WO2021107139A1 - Grinding system, correction amount estimation device, computer program, and grinding method - Google Patents

Grinding system, correction amount estimation device, computer program, and grinding method Download PDF

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Publication number
WO2021107139A1
WO2021107139A1 PCT/JP2020/044359 JP2020044359W WO2021107139A1 WO 2021107139 A1 WO2021107139 A1 WO 2021107139A1 JP 2020044359 W JP2020044359 W JP 2020044359W WO 2021107139 A1 WO2021107139 A1 WO 2021107139A1
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Prior art keywords
correction amount
grinding
reaction force
machining
difference
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PCT/JP2020/044359
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French (fr)
Japanese (ja)
Inventor
剛 横矢
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株式会社安川電機
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Priority to CN202080073652.1A priority Critical patent/CN114616076A/en
Priority to JP2021561572A priority patent/JP7294448B2/en
Publication of WO2021107139A1 publication Critical patent/WO2021107139A1/en

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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B24GRINDING; POLISHING
    • B24BMACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B27/00Other grinding machines or devices
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B24GRINDING; POLISHING
    • B24BMACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
    • B24B49/00Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
    • B24B49/16Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation taking regard of the load

Definitions

  • the present invention relates to a grinding system, a correction amount estimation device, a computer program, and a grinding method.
  • the polishing processing condition data indicating the processing condition of the polishing process is observed as a state variable representing the current state of the environment, and based on the state variable, the wear of the polishing tool with respect to the processing condition of the polishing process.
  • a polishing tool wear amount prediction device including a machine learning device that performs learning or prediction using a learning model that models a quantity is described.
  • Patent Document 1 predicts the wear of the polishing tool at this point by using a machine learning device, and estimates the wear of the polishing tool based on various machining conditions, but the amount of wear of the tool is It is difficult to accurately estimate the amount of tool wear according to the present invention because it strongly depends on the state of the workpiece.
  • the present invention has been made in view of such circumstances, and an object of the present invention is to obtain a correction amount in a pressing direction that reflects the state of a workpiece in a grinding system.
  • the grinding system has a reaction force-related information acquisition unit that acquires reaction force-related information regarding the pressing reaction force of a grinding tool during grinding, and a reaction force-related information acquisition unit that acquires reaction force-related information regarding the pressing reaction force of a grinding tool during grinding.
  • a difference calculation unit that calculates the difference between the two acquired reaction force-related information, a correction amount estimation unit that estimates a correction amount in the pressing direction of the grinding tool based on the difference, a target position, and the correction. It has a machining control unit that controls the position of the grinding tool at the time of machining based on the amount.
  • the correction amount estimation unit may input the difference into the machine learning model to obtain the correction amount.
  • the grinding system has a cumulative correction amount calculation unit for calculating a cumulative correction amount which is a cumulative value of the correction amount for the grinding wheel of the grinding tool, and the machining control unit. May control the position of the grinding tool at the time of machining based on the target position and the cumulative correction amount when machining different workpieces.
  • the machining target portion of the workpiece may be classified, and the correction amount estimation unit may estimate the correction amount for each classification.
  • the grinding tool is supported by the motor control support mechanism, and the reaction force related information may be the external force torque of the motor in the pressing direction of the grinding tool. ..
  • the reaction force related information is input to the second machine learning model, and the estimated number of repeated machining times at the same location of the workpiece is estimated. May have a part.
  • the correction amount estimation device is a difference in reaction force-related information regarding the pressing reaction force of the grinding tool during the grinding process, which is acquired at different repetitive times for the repetitive machining at the same place of the workpiece. It has a difference calculation unit for calculating the difference, and a correction amount estimation unit for estimating the correction amount in the pressing direction of the grinding tool based on the difference.
  • the computer program uses the computer to obtain reaction force-related information regarding the pressing reaction force of the grinding tool during the grinding process of 2 acquired at different repeated times for the repeated machining at the same location of the workpiece. It functions as a correction amount estimation device having a difference calculation unit for calculating the difference and a correction amount estimation unit for estimating the correction amount in the pressing direction of the grinding tool based on the difference.
  • reaction force-related information regarding the pressing reaction force of the grinding tool during grinding is acquired, and the repeated machining at the same location of the workpiece is obtained at different repeated times.
  • the difference in the reaction force related information is calculated, the correction amount in the pressing direction of the grinding tool is estimated based on the difference, and the target position and the position of the grinding tool at the time of machining are controlled based on the correction amount.
  • FIG. 1 is a schematic view showing an overall configuration of an example of a grinding system 100 according to a preferred embodiment of the present invention.
  • the grinding system 100 includes a grinding tool 1 and a support mechanism 3 that supports the grinding tool 1 so as to be relatively movable with respect to the workpiece 2 and enables grinding of the workpiece 2. Further, FIG. 1 shows a support base 4 for supporting the workpiece 2 being machined and a controller 5 for controlling the support mechanism 3.
  • the grinding tool 1 is a disc grinder in this embodiment, and has a grinder main body 6 and a grinding wheel 7 attached to the tip thereof. Further, the support mechanism 3 uses a general multi-axis industrial robot, and a grinding tool 1 is attached as an end effector thereof.
  • the controller 5 is a robot controller.
  • the grinding system 100 grinds the unnecessary raised portion of the weld bead 8 to the surface. Illustrates those whose processing purpose is to remove the swelling so that it becomes smooth.
  • the grinding system 100 shown in FIG. 1 is an example, and there is no problem in the gist of the present invention even if the specific configuration of each part is replaced with another configuration.
  • the grinding tool 1 is shown in FIG. 1 as using a generally commercially available disc grinder, the grinding tool 1 may be designed and manufactured exclusively for the grinding system 100.
  • the support mechanism 3 is shown here as using a vertical articulated robot, various mechanisms such as a SCARA robot, a Cartesian robot, and a gantry mechanism may be used in addition to the support mechanism 3, and the grinding tool 1 is supported. Not only the side but also the support base 4 that supports the workpiece 2 may be movable.
  • the controller 5 is not limited to a robot controller, but may be any device such as a servo controller, PLC (Programmable Logic Controller), or a PC for automatically controlling the support mechanism 3. It may be a combination of a plurality of devices described above.
  • the workpiece 2 is not limited as long as it is an object that requires grinding, and in addition to surface grinding such as removal of unnecessary parts of the weld bead 8 shown in the figure, the inner surface of the object to be rotated or the inner surface of the object to be rotated or It may require various grinding processes such as external grinding and cutting.
  • FIG. 2 is a block diagram showing the functional configuration of the grinding system 100.
  • the controller 5 is provided with a machining control unit 50, and by controlling the position of the support mechanism 3 by program control, the grinding tool 1 supported by the support mechanism 3 is desired for the workpiece 2. Move along the trajectory of.
  • the machining control unit 50 includes a motor controller for driving the electric motor, and each of the support mechanisms 3 includes a motor controller. The motor is servo-controlled.
  • the support mechanism 3 is provided with a reaction force-related information acquisition unit 30, which acquires reaction force-related information regarding the pressing reaction force of the grinding tool 1 during grinding.
  • the reaction force-related information means information that can indicate the pressing reaction force of the grinding tool 1 directly or by some conversion, and may be the pressing reaction force itself or other information. May be good.
  • the pressing reaction force itself can be obtained, for example, by attaching the grinding tool 1 to the support mechanism 3 via a load cell and directly measuring the grinding tool 1.
  • the reaction force torque acting on the motor of the support mechanism 3 in the pressing direction of the grinding tool 1, that is, the torque command value is used as the reaction force related information. There is.
  • Adopting motor control information, particularly current command value or torque command value, as reaction force related information is economical because the support mechanism 3 does not require a special additional configuration for acquiring reaction force related information. ..
  • the torque command value of the motor of the support mechanism 3 will be used as the reaction force related information.
  • the controller 5 is provided with a correction unit 51 in addition to the processing control unit 50, and outputs a cumulative correction amount to the processing control unit 50 in the present embodiment.
  • a correction unit 51 in addition to the processing control unit 50, and outputs a cumulative correction amount to the processing control unit 50 in the present embodiment.
  • the meaning of the correction by the correction unit 51 will be described before the detailed description of the configuration of the correction unit 51.
  • wear of the grinding wheel 7 is unavoidable as the processing progresses. Since the wear of the grinding wheel 7 means the retreat of the machining point of the grinding process, when the grinding tool 1 is ground by the position control by the support mechanism 3, the machining point is accompanied by the progress of the wear of the grinding wheel 7. However, it becomes impossible to process the desired shape of the workpiece 2. Therefore, in order to perform accurate grinding, it is necessary to estimate the amount of wear of the grinding wheel 7 by some method and add a correction corresponding to it to the position control by the support mechanism 3.
  • the correction unit 51 is provided with a difference calculation unit 52, and the difference between the reaction force-related information of 2 acquired at different repetitive times for the repetitive machining at the same location of the workpiece 2 is obtained. calculate.
  • the desired shape of the workpiece 2 can be obtained by one grinding, so the same portion is ground a plurality of times along the same trajectory until the desired shape is obtained.
  • FIG. 3 shows the torque command value which is the reaction force related information obtained in the nth grinding process and the n + 1th grinding process in the repeated machining of the workpiece 2 at the same location in the grinding system 100, and the torque command value thereof. It is a figure which shows the example of the difference.
  • the upper graph of FIG. 3 shows the nth torque command value with a solid line, the n + 1th torque command value is shown with a broken line, and the lower graph shows the n + 1th torque command value from the nth torque command value.
  • the difference obtained by subtracting the torque command value is shown by the solid line.
  • the torque command value reflects the pressing reaction force when the grinding tool 1 is pressed against the workpiece 2 during grinding, the larger the portion requiring processing, the stronger the pressing reaction force is generated on the support mechanism 3. Therefore, the waveform of the torque command value reflects the shape of the portion of the work portion 2 that requires machining. Then, as the grinding process progresses, the portion requiring processing is scraped and its size becomes smaller, so that the torque command value in the subsequent repeated times becomes smaller than the torque command value in the previous repeated times. There is a tendency.
  • the difference in the torque command value acquired in the different iterations reflects the difference in the shape of the portion requiring machining in the previous iterations and the shape of the portion requiring machining in the later iterations. It is nothing but a reflection of the amount of change in the shape of the part that requires processing between the first and subsequent iterations.
  • the difference shown in the lower graph is associated with the amount of change in the shape of the portion requiring machining caused by the nth machining, that is, the amount of grinding by the grinding tool 1.
  • the correction amount estimation unit 53 provided in the correction unit 51 further estimates the correction amount in the pressing direction of the grinding system 100.
  • the algorithm for estimating the correction amount may be any algorithm as long as the correction amount can be reasonably estimated based on the difference.
  • the correction amount may be any correction amount necessary for performing the grinding process with high accuracy, including the deviation of the processing point of the grinding tool 1 due to the wear of the grinding wheel 7.
  • the moving distance of the machining point due to the wear of the grinding wheel 7 (hereinafter, simply referred to as "wear amount") is used as the correction amount, but in addition to this moving distance, the support mechanism due to the reaction force during machining is used.
  • the amount including the amount of mechanical change such as the deflection compensation of 3 may be used as the correction amount.
  • the correction amount estimation unit of the grinding system 100 uses a machine learning model in which the relationship between the difference and the wear amount has been learned in advance, and inputs the difference into the machine learning model. As a result, a correction amount is obtained as an output.
  • the architecture of this machine learning model is not particularly limited, but since the difference as an input value is time series data, it is preferable to use an RNN (recurrent neural network). The learning of the machine learning model will be described later.
  • the correction amount obtained in this way indicates the amount of wear of the grinding wheel 7 caused by the grinding process between the two different repetitions for which the torque command value was acquired. Since the wear of the grinding wheel 7 is accumulated each time the grinding wheel is processed, the correction for the position control of the support mechanism 3 is finally based on the accumulated value of the correction amount.
  • the cumulative correction amount calculation unit 54 provided in the correction unit 51 accumulates the correction amount each time the correction amount estimation unit 53 estimates the correction amount, calculates and holds the cumulative correction amount, and performs such cumulative correction.
  • the amount is output to the machining control unit 50.
  • this cumulative correction amount is an amount corresponding to the amount of wear of the individual grinding wheel 7 of the grinding tool 1, it is held as an amount for the grinding wheel 7 in use, and when the grinding wheel 7 is replaced, the amount is retained. The value is reset.
  • the machining control unit 10 receives the cumulative correction amount obtained as described above from the correction unit 51, and at the time of grinding, the grinding tool is based on the cumulative correction amount in addition to the preset target position. Control the position of 1. More specifically, the cumulative correction amount is added to the coordinates in the pressing direction of the target position of the grinding tool 1. Therefore, it can be said that the machining control unit 50 controls the position of the grinding tool 1 at the time of machining based on the target position and the correction amount estimated by the correction amount estimation unit 53.
  • the cumulative correction amount is the amount for the grinding wheel 7 in use, naturally, even when the grinding process for one workpiece 2 is completed and replaced with another different workpiece 2. It will continue to be used unless the grinding wheel 7 is replaced. Therefore, when machining a different workpiece 2, the machining control unit 50 controls the position of the grinding tool 1 at the time of machining based on the target position and the cumulative correction amount.
  • the correction unit 51 is configured as an independent device in the controller 5 of the grinding system 100 described above, this can be considered as a correction amount estimation device.
  • the correction amount estimation device is an independent device having the difference calculation unit 52, the correction amount estimation unit 53, and the cumulative correction amount calculation unit 54 described above.
  • the correction amount estimation device may be designed as a dedicated device or may be realized by using a general computer.
  • FIG. 5 is a diagram showing a configuration of a general computer 11 that can be used as a correction amount estimation device.
  • the computer 11 includes a CPU (Central Processing Unit) 11a as a processor, a RAM (Random Access Memory) 11b as a memory, an external storage device 11c, a GC (Graphics Controller) 11d, and input devices 11e and 11f (Inpur / Output) 306.
  • the data bus 11g is connected so that electric signals can be exchanged with each other.
  • the hardware configuration of the computer 11 shown here is an example, and other configurations may be used.
  • the external storage device 11c is a device that can statically record information such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). Further, the signal from the GC 11d is output to a monitor 11h such as a CRT (Cathode Ray Tube) or a so-called flat panel display in which the user visually recognizes the image, and is displayed as an image.
  • the input device 11e is one or more devices such as a keyboard, mouse, and touch panel for the user to input information
  • the I / O 11f is one or more interfaces for the computer 11 to exchange information with an external device. Is.
  • the I / O 11f may include various ports for wired connection and a controller for wireless connection.
  • the computer program for making the computer 11 function as the machine learning data generation device 1 and the machine learning device 2 is stored in the external storage device 11c, read into the RAM 11b as needed, and executed by the CPU 11a. That is, the RAM 11b stores the code for realizing the various functions shown as the correction unit 51 in FIG. 2 by being executed by the CPU 11a. Even if such a computer program is recorded and provided on an appropriate computer-readable information recording medium such as an appropriate optical disk, magneto-optical disk, or flash memory, the computer program is provided via an external information communication line such as the Internet via the I / O 11f. May be provided.
  • FIG. 5 shows the nth repetitive machining at the same location of the workpiece 2 when the length of the machining target for grinding is long or the shape of the machining target portion changes significantly during machining. It is a figure which shows the example of the torque command value which is the reaction force related information obtained in the grinding process and the n + 1th grinding process, and the difference thereof.
  • the torque command value obtained in the same repeated round is divided into a plurality of sections, and in the example shown in FIG. 5, the section a, the section b, and the section c are divided into three sections, and similarly, the nth time.
  • the difference between the torque command value and the n + 1th torque command value is also divided into the same three sections. Then, the difference for each division may be input to the correction amount estimation unit 53, and as a result, the correction amount for each division may be obtained.
  • the final cumulative correction amount is the cumulative value of the correction amount for each category obtained in this way.
  • each division is performed with respect to time, but since the grinding process is performed by the grinding tool 1 that moves on the workpiece 2 with the passage of time, this division is performed at the machining target location of the workpiece 2. It is nothing but a division of.
  • the processing target portion of the work 2 may be classified at regular intervals, or may be classified at arbitrary intervals according to the properties of the work 2.
  • the learning of the machine learning model used in the correction amount estimation unit 53 is any method as long as the machine learning model can be learned so as to input the difference and output the correction amount. It is also good and is not particularly limited. Therefore, an example of such a learning method will be described below.
  • FIG. 6 is a block diagram showing a functional configuration of a learning device 101 of a machine learning model using a grinding machine.
  • the same reference numerals are given to the configurations equivalent to those of the grinding system 100 shown in FIG. 2, and the duplicated description thereof will be omitted.
  • the machine learning device 101 is attached to a support mechanism 3 to which a grinding tool 1 is attached and having a reaction force related information acquisition unit 30, and a controller 5 having a machining control unit 50 that controls the support mechanism 3.
  • the learning device 9 and the cumulative correction amount measuring device 10 are provided.
  • the cumulative correction amount measuring device 10 is a measuring device that measures the amount of wear of the grinding wheel 7 of the grinding tool 1 attached to the support mechanism 3.
  • the amount of wear is measured by measuring the surface position of the grinding wheel 7 with an arbitrary sensor, for example, a non-contact laser sensor or an arbitrary contact sensor, or by driving the support mechanism 3 to drive the grinding tool 1 in advance. This may be done by pressing against the installed reference surface and detecting the coordinates of the support mechanism 3 at the time when the grinding wheel 7 and the reference surface come into contact with each other. That is, the cumulative correction amount measuring device 10 may be an independent device, or may be configured by utilizing one or a plurality of existing devices of the machine learning device 101.
  • the amount of wear of the grinding wheel 7 is measured, that is, the cumulative value of the wear caused by the grinding process so far. Therefore, in this example, the cumulative correction amount is measured.
  • the reaction force-related information acquisition unit 30 acquires the measured value of the reaction force-related information at the time of machining at any repetitive times, and also at any repetitive times. The actual measured value of the cumulative correction amount can be obtained.
  • the learning device 9 has a difference calculation unit 90, a machine learning model 91, and a correction amount calculation unit 92.
  • the difference calculation unit 90 calculates the difference between the reaction force-related information acquired by the reaction force-related information acquisition unit 30, here, by inputting the torque command value, the difference between the two reaction force-related information acquired in different iterative times. can do.
  • correction amount calculation unit 92 can actually calculate the correction amount generated by the processing during the repetition times from the difference between the measured values of the cumulative correction amounts of 2 acquired in the different repetition times.
  • the learner 9 learns the machine learning model 91 by repeatedly learning the machine learning model 91 using the difference obtained based on the actual measurement as input data and the correction amount obtained based on the actual measurement as the correct answer data. Can be made to.
  • the hardware that realizes the learner 9 is not particularly limited.
  • the learner 9 may be realized as a part of the controller 5, or for example, the general computer 11 shown in FIG. 4 may be used as the learner 9.
  • the learning device 9 is directly connected to the instruction mechanism 3 and the cumulative correction amount measuring device 10, and machine learning is performed every time the support mechanism 3 is driven to execute the grinding process.
  • the configuration in which the model 91 is trained is shown, but in addition to this, a large number of reaction force-related information and actual measurement values of the cumulative correction amount are acquired and accumulated in advance, and later, a learning device 9 independently configured is used. It may be used to perform learning collectively.
  • the grinding system 100 may have a configuration for estimating the estimated number of repeated machining, which is an estimated value of the number of repeated machining required to complete the machining when performing repeated machining at the same location of the workpiece 2.
  • a configuration for estimating the estimated number of repeated machining in the grinding system 100 will be described.
  • FIG. 7 shows an outline of the system configuration.
  • the control torque data collected by the robot while performing polishing work is used to generate a model that estimates the state of the work target.
  • a disc grinder for polishing is attached to the robot's minions.
  • a damper mechanism is provided between the disc grinder and the hand, and even if it comes into contact, a certain amount of external force is absorbed.
  • the robot controller (hereinafter referred to as RC) collects work data using the MotoPlus (Note) application and saves it in the USB memory. Using the saved data, learning is performed with the machine learning software in the PC.
  • a neural network hereinafter referred to as NN consisting of four layers including the input layer and the output layer is used.
  • the learned NN model is downloaded to RC via a USB memory.
  • the RC inputs the control torque data at the time of work to this NN, and estimates the work state based on the output result.
  • MotoPlus A function to develop application software that operates inside RC
  • the hand force F uses the Jacobian matrix J, where ⁇ d is the disturbance torque that is the difference between the control torque estimated from the operating trajectory and the actual control torque. , Can be calculated by the number 1.
  • the contact work state S including the tool is a system in which F is input, if the system function is G, the number is 2.
  • Equation 3 a system function having a disturbance torque ⁇ d as an input as shown in Equation 3.
  • the estimated working state is the state of achievement of polishing.
  • FIG. 1 The work to be polished to be verified is shown in FIG. This involves welding work on an iron plate with a thickness of 4 mm and arbitrarily attaching a welding bead, and the target is polishing of this welding bead.
  • the length of the weld bead is about 155 mm and the height is about 2.5 mm.
  • FIG. 1 A 6-axis vertical articulated robot MOTOMAN-GP7 (hereinafter referred to as GP7) is used as the robot, and YRC1000 is used as the RC.
  • GP7 6-axis vertical articulated robot MOTOMAN-GP7
  • YRC1000 is used as the RC.
  • This disc grinder can be polished by pressing the rotating surface by attaching a grindstone.
  • a resinoid flexible toishi manufactured by HiKOKI is used as the grindstone.
  • the welding bead to be polished is polished while being fixed to a table installed in front of the robot.
  • the specifications of the disc grinding machine and the grindstone are shown in Tables 1 and 2 of FIG. 10, respectively.
  • FIG. 11 is an outline of the polishing operation. Polishing is performed by pulling the hand toward the front of the robot. This operation has been taught in advance, and the work uses only the three axes (second, third, and fifth axes) of the GP7 axes.
  • polishing operation is performed 10 times for one weld bead.
  • polishing is approximately completed in these 10 polishing operations.
  • the polishing of 10 workpieces is completed in order to obtain data with a new grindstone, the grindstone is replaced.
  • FIG. 12 shows the appearance of the weld bead every two times of polishing. Every time it is polished, the weld bead is approaching flat, and after 10 times of polishing, the polishing is achieved.
  • Table 3 of FIG. 14 shows the results estimated using the evaluation data from the NN model generated by learning. This is a table called a mixing matrix, which shows the estimated number of labels for each correct label (in this case, the number of polishings).
  • the gray frame on the diagonal is the number that can be accurately estimated for each label.
  • 49 data can be estimated within the correct label or ⁇ 1 time range, and it can be said that the estimation is approximately accurate.
  • FIG. 16 is a block diagram showing a functional configuration of the grinding system 100 including the repetition number estimation unit 55. Since each configuration of the grinding system 100 according to this example is basically the same as that shown in FIG. 2, the same reference numerals are given to the same configurations, and duplicate description will be omitted. The only difference from that shown in FIG. 2 is that the controller 5 is provided with the estimated number of iterations estimation unit 55.
  • the estimated number of iterations estimation unit 55 estimates the estimated number of iterations at the same location of the workpiece 2, so that the obtained estimated number of iterations is used to estimate the remaining time required for the current machining. It can be useful for determining the timing of replacing the grinding wheel 7.

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Abstract

A grinding system (100) comprising a reaction force related information acquisition unit that acquires reaction force related information about the pressing reaction force of a grinding tool during grinding processing, a difference calculation unit that acquires the difference between two pieces of the reaction force related information acquired in different repetitive executions of repetitive processing on the same part of the workpiece, a correction amount estimation unit that estimates a correction amount in the pressing direction of the grinding tool on the basis of the difference, and a processing control unit that controls the position of the grinding tool during processing on the basis of a target position and the correction amount.

Description

研削システム、補正量推定装置、コンピュータプログラム及び研削方法Grinding system, correction amount estimation device, computer program and grinding method
 本発明は、研削システム、補正量推定装置、コンピュータプログラム及び研削方法に関する。 The present invention relates to a grinding system, a correction amount estimation device, a computer program, and a grinding method.
 特許文献1には、研磨加工の加工条件を示す研磨加工条件データを、環境の現在状態を表す状態変数として観測し、該状態変数に基づいて、前記研磨加工の加工条件に対する前記研磨工具の摩耗量をモデル化した学習モデルを用いた学習乃至予測をする機械学習装置を備える研磨工具摩耗量予測装置が記載されている。 In Patent Document 1, the polishing processing condition data indicating the processing condition of the polishing process is observed as a state variable representing the current state of the environment, and based on the state variable, the wear of the polishing tool with respect to the processing condition of the polishing process. A polishing tool wear amount prediction device including a machine learning device that performs learning or prediction using a learning model that models a quantity is described.
特開2019-139755号公報Japanese Unexamined Patent Publication No. 2019-139755
 研削機による工作物の研削は、所望の加工結果が得られるまで繰り返し研削工具を加工箇所に押し付け研削することによりなされる。そして、加工が進むにしたがって、工作物だけでなく、研削工具も同時に摩耗していくため、加工精度を確保するためには加工後の工作物を測定し再加工の必要の有無や条件の判断が必要であり、生産性の向上がむつかしい。 Grinding of a workpiece by a grinding machine is performed by repeatedly pressing a grinding tool against a machining site until a desired machining result is obtained. As the machining progresses, not only the workpiece but also the grinding tool wears at the same time. Therefore, in order to ensure the machining accuracy, the workpiece after machining is measured to determine the necessity and conditions of reworking. Is necessary, and it is difficult to improve productivity.
 特許文献1記載の発明はこの点研磨工具の摩耗を機械学習装置を利用して予測するものであり、種々の加工条件に基づいて研磨工具の摩耗を推定しているが、工具の摩耗量は工作物の状態に強く依存するため、同発明により工具の摩耗量を正確に見積もることは困難である。 The invention described in Patent Document 1 predicts the wear of the polishing tool at this point by using a machine learning device, and estimates the wear of the polishing tool based on various machining conditions, but the amount of wear of the tool is It is difficult to accurately estimate the amount of tool wear according to the present invention because it strongly depends on the state of the workpiece.
 本発明は、かかる事情に鑑みてなされたものであり、その目的は、研削システムにおいて、工作物の状態を反映した押し付け方向の補正量を取得することである。 The present invention has been made in view of such circumstances, and an object of the present invention is to obtain a correction amount in a pressing direction that reflects the state of a workpiece in a grinding system.
 上記課題を解決すべく本出願において開示される発明は種々の側面を有しており、それら側面の代表的なものの概要は以下のとおりである。 The invention disclosed in this application in order to solve the above problems has various aspects, and the outline of typical ones of these aspects is as follows.
 本発明の一側面に係る研削システムは、研削加工中の研削工具の押し付け反力に関する反力関連情報を取得する反力関連情報取得部と、工作物の同一箇所における反復加工について異なる反復回において取得された2の前記反力関連情報の差分を算出する差分算出部と、前記差分に基づいて、前記研削工具の押し付け方向の補正量を推定する補正量推定部と、目標位置と、前記補正量に基づいて加工時における前記研削工具の位置を制御する加工制御部と、を有する。 The grinding system according to one aspect of the present invention has a reaction force-related information acquisition unit that acquires reaction force-related information regarding the pressing reaction force of a grinding tool during grinding, and a reaction force-related information acquisition unit that acquires reaction force-related information regarding the pressing reaction force of a grinding tool during grinding. A difference calculation unit that calculates the difference between the two acquired reaction force-related information, a correction amount estimation unit that estimates a correction amount in the pressing direction of the grinding tool based on the difference, a target position, and the correction. It has a machining control unit that controls the position of the grinding tool at the time of machining based on the amount.
 また、本発明の別の一側面に係る研削システムでは、前記補正量推定部は、前記差分を機械学習モデルに入力して前記補正量を得てよい。 Further, in the grinding system according to another aspect of the present invention, the correction amount estimation unit may input the difference into the machine learning model to obtain the correction amount.
 また、本発明の別の一側面に係る研削システムでは、前記研削工具の研削砥石についての前記補正量の累積値である累積補正量を算出する累積補正量算出部を有し、前記加工制御部は、異なる工作物に対して加工を行う場合、目標位置及び前記累積補正量に基づいて加工時における前記研削工具の位置を制御してよい。 Further, the grinding system according to another aspect of the present invention has a cumulative correction amount calculation unit for calculating a cumulative correction amount which is a cumulative value of the correction amount for the grinding wheel of the grinding tool, and the machining control unit. May control the position of the grinding tool at the time of machining based on the target position and the cumulative correction amount when machining different workpieces.
 また、本発明の別の一側面に係る研削システムでは、前記工作物の加工対象箇所を区分し、前記補正量推定部は、区分ごとに前記補正量を推定してよい。 Further, in the grinding system according to another aspect of the present invention, the machining target portion of the workpiece may be classified, and the correction amount estimation unit may estimate the correction amount for each classification.
 また、本発明の別の一側面に係る研削システムでは、前記研削工具は電動機制御支持機構により支持され、前記反力関連情報は、前記研削工具の押し付け方向についての電動機の外力トルクであってよい。 Further, in the grinding system according to another aspect of the present invention, the grinding tool is supported by the motor control support mechanism, and the reaction force related information may be the external force torque of the motor in the pressing direction of the grinding tool. ..
 また、本発明の別の一側面に係る研削システムでは、前記反力関連情報を第2の機械学習モデルに入力し、前記工作物の同一箇所における推定反復加工回数を推定する推定反復加工回数推定部を有してよい。 Further, in the grinding system according to another aspect of the present invention, the reaction force related information is input to the second machine learning model, and the estimated number of repeated machining times at the same location of the workpiece is estimated. May have a part.
 また、本発明の一側面に係る補正量推定装置は、工作物の同一箇所における反復加工について異なる反復回において取得された2の研削加工中の研削工具の押し付け反力に関する反力関連情報の差分を算出する差分算出部と、前記差分に基づいて、前記研削工具の押し付け方向の補正量を推定する補正量推定部と、を有する。 Further, the correction amount estimation device according to one aspect of the present invention is a difference in reaction force-related information regarding the pressing reaction force of the grinding tool during the grinding process, which is acquired at different repetitive times for the repetitive machining at the same place of the workpiece. It has a difference calculation unit for calculating the difference, and a correction amount estimation unit for estimating the correction amount in the pressing direction of the grinding tool based on the difference.
 また、本発明の一側面に係るコンピュータプログラムは、コンピュータを、工作物の同一箇所における反復加工について異なる反復回において取得された2の研削加工中の研削工具の押し付け反力に関する反力関連情報の差分を算出する差分算出部と、前記差分に基づいて、前記研削工具の押し付け方向の補正量を推定する補正量推定部と、を有する補正量推定装置として機能させる。 Further, the computer program according to one aspect of the present invention uses the computer to obtain reaction force-related information regarding the pressing reaction force of the grinding tool during the grinding process of 2 acquired at different repeated times for the repeated machining at the same location of the workpiece. It functions as a correction amount estimation device having a difference calculation unit for calculating the difference and a correction amount estimation unit for estimating the correction amount in the pressing direction of the grinding tool based on the difference.
 また、本発明の一側面に係る研削方法は、研削加工中の研削工具の押し付け反力に関する反力関連情報を取得し、工作物の同一箇所における反復加工について異なる反復回において取得された2の前記反力関連情報の差分を算出し、前記差分に基づいて、前記研削工具の押し付け方向の補正量を推定し、目標位置と、前記補正量に基づいて加工時における前記研削工具の位置を制御する。 Further, in the grinding method according to one aspect of the present invention, reaction force-related information regarding the pressing reaction force of the grinding tool during grinding is acquired, and the repeated machining at the same location of the workpiece is obtained at different repeated times. The difference in the reaction force related information is calculated, the correction amount in the pressing direction of the grinding tool is estimated based on the difference, and the target position and the position of the grinding tool at the time of machining are controlled based on the correction amount. To do.
本発明の好適な実施形態に係る研削システムの例の全体構成を示す概略図である。It is the schematic which shows the whole structure of the example of the grinding system which concerns on a preferred embodiment of this invention. 研削システムの機能構成を示すブロック図である。It is a block diagram which shows the functional structure of a grinding system. 第n回目の研削加工と、第n+1回目の研削加工において得られた反力関連情報であるトルク指令値と、その差分の例を示す図である。It is a figure which shows the example of the torque command value which is the reaction force related information obtained in the nth grinding process and the n + 1th grinding process, and the difference thereof. 第n回目の研削加工と、第n+1回目の研削加工において得られた反力関連情報であるトルク指令値と、その差分の例を示す図である。It is a figure which shows the example of the torque command value which is the reaction force related information obtained in the nth grinding process and the n + 1th grinding process, and the difference thereof. 研削システムを利用した機械学習モデルの学習装置の機能構成を示すブロック図である。It is a block diagram which shows the functional structure of the learning apparatus of the machine learning model using a grinding system. 研削システムにおいて、推定反復加工回数を推定するための構成の概要を示す図である。It is a figure which shows the outline of the structure for estimating the estimated number of repeated machining in a grinding system. システム構成の概要を示す図である。It is a figure which shows the outline of the system configuration. 検証する研磨対象ワークを示す図である。It is a figure which shows the work to be polished to be verified. 検証システムのロボット構成を示す図である。It is a figure which shows the robot composition of the verification system. ディスクグラインダと砥石のスペックを示す表である。It is a table which shows the specifications of a disc grinding machine and a grindstone. 研磨動作の概要を示す図である。It is a figure which shows the outline of the polishing operation. 研磨回数2回毎の溶接ビードの外観を示す図である。It is a figure which shows the appearance of the welding bead every 2 times of polishing. 研磨動作2,4,6,8,10回目における外乱トルクτのデータの一例を示す図である。It is a diagram illustrating an example of the data of the disturbance torque tau d in the polishing operation 2,4,6,8,10 time. 学習により生成したNNモデルから評価データを用いて推定した結果を示す表である。It is a table which shows the result estimated using the evaluation data from the NN model generated by learning. 同じ砥石の研磨作業を11~15個目のワークまで続けて研磨動作10回を実施した場合の推定結果を示す表である。It is a table which shows the estimation result when the polishing operation of the same grindstone was performed 10 times in succession to the 11th to 15th workpieces. 反復回数推定部を備えた研削システムの機能構成を示すブロック図である。It is a block diagram which shows the functional structure of the grinding system which includes the iteration number estimation part.
 図1は、本発明の好適な実施形態に係る研削システム100の例の全体構成を示す概略図である。 FIG. 1 is a schematic view showing an overall configuration of an example of a grinding system 100 according to a preferred embodiment of the present invention.
 研削システム100は、研削工具1と、研削工具1を工作物2に相対的に移動可能に支持し、工作物2に対する研削加工を可能とする支持機構3を含む。また、図1には、加工中の工作物2を支持する支持台4と、支持機構3を制御する制御器5が示されている。 The grinding system 100 includes a grinding tool 1 and a support mechanism 3 that supports the grinding tool 1 so as to be relatively movable with respect to the workpiece 2 and enables grinding of the workpiece 2. Further, FIG. 1 shows a support base 4 for supporting the workpiece 2 being machined and a controller 5 for controlling the support mechanism 3.
 ここで、研削工具1は、本実施形態ではディスクグラインダーであり、グラインダー本体6と、その先端に取り付けられた研削砥石7を有している。また、支持機構3は、一般的な多軸産業用ロボットを用いており、そのエンドエフェクタとして研削工具1が取り付けられている。制御器5は、ロボットコントローラである。 Here, the grinding tool 1 is a disc grinder in this embodiment, and has a grinder main body 6 and a grinding wheel 7 attached to the tip thereof. Further, the support mechanism 3 uses a general multi-axis industrial robot, and a grinding tool 1 is attached as an end effector thereof. The controller 5 is a robot controller.
 また、工作物2は、ここでは、鋼板をビード溶接した部材であり、その溶接ビード8が畝状に盛り上がっているため、研削システム100は、その溶接ビード8の不要な盛り上がりを研削加工により表面が滑らかになるよう除去することをその加工目的とするものを例示している。 Further, since the workpiece 2 is a member obtained by bead-welding a steel plate and the weld bead 8 is raised in a ridge shape, the grinding system 100 grinds the unnecessary raised portion of the weld bead 8 to the surface. Illustrates those whose processing purpose is to remove the swelling so that it becomes smooth.
 なお、図1に示した研削システム100は一例であり、その具体的な各部の構成を他の構成に置き換えても本発明の要旨に差し支えはない。例えば、研削工具1は、図1では一般的に市販されるディスクグラインダーを使用したものとして示しているが、研削システム100専用に研削工具1を設計・製作してもよい。また、支持機構3はここでは垂直多関節ロボットを用いたものとして示しているが、これ以外に、スカラロボット、直交ロボットや、ガントリ機構など各種機構を用いてよいし、研削工具1を支持する側だけでなく、工作物2を支持する支持台4が可動するものであってもよい。 Note that the grinding system 100 shown in FIG. 1 is an example, and there is no problem in the gist of the present invention even if the specific configuration of each part is replaced with another configuration. For example, although the grinding tool 1 is shown in FIG. 1 as using a generally commercially available disc grinder, the grinding tool 1 may be designed and manufactured exclusively for the grinding system 100. Further, although the support mechanism 3 is shown here as using a vertical articulated robot, various mechanisms such as a SCARA robot, a Cartesian robot, and a gantry mechanism may be used in addition to the support mechanism 3, and the grinding tool 1 is supported. Not only the side but also the support base 4 that supports the workpiece 2 may be movable.
 さらに、制御器5はロボットコントローラだけでなく、サーボコントローラやPLC(Programable Logic Controller)、PCなど、支持機構3を自動制御するための機器であればどのようなものであってもよく、またこれらの複数の機器を組み合わせたものであってもよい。そして、工作物2は、研削加工を要する対象であればどのようなものであるかは限定されず、図示した溶接ビード8の不要部の除去のような平面研削のほか、回転対象の内面又は外面研削、切断等、種々の研削加工を必要とするものであってよい。 Further, the controller 5 is not limited to a robot controller, but may be any device such as a servo controller, PLC (Programmable Logic Controller), or a PC for automatically controlling the support mechanism 3. It may be a combination of a plurality of devices described above. The workpiece 2 is not limited as long as it is an object that requires grinding, and in addition to surface grinding such as removal of unnecessary parts of the weld bead 8 shown in the figure, the inner surface of the object to be rotated or the inner surface of the object to be rotated or It may require various grinding processes such as external grinding and cutting.
 図2は、研削システム100の機能構成を示すブロック図である。制御器5には、加工制御部50が設けられており、支持機構3に対してプログラム制御による位置制御を行うことにより、支持機構3によって支持された研削工具1を工作物2に対して所望の軌跡で移動させる。本実施形態では、支持機構3は電動機により駆動される電動機制御支持機構であるため、加工制御部50には、電動機を駆動するためのモータコントローラが含まれており、支持機構3に含まれる各電動機をサーボ制御するものとなっている。 FIG. 2 is a block diagram showing the functional configuration of the grinding system 100. The controller 5 is provided with a machining control unit 50, and by controlling the position of the support mechanism 3 by program control, the grinding tool 1 supported by the support mechanism 3 is desired for the workpiece 2. Move along the trajectory of. In the present embodiment, since the support mechanism 3 is an electric motor control support mechanism driven by an electric motor, the machining control unit 50 includes a motor controller for driving the electric motor, and each of the support mechanisms 3 includes a motor controller. The motor is servo-controlled.
 支持機構3には、反力関連情報取得部30が設けられ、研削加工中の研削工具1の押し付け反力に関する反力関連情報を取得するようになっている。ここで、反力関連情報は、直接、又は何らかの換算により、研削工具1の押し付け反力を示しうる情報を意味しており、押し付け反力そのものであってもよいし、その他の情報であってもよい。押し付け反力そのものは、例えば、支持機構3にロードセルを介して研削工具1を取り付け、これを直接測定することなどによって得られる。その他の情報としては、本実施形態に係る研削システム100においては、研削工具1の押し付け方向についての支持機構3の電動機に作用する反力トルク、すなわち、トルク指令値を反力関連情報として用いている。電動機の制御情報、特に、電流指令値やトルク指令値を反力関連情報として採用すると、支持機構3に、反力関連情報を取得するための特別の追加の構成を要しないため経済的である。以降、本実施形態においては、反力関連情報として、支持機構3の電動機のトルク指令値を用いるものとする。 The support mechanism 3 is provided with a reaction force-related information acquisition unit 30, which acquires reaction force-related information regarding the pressing reaction force of the grinding tool 1 during grinding. Here, the reaction force-related information means information that can indicate the pressing reaction force of the grinding tool 1 directly or by some conversion, and may be the pressing reaction force itself or other information. May be good. The pressing reaction force itself can be obtained, for example, by attaching the grinding tool 1 to the support mechanism 3 via a load cell and directly measuring the grinding tool 1. As other information, in the grinding system 100 according to the present embodiment, the reaction force torque acting on the motor of the support mechanism 3 in the pressing direction of the grinding tool 1, that is, the torque command value is used as the reaction force related information. There is. Adopting motor control information, particularly current command value or torque command value, as reaction force related information is economical because the support mechanism 3 does not require a special additional configuration for acquiring reaction force related information. .. Hereinafter, in the present embodiment, the torque command value of the motor of the support mechanism 3 will be used as the reaction force related information.
 制御器5には、加工制御部50に加え、補正部51が設けられており、加工制御部50に対し、本実施形態では累積補正量を出力するようになっている。ここで、補正部51の構成の詳細な説明の前に、補正部51による補正の意味について説明する。 The controller 5 is provided with a correction unit 51 in addition to the processing control unit 50, and outputs a cumulative correction amount to the processing control unit 50 in the present embodiment. Here, the meaning of the correction by the correction unit 51 will be described before the detailed description of the configuration of the correction unit 51.
 研削工具1による研削加工においては、加工の進展に伴い、研削砥石7の摩耗が避けられない。そして、研削砥石7の摩耗は、研削加工の加工点の後退を意味するため、研削工具1を支持機構3による位置制御によって研削加工を行うと、研削砥石7摩耗の進展に伴って、加工点がずれ、工作物2に対し所望の形状の加工が行えなくなってしまう。そのため、正確な研削を行うためには、研削砥石7の摩耗量を何らかの方法により見積もり、それに見合った補正を支持機構3による位置制御に加えなければならない。 In the grinding process using the grinding tool 1, wear of the grinding wheel 7 is unavoidable as the processing progresses. Since the wear of the grinding wheel 7 means the retreat of the machining point of the grinding process, when the grinding tool 1 is ground by the position control by the support mechanism 3, the machining point is accompanied by the progress of the wear of the grinding wheel 7. However, it becomes impossible to process the desired shape of the workpiece 2. Therefore, in order to perform accurate grinding, it is necessary to estimate the amount of wear of the grinding wheel 7 by some method and add a correction corresponding to it to the position control by the support mechanism 3.
 研削砥石7の摩耗量を加工の都度高精度に測定するのは、測定機の設置や調整など研削システム100の複雑化やコスト増のほか、保守管理がむつかしくなる。一方で、研削砥石7の摩耗量を推定しようとしても、研削砥石7の摩耗量は、研削加工における工作物2の研削量に大きく依存するため、工作物2の加工対象箇所の形状などにより大きく変化してしまい、研削システム100側の条件のみによってこれを推定することは難しい。 Measuring the amount of wear of the grinding wheel 7 with high accuracy each time it is processed makes the grinding system 100 complicated and costly, such as installation and adjustment of a measuring machine, and maintenance management becomes difficult. On the other hand, even if an attempt is made to estimate the amount of wear of the grinding wheel 7, the amount of wear of the grinding wheel 7 largely depends on the amount of grinding of the workpiece 2 in the grinding process. It changes, and it is difficult to estimate this only by the conditions on the grinding system 100 side.
 そこで、本実施形態に係る研削システム100では、補正部51に差分算出部52が設けられ、工作物2の同一箇所における反復加工について異なる反復回において取得された2の反力関連情報の差分を算出する。 Therefore, in the grinding system 100 according to the present embodiment, the correction unit 51 is provided with a difference calculation unit 52, and the difference between the reaction force-related information of 2 acquired at different repetitive times for the repetitive machining at the same location of the workpiece 2 is obtained. calculate.
 一般に、研削加工においては、一度の研削によって所望の工作物2の形状を得られることは少ないため、同一箇所を所望の形状が得られるまで複数回同じ軌跡に沿って研削する。 Generally, in the grinding process, it is rare that the desired shape of the workpiece 2 can be obtained by one grinding, so the same portion is ground a plurality of times along the same trajectory until the desired shape is obtained.
 図3は、研削システム100において、工作物2の同一箇所における反復加工において、第n回目の研削加工と、第n+1回目の研削加工において得られた反力関連情報であるトルク指令値と、その差分の例を示す図である。図3の上段のグラフには、第n回目のトルク指令値を実線で、第n+1回目のトルク指令値を破線で示し、下段のグラフには、第n回目のトルク指令値から第n+1回目のトルク指令値を引いた差分を実線で示した。 FIG. 3 shows the torque command value which is the reaction force related information obtained in the nth grinding process and the n + 1th grinding process in the repeated machining of the workpiece 2 at the same location in the grinding system 100, and the torque command value thereof. It is a figure which shows the example of the difference. The upper graph of FIG. 3 shows the nth torque command value with a solid line, the n + 1th torque command value is shown with a broken line, and the lower graph shows the n + 1th torque command value from the nth torque command value. The difference obtained by subtracting the torque command value is shown by the solid line.
 トルク指令値は、研削加工時に研削工具1を工作物2に押し付けたときの押し付け反力を反映するため、加工を要する部分が大きいほど、支持機構3に強い押し付け反力が生じる。そのため、トルク指令値の波形は、工作部2の加工を要する部分の形状を反映したものとなっている。そして、研削加工が進展するにしたがって、加工を要する部分は削れてその大きさが小さくなっていくため、後の反復回におけるトルク指令値のほうが、先の反復回におけるトルク指令値よりも小さくなる傾向にある。 Since the torque command value reflects the pressing reaction force when the grinding tool 1 is pressed against the workpiece 2 during grinding, the larger the portion requiring processing, the stronger the pressing reaction force is generated on the support mechanism 3. Therefore, the waveform of the torque command value reflects the shape of the portion of the work portion 2 that requires machining. Then, as the grinding process progresses, the portion requiring processing is scraped and its size becomes smaller, so that the torque command value in the subsequent repeated times becomes smaller than the torque command value in the previous repeated times. There is a tendency.
 そして、異なる反復回において取得されたトルク指令値の差は、先の反復回における加工を要する部分の形状と、後の反復回における加工を要する部分の形状の差を反映したものとなるため、先の反復回から後の反復回の間の加工を要する部分の形状の変化量を反映するものに他ならない。図3に示した例だと、下段のグラフに示した差分は、n回目の加工によって生じた加工を要する部分の形状の変化量、すなわち、研削工具1による研削量に関連付けられる。 Then, the difference in the torque command value acquired in the different iterations reflects the difference in the shape of the portion requiring machining in the previous iterations and the shape of the portion requiring machining in the later iterations. It is nothing but a reflection of the amount of change in the shape of the part that requires processing between the first and subsequent iterations. In the example shown in FIG. 3, the difference shown in the lower graph is associated with the amount of change in the shape of the portion requiring machining caused by the nth machining, that is, the amount of grinding by the grinding tool 1.
 よって、本実施形態に係る研削システム100では、この差分に基づいて、さらに、補正部51に設けられた補正量推定部53により、研削システム100の押し付け方向の補正量を推定する。 Therefore, in the grinding system 100 according to the present embodiment, based on this difference, the correction amount estimation unit 53 provided in the correction unit 51 further estimates the correction amount in the pressing direction of the grinding system 100.
 この補正量の推定のアルゴリズムは、差分に基づいて補正量を合理的に推定できるものであればいかなるものであっても差し支えはない。また、この補正量は、研削砥石7の摩耗による研削工具1の加工点のずれを含む、研削加工を高精度に行う際に必要な補正量であればどのようなものであってもよい。本実施形態では、補正量として、研削砥石7の摩耗による加工点の移動距離(以降では単に「摩耗量」という。)を用いているが、この移動距離に加え、加工時反力による支持機構3のたわみ補償などの機械的変化量を含む量を補正量とするなどしてもよい。 The algorithm for estimating the correction amount may be any algorithm as long as the correction amount can be reasonably estimated based on the difference. Further, the correction amount may be any correction amount necessary for performing the grinding process with high accuracy, including the deviation of the processing point of the grinding tool 1 due to the wear of the grinding wheel 7. In the present embodiment, the moving distance of the machining point due to the wear of the grinding wheel 7 (hereinafter, simply referred to as "wear amount") is used as the correction amount, but in addition to this moving distance, the support mechanism due to the reaction force during machining is used. The amount including the amount of mechanical change such as the deflection compensation of 3 may be used as the correction amount.
 そして、すでに述べたように、図3の下段のグラフに示したような差分と、必要な補正量との間には相関があると考えられるが、両者の間の精度の高い変換関係を発見するのは、必ずしも容易ではない。そこで、本実施形態に係る研削システム100の補正量推定部は、この差分と摩耗量との間の関係をあらかじめ学習させておいた機械学習モデルを用い、かかる機械学習モデルに差分を入力することにより、出力として補正量を得ている。 Then, as already described, it is considered that there is a correlation between the difference shown in the lower graph of FIG. 3 and the required correction amount, but a highly accurate conversion relationship between the two was discovered. It's not always easy to do. Therefore, the correction amount estimation unit of the grinding system 100 according to the present embodiment uses a machine learning model in which the relationship between the difference and the wear amount has been learned in advance, and inputs the difference into the machine learning model. As a result, a correction amount is obtained as an output.
 なお、この機械学習モデルのアーキテクチャは特に限定されるものではないが、入力値である差分が時系列データであるため、RNN(再帰型ニューラルネットワーク)によるものが好適である。機械学習モデルの学習については後述する。 The architecture of this machine learning model is not particularly limited, but since the difference as an input value is time series data, it is preferable to use an RNN (recurrent neural network). The learning of the machine learning model will be described later.
 このようにして得られた補正量は、トルク指令値を取得した異なる2の反復回の間の研削加工によって生じた研削砥石7の摩耗量を示す。そして、研削砥石7の摩耗は、加工の都度累積していくため、最終的に支持機構3の位置制御に対してする補正は、この補正量の累積値に基づくものとなる。 The correction amount obtained in this way indicates the amount of wear of the grinding wheel 7 caused by the grinding process between the two different repetitions for which the torque command value was acquired. Since the wear of the grinding wheel 7 is accumulated each time the grinding wheel is processed, the correction for the position control of the support mechanism 3 is finally based on the accumulated value of the correction amount.
 そのため、補正部51に設けられた累積補正量算出部54では、補正量推定部53により補正量の推定がなされる都度、これを累積して累積補正量を算出して保持し、かかる累積補正量を加工制御部50に出力する。 Therefore, the cumulative correction amount calculation unit 54 provided in the correction unit 51 accumulates the correction amount each time the correction amount estimation unit 53 estimates the correction amount, calculates and holds the cumulative correction amount, and performs such cumulative correction. The amount is output to the machining control unit 50.
 この累積補正量は、研削工具1の研削砥石7の個体の摩耗量に対応する量であるから、使用中の研削砥石7についての量として保持され、研削砥石7が交換された場合にはその値はリセットされる。 Since this cumulative correction amount is an amount corresponding to the amount of wear of the individual grinding wheel 7 of the grinding tool 1, it is held as an amount for the grinding wheel 7 in use, and when the grinding wheel 7 is replaced, the amount is retained. The value is reset.
 加工制御部10は、以上のようにして得られた累積補正量を補正部51から受領し、研削加工の際に、あらかじめプログラムされた目標位置に加え、累積補正量にも基づいて、研削工具1の位置を制御する。より具体的には、研削工具1の目標位置の押し付け方向の座標に累積補正量を加算する。したがって、加工制御部50は、その目標位置と、補正量推定部53により推定された補正量に基づいて、加工時における研削工具1の位置を制御しているといえる。 The machining control unit 10 receives the cumulative correction amount obtained as described above from the correction unit 51, and at the time of grinding, the grinding tool is based on the cumulative correction amount in addition to the preset target position. Control the position of 1. More specifically, the cumulative correction amount is added to the coordinates in the pressing direction of the target position of the grinding tool 1. Therefore, it can be said that the machining control unit 50 controls the position of the grinding tool 1 at the time of machining based on the target position and the correction amount estimated by the correction amount estimation unit 53.
 また、累積補正量は、使用中の研削砥石7についての量であるから、当然に、ある工作物2に対する研削加工が完了し、別の異なる工作物2に交換された場合であっても、研削砥石7が交換されない限りは引き続き使用される。したがって、加工制御部50は、異なる工作物2に対して加工を行う場合、目標位置及び累積補正量に基づいて加工時における研削工具1の位置を制御することになる。 Further, since the cumulative correction amount is the amount for the grinding wheel 7 in use, naturally, even when the grinding process for one workpiece 2 is completed and replaced with another different workpiece 2. It will continue to be used unless the grinding wheel 7 is replaced. Therefore, when machining a different workpiece 2, the machining control unit 50 controls the position of the grinding tool 1 at the time of machining based on the target position and the cumulative correction amount.
 なお、以上説明した研削システム100の制御器5において、補正部51を独立した機器として構成した場合には、これを補正量推定装置として観念することができる。その場合は、補正量推定装置は、上で説明した差分算出部52、補正量推定部53、及び累積補正量算出部54を有する独立した機器である。 When the correction unit 51 is configured as an independent device in the controller 5 of the grinding system 100 described above, this can be considered as a correction amount estimation device. In that case, the correction amount estimation device is an independent device having the difference calculation unit 52, the correction amount estimation unit 53, and the cumulative correction amount calculation unit 54 described above.
 補正量推定装置は、専用の装置として設計してもよいし、一般的なコンピュータを用いて実現してもよい。図5は、補正量推定装置として用いることのできる一般的なコンピュータ11の構成を示す図である。コンピュータ11は、プロセッサであるCPU(Central Processing Unit)11a、メモリであるRAM(Random Access Memory)11b、外部記憶装置11c、GC(Graphics Controller)11d、入力デバイス11e及び11f(Inpur/Output)306がデータバス11gにより相互に電気信号のやり取りができるよう接続されている。なお、ここで示したコンピュータ11のハードウェア構成は一例であり、これ以外の構成のものであってもよい。 The correction amount estimation device may be designed as a dedicated device or may be realized by using a general computer. FIG. 5 is a diagram showing a configuration of a general computer 11 that can be used as a correction amount estimation device. The computer 11 includes a CPU (Central Processing Unit) 11a as a processor, a RAM (Random Access Memory) 11b as a memory, an external storage device 11c, a GC (Graphics Controller) 11d, and input devices 11e and 11f (Inpur / Output) 306. The data bus 11g is connected so that electric signals can be exchanged with each other. The hardware configuration of the computer 11 shown here is an example, and other configurations may be used.
 外部記憶装置11cはHDD(Hard Disk Drive)やSSD(Solid State Drive)等の静的に情報を記録できる装置である。またGC11dからの信号はCRT(Cathode Ray Tube)やいわゆるフラットパネルディスプレイ等の、使用者が視覚的に画像を認識するモニタ11hに出力され、画像として表示される。入力デバイス11eはキーボードやマウス、タッチパネル等の、ユーザが情報を入力するための一又は複数の機器であり、I/O11fはコンピュータ11が外部の機器と情報をやり取りするための一又は複数のインタフェースである。I/O11fには、有線接続するための各種ポート及び、無線接続のためのコントローラが含まれていてよい。 The external storage device 11c is a device that can statically record information such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). Further, the signal from the GC 11d is output to a monitor 11h such as a CRT (Cathode Ray Tube) or a so-called flat panel display in which the user visually recognizes the image, and is displayed as an image. The input device 11e is one or more devices such as a keyboard, mouse, and touch panel for the user to input information, and the I / O 11f is one or more interfaces for the computer 11 to exchange information with an external device. Is. The I / O 11f may include various ports for wired connection and a controller for wireless connection.
 コンピュータ11を機械学習データ生成装置1及び機械学習装置2として機能させるためのコンピュータプログラムは外部記憶装置11cに記憶され、必要に応じてRAM11bに読みだされてCPU11aにより実行される。すなわち、RAM11bには、CPU11aにより実行されることにより、図2の補正部51として示した各種機能を実現させるためのコードが記憶されることとなる。かかるコンピュータプログラムは、適宜の光ディスク、光磁気ディスク、フラッシュメモリ等の適宜のコンピュータ可読情報記録媒体に記録されて提供されても、I/O11fを介して外部のインターネット等の情報通信回線を介して提供されてもよい。 The computer program for making the computer 11 function as the machine learning data generation device 1 and the machine learning device 2 is stored in the external storage device 11c, read into the RAM 11b as needed, and executed by the CPU 11a. That is, the RAM 11b stores the code for realizing the various functions shown as the correction unit 51 in FIG. 2 by being executed by the CPU 11a. Even if such a computer program is recorded and provided on an appropriate computer-readable information recording medium such as an appropriate optical disk, magneto-optical disk, or flash memory, the computer program is provided via an external information communication line such as the Internet via the I / O 11f. May be provided.
 図5は、研削加工の加工対象の長さが長く、あるいは加工対象部分の形状が加工途中で大きく変化しているなどの場合における、工作物2の同一箇所における反復加工において、第n回目の研削加工と、第n+1回目の研削加工において得られた反力関連情報であるトルク指令値と、その差分の例を示す図である。 FIG. 5 shows the nth repetitive machining at the same location of the workpiece 2 when the length of the machining target for grinding is long or the shape of the machining target portion changes significantly during machining. It is a figure which shows the example of the torque command value which is the reaction force related information obtained in the grinding process and the n + 1th grinding process, and the difference thereof.
 この場合、同一反復回において得られたトルク指令値を複数の区間、図5に示した例では、区間a、区間b及び区間cの3つの区間に区分しており、同様に第n回目のトルク指令値と第n+1回目のトルク指令値との差分もまた、同様の3つの区間に区分している。そして、補正量推定部53には、この区分ごとの差分を入力し、結果として、区分ごとの補正量を得るようにしてよい。 In this case, the torque command value obtained in the same repeated round is divided into a plurality of sections, and in the example shown in FIG. 5, the section a, the section b, and the section c are divided into three sections, and similarly, the nth time. The difference between the torque command value and the n + 1th torque command value is also divided into the same three sections. Then, the difference for each division may be input to the correction amount estimation unit 53, and as a result, the correction amount for each division may be obtained.
 最終的な累積補正量は、このようにして得られた区分ごとの補正量の累積値となる。図4では、各区分は時間に対しなされているが、研削加工は時間の経過に伴って工作物2上を移動する研削工具1によってなされるから、この区分は、工作物2の加工対象箇所を区分するものに他ならない。このように適切に工作物2の加工対象箇所を区分し、区分ごとに補正量を推定するようにすると、研削加工の途中で加工条件が大きく変動するような場合に高い精度で補正量が得られる。 The final cumulative correction amount is the cumulative value of the correction amount for each category obtained in this way. In FIG. 4, each division is performed with respect to time, but since the grinding process is performed by the grinding tool 1 that moves on the workpiece 2 with the passage of time, this division is performed at the machining target location of the workpiece 2. It is nothing but a division of. By appropriately classifying the machining target parts of the workpiece 2 and estimating the correction amount for each classification in this way, the correction amount can be obtained with high accuracy when the machining conditions fluctuate significantly during the grinding process. Be done.
 例えば、工作物2の加工対象箇所の前半分と後半分で溶接ビード8の大きさが顕著に異なっているなど、加工条件が大きく異なる場合、それらをまとめて補正量の推定を行うよりも、加工条件が概ね等しいと考えられる前半分とあと半分に分けて補正量の推定を行った方が、より正確な補正量の推定が行えるためである。工作物2の加工対象箇所の区分は、一定間隔ごとに区分を行ってもよいし、工作物2の性状に応じて、任意の間隔で区分を行ってもよい。 For example, when the machining conditions are significantly different, such as the size of the weld bead 8 being significantly different between the front half and the rear half of the machining target portion of the workpiece 2, it is better than estimating the correction amount collectively. This is because the correction amount can be estimated more accurately by estimating the correction amount separately for the first half and the second half, which are considered to have substantially the same processing conditions. The processing target portion of the work 2 may be classified at regular intervals, or may be classified at arbitrary intervals according to the properties of the work 2.
 補正量推定部53において用いる機械学習モデルの学習は、上で説明したように、差分を入力とし、補正量を出力するように機械学習モデルを学習できる方法であればどのようなものであってもよく、特に限定されるものではない。よって、以下には、かかる学習方法の一例を記載する。 As explained above, the learning of the machine learning model used in the correction amount estimation unit 53 is any method as long as the machine learning model can be learned so as to input the difference and output the correction amount. It is also good and is not particularly limited. Therefore, an example of such a learning method will be described below.
 図6は、研削機を利用した機械学習モデルの学習装置101の機能構成を示すブロック図である。なお、同図では、図2に示した研削システム100と同等の構成には同符号を付し、その重複する説明は省略するものとする。 FIG. 6 is a block diagram showing a functional configuration of a learning device 101 of a machine learning model using a grinding machine. In the figure, the same reference numerals are given to the configurations equivalent to those of the grinding system 100 shown in FIG. 2, and the duplicated description thereof will be omitted.
 機械学習装置101は、研削システム100と同様の、研削工具1が取り付けられ、反力関連情報取得部30を有する支持機構3と、支持機構3を制御する加工制御部50を有する制御器5に加え、学習器9及び累積補正量測定器10が設けられた構成となっている。 Similar to the grinding system 100, the machine learning device 101 is attached to a support mechanism 3 to which a grinding tool 1 is attached and having a reaction force related information acquisition unit 30, and a controller 5 having a machining control unit 50 that controls the support mechanism 3. In addition, the learning device 9 and the cumulative correction amount measuring device 10 are provided.
 累積補正量測定器10は、本例では、支持機構3に取り付けられた研削工具1の研削砥石7の摩耗量を測定する測定器である。この摩耗量の測定は、研削砥石7の表面位置を任意のセンサ、例えば非接触式レーザセンサや、任意の接触式センサによって測定したり、又は、支持機構3を駆動して研削工具1をあらかじめ設置した基準面に押し当て、研削砥石7と基準面が接触した時点の支持機構3の座標を検出したりすることによってなされてよい。すなわち、累積補正量測定器10は、独立した機器であってもよいし、機械学習装置101の既存の1又は複数の機器を利用して構成されるものであってもよい。 In this example, the cumulative correction amount measuring device 10 is a measuring device that measures the amount of wear of the grinding wheel 7 of the grinding tool 1 attached to the support mechanism 3. The amount of wear is measured by measuring the surface position of the grinding wheel 7 with an arbitrary sensor, for example, a non-contact laser sensor or an arbitrary contact sensor, or by driving the support mechanism 3 to drive the grinding tool 1 in advance. This may be done by pressing against the installed reference surface and detecting the coordinates of the support mechanism 3 at the time when the grinding wheel 7 and the reference surface come into contact with each other. That is, the cumulative correction amount measuring device 10 may be an independent device, or may be configured by utilizing one or a plurality of existing devices of the machine learning device 101.
 この時測定されるのは、研削砥石7の摩耗量、すなわち、これまでの研削加工による摩耗の累積値であるから、本例においては、累積補正量を実測していることに他ならない。 At this time, the amount of wear of the grinding wheel 7 is measured, that is, the cumulative value of the wear caused by the grinding process so far. Therefore, in this example, the cumulative correction amount is measured.
 従って、任意の工作物2に対して反復加工を行う際に、任意の反復回において加工時の反力関連情報の実測値を反力関連情報取得部30により取得するとともに、同じく任意の反復回について、累積補正量の実測値を得ることができる。 Therefore, when performing repetitive machining on an arbitrary workpiece 2, the reaction force-related information acquisition unit 30 acquires the measured value of the reaction force-related information at the time of machining at any repetitive times, and also at any repetitive times. The actual measured value of the cumulative correction amount can be obtained.
 学習器9は、差分算出部90、機械学習モデル91及び補正量算出部92を有している。差分算出部90は、反力関連情報取得部30により取得された反力関連情報、ここではトルク指令値を入力することにより、異なる反復回において取得された2の反力関連情報の差分を算出することができる。 The learning device 9 has a difference calculation unit 90, a machine learning model 91, and a correction amount calculation unit 92. The difference calculation unit 90 calculates the difference between the reaction force-related information acquired by the reaction force-related information acquisition unit 30, here, by inputting the torque command value, the difference between the two reaction force-related information acquired in different iterative times. can do.
 また、補正量算出部92は、異なる反復回において取得された2の累積補正量の実測値の差から、当該反復回の間における加工により生じた補正量を現実に算出することができる。 Further, the correction amount calculation unit 92 can actually calculate the correction amount generated by the processing during the repetition times from the difference between the measured values of the cumulative correction amounts of 2 acquired in the different repetition times.
 したがって、学習器9は、実測に基づいて得られた差分を入力データ、同じく実測に基づいて得られた補正量を正解データとして機械学習モデル91を繰り返し学習させることにより、機械学習モデル91を学習させることができる。 Therefore, the learner 9 learns the machine learning model 91 by repeatedly learning the machine learning model 91 using the difference obtained based on the actual measurement as input data and the correction amount obtained based on the actual measurement as the correct answer data. Can be made to.
 なお、学習器9を実現するハードウェアは特に限定されない。制御器5の一部として学習器9が実現されてもよいし、例えば、図4に示した一般的なコンピュータ11を学習器9として使用してもよい。また、図6に示した機械学習装置101の例では、学習器9が直接指示機構3及び累積補正量測定器10と接続され、支持機構3を駆動して研削加工を実行するごとに機械学習モデル91の学習がなされる構成を示したが、これ以外に、反力関連情報と累積補正量の実測値をあらかじめ多数取得して蓄積しておき、後に、独立して構成した学習器9を用いてまとめて学習を行うようにしてもよい。 The hardware that realizes the learner 9 is not particularly limited. The learner 9 may be realized as a part of the controller 5, or for example, the general computer 11 shown in FIG. 4 may be used as the learner 9. Further, in the example of the machine learning device 101 shown in FIG. 6, the learning device 9 is directly connected to the instruction mechanism 3 and the cumulative correction amount measuring device 10, and machine learning is performed every time the support mechanism 3 is driven to execute the grinding process. The configuration in which the model 91 is trained is shown, but in addition to this, a large number of reaction force-related information and actual measurement values of the cumulative correction amount are acquired and accumulated in advance, and later, a learning device 9 independently configured is used. It may be used to perform learning collectively.
 さらに、研削システム100は、工作物2の同一箇所における反復加工を行う際に、加工の完了までに要する反復回数の推定値である推定反復加工回数を推定する構成を有していてもよい。以下、研削システム100において、推定反復加工回数を推定するための構成を記す。 Further, the grinding system 100 may have a configuration for estimating the estimated number of repeated machining, which is an estimated value of the number of repeated machining required to complete the machining when performing repeated machining at the same location of the workpiece 2. Hereinafter, the configuration for estimating the estimated number of repeated machining in the grinding system 100 will be described.
<システム概要>
 図7にシステム構成の概要を示す。本システムではロボットが研磨作業を行いながら収集した制御トルクデータを用いて,作業対象の状態を推定するモデル生成をする。ロボットの手先には研磨作業を行うためのディスクグラインダを取付ける。ディスクグラインダと手先の間には,ダンパー機構が備わっており,接触してもある程度の外力は吸収される。ロボットコントローラ(以降,RC)は,MotoPlus(注)アプリにより作業時のデータを収集してUSBメモリへ保存する。保存したデータを用いて,PC内の機械学習用ソフトウェアで学習を行う。学習モデルには,入力層,出力層を含めて四層からなるニューラルネットワーク(以降,NN)を用いる。学習したNNモデルをUSBメモリを介して,RCにダウンロードする。RCは作業時の制御トルクデータをこのNNに入力し,その出力結果に基づいて作業状態を推定する。
(注)MotoPlus:RC内部で動作するアプリケーションソフトウェアを開発する機能
<System overview>
FIG. 7 shows an outline of the system configuration. In this system, the control torque data collected by the robot while performing polishing work is used to generate a model that estimates the state of the work target. A disc grinder for polishing is attached to the robot's minions. A damper mechanism is provided between the disc grinder and the hand, and even if it comes into contact, a certain amount of external force is absorbed. The robot controller (hereinafter referred to as RC) collects work data using the MotoPlus (Note) application and saves it in the USB memory. Using the saved data, learning is performed with the machine learning software in the PC. For the learning model, a neural network (hereinafter referred to as NN) consisting of four layers including the input layer and the output layer is used. The learned NN model is downloaded to RC via a USB memory. The RC inputs the control torque data at the time of work to this NN, and estimates the work state based on the output result.
(Note) MotoPlus: A function to develop application software that operates inside RC
<作業状態の推定方法>
 一般的なセンサレスによる手先力の推定手法では,手先力Fは,動作軌道から推定される制御トルクと実際の制御トルクとの差分である外乱トルクをτとしたとき,ヤコビアン行列Jを用いて,数1で算出できる。
<Estimation method of working condition>
In a general sensorless hand force estimation method, the hand force F uses the Jacobian matrix J, where τ d is the disturbance torque that is the difference between the control torque estimated from the operating trajectory and the actual control torque. , Can be calculated by the number 1.
Figure JPOXMLDOC01-appb-M000001
手先力推定
Figure JPOXMLDOC01-appb-M000001
Hand force estimation
 ツールを内包した接触作業の状態SはFを入力したシステムとなるため,そのシステム関数をGとすると,数2となる。 Since the contact work state S including the tool is a system in which F is input, if the system function is G, the number is 2.
Figure JPOXMLDOC01-appb-M000002
手先力を入力とした作業状態推定
Figure JPOXMLDOC01-appb-M000002
Work state estimation with manual force as input
 ここで,数式3のように外乱トルクτを入力としたシステム関数に置き換える。 Here, it is replaced with a system function having a disturbance torque τ d as an input as shown in Equation 3.
Figure JPOXMLDOC01-appb-M000003
外乱トルクを入力とした作業状態推定
Figure JPOXMLDOC01-appb-M000003
Work state estimation with disturbance torque as input
Figure JPOXMLDOC01-appb-I000004
Figure JPOXMLDOC01-appb-I000004
<技術評価>
 本技術を用いた作業状態推定について評価する。推定する作業状態は,研磨の達成状態とする。
<Technical evaluation>
Evaluate the work state estimation using this technology. The estimated working state is the state of achievement of polishing.
<作業対象ワーク>
 検証する研磨対象ワークを図8に示す。これは厚さ4mmの鉄板に溶接作業を行って,溶接ビードを恣意的につけており,この溶接ビードの研磨を対象とする。溶接ビードの長さは,約155mm,高さは約2.5mmである。
<Work to be worked>
The work to be polished to be verified is shown in FIG. This involves welding work on an iron plate with a thickness of 4 mm and arbitrarily attaching a welding bead, and the target is polishing of this welding bead. The length of the weld bead is about 155 mm and the height is about 2.5 mm.
<検証システムの構成>
 検証システムのロボット構成を図9に示す。ロボットには6軸垂直多関節ロボットMOTOMAN-GP7(以降,GP7)を用い,RCにはYRC1000を用いる。GP7の先端にHiKOKI社製の電気ディスクグラインダG10SH5を取付ける。このディスクグラインンダは砥石を取り付けることでその回転面を押し付けて研磨を行うことができる。砥石にはHiKOKI社製のレジノイドフレキシブルトイシを用いる。研磨対象である溶接ビードはロボット前方に設置した台に固定した状態で研磨作業を行う。ディスクグラインダと砥石のスペックを図10の表1,表2にそれぞれ示す。
<Verification system configuration>
The robot configuration of the verification system is shown in FIG. A 6-axis vertical articulated robot MOTOMAN-GP7 (hereinafter referred to as GP7) is used as the robot, and YRC1000 is used as the RC. Attach the electric disc grinder G10SH5 manufactured by HiKOKI to the tip of GP7. This disc grinder can be polished by pressing the rotating surface by attaching a grindstone. A resinoid flexible toishi manufactured by HiKOKI is used as the grindstone. The welding bead to be polished is polished while being fixed to a table installed in front of the robot. The specifications of the disc grinding machine and the grindstone are shown in Tables 1 and 2 of FIG. 10, respectively.
<研磨作業対象>
 図11は研磨動作の概要である。手先をロボット手前側へ引く動作で研磨を実行する。この動作はあらかじめティーチング済みであり,GP7の各軸のうち,3軸(第2,3,5軸)のみを用いた作業となっている。
<Target for polishing work>
FIG. 11 is an outline of the polishing operation. Polishing is performed by pulling the hand toward the front of the robot. This operation has been taught in advance, and the work uses only the three axes (second, third, and fifth axes) of the GP7 axes.
 実際に研磨を行いデータを収集する。1個の溶接ビードに対して10回研磨動作を行う。新品の砥石を用いた場合この10回の研磨動作で研磨はおおよそ完了することが経験上分かっている。また,新品の砥石によるデータを取るために10個のワークの研磨を終えたら,砥石を交換する。図12は,研磨回数2回毎の溶接ビードの外観を示す。研磨する度に,溶接ビードが平らに近づいていっており,10回の研磨で,研磨を達成した状態となる。 Actually polish and collect data. Polishing operation is performed 10 times for one weld bead. Experience has shown that when a new grindstone is used, polishing is approximately completed in these 10 polishing operations. Also, when the polishing of 10 workpieces is completed in order to obtain data with a new grindstone, the grindstone is replaced. FIG. 12 shows the appearance of the weld bead every two times of polishing. Every time it is polished, the weld bead is approaching flat, and after 10 times of polishing, the polishing is achieved.
<評価結果>
 50個のワークを10回研磨した計500データを用意した。このうち新品の砥石に交換してから5個目のワークを研磨した計50データを評価データとし,残りの450データで学習を行った。図13は研磨動作2,4,6,8,10回目における外乱トルクτのデータの一例である。
<Evaluation result>
A total of 500 data were prepared by polishing 50 workpieces 10 times. Of these, a total of 50 data obtained by polishing the fifth work after replacing with a new grindstone was used as evaluation data, and learning was performed using the remaining 450 data. Figure 13 is an example of a data of the disturbance torque tau d in the polishing operation 2,4,6,8,10 time.
 学習により生成したNNモデルから評価データを用いて推定した結果を図14の表3に示す。これは混合行列と呼ばれる表で,各正解ラベル(今回の場合は研磨回数)に対する推定されたラベルの個数を示している。対角線上の灰色の枠が各ラベルに対して正確に推定できた個数となる。結果,50データのうち,49データが正解のラベルもしくは±1回の範囲で推定できており,おおよそ精度よく推定できていることが言える。 Table 3 of FIG. 14 shows the results estimated using the evaluation data from the NN model generated by learning. This is a table called a mixing matrix, which shows the estimated number of labels for each correct label (in this case, the number of polishings). The gray frame on the diagonal is the number that can be accurately estimated for each label. As a result, of the 50 data, 49 data can be estimated within the correct label or ± 1 time range, and it can be said that the estimation is approximately accurate.
 推論結果に失敗がある原因として,以下が想定される。今回推定するラベルとして研磨回数を採用したが,実際には研磨を行うごとに砥石が劣化し,同じ研磨回数でも研磨の良し悪しに差が生じて,より多くもしくは小さい研磨回数に誤認した。実際に,同じ砥石の研磨作業を11~15個目のワークまで続けて研磨動作10回を実施した場合の推定結果は図15の表4のようになり,同じ砥石で研磨を続けるほど,ワークの研磨達成状況に到達していないと推定されることがわかる。これは言い換えると,砥石の劣化により十分に研磨ができていないことが判定可能である。 The following are assumed as the causes of failure in the inference result. The number of polishings was used as the label estimated this time, but in reality, the grindstone deteriorated with each polishing, and even with the same number of polishings, there was a difference in the quality of polishing, and it was mistaken for a larger or smaller number of polishings. Actually, the estimation result when the polishing operation of the same grindstone is continued up to the 11th to 15th workpieces and the polishing operation is performed 10 times is as shown in Table 4 of FIG. It can be seen that it is presumed that the polishing achievement status of In other words, it can be determined that the grindstone is not sufficiently polished due to deterioration.
 すなわち,研磨動作を1回行う度に研磨状態を推定し,10回目の研磨動作と推定されたら,研磨作業を停止することにより,適切な研磨回数で自動終了させることに応用できると言える。 That is, it can be said that it can be applied to estimate the polishing state each time the polishing operation is performed, and when it is estimated to be the 10th polishing operation, stop the polishing operation to automatically end the polishing operation with an appropriate number of times.
 以上の通り説明した構成により、推定反復加工回数を推定する推定反復回数推定部55を構成することができる。図16は、反復回数推定部55を備えた研削システム100の機能構成を示すブロック図である。本例に係る研削システム100の各構成は基本的には図2にて示したものと同一であるから、等しい構成には同符号を付して重複する説明は省略する。そして、図2に示したものとは、制御器5に推定反復回数推定部55が設けられている点のみが相違している。 With the configuration described above, the estimated number of iterations estimation unit 55 for estimating the estimated number of iterations can be configured. FIG. 16 is a block diagram showing a functional configuration of the grinding system 100 including the repetition number estimation unit 55. Since each configuration of the grinding system 100 according to this example is basically the same as that shown in FIG. 2, the same reference numerals are given to the same configurations, and duplicate description will be omitted. The only difference from that shown in FIG. 2 is that the controller 5 is provided with the estimated number of iterations estimation unit 55.
 推定反復回数推定部55は本例では、工作物2の同一箇所における推定反復加工回数を推定するものであるから、得られた推定反復加工回数を、現在行っている加工に要する残り時間の推定や、研削砥石7を交換するタイミングの判断に役立てることができる。

 
In this example, the estimated number of iterations estimation unit 55 estimates the estimated number of iterations at the same location of the workpiece 2, so that the obtained estimated number of iterations is used to estimate the remaining time required for the current machining. It can be useful for determining the timing of replacing the grinding wheel 7.

Claims (9)

  1.  研削加工中の研削工具の押し付け反力に関する反力関連情報を取得する反力関連情報取得部と、
     工作物の同一箇所における反復加工について異なる反復回において取得された2の前記反力関連情報の差分を取得する差分算出部と、
     前記差分に基づいて、前記研削工具の押し付け方向の補正量を推定する補正量推定部と、
     目標位置と、前記補正量に基づいて加工時における前記研削工具の位置を制御する加工制御部と、
     を有する研削システム。
    A reaction force-related information acquisition unit that acquires reaction force-related information related to the pressing reaction force of a grinding tool during grinding,
    A difference calculation unit that acquires the difference between the two reaction force-related information acquired at different repetitive times for repetitive machining at the same location of the workpiece, and
    A correction amount estimation unit that estimates a correction amount in the pressing direction of the grinding tool based on the difference,
    A machining control unit that controls the position of the grinding tool at the time of machining based on the target position and the correction amount.
    Grinding system with.
  2.  前記補正量推定部は、前記差分を機械学習モデルに入力して前記補正量を得る、請求項1に記載の研削システム。 The grinding system according to claim 1, wherein the correction amount estimation unit inputs the difference into a machine learning model to obtain the correction amount.
  3.  前記研削工具の研削砥石についての前記補正量の累積値である累積補正量を算出する累積補正量算出部を有し、
     前記加工制御部は、異なる工作物に対して加工を行う場合、目標位置及び前記累積補正量に基づいて加工時における前記研削工具の位置を制御する請求項1又は2に記載の研削システム。
    It has a cumulative correction amount calculation unit that calculates a cumulative correction amount that is a cumulative value of the correction amount for the grinding wheel of the grinding tool.
    The grinding system according to claim 1 or 2, wherein the machining control unit controls the position of the grinding tool at the time of machining based on the target position and the cumulative correction amount when machining different workpieces.
  4.  前記工作物の加工対象箇所を区分し、前記補正量推定部は、区分ごとに前記補正量を推定する、請求項1~3のいずれか1項に記載の研削システム。 The grinding system according to any one of claims 1 to 3, wherein the processing target portion of the workpiece is classified, and the correction amount estimation unit estimates the correction amount for each classification.
  5.  前記研削工具は電動機制御支持機構により支持され、前記反力関連情報は、前記研削工具の押し付け方向についての電動機の外力トルクである、請求項1~4のいずれか1項に記載の研削システム。 The grinding system according to any one of claims 1 to 4, wherein the grinding tool is supported by an electric motor control support mechanism, and the reaction force related information is an external force torque of the electric motor in a pressing direction of the grinding tool.
  6.  前記反力関連情報を第2の機械学習モデルに入力し、前記工作物の同一箇所における推定反復加工回数を推定する推定反復加工回数推定部を有する請求項1~5のいずれか1項に記載の研削システム。 The item according to any one of claims 1 to 5, which has an estimated repetitive machining number estimation unit that inputs the reaction force-related information into the second machine learning model and estimates the estimated repetitive machining number at the same location of the workpiece. Grinding system.
  7.  工作物の同一箇所における反復加工について異なる反復回において取得された2の研削加工中の研削工具の押し付け反力に関する反力関連情報の差分を取得する差分算出部と、
     前記差分に基づいて、前記研削工具の押し付け方向の補正量を推定する補正量推定部と、
     を有する補正量推定装置。
    A difference calculation unit that acquires the difference in reaction force-related information regarding the pressing reaction force of the grinding tool during the grinding process, which was acquired at different repetitive times for the repetitive machining at the same location of the workpiece.
    A correction amount estimation unit that estimates a correction amount in the pressing direction of the grinding tool based on the difference,
    Correction amount estimation device having.
  8.  コンピュータを、
     工作物の同一箇所における反復加工について異なる反復回において取得された2の研削加工中の研削工具の押し付け反力に関する反力関連情報の差分を取得する差分算出部と、
     前記差分に基づいて、前記研削工具の押し付け方向の補正量を推定する補正量推定部と、
     を有する補正量推定装置として機能させるためのコンピュータプログラム。
    Computer,
    A difference calculation unit that acquires the difference in reaction force-related information regarding the pressing reaction force of the grinding tool during the grinding process, which was acquired at different repetitive times for the repetitive machining at the same location of the workpiece.
    A correction amount estimation unit that estimates a correction amount in the pressing direction of the grinding tool based on the difference,
    A computer program for functioning as a correction amount estimation device.
  9.  研削加工中の研削工具の押し付け反力に関する反力関連情報を取得し、
     工作物の同一箇所における反復加工について異なる反復回において取得された2の前記反力関連情報の差分を取得し、
     前記差分に基づいて、前記研削工具の押し付け方向の補正量を推定し、
     目標位置と、前記補正量に基づいて加工時における前記研削工具の位置を制御する、
     研削方法。

     
    Obtain reaction force related information regarding the pressing reaction force of the grinding tool during grinding,
    The difference between the two reaction force-related information acquired at different repetitive times for repetitive machining at the same location of the workpiece is acquired.
    Based on the difference, the correction amount in the pressing direction of the grinding tool is estimated.
    The position of the grinding tool at the time of machining is controlled based on the target position and the correction amount.
    Grinding method.

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