabilization time based on the generated measurement data, and outputs the generated evaluation index data to the generation device 10A.

[0203] The sensor 30A with a processing device is realized by, for example, a computer device having a sensing device for measuring the position of the driven object 24, a processor, a memory, and an input / output interface, and the processor executing a program stored in the memory. Such a computer device is, for example, a personal computer (PC).

[0204] Alternatively, the sensor 30A with a processing device may include, for example, an image processing device. In this case, the sensor 30A with a processing device may perform image processing on an image of the driven object 24 using, for example, the image processing device to measure the position of the driven object 24.

[0205] The input unit 11A acquires evaluation index data output from the sensor 30A with a processing device.

[0206] The control circuit 13A updates the control parameters based on the evaluation index data acquired by the input unit 11A using the machine learning model 14 that learns the relationship between the evaluation index data and the control parameters.

[0207] <Action>

[0208] Next, the operation of the control parameter generation system 1A having the above configuration will be described.

[0209] The control parameter generation system 1A performs a second control parameter adjustment process in which a part of the process is changed from the first control parameter adjustment process according to the first embodiment.

[0210] Fig.13 is a sequence diagram of the second control parameter adjustment process. Fig.14 This is a flowchart of the second control parameter adjustment process.

[0211] like Fig.13 , Fig.14 As shown, the second control parameter adjustment process includes the process of step S110 to the process of step S165. Among these processes, the process of step S110 to the process of step S130 and the process of step S150 to the process of step S165 are the same as the process of step S10 to the process of step S30 and the process of step S50 to the process of step S65 in the first control parameter adjustment process, respectively.

[0212] Therefore, here, the description will be mainly focused on the processing of step S135 to step S145.

[0213] When the processing of step S130 is completed, the sensor 30A with a processing device measures the position of the driven object 24 (step S135), and generates measurement data indicating the position of the driven object 24 after the time when the driven object 24 reaches the allowed position that can be evaluated as reaching the given target position. In addition, the sensor 30A with a processing device generates evaluation index data indicating the vibration of the driven object 24 after the stabilization time based on the generated measurement data (step S140), and outputs the generated evaluation index data to the input unit 11A. Then, the input unit 11A acquires the evaluation index data output from the sensor 30A with a processing device (step S145).

[0214] When the process of step S145 is completed, the control parameter generating system 1A proceeds to the process of step S150 .

[0215] When the generation of the evaluation index data satisfies the given condition in the process of step S150 (step S150 : Yes), the control parameter generation system 1A ends the second control parameter adjustment process.

[0216] <Investigation>

[0217] As described above, the control parameter generation system 1A having the above configuration, like the control parameter generation system 1 according to the first embodiment, repeatedly generates control parameters by executing the second control parameter adjustment process until the generation of the evaluation index value data satisfies the given conditions, thereby adjusting the control parameters.

[0218] Therefore, according to the control parameter generation system 1A, similarly to the control parameter generation system 1 , it is possible to generate appropriate control parameters in a relatively short time without relying on a skilled person.

[0219] (Implementation method 3)

[0220] Hereinafter, a control parameter generation system according to a third embodiment will be described. The third embodiment is configured by changing a part of the structure of the control parameter generation system 1 according to the first embodiment.

[0221] The control parameter generation system involved in Implementation 3 is an example of a system in which, during the adjustment of the control parameters, when the vibration represented by the evaluation index data exceeds a given threshold, the updating of the control parameters is interrupted, and then when the vibration represented by the evaluation index data becomes below a given threshold, the interrupted updating of the control parameters is restarted.

[0222] Here, regarding the control parameter generation system involved in embodiment 3, the same components as those of the control parameter generation system 1 are assumed to have been described and marked with the same symbols, and their detailed description is omitted, and the description focuses on the differences from the control parameter generation system 1.

[0223] Fig.15 This is a schematic diagram showing an overview of a control parameter generating system 1B according to the third embodiment.

[0224] Fig.16 is a block diagram showing the structure of the control parameter generating system 1B.

[0225] like Fig.15 As shown, the control parameter generation system 1 according to the first embodiment is configured by changing the generation device 10 to a generation device 10B and adding a display device 40 to form a control parameter generation system 1B. Fig.16 As shown, the output unit 12 of the generating device 10 is changed to the output unit 12B, and the control circuit 13 is changed to the control circuit 13B to form the generating device 10B. In addition, the display device 40 may be provided separately from the generating device 10B, or may be included in the generating device 10B. In addition, the display device 40 and the display unit 17 may be the same. In the case where the display device 40 is included as a part of the display unit 17, for example, the display unit 17 may be composed of a plurality of display devices and the display device 40 may be one of them. In addition, the display unit 17 may also display the information shown by the display device 40.

[0226] The control circuit 13B performs the same operation as that performed by the control circuit 13, and further performs the following operation.

[0227] When the evaluation index data is generated, the control circuit 13B determines whether the vibration of the driven object 24 indicated by the generated evaluation index data exceeds a given threshold value.

[0228] The control circuit 13B uses the machine learning model 14 to update the control parameters when the vibration of the driven object 24 represented by the evaluation index data is below a given threshold, and interrupts the update of the control parameters when the vibration of the driven object 24 represented by the evaluation index data exceeds the given threshold.

[0229] The control circuit 13 further restarts the interrupted updating of the control parameters after interrupting the updating of the control parameters, when the vibration of the driven object 24 represented by the evaluation index data is below a given threshold value, based on the evaluation index data generated based on the measurement data acquired after the moment when the vibration of the driven object 24 represented by the evaluation index data is below the given threshold value.

[0230] When generating the evaluation index data, the control circuit 13B generates the evaluation index data in association with the generation time of the evaluation index data, and updates the adjustment history based on the generated evaluation index data and the generation time of the evaluation index data.

[0231] Fig.17 2 is a data structure diagram showing an example of the data structure of the adjustment history updated by the control circuit 13B.

[0232] like Fig.17 As shown, as an example, the number of updates, the time stamp, the optimal evaluation value, and the control parameter value at the time of the optimal evaluation value are associated with each other to form an adjustment history.

[0233] The number of updates is the number of times the control circuit 13B generates the control parameters after the control parameter generation system 1B starts adjusting the control parameters, that is, the number of times the control circuit 13B updates the control parameters.

[0234] The time stamp is the time at which the control circuit 13B generates evaluation index data of the number of times indicated by the corresponding update frequency.

[0235] The optimal evaluation value is the optimal evaluation index data value (here, the minimum evaluation index data value) generated by the control circuit 13B after the control parameter generation system 1B starts adjusting the control parameters until the time indicated by the corresponding time stamp.

[0236] The control parameter value at the time of the optimum evaluation value is the value of the control parameter when the control circuit 13B calculates the corresponding optimum evaluation value.

[0237] Back again Fig.15 , Fig.16 , continue with the description of the control parameter generating system 1B.

[0238] The output unit 12B performs the same operation as that performed by the output unit 12, and further performs the following operation.

[0239] When the control circuit 13B updates the adjustment history, the output unit 12B outputs the updated adjustment history to the display device 40 .

[0240] When control circuit 13B determines that the vibration of driven object 24 indicated by the evaluation index data exceeds a given threshold, output unit 12B outputs a message to interrupt the updating of the control parameters (hereinafter also referred to as an “update interrupt message”) to display device 40 .

[0241] The display device 40 generates and displays an image based on the data and message output from the output unit 12B.

[0242] Fig.18 This is an example of an image displayed on the display device 40 . Fig.18 This is an example of an image showing transition of the optimal evaluation value (hereinafter also referred to as “optimal evaluation value transition image”) which is displayed when the adjustment history is transmitted from the output unit 12B.

[0243] exist Fig.18 In , the horizontal axis is the number of updates of the control parameters, or the elapsed time (adjustment time) from the start of adjustment of the control parameters, and the vertical axis is the optimal evaluation value.

[0244] Fig.19 is another example of an image displayed by the display device 40. Fig.19 This is an example of an image displayed when an update suspension message is transmitted from the output unit 12B while the optimal evaluation value transition image is displayed, and is an image in which a message image indicating the contents of the update suspension message is superimposed on the displayed optimal evaluation value transition image.

[0245] Fig.19 The dotted line in is a virtual line indicating the transition of the optimum evaluation value when the adjustment of the control parameter progresses ideally, and is not actually displayed in the adjustment restart time point designation image.

[0246] Fig.19 The one-dot chain line in is a virtual line indicating the transition of the optimal evaluation value expected when the adjustment of the control parameter is not interrupted, and is not actually displayed in the adjustment restart time point designation image.

[0247] As shown by the dotted line and the single-dot line, it is expected that the adjustment of the control parameters will not proceed ideally if the adjustment of the control parameters is not interrupted even though the vibration of the driven object 24 represented by the evaluation index data exceeds the given threshold, and there is a concern that the adjustment of the control parameters that satisfies the user of the control parameter generation system 1B cannot be achieved.

[0248] When the display device 40 displays the optimum evaluation value transition image without superimposing the message image, the user can visually confirm that the updating of the control parameter is not interrupted.

[0249] Furthermore, when the display device 40 displays a message image superimposed on the optimum evaluation value transition image, the user can visually confirm that the updating of the control parameter is interrupted.

[0250] <Action>

[0251] Next, the operation of the control parameter generation system 1B having the above configuration will be described.

[0252] The control parameter generation system 1B performs a third control parameter adjustment process in which a part of the process is changed from the first control parameter adjustment process according to the first embodiment.

[0253] Fig. 20 This is a flowchart of the third control parameter adjustment process.

[0254] like Fig. 20 As shown, the third control parameter adjustment process includes the process of step S210 to the process of step S277. Among these processes, the process of step S210 to the process of step S240 and the process of step S250 to the process of step S265 are the same processes as the process of step S10 to the process of step S40 and the process of step S50 to the process of step S65 in the first control parameter adjustment process, respectively.

[0255] Therefore, here, the description will focus on the processing of step S270 to step S277.

[0256] When the processing of step S240 is completed, the control circuit 13B generates evaluation index data indicating the vibration of the driven object 24 after the stabilization time in correspondence with the generation time of the evaluation index data based on the measurement data (step S270). Furthermore, the control circuit 13B updates the adjustment history based on the generated evaluation index data and the generation time of the evaluation index data (step S271).

[0257] When the adjustment history is updated, the output unit 12B outputs the updated adjustment history to the display device 40 (step S272 ). Then, the display device 40 displays the optimum evaluation value transition image based on the adjustment history (step S273 ).

[0258] On the other hand, the control circuit 13B determines whether the vibration of the driven object 24 indicated by the generated evaluation index data exceeds a given threshold value (step S274 ).

[0259] When the control circuit 13B determines that the vibration of the driven object 24 represented by the generated evaluation index data exceeds the given threshold value (step S275: Yes), the output unit 12B outputs an update interrupt message to the display device 40 (step S276). Then, the display device 40 displays a message image indicating the content of the update interrupt message and the optimal evaluation value transition image in a superimposed manner (step S277).

[0260] When the process of step S277 is completed, the control parameter generation system 1B proceeds to the process of step S225.

[0261] When the control circuit 13B determines in the process of step S275 that the vibration of the driven object 24 indicated by the generated evaluation index data does not exceed the given threshold value (step S275 : No), the control parameter generation system 1B proceeds to the process of step S250 .

[0262] <Investigation>

[0263] As described above, according to the control parameter generation system 1B of the above structure, when the vibration of the driven object 24 represented by the evaluation index data exceeds a given threshold value, the updating of the control parameters is interrupted, thereby being able to suppress the adverse effect of the vibration of the production device 20 on the adjustment of the control parameters during the period of adjusting the control parameters.

[0264] Therefore, according to the control parameter generation system 1B having the above configuration, it is possible to suppress the frequency with which the control parameters need to be readjusted.

[0265] (Implementation method 4)

[0266] Hereinafter, a control parameter generation system according to a fourth embodiment will be described. The fourth embodiment is configured by changing a part of the structure of the control parameter generation system 1B according to the third embodiment.

[0267] The control parameter generation system 1B is an example of a system in which the sensor 30 measures the position of the driven object 24 and outputs the measurement data, and the generation device 10B acquires the measurement data output from the sensor 30 .

[0268] In contrast, the control parameter generating system involved in embodiment 4 is an example of a system in which the sensor with a processing device involved in embodiment 4 measures the position of the driven object 24 and outputs evaluation index data, and the generating device involved in embodiment 4 obtains the evaluation index data output from the sensor with a processing device involved in embodiment 4.

[0269] Here, regarding the control parameter generation system involved in embodiment 4, the same components as those of the control parameter generation system 1B are assumed to have been described and are marked with the same symbols, and their detailed description is omitted. The description focuses on the differences from the control parameter generation system 1B.

[0270] Fig.21 This is a schematic diagram showing an overview of a control parameter generation system 1C according to the fourth embodiment.

[0271] Fig. 22 : is a block diagram showing the structure of the control parameter generation system 1C.

[0272] like Fig.21As shown, the control parameter generation system 1C is configured by changing the generation device 10B to a generation device 10C from the control parameter generation system 1B according to the third embodiment, and changing the sensor 30 to a sensor 30C with a processing device. Fig. 22 As shown, the generation device 10C is configured by changing the input unit 11 of the generation device 10B to an input unit 11C and changing the control circuit 13B to a control circuit 13C.

[0273] The sensor 30C with a processing device performs the same operation as the sensor 30A with a processing device, and further performs the following operation.

[0274] When the sensor 30C with a processing device generates the evaluation index data, the evaluation index data is generated in correspondence with the generation time of the evaluation index data. Moreover, when the sensor 30C with a processing device outputs the generated evaluation index data to the generating device 10C, the generation time of the generated evaluation index data is output in correspondence with the evaluation index data.

[0275] The input unit 11C performs the same operation as that performed by the input unit 11, and further performs the following operation.

[0276] The input unit 11C acquires the evaluation index data output from the sensor 30C with a processing device in association with the generation time of the evaluation index data.

[0277] The control circuit 13C performs the same operation as that performed by the control circuit 13B, and further performs the following operation.

[0278] In a case where the input unit 11C does not acquire measurement data from the sensor 30C with a processing device but acquires the evaluation index data in correspondence with the time when the evaluation index data is generated, instead of generating the evaluation index data based on the measurement data and the time when the evaluation index data is generated in correspondence, the control circuit 13C acquires the evaluation index data acquired by the input unit 11C in correspondence with the time when the evaluation index data is generated.

[0279] <Action>

[0280] Next, the operation of the control parameter generation system 1C having the above configuration will be described.

[0281] The control parameter generation system 1C performs a fourth control parameter adjustment process in which a part of the process is changed from the third control parameter adjustment process according to the third embodiment.

[0282] Fig.23 This is a flowchart of the fourth control parameter adjustment process.

[0283] like Fig.23 As shown, the fourth control parameter adjustment process includes the process of step S310 to the process of step S377. Among these processes, the process of step S310 to the process of step S330, the process of step S350 to the process of step S365, and the process of step S371 to the process of step S377 are the same processes as the process of step S210 to the process of step S230, the process of step S250 to the process of step S265, and the process of step S271 to the process of step S277 in the third control parameter adjustment process, respectively.

[0284] Therefore, here, the description will be mainly focused on the processing of step S335 to step S340 and the processing of step S370.

[0285] When the processing of step S330 is completed, the sensor 30C with a processing device measures the position of the driven object 24 (step S335), and generates measurement data indicating the position of the driven object 24 after the moment when the driven object 24 reaches the allowed position that can be evaluated as reaching the given target position. Moreover, the sensor 30C with a processing device generates the evaluation index data indicating the vibration of the driven object 24 after the stabilization moment in correspondence with the generation time of the evaluation index data based on the generated measurement data (step S340), and outputs the generated evaluation index data to the input unit 11C in correspondence with the generation time of the evaluation index data. Then, the input unit 11C obtains the evaluation index data output from the sensor 30C with a processing device in correspondence with the generation time of the evaluation index data (step S370). Moreover, the control circuit 13C updates the adjustment history based on the obtained evaluation index data and the generation time of the evaluation index data (step S371).

[0286] When the process of step S371 is completed, the control parameter generation system 1C proceeds to the process of step S372.

[0287] <Investigation>

[0288] According to the control parameter generating system 1C of the above-mentioned structure, similarly to the control parameter generating system 1B involved in the third embodiment, when the vibration of the driven object 24 represented by the evaluation index data exceeds a given threshold value, the updating of the control parameters is interrupted, thereby being able to suppress the adverse effect of the vibration of the production device 20 on the adjustment of the control parameters during the period of adjusting the control parameters.

[0289] Therefore, according to the control parameter generation system 1C having the above configuration, it is possible to suppress the frequency with which the control parameters need to be readjusted.

[0290] (Replenish)

[0291] As described above, as an illustration of the technology disclosed in this application, the description is based on embodiments 1 to 4. However, the present disclosure is not limited to these embodiments 1 to 4. As long as it does not depart from the main purpose of the present disclosure, the embodiments obtained by implementing various modifications that a person skilled in the art can think of to the present embodiment or the present modification, and the methods constructed by combining the constituent elements in different embodiments or modifications may also be included in the scope of one or more methods of the present disclosure.

[0292] (1) In Embodiment 1, as an example, the generation device 10 is described as being implemented by one computer device. However, as long as the same function can be achieved, it is not necessary to limit the generation device 10 to an example implemented by one computer device. The generation device 10 may be implemented by, for example, a plurality of computer devices that can communicate with each other.

[0293] Fig.24 This is a block diagram showing an example of the configuration of the generating device 10 when the generating device 10 is implemented by a plurality of computer devices that can communicate with each other.

[0294] like Fig.24 As shown, the generating device 10 can also be implemented by (1) a first computer device 100 including an input unit 11, an output unit 12, an operation receiving unit 15, an image generating unit 16, a display unit 17 and a communication unit 18, (2) a second computer device 110 including a control circuit 13, a machine learning model 14 and a communication unit 19, and (3) a network 120 connecting the communication unit 18 and the communication unit 19 so that they can communicate.

[0295] In this case, for example, the first computer device 100 may be a personal computer (PC) and the second computer device 110 may be a server.

[0296] (2) In the first embodiment, the control parameter generation system 1 is described as having the following structure: (i) the sensor 30 measures the position of the driven object 24 and outputs measurement data indicating the position of the driven object 24 after the driven object 24 reaches an allowable position that can be evaluated as reaching a given target position, and (ii) the input unit 11 acquires the measurement data output from the sensor 30.

[0297] However, the above-mentioned structure of the control parameter generating system 1 is an example, and as long as the input unit 11 can obtain measurement data indicating the position of the driven object 24 after the moment when the driven object 24 reaches an allowable position that can be evaluated as reaching a given target position, there is no need to limit the control parameter generating system 1 to the above-mentioned structure.

[0298] Fig.251 is a block diagram showing the structure of a control parameter generation system 1D as another example of the structure of the control parameter generation system 1 , that is, a control parameter generation system 1D that does not include the sensor 30 .

[0299] In the control parameter generation system 1D, the servo motor 23 has a built-in sensor for detecting the motor position (for example, if the servo motor 23 is a rotary motor, the rotation position of the servo motor 23). The servo motor 23 outputs an encoding value indicating the motor position of the servo motor 23 detected by the built-in sensor. That is, the production device 20D outputs the encoding value.

[0300] In addition, generally, such a sensor for detecting the position of a motor is also called an encoder. In addition, generally, a servo motor has an encoder built therein.

[0301] Here, the position of the servo motor 23 represented by the code value is in a one-to-one correspondence with the position of the driven object 24. Therefore, the code value output by the servo motor 23 represents the position of the servo motor 23 and also represents the position of the driven object 24. Therefore, it can be said that the sensor built into the servo motor 23 also detects the position of the driven object 24 and also outputs a code value representing the position of the driven object 24.

[0302] Furthermore, in the control parameter generating system 1D, the input unit 11 acquires the encoder value output from the servo motor 23 as measurement data.

[0303] (3) The present disclosure may be implemented in a general or specific manner by a system, device, method, integrated circuit, program, or non-transitory recording medium such as a computer-readable CD-ROM. In addition, the present disclosure may be implemented by any combination of a system, device, method, integrated circuit, program, and non-transitory recording medium. For example, the present disclosure may be implemented as a program for causing a computer device to execute the processing performed by a generating device.

[0304] (4) The production device 20, 20D of the present disclosure may also be equipped with the generation device 10, 10A to 10C. The production device 20, 20D of the present disclosure may also be equipped with the control parameter generation system 1, 1A to 1D. In addition, the display device 40 may be configured separately from the generation device 10, 10A to 10C, or independently from the generation device 10, 10A to 10C. In addition, the display device 40 may also be assembled in the production device 20, 20D. In addition, the sensor 30 and the sensor 30A, 30C with a processing device may also be configured separately from the production device 20, 20D, or independently from the production device 20, 20D. In addition, the sensor 30 and the sensor 30A, 30C with a processing device may also be configured by assembling the generation device 10, 10A to 10C. In addition, the production device 20, 20D of the present disclosure is not limited to a device for producing a product, and may include equipment, complete equipment, and a factory for producing a product. In addition, the present disclosure can be applied to a transport device other than the production device 20 or 20D. In addition, the control parameter generation system 1 or 1A to 1D of the present disclosure can be applied to a device, equipment, plant, warehouse, or factory that produces a product.

[0305] Industrial Applicability

[0306] The present disclosure can be widely utilized in systems for generating control parameters, and the like.

[0307] Explanation of symbols

[0308] 1.1A, 1B, 1C, 1D Control parameter generation system

[0309] 10, 10A, 10B, 10C Generating device

[0310] 11, 11A, 11C Input

[0311] 12, 12B Output

[0312] 13, 13A, 13B, 13C Control circuit

[0313] 14 Machine Learning Model

[0314] 15 Operation Reception Department

[0315] 16 Image generation unit

[0316] 17 Display

[0317] 18, 19 Ministry of Communications

[0318] 20, 20D production equipment

[0319] 21 Memory

[0320] 22 Control circuit

[0321] 23 Servo motor

[0322] 24 Driven object

[0323] 30 Sensors

[0324] 30A, 30C Sensor with processing unit

[0325] 40 Display device

[0326] 100 No. 1 computer device

[0327] 110 Second computer device

[0328] 120 network.

Claims

1. A control parameter generation method, the generation method generating a control parameter, the control parameter being generated by a control parameter generation device and being used by the production device while a sensor confirms the state of the production device, The production device comprises: a servo motor; a control circuit for controlling the servo motor; a memory for storing the control parameters used when the control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor measures the position of the driven object after the driven object reaches an allowed position based on an instruction for making the position of the driven object a given target position, and outputs measurement data representing the position, wherein the allowed position is a position that can be evaluated as reaching the given target position. The method for generating the control parameters includes: acquiring measurement data indicating the position outputted from the sensor, generating, based on the measurement data, evaluation index data indicating vibration of the driven object after the time when the driven object reaches the allowed position; Based on the evaluation index data, when the vibration represented by the evaluation index data is below a given threshold, updating the control parameter using a machine learning model that learns the relationship between the evaluation index data and the control parameter, outputting the updated control parameters to the production device for storage in the memory, When the vibration indicated by the evaluation index data exceeds the given threshold, updating of the control parameter is interrupted.

2. The method for generating control parameters according to claim 1, wherein: When the vibration indicated by the evaluation index data exceeds the given threshold, a message to interrupt the updating of the control parameter is further output to the display device.

3. The method for generating control parameters according to claim 1, wherein: After interrupting the updating of the control parameters, when the vibration represented by the evaluation index data becomes below the given threshold, the interrupted updating of the control parameters is started again based on the evaluation index data generated based on the measurement data acquired after the moment when the vibration represented by the evaluation index data becomes below the given threshold.

4. The method for generating control parameters according to claim 1, wherein: The production device includes any one of an installation device, a processing device, or a working device. 5 . A program for causing a computer of an information processing device to execute the control parameter generation method according to claim 1 , wherein the information processing device is connected to the production device. 6 . A recording medium having a program recorded thereon, the program being used to cause a computer of an information processing device to execute the control parameter generation method according to claim 1 , the information processing device being connected to the production device.

7. A control parameter generating device, generating a control parameter used by a production device while a sensor confirms the state of the production device, The production device comprises: a servo motor; a first control circuit for controlling the servo motor; a memory for storing control parameters used when the first control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor measures the position of the driven object after the driven object reaches an allowed position based on an instruction for making the position of the driven object a given target position, and outputs measurement data representing the position, wherein the allowed position is a position that can be evaluated as reaching the given target position. The control parameter generating device comprises: an input unit for acquiring measurement data indicating the position output from the sensor; a second control circuit that generates, based on the measurement data, evaluation index data indicating vibration of the driven object after the driven object reaches the permitted position, and based on the evaluation index data, updates the control parameter using a machine learning model that learns a relationship between the evaluation index data and the control parameter when the vibration indicated by the evaluation index data is below a given threshold value; and an output unit, outputting the updated control parameter to the production device to be stored in the memory, The second control circuit further interrupts the updating of the control parameter when the vibration indicated by the evaluation index data exceeds the predetermined threshold value.

8. A method for generating a control parameter, wherein the control parameter is generated by a control parameter generating device and used by the production device while a sensor attached to a processing device confirms the state of the production device, The production device comprises: a servo motor; a control circuit for controlling the servo motor; a memory for storing control parameters used when the control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor with a processing device measures the position of the driven object after the driven object reaches an allowed position based on an instruction for making the position of the driven object a given target position, and outputs evaluation index data representing the vibration of the driven object after the driven object reaches the allowed position, wherein the allowed position is a position that can be evaluated as reaching the given target position. The method for generating the control parameters includes: acquiring evaluation index data indicating vibration of the driven object after the driven object reaches the permitted position, output from the sensor with the processing device; Based on the evaluation index data, when the vibration represented by the evaluation index data is below a given threshold, updating the control parameter using a machine learning model that learns the relationship between the evaluation index data and the control parameter, outputting the updated control parameters to the production device for storage in the memory, When the vibration indicated by the evaluation index data exceeds the given threshold, updating of the control parameter is interrupted.

9. The method for generating control parameters according to claim 8, wherein: When the vibration indicated by the evaluation index data exceeds the given threshold, a message to interrupt the updating of the control parameter is further output to the display device.

10. The method for generating control parameters according to claim 8, wherein: After interrupting the updating of the control parameters, when the vibration represented by the evaluation index data becomes below the given threshold, the interrupted updating of the control parameters is started again based on the evaluation index data acquired after the moment when the vibration represented by the evaluation index data becomes below the given threshold.

11. The method for generating control parameters according to claim 8, wherein: In the sensor with a processing device, the evaluation index data is generated based on the position of the driven object measured by the sensor with a processing device.

12. The method for generating control parameters according to claim 8, wherein: The sensor with accompanying processing means includes an image processing means.

13. The method for generating control parameters according to claim 8, wherein: The production device includes any one of an installation device, a processing device, or a working device.

14. A program for causing a computer of an information processing device to execute the control parameter generation method according to any one of claims 8 to 13, wherein the information processing device is connected to the production device. 15 . A recording medium having a program recorded thereon, the program being for causing a computer of an information processing device to execute the control parameter generation method according to claim 8 , the information processing device being connected to the production device.

16. A control parameter generating device for generating control parameters used by a production device while a sensor attached to a processing device confirms the state of the production device, The production device comprises: a servo motor; a first control circuit for controlling the servo motor; a memory for storing control parameters used when the first control circuit controls the servo motor; and a driven object driven by the servo motor. The sensor with a processing device measures the position of the driven object after the driven object reaches an allowed position based on an instruction for making the position of the driven object a given target position, and outputs evaluation index data representing the vibration of the driven object after the driven object reaches the allowed position, wherein the allowed position is a position that can be evaluated as reaching the given target position. The control parameter generating device comprises: an input unit for acquiring evaluation index data indicating vibration of the driven object after the driven object reaches the permitted position, output from the sensor with the processing device; a second control circuit that updates the control parameter based on the evaluation index data using a machine learning model that learns the relationship between the evaluation index data and the control parameter when the vibration represented by the evaluation index data is below a given threshold value; and an output unit, outputting the updated control parameter to the production device to be stored in the memory, The second control circuit further interrupts the updating of the control parameter when the vibration indicated by the evaluation index data exceeds the predetermined threshold value.

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