WO2023238192A1 - Dispositif de correction de déplacement thermique et support d'enregistrement lisible par ordinateur dans lequel un programme est enregistré - Google Patents

Dispositif de correction de déplacement thermique et support d'enregistrement lisible par ordinateur dans lequel un programme est enregistré Download PDF

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Publication number
WO2023238192A1
WO2023238192A1 PCT/JP2022/022793 JP2022022793W WO2023238192A1 WO 2023238192 A1 WO2023238192 A1 WO 2023238192A1 JP 2022022793 W JP2022022793 W JP 2022022793W WO 2023238192 A1 WO2023238192 A1 WO 2023238192A1
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WIPO (PCT)
Prior art keywords
temperature data
thermal displacement
temperature
machine
unit
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PCT/JP2022/022793
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English (en)
Japanese (ja)
Inventor
啓太 羽田
嘉孝 久保
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ファナック株式会社
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Priority to PCT/JP2022/022793 priority Critical patent/WO2023238192A1/fr
Publication of WO2023238192A1 publication Critical patent/WO2023238192A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q15/00—Automatic control or regulation of feed movement, cutting velocity or position of tool or work
    • B23Q15/007—Automatic control or regulation of feed movement, cutting velocity or position of tool or work while the tool acts upon the workpiece
    • B23Q15/18—Compensation of tool-deflection due to temperature or force
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
    • B23Q15/00—Automatic control or regulation of feed movement, cutting velocity or position of tool or work
    • B23Q15/20—Automatic control or regulation of feed movement, cutting velocity or position of tool or work before or after the tool acts upon the workpiece
    • B23Q15/22—Control or regulation of position of tool or workpiece
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/404—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by control arrangements for compensation, e.g. for backlash, overshoot, tool offset, tool wear, temperature, machine construction errors, load, inertia
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/4155—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by programme execution, i.e. part programme or machine function execution, e.g. selection of a programme

Definitions

  • the present invention relates to a thermal displacement correction device and a computer-readable recording medium on which a program is recorded.
  • Thermal deformation of manufacturing machines is one of the factors that impede machining accuracy.
  • internal heat sources such as the manufacturing machine's motor, and external heat sources such as sunlight, lighting, and air conditioning. is necessary.
  • a model for estimating the amount of thermal displacement is generated by, for example, a manufacturer of manufacturing machinery. Manufacturers of manufacturing machines, for example, install their manufacturing machines in variable temperature rooms and collect learning data used to generate models while reproducing various temperature environments. A model is then generated by machine learning using the collected data. However, if the temperature environment of the manufacturing site where the manufacturing machine is installed is different from the expected one, the model generated by the manufacturer of the manufacturing machine may not be able to estimate the amount of thermal displacement with sufficient accuracy. In such a case, it becomes necessary to collect data used for learning at an appropriate timing at the manufacturing site and perform additional learning using this data.
  • the learning data used for additional learning requires temperature data including the temperature of each part of the manufacturing machine and the ambient temperature of the installation environment, and displacement data including the amount of displacement of the manufacturing machine.
  • FIG. 5 is a schematic diagram showing an example of a manufacturing machine used when creating learning data.
  • the temperature of each part of the manufacturing machine can be obtained by measuring it with a temperature sensor attached to each part of the manufacturing machine. Further, the amount of displacement of the manufacturing machine can be obtained by measuring the position of the measurement reference with a touch probe attached to the main shaft. Additional learning is then performed using the temperature data and displacement data obtained in this way.
  • Temperature data can be measured even while a workpiece is being processed using a temperature sensor attached to the manufacturing machine.
  • displacement data cannot be measured unless machining of the workpiece is interrupted and a touch probe is attached in place of the tool.
  • machining of the workpiece is interrupted and a touch probe is attached in place of the tool.
  • it is necessary to stop processing. This has the problem of increasing cycle time. Therefore, there is a need for a more efficient method of updating models.
  • Patent Document 1 it is determined whether the model needs to be updated based on the adjustment value calculated from the correction value obtained by actually operating the machining program, and additional measurements are performed only when it is determined that the model needs to be updated. . This reduces the number of additional measurements, making model updates more efficient.
  • the adjustment values in this technique vary depending on the machining environment and machining program, so when changing the machining environment or machining program, actual measurements must be performed to obtain the adjustment values. Therefore, even if applied to manufacturing machines where the machining environment and machining programs are frequently changed, the effect of improving the efficiency of model updating will not be sufficiently achieved.
  • the thermal displacement correction device stores learned temperature data in a database in advance. Then, the temperature measured by the temperature sensor during actual operation of the manufacturing machine is compared with the temperature data stored in the database, and if there is similar temperature data, displacement measurement is not performed, and if there is no similar temperature data, the displacement measurement is not performed.
  • the above problem can be solved by measuring displacement only at and performing additional learning.
  • One aspect of the present disclosure is a thermal displacement correction device that has a function related to thermal displacement correction that estimates and corrects thermal displacement of a machine by modeling the relationship between temperature and the amount of thermal displacement using machine learning.
  • a temperature data acquisition unit that acquires temperature data related to the temperature of each part of the machine, a displacement data acquisition unit that acquires displacement data related to the amount of thermal displacement of the machine, and a temperature data acquisition unit that acquires temperature data related to the temperature of each part of the machine; a temperature data storage section to store; a temperature data comparison section that compares the temperature data measured by the temperature data acquisition section with the temperature data stored in the temperature data storage section; and a determination unit that determines whether or not it is necessary for the displacement data acquisition unit to measure the amount of thermal displacement of the machine, and whether or not it is necessary to update the model, based on the thermal displacement data acquisition unit.
  • Another aspect of the present disclosure is to use a computer as a thermal displacement correction device having a function related to thermal displacement correction that estimates and corrects thermal displacement of a machine by modeling the relationship between temperature and thermal displacement amount by machine learning.
  • a computer-readable recording medium recording a program to be operated, the temperature data acquisition unit acquiring temperature data related to the temperature of each part of the machine, and the displacement data acquisition unit acquiring displacement data related to the amount of thermal displacement of the machine.
  • a temperature data storage unit that stores temperature data used in the machine learning
  • a temperature data comparison unit that compares the temperature data measured by the temperature data acquisition unit with the temperature data stored in the temperature data storage unit
  • operating the computer as a determination unit that determines whether or not the displacement data acquisition unit should measure the amount of thermal displacement of the machine and whether or not the model needs to be updated, based on the comparison result by the temperature data comparison unit
  • a computer-readable recording medium that records a program.
  • FIG. 1 is a hardware configuration diagram of a thermal displacement correction device according to an embodiment.
  • FIG. 2 is a block diagram illustrating the functions of a thermal displacement correction device according to an embodiment.
  • 1 is a schematic diagram of a manufacturing machine equipped with a temperature sensor;
  • FIG. It is a figure which illustrates the temperature data set memorize
  • FIG. 2 is a schematic diagram showing an example of a manufacturing machine used when creating learning data.
  • FIG. 1 is a schematic hardware configuration diagram showing the main parts of a thermal displacement correction device according to an embodiment of the present invention.
  • the thermal displacement correction device 1 of the present invention can be mounted on a control device that controls a manufacturing machine 2 such as a machine tool, for example.
  • the thermal displacement correction device 1 may be a personal computer attached to a control device that controls the manufacturing machine 2, a personal computer connected to the control device via a wired/wireless network (not shown), a cell computer, a fog computer, etc. It can be implemented on a computer, such as a computer or a cloud server.
  • an example is shown in which the thermal displacement correction device 1 is mounted on a control device that controls a manufacturing machine 2.
  • the CPU 11 included in the thermal displacement correction device 1 is a processor that controls the thermal displacement correction device 1 as a whole.
  • the CPU 11 reads out a system program stored in the ROM 12 via the bus 22, and controls the entire thermal displacement correction device 1 according to the system program.
  • the RAM 13 temporarily stores temporary calculation data, display data, and various data input from the outside.
  • the non-volatile memory 14 is composed of, for example, a memory backed up by a battery (not shown) or a SSD (Solid State Drive), and the stored state is maintained even when the power of the thermal displacement correction device 1 is turned off.
  • the nonvolatile memory 14 stores machine control programs and data read from the external device 72 via the interface 15, machine control programs and data input via the input device 71, and machine control programs and data obtained from the manufacturing machine 2. Control programs, data, etc. are stored.
  • the data stored in the nonvolatile memory 14 may be expanded to the RAM 13 at the time of execution/use. Further, various system programs such as a known analysis program are written in the ROM 12 in advance.
  • the interface 15 is an interface for connecting the CPU 11 of the thermal displacement correction device 1 to an external device 72 such as a USB device. From the external device 72 side, for example, pre-stored control programs and data related to the operation of each manufacturing machine 2 can be read. Further, the control program, setting data, etc. edited within the thermal displacement correction device 1 can be stored in external storage means via the external device 72.
  • a PLC (programmable logic controller) 16 is a sequence program built into the thermal displacement correction device 1 and controls the manufacturing machine 2, peripheral equipment (not shown) of the manufacturing machine 2, sensors attached to the manufacturing machine 2, etc. Control is performed by inputting/outputting signals to and from devices such as through the I/O unit 17.
  • the thermal displacement correction device 1 is connected to sensors such as a temperature sensor 3 attached to the manufacturing machine 2 and a touch probe 4 used for measuring thermal displacement.
  • the temperature sensor 3 is used to measure the temperature of each part of the manufacturing machine 2 and the ambient temperature. Further, the touch probe 4 is used to measure the amount of displacement of each part of the manufacturing machine 2, etc.
  • the display device 70 outputs and displays each data read into the memory, data obtained as a result of executing a program, etc. via the interface 18. Further, an input device 71 including a keyboard, a pointing device, etc. passes commands, data, etc. based on operations by an operator to the CPU 11 via the interface 19.
  • An axis control circuit 30 for controlling each axis of the manufacturing machine 2 receives an axis movement command amount from the CPU 11 and outputs the axis command to the servo amplifier 40. Upon receiving this command, the servo amplifier 40 drives a servo motor 50 that moves an axis of the manufacturing machine 2.
  • the shaft servo motor 50 has a built-in position/velocity detector, and feeds back a position/velocity feedback signal from this position/velocity detector to the axis control circuit 30 to perform position/velocity feedback control.
  • the actual number of axes provided in the manufacturing machine 2 to be controlled is (For example, three for the manufacturing machine 2 with three linear axes, and five for a five-axis processing machine).
  • the spindle control circuit 60 receives a spindle rotation command to the spindle of the manufacturing machine 2 and outputs a spindle speed signal to the spindle amplifier 61.
  • the spindle amplifier 61 receives this spindle speed signal, rotates the spindle motor 62 of the main shaft at the commanded rotational speed, and drives the tool.
  • a position coder 63 is coupled to the spindle motor 62, and the position coder 63 outputs a feedback pulse in synchronization with the rotation of the main shaft, and the feedback pulse is read by the CPU 11.
  • the interface 21 is an interface for connecting the thermal displacement correction device 1 and the machine learning device 100.
  • the machine learning device 100 includes a processor 101 that controls the entire machine learning device 100, a ROM 102 that stores system programs, etc., a RAM 103 that stores temporary storage in each process related to machine learning, and a memory that stores learning models and the like.
  • a non-volatile memory 104 is provided.
  • the machine learning device 100 acquires various information that can be acquired by the thermal displacement correction device 1 via the interface 21 (for example, temperature data of each part of the manufacturing machine 2, data of ambient temperature, displacement data of each part of the manufacturing machine 2, etc.). It can be observed.
  • the thermal displacement correction device 1 receives information output from the machine learning device 100, corrects the thermal displacement correction amount, controls the manufacturing machine 2, displays it on the display device 70, and performs other operations via a network (not shown). Sends information to the device, etc.
  • FIG. 2 is a schematic block diagram showing the functions of the thermal displacement correction device 1 according to an embodiment of the present invention.
  • Each function provided in the thermal displacement correction apparatus 1 according to the present embodiment is implemented by the CPU 11 of the thermal displacement correction apparatus 1 and the processor 101 of the machine learning device 100 shown in FIG. This is realized by controlling the operations of each part of the learning device 100.
  • the thermal displacement correction device 1 of this embodiment includes a temperature data acquisition section 110, a displacement data acquisition section 120, a temperature data comparison section 130, a determination section 140, an output section 150, a learning section 160, and an estimation section 170. Further, on the RAM 13 to nonvolatile memory 14 of the thermal displacement correction device 1, a temperature data storage section 210 is prepared in advance, which is an area for storing temperature data used to generate a model by machine learning. Further, on the RAM 103 to nonvolatile memory 104 of the machine learning device 100, temperature data stored in the temperature data storage unit 210 and displacement data paired with the temperature data are generated by machine learning. A model storage unit 220 is prepared in advance in which the model is stored.
  • the temperature data acquisition unit 110 acquires a set DS of temperature data related to the temperature of each part of the manufacturing machine 2.
  • This temperature data set DS may include the temperature of the installation environment of the manufacturing machine 2.
  • the temperature data acquisition unit 110 may acquire temperature data from a temperature sensor 3 attached to the manufacturing machine 2, for example. Although this temperature data may be acquired while the manufacturing machine 2 is not operating, it is desirable to acquire it while the manufacturing machine 2 is operating. This is to obtain the difference in temperature of each part of the manufacturing machine 2 depending on the contents of the machining program. As illustrated in FIG. 3, it is desirable that a plurality of temperature sensors 3 be attached to each part of the manufacturing machine 2.
  • the temperature data acquired by the temperature data acquisition unit 110 may be instantaneous values of temperatures measured by each temperature sensor 3 at a predetermined point in time, but is preferably a time series of temperatures measured by the temperature sensors 3 in a predetermined period. Preferably data. This is because the amount of thermal displacement of each part of the manufacturing machine 2 is caused by the change in temperature of each part during a predetermined period in the past. In this way, it is preferable that the temperature data acquisition unit 110 acquires a plurality of temperature data obtained by measuring changes in the environmental temperature and the temperature of each part of the manufacturing machine 2 over a predetermined period of time.
  • the displacement data acquisition unit 120 acquires displacement data related to the amount of thermal displacement of each part of the manufacturing machine 2.
  • the displacement data acquisition unit 120 may automatically measure the amount of thermal displacement of each part of the manufacturing machine 2 in response to a command to acquire displacement data, and acquire it as displacement data.
  • an operator may operate the manufacturing machine 2 and manually measure the amount of thermal displacement of each part of the manufacturing machine 2, and obtain it as displacement data.
  • the displacement data acquisition unit 120 displays a message on the display device 70, etc., instructing the manufacturing machine 2 to stop processing operation and measure the amount of thermal displacement. do.
  • the operator stops the processing operation of the workpiece by the manufacturing machine 2 and performs the work of measuring the amount of thermal displacement.
  • the displacement data acquisition unit 120 acquires displacement data related to the amount of thermal displacement of the manufacturing machine 2 based on the work result.
  • the manufacturing machine 2 is a machine tool
  • the amount of thermal displacement of the manufacturing machine 2 can be measured by attaching a touch probe to the main shaft and detecting the measurement standard with the touch probe as illustrated in FIG. . Such operations cannot be performed while the workpiece is being processed. Therefore, the work of measuring the amount of thermal displacement is performed by temporarily stopping the workpiece machining operation of the manufacturing machine 2.
  • the temperature data comparison unit 130 compares the temperature data set DS acquired by the temperature data acquisition unit 110 at a predetermined timing with the temperature data set stored in the temperature data storage unit 210.
  • the predetermined timing may be every predetermined cycle, or may be at a time when an instruction is given by an operator's operation.
  • the temperature data comparison unit 130 outputs the comparison result between the temperature data sets to the determination unit 140.
  • the temperature data storage unit 210 stores in advance a plurality of temperature data sets used in machine learning processing when generating the model stored in the model storage unit 220.
  • FIG. 4 shows an example of a temperature data set stored in the temperature data storage unit 210.
  • temperature data Et j (t) of the installation environment of manufacturing machine 2 and temperature data T 1j (t) to temperature data T ij (t) of each part of the machine measured by each temperature sensor 1 to i are shown.
  • Data sets DS1 to DSj including the following are stored in the temperature data storage unit 210.
  • the temperature data comparison unit 130 calculates the degree of similarity between the temperature data set DS acquired by the temperature data acquisition unit 110 and each temperature data set stored in the temperature data storage unit 210, for example. do.
  • a well-known method such as CCF (Cross Correlation Function) or DTW (Dynamic Time Warping) may be used.
  • the degree of similarity between the data sets may be calculated by calculating a statistical value such as an average value regarding the degree of similarity between the respective time series data forming the data sets.
  • the highest degree of similarity may be used as the comparison result.
  • the determination unit 140 determines whether or not the displacement data acquisition unit 120 should measure the amount of thermal displacement of the manufacturing machine 2 based on the comparison result between the temperature data sets input from the temperature data comparison unit 130, and determines whether or not the displacement data acquisition unit 120 should measure the amount of thermal displacement of the manufacturing machine 2, and the model storage unit 220. Determine whether or not the stored model needs to be updated. For example, when the temperature data set DS acquired by the temperature data acquisition unit 110 is not similar to the temperature data set group stored in the temperature data storage unit 210, the determination unit 140 determines whether or not to measure the amount of thermal displacement of the manufacturing machine 2. and determine that it is necessary to update the model.
  • the determining unit 140 compares a predetermined threshold Sim th set in advance with the similarity input from the temperature data comparing unit 130, and if the similarity is equal to or less than the threshold Sim th , the determining unit 140 It may be determined that additional learning is necessary for the model stored in 220. If the determining unit 140 determines that it is necessary to measure the amount of thermal displacement of the manufacturing machine 2, it instructs the displacement data acquisition unit 120 to measure the amount of thermal displacement of the manufacturing machine 2 according to instructions from the operator. do.
  • the determining unit 140 when determining that it is necessary to update the model, sends a set of temperature data acquired by the temperature data acquiring unit 110 to the learning unit 160 of the machine learning device 100 according to an instruction from the operator, and a displacement A command is given to perform machine learning using the displacement data acquired by the data acquisition unit 120 and update the model. Judgment unit 140 outputs the judgment result to output unit 150.
  • the output unit 150 outputs and displays the result of the determination by the determination unit 140 on the display device 70.
  • the output unit 150 may display on the display device 70, in addition to the determination result by the determination unit 140, the degree of similarity between the respective data sets compared by the temperature data comparison unit 130. Further, depending on the relationship between the degree of similarity and the threshold value, the operator may be able to determine whether to measure the amount of thermal displacement of the manufacturing machine 2 or update the model based on these displays.
  • the output unit 150 may record and output the results of the determination by the determination unit 140 to the RAM 13 to the nonvolatile memory 14 and the external device 72. Further, the output unit 150 may output the result of the determination by the determination unit 140 to other computers such as a fog computer or a host computer via a network (not shown).
  • the learning unit 160 performs machine learning processing using the temperature data set acquired by the temperature data acquisition unit 110 and the displacement data acquired by the displacement data acquisition unit 120 based on the command from the determination unit 140, and creates a model.
  • the model stored in the storage unit 220 is updated.
  • the temperature data set used for learning by the learning unit 160 is additionally stored in the temperature data storage unit 210.
  • the estimation unit 170 uses the temperature data set acquired by the temperature data acquisition unit 110 during processing operation of the manufacturing machine 2 to perform estimation processing of the amount of thermal displacement based on the model stored in the model storage unit 220.
  • the amount of thermal displacement estimated by the estimation unit 170 is used for thermal displacement correction processing during processing operation of the manufacturing machine 2.
  • the thermal displacement correction device 1 measures the environmental temperature and the temperature of each part of the manufacturing machine 2 during actual operation. Then, it is compared with the temperature data set that has already been used to train the model. If there are similar temperature data sets, the current temperature situation is considered to have already been reflected in the model. Even if learning was performed by measuring the amount of thermal displacement of the manufacturing machine 2, no improvement in the performance of the model can be expected. There is also a risk of overfitting. Therefore, in such a case, there is no need to measure the amount of thermal displacement until the machining operation of the manufacturing machine 2 is stopped. On the other hand, if there is no similar temperature data set, it is highly likely that the current temperature situation has not been reflected in the model. It is expected that the performance of the model will improve by acquiring the current data and updating the model using the acquired data. In this way, the thermal displacement correction device 1 according to the present embodiment can more efficiently update the model for estimating the amount of thermal displacement.
  • a model for estimating the amount of thermal displacement of a manufacturing machine can be created by, for example, installing the manufacturing machine in a variable temperature room, measuring the amount of thermal displacement under multiple temperature environments, and performing machine learning using the data obtained. It is desirable to generate it before actual operation. However, if the manufacturing machine is a large machine, it may not be possible to put it into a variable temperature chamber, making it difficult to create a model using data obtained under multiple temperature environments before actual operation. In such cases, data used for machine learning is collected at appropriate times while the manufacturing machines are actually operated at the manufacturing site, and the model is updated each time. Even in such cases, processing operations must be stopped in order to collect data, so data can be collected to efficiently update the model without unnecessarily reducing cycle time. There is a need. Even in such a case, the thermal displacement correction device according to this embodiment functions effectively.
  • the temperature data comparison unit 130 compares the temperature data set DS acquired by the temperature data acquisition unit 110 and the temperature data set stored in the temperature data storage unit 210.
  • a machine learning method may be used. More specifically, for example, a cluster of existing data sets is created from the temperature data set stored in the temperature data storage unit 210 using a known cluster analysis method, and a cluster is created between the temperature data acquisition unit 110 and the cluster. It is also possible to consider the closeness of the distance as the degree of similarity for comparison. If the distance from the cluster is greater than or equal to a predetermined threshold, the determining unit 140 may determine that it is necessary to measure the amount of thermal displacement of the manufacturing machine 2 and update the model. For cluster analysis, known methods such as regression analysis and k-means method can be used.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Manufacturing & Machinery (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Automatic Control Of Machine Tools (AREA)
  • Numerical Control (AREA)

Abstract

Un dispositif de correction de déplacement thermique selon la présente invention comprend : une unité d'acquisition de données de température qui acquiert des données de température concernant la température de chaque partie d'une machine ; une unité d'acquisition de données de déplacement qui acquiert des données de déplacement concernant la quantité de déplacement thermique de la machine ; une unité de stockage de données de température qui stocke des données de température utilisées dans l'apprentissage automatique ; une unité de comparaison de données de température qui compare les données de température mesurées par l'unité d'acquisition de données de température et les données de température stockées par l'unité de stockage de données de température ; et une unité de détermination qui, sur la base du résultat de la comparaison par l'unité de comparaison de données de température, détermine s'il est nécessaire de mesurer la quantité de déplacement thermique de la machine à l'aide de l'unité d'acquisition de données de déplacement.
PCT/JP2022/022793 2022-06-06 2022-06-06 Dispositif de correction de déplacement thermique et support d'enregistrement lisible par ordinateur dans lequel un programme est enregistré WO2023238192A1 (fr)

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JP2019198928A (ja) * 2018-05-16 2019-11-21 ファナック株式会社 熱変位補正装置
KR20200131475A (ko) * 2019-05-14 2020-11-24 두산공작기계 주식회사 공작기계의 열변위 보정 방법 및 시스템
KR20210023334A (ko) * 2019-08-23 2021-03-04 현대위아 주식회사 공작기계의 열변위 보상장치 및 그 방법
JP2021104564A (ja) * 2019-12-26 2021-07-26 ファナック株式会社 熱変位補正装置

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