CN117795308A - Control device and control method - Google Patents
Control device and control method Download PDFInfo
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- CN117795308A CN117795308A CN202180101466.9A CN202180101466A CN117795308A CN 117795308 A CN117795308 A CN 117795308A CN 202180101466 A CN202180101466 A CN 202180101466A CN 117795308 A CN117795308 A CN 117795308A
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- 230000005540 biological transmission Effects 0.000 abstract description 10
- 238000001746 injection moulding Methods 0.000 description 38
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H7/00—Gearings for conveying rotary motion by endless flexible members
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/023—Power-transmitting endless elements, e.g. belts or chains
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
Abstract
The control device of the present disclosure estimates the tension of a power transmission belt provided in an industrial machine to control the operation of the belt, and comprises: a control unit that controls a diagnostic operation for driving the belt; a data acquisition unit that acquires data obtained from the diagnostic operation; an alarm condition storage unit that stores an alarm condition table listing alarm conditions in which at least a range of belt tension and an alarm operation related to control of the industrial machine are associated with respective alarm levels; and an alarm operation command unit that, with reference to the alarm condition storage unit, when the tension of the belt obtained based on the data acquired by the data acquisition unit satisfies a predetermined alarm condition, commands the control unit to perform an alarm operation corresponding to the satisfied alarm condition.
Description
Technical Field
The present invention relates to a control device and a control method.
Background
In industrial machines, there is a structure in which rotational power of a motor is transmitted to a shaft via a power transmission mechanism such as a belt. In the case of a belt, if the tension (tension) of the belt is too weak or too strong as compared with the use condition, the life of the belt is reduced, which is a factor of failing to drive the shaft normally. In addition, if the tension of the belt decreases during operation of the industrial machine, a belt slip, tooth jump, or other obstacle occurs. Therefore, inspection and maintenance work for using the tension of the belt within a prescribed range is required.
Further, the belt is subject to aged deterioration (e.g., elongation, wear, deterioration of elasticity) due to environmental conditions (e.g., room temperature, humidity, etc.), operating conditions (e.g., motor rotational speed, load, etc.) in which the industrial machine is installed, and therefore, the tension of the belt constantly changes. Therefore, it is desirable to periodically perform inspection and maintenance work on the tension of the belt.
Regarding measurement and inspection of the tension of the belt, it is known to stop the operation of the industrial machine, cut off the power supply to the motor driving the industrial machine for safety, and measure the tension of the belt using a tensiometer such as an acoustic belt tensiometer in a state where the rotation of the belt is stopped. In addition, when measuring the tension of the belt, a work is required to disassemble the structural members of the industrial machine to expose the belt, such as removing the safety cover covering the belt from the industrial machine.
As a related art related to inspection of a belt, patent document 1 shows the following: the tension monitoring device (acoustic wave sensor) is provided in the driving force transmission device of the injection molding machine having the timing belt to monitor the tension of the belt, thereby reducing the measurement work. As means for measuring the state of the belt, it is known to measure the frequency characteristic of the motor in a state where a load such as a transmission mechanism is connected to the motor. By analyzing the frequency characteristics, it is possible to diagnose the operational characteristics such as the resonance frequency, responsiveness, stability, and the like of the transmission mechanism.
Patent document 2 discloses that white noise (for example, a sine wave signal) is applied to detect a frequency characteristic and a resonance frequency, and patent document 3 discloses that measurement accuracy of the frequency characteristic is improved. Patent document 4 discloses the following: in the diagnosis operation of the drive belt, the frequency characteristic (frequency-gain characteristic) of the motor is analyzed, and the tension value of the belt is estimated by performing machine learning on data including the range of the resonance frequency.
Prior art literature
Patent literature
Patent document 1: japanese patent laid-open No. 11-262932
Patent document 2: japanese patent laid-open No. 2000-279990
Patent document 3: japanese patent application laid-open No. 2015-158734
Patent document 4: japanese patent laid-open No. 2021-060313
Disclosure of Invention
Problems to be solved by the invention
In response to detection of an abnormality in the tension of the belt, control is performed to notify the operator of the abnormality or to stop the operation, but the operation of the belt and the continuation of the operation are not performed. Specifically, the industrial machine is not controlled by switching the speed and torque of the motor driving the belt to safe operation command values.
In general, when a structural member having high rigidity such as a bearing or a bush is damaged, an industrial machine cannot be immediately operated, and production cannot be continued. However, even if the belt is slightly loosened, the belt does not immediately reach a critical condition (such as a shift in position due to the jump teeth of the belt, or breakage of the belt) as long as the tension of the belt is within a design tolerance range, and the operation and production can be continued.
Therefore, when the belt is loosened within the allowable range after the belt is detected, the operation command value associated with the belt tension is switched to safely continue the production.
Means for solving the problems
The control device of the present disclosure analyzes the frequency characteristics (frequency-gain characteristics, etc.) of feedback data based on feedback data (e.g., speed, position, torque) obtained by diagnosing the tension of the belt, and detects an extreme point. Then, the frequency of the detected extreme point is set to be the resonance frequency or the antiresonance frequency, and the belt tension is estimated based on the correlation between the resonance frequency or antiresonance frequency and the belt tension. If the estimated belt tension reaches a predetermined alarm level, the drive command value (for example, a speed command value or a torque command value) for controlling the driving of the belt is restricted or a warning message is notified, thereby solving the above-described problem.
Further, one aspect of the present disclosure is a control device that estimates a tension of a belt that transmits power and that is provided in an industrial machine, and that controls an operation of the belt, the control device including: a control unit that controls a diagnostic operation for driving the belt; a data acquisition unit that acquires data obtained from the diagnostic operation; an alarm condition storage unit that stores an alarm condition table that lists alarm conditions that relate at least a range of belt tension and an alarm operation related to control of the industrial machine to an alarm level; and an alarm operation command unit that, with reference to the alarm condition storage unit, when the tension of the belt obtained based on the data acquired by the data acquisition unit satisfies a predetermined alarm condition, commands the control unit to perform an alarm operation corresponding to the satisfied alarm condition.
Another aspect of the present disclosure is a control method for estimating a tension of a power transmission belt provided in an industrial machine to control an operation of the belt, the control method including: a step of performing control of a diagnostic operation for driving the belt; a step of acquiring data obtained from the diagnostic operation; and a step of controlling the industrial machine based on an alarm operation corresponding to the alarm condition that is satisfied when the tension of the belt obtained based on the data acquired in the step of acquiring satisfies the predetermined alarm condition, with reference to an alarm condition storage unit that stores an alarm condition table that includes alarm conditions that relate at least a range of the tension of the belt and an alarm operation related to control of the industrial machine to an alarm level.
Effects of the invention
According to one aspect of the present disclosure, even in a state where the belt tension is relaxed, the rotation speed and the rotation torque of the motor for driving the belt are switched to the operation command values of the safety level and the operation is continued as long as the estimated tension is within the allowable range in terms of design, and therefore, the yield and the productivity (production efficiency) can be improved.
Drawings
Fig. 1 is a schematic hardware configuration diagram of a control device according to a first embodiment of the present invention.
Fig. 2 is a schematic configuration diagram of an injection molding machine.
Fig. 3 is a block diagram showing the schematic function of the control device according to the first embodiment of the present invention.
Fig. 4 is a diagram showing an example of frequency analysis of the velocity feedback.
Fig. 5 is a diagram showing an example of the tension determination table.
Fig. 6 is a diagram showing an example of an alarm condition table.
Fig. 7 is a diagram showing an example of an alarm screen displayed by the alarm operation instruction unit.
Fig. 8 is a schematic hardware configuration diagram of a control device according to a second embodiment of the present invention.
Fig. 9 is a block diagram showing the schematic function of a control device according to a second embodiment of the present invention.
Fig. 10 is a schematic hardware configuration diagram of a control device according to another embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.
Fig. 1 is a schematic hardware configuration diagram showing a main part of a control device according to a first embodiment of the present invention. The control device 1 of the present invention can be mounted as a control device for controlling an industrial machine based on a control program, for example. The control device 1 of the present invention can be mounted on a personal computer provided in parallel with a control device for controlling an industrial machine according to a control program, a personal computer connected to the control device via a wired/wireless network, a cell computer, a mist computer 6, and a cloud server 7. In the present embodiment, an example is shown in which the control device 1 is installed as a control device for controlling the injection molding machine 2 as an industrial machine based on a control program.
The CPU11 included in the control device 1 of the present embodiment is a processor that integrally controls the control device 1. The CPU11 reads out a system program stored in the ROM12 via the bus 22, and controls the entire control device 1 according to the system program. The RAM13 temporarily stores temporary calculation data, display data, various data input from the outside, and the like.
The nonvolatile memory 14 is configured by, for example, a battery-backed up memory, SSD (Solid State Drive), or the like, which is not shown, and maintains a stored state even when the power supply of the control device 1 is turned off. The nonvolatile memory 14 stores data acquired from the injection molding machine 2, control programs read from the external device 72 via the interface 15, data, control programs input via the input device 71, data, control programs acquired from other devices via the network 5, data, and the like. The control program and data stored in the nonvolatile memory 14 may be developed in the RAM13 at the time of execution and at the time of use. Various system programs such as a well-known analysis program are written in advance in the ROM 12.
The interface 15 is an interface for connecting the CPU11 of the control device 1 to an external device 72 such as a USB device. For example, a control program for controlling the injection molding machine 2, setting data, and the like are read from the external device 72 side. The control program, setting data, and the like edited in the control device 1 can be stored in the external storage unit via the external device 72. The PMC (programmable machine controller) 16 executes a trapezoidal program, and outputs signals to and controls the injection molding machine 2 and peripheral devices (for example, a mold changing device, an actuator such as a robot, and a plurality of sensors 3 such as a temperature sensor and a humidity sensor attached to the injection molding machine 2) of the injection molding machine 2 via the I/O unit 19. Signals of various switches, peripheral devices, and the like provided on the operation panel of the main body of the injection molding machine 2 are received, subjected to necessary signal processing, and then transferred to the CPU11.
The interface 20 is an interface for connecting the CPU of the control apparatus 1 to the wired or wireless network 5. Other industrial machines 4 such as machine tools and electric discharge machines, mist computers 6, cloud servers 7, and the like are connected to the network 5, and exchange data with the control device 1.
The data read in the memory, the data obtained as a result of executing the program or the like, and the like are output to the display device 70 via the interface 17 and displayed. The input device 71, which is constituted by a keyboard, a pointing device, or the like, transfers instructions, data, or the like based on an operation by an operator to the CPU11 via the interface 18.
The axis control circuit 30 for controlling the axis of the injection molding machine 2 receives the movement command amount of the axis from the CPU11, and outputs the axis command to the servo amplifier 40. The servo amplifier 40 receives the command and drives a servo motor 50 for moving the shaft of the injection molding machine 2. The servo motor 50 of the shaft incorporates a position and speed detector, and feeds back a position and speed feedback signal from the position and speed detector to the shaft control circuit 30 to perform feedback control of the position and speed. In the hardware configuration of fig. 1, the shaft control circuit 30, the servo amplifier 40, and the servo motor 50 are shown one by one, but the corresponding amounts of the number of shafts of the injection molding machine 2 to be controlled are actually prepared. At least one of the servo motors 50 is connected to a predetermined shaft of the injection molding machine 2 via a belt as a power transmission unit.
Fig. 2 is a schematic configuration diagram of the injection molding machine 2. The injection molding machine 2 is mainly composed of a mold clamping unit 401 and an injection unit 402. The clamping unit 401 has a movable platen 416 and a fixed platen 414. A movable-side die 412 is attached to the movable platen 416, and a fixed-side die 411 is attached to the fixed platen 414. The injection molding machine 2 is provided with a servomotor 50. Further, by driving the servomotor 50 and driving a ball screw, not shown, via a power transmission means such as the belt 420 and the pulley 422, the movable platen 416 can be advanced or retracted in the direction of the fixed platen 414.
On the other hand, injection unit 402 is constituted by an injection cylinder 426, a hopper 436 for accumulating a resin material supplied to injection cylinder 426, and a nozzle 440 provided at the tip of injection cylinder 426. The injection unit 402 can advance or retract the injection cylinder 426 in the direction of the fixed platen 414 by driving a servo motor, not shown.
In a molding cycle for manufacturing 1 molded article, the mold closing and closing operation is performed by the movement of the movable platen 416 by the mold closing unit 401, the nozzle 440 is pressed against the fixed side mold 411 by the injection unit 402, and then the measured resin is injected into the mold in the injection cylinder 426. These operations are controlled by instructions from the control device 1, not shown.
Further, a sensor 3, not shown, is mounted to each part of the injection molding machine 2, and detects various physical quantities necessary for controlling the molding operation. Examples of the detected physical quantity include motor current, voltage, torque, position, speed, acceleration of the driving unit, pressure in the mold, temperature of the injection cylinder 426, flow rate, flow velocity, vibration, sound, and the like. The detected physical quantity is sent to the control device 1. The physical quantities detected by the control device 1 are stored in the RAM13, the nonvolatile memory 14, and the like.
Here, when the belt 420 is loosened, the friction between the belt 420 and the pulley 422 is reduced, the resistance to the centrifugal force generated when the belt 420 is driven to rotate is reduced, and the power transmission capability from the servomotor 50 is reduced. If the tension of the belt 420 is lowered, the belt 420 floats, or the belt 420 slips in the case where the belt 420 is a flat belt, a V belt, or a tooth jump occurs in the case of a toothed belt. As a result, the positioning accuracy of the control target (for example, the driven pulley 422, a not-shown toggle joint, a transmission mechanism portion such as the movable platen 416, or a mold) by the belt 420 is deteriorated, or the driving force of the servomotor 50 cannot be sufficiently transmitted to the control target. In this case, if the operation is continued, a command for changing the control is required in accordance with the situation.
Fig. 3 is a schematic block diagram showing functions of the control device 1 according to the first embodiment of the present invention. The functions of the control device 1 according to the present embodiment are realized by the CPU11 of the control device 1 shown in fig. 1 executing a system program and controlling the operations of the respective units of the control device 1.
The control device 1 of the present embodiment includes: the control unit 110, the data acquisition unit 120, the detection unit 130, the tension estimation unit 150, and the alarm operation command unit 160. In the RAM13 or nonvolatile memory 14 of the control device 1, a control program 200 for controlling the servomotor 50 provided in the injection molding machine 2 is stored in advance, and as a region for storing data acquired by the data acquisition unit 120 from the servomotor 50, the sensor 3, and the like, an acquisition data storage unit 210, an alarm condition storage unit 220 which is a region in which alarm conditions are stored in advance, and a tension determination table storage unit 152 which is a region in which a tension determination table defining a relationship between a resonance frequency or an antiresonance frequency and a belt tension is stored in advance are prepared.
The control unit 110 is realized by executing a system program read from the ROM12 by the CPU11 included in the control device 1 shown in fig. 1, and mainly performing arithmetic processing using the RAM13 and the nonvolatile memory 14 based on the CPU11, control processing of each part of the injection molding machine 2 using the shaft control circuit 30 and the PMC16, and input/output processing via the interface 18. The control unit 110 analyzes the blocks of the control program 200, and controls the respective units of the injection molding machine 2 based on the analysis result. For example, when the block command of the control program 200 is to drive each shaft of the injection molding machine 2, the control unit 110 generates movement command data in accordance with the block command and outputs the movement command data to the servo motor 50. When a block command of the control program 200 causes a peripheral device such as the sensor 3 mounted on the injection molding machine 2 to operate, for example, the control unit 110 generates a predetermined signal for causing the peripheral device to operate, and outputs the signal to the PMC16. In addition, the control unit 110 can output general instructions related to control of the injection molding machine 2, such as injection of resin, to the injection molding machine 2 in accordance with instructions of the program blocks of the control program 200. On the other hand, the control unit 110 acquires data detected by the sensor 3 such as the position feedback, the speed feedback, the torque feedback, the temperature sensor, the humidity sensor, and the like of the servomotor 50, and outputs the data to the data acquisition unit 120.
The control program 200 includes a program block for preliminarily instructing the servo motor 50 for driving the belt to perform a scanning operation at a rotation speed (frequency) within a predetermined range. The control program 200 includes a program block for instructing acquisition of position feedback, speed feedback, and torque feedback of the servo motor 50 during the scanning operation as time-series data, and a program block for instructing acquisition of data based on the sensor 3 at least at any one of the start of the scanning operation, during the scanning operation, and at the end of the scanning operation.
The data acquisition unit 120 acquires feedback data such as position feedback, speed feedback, torque feedback, and the like acquired from the servomotor 50 during operation of the injection molding machine 2, and data detected by the sensor 3, and stores the acquired data in the acquired data storage unit 210. Feedback data such as position feedback, speed feedback, and torque feedback acquired by the data acquisition unit 120 is time-series data. The data acquired by the data acquisition unit 120 may be a data value acquired at a predetermined timing. The data acquisition unit 120 may acquire data detected by the other industrial machines 4 from the other industrial machines 4 via the network 5. Further, data input from the input device 71 by the operator and data input via the external device 72 may be acquired.
The detection unit 130 calculates frequency response data indicating the frequency characteristics from the data acquired by the data acquisition unit 120. Then, the calculated frequency response data is analyzed, and an extreme point of the frequency response data is detected. The extreme point is a point at which a maximum value (maximum value) or a minimum value (minimum value) is locally taken in the vicinity of the point. The frequency of the detected extreme point becomes a candidate of the resonance frequency or the antiresonance frequency. For example, the frequency that becomes the maximum value becomes a candidate of the resonance frequency, and the frequency that becomes the minimum value becomes a candidate of the antiresonance frequency. The detection unit 130 outputs the frequency (resonance frequency, antiresonance frequency) of the detected extreme point to the tension estimation unit 150.
The detection unit 130 calculates, for example, frequency response data indicating a frequency-gain characteristic obtained by frequency-analyzing feedback data of the servo motor 50 stored in the acquired data storage unit 210. The frequency response data may be data obtained by sampling values in a range of frequencies in a predetermined range in a predetermined frequency period. Fig. 4 is a diagram showing an example of frequency analysis of the velocity feedback. In the example of fig. 4, the extreme points P0, P1, P2, … are detected, the maximum value is represented by a black circle, and the minimum value is represented by a white circle. In the figure, a first minimum value of gain is detected at the position of frequency fa, a first maximum value is detected at the position of frequency fb, a second minimum value is detected at the position of frequency fc, a second maximum value is detected at the position of frequency fd, and several extrema are detected. The detection unit 130 analyzes the change in the value of the frequency response data, and detects such a maximum value and a minimum value. When detecting an extremum from frequency response data representing frequency characteristics, the detection unit 130 may perform equalization such as moving average and partial regression on the frequency response data in order to remove noise and the like. The frequency response data may be frequency response data indicating frequency-phase characteristics.
The tension estimating unit 150 estimates a tension value of the belt attached to the injection molding machine 2 based on the frequency (resonance frequency or antiresonance frequency) of the extreme point detected by the detecting unit 130. The tension estimation unit 150 includes a tension determination table storage unit 152 and an estimation unit 154.
The estimating unit 154 refers to the tension determination table stored in the tension determination table storage unit 152, and estimates the tension value of the belt based on the frequency (resonance frequency or antiresonance frequency) of the extreme point detected by the detecting unit 130. Fig. 5 shows an example of the tension determination table stored in the tension determination table storage unit 152. As illustrated in fig. 5, the tension determination table is a table listing tension determination conditions that define the correspondence between the range of resonance frequencies or antiresonance frequencies and the estimated value of the belt tension for each machine type and each belt type. In the tension determination table illustrated in fig. 5, a correspondence relationship between the range of the primary antiresonance frequency and the estimated belt tension value is defined. For example, experiments or the like may be performed in advance, and the tension determination table may be prepared based on data obtained by measuring the tension of the belt using a tension meter such as an acoustic belt tension meter. The estimating unit 154 reads out a belt tension estimated value corresponding to the frequency of the extreme point detected by the detecting unit 130 from the tension determination table, and outputs the read-out belt tension estimated value as an estimated value of the belt tension. For example, when the type of machine is a small-sized injection molding machine, the type of belt is a standard, and the primary antiresonance frequency detected by the detection unit 130 is 120 hz, the estimation unit 154 can obtain 110 n as the belt tension estimation value by referring to the tension determination table.
The tension of the belt as the estimation result of the estimation unit 154 may be displayed and outputted on the display device 70, for example. The output may be transmitted to a personal computer such as a monitor terminal, the mist computer 6, and the cloud server 7 via the network 5.
When the estimated value of the belt tension estimated by the tension estimating unit 150 satisfies the alarm condition, the alarm operation instructing unit 160 instructs the control unit 110 to perform an alarm operation corresponding to the satisfied alarm condition. Fig. 6 shows an example of the alarm condition table stored in the alarm condition storage unit 220 referred to by the alarm operation instruction unit 160. As illustrated in fig. 6, the alarm condition table is a table listing alarm conditions defined by associating at least a range of belt tension and an alarm operation related to control of the injection molding machine 2 with each alarm level for each machine type and belt type. Examples of the alarm operation related to control include limitation of the rotational speed of the servomotor 50, limitation of the torque, operation of applying a brake for braking the movement of the servomotor 50, display of a warning message, and continuous conditions of operation (upper limit of operation duration, upper limit of the number of productions, etc.). Regarding the alarm condition table, for example, a durability test in the case where the belt is brought to a predetermined tension is performed in advance, and the rotational speed, torque, operation duration, and the like at which the injection molding machine 2 can be safely operated without failure may be determined.
The alarm operation command unit 160 may command the control unit 110 to control the alarm operation and display the level of the alarm, the content of the operation restriction, and the like on the display device 70. Fig. 7 is a diagram showing an example of an alarm screen displayed on the display device 70 by the alarm operation instruction unit 160. The alarm screen may include, in addition to the current alarm level, a current value of an estimated value of the belt tension, a graph showing a change with time until the current value, and the like. As specific details of the alarm operation, the content of the limitation on the servomotor 50, the remaining duration of the operation, and the like may be displayed.
The control device 1 having the above-described configuration estimates the belt tension from feedback data obtained by a conventional control device without using a measuring instrument such as an acoustic belt tension meter. Even when the belt tension is relaxed, the operation of the machine is continued by switching the rotational speed and rotational torque of the motor driving the belt to the operation command values of the safety level without stopping the operation of the machine if the estimated tension is within the allowable range in design, and therefore, the yield and productivity (production efficiency) can be improved. Even in a loose state of the belt, the machine continues to operate without applying an excessive load, and therefore, the belt can be prevented from being consumed and deteriorated, and the operating life of the machine can be prolonged.
If the operation of the machine is stopped at the moment when a fatal condition (such as a shift in the position of the jump teeth of the belt, or breakage of the belt) occurs in the belt, the movable part of the machine and the production facility may be damaged, and a large amount of man-hours and labor are required for the recovery work of the production. However, the control device according to the present embodiment can stop the machine in a safe state (a safe standby process in the production process) after reaching the warning level, and can avoid unexpected production failure and secondary disasters.
Further, since abnormality of the belt is detected and notified at a stage of tension relaxation of the belt before occurrence of a fatal obstacle, an operator can prepare a new maintenance member or equipment such as a belt or a measuring instrument before the machine is damaged, or perform maintenance work (inspection or maintenance work) such as replacement of the belt, thereby reducing downtime of the machine and preventing maintenance.
Fig. 8 is a schematic hardware configuration diagram showing a main part of a control device according to a second embodiment of the present invention. The control device 1 of the present invention learns the correlation between frequency response data obtained when a belt mounted on an injection molding machine 2 as an industrial machine is scanned at a rotation speed (frequency) within a predetermined range and the tension of the belt. Further, the tension of the belt is estimated using a learning model as a learning result thereof. The control device of the present embodiment has the same configuration as the control device of the first embodiment except that the control device has a machine learning device.
The interface 21 provided in the control device 1 of the present embodiment is an interface for connecting the CPU11 and the machine learning device 300. The machine learning device 300 includes: a processor 301 that integrally controls the whole of the machine learning device 300; a ROM302 storing a system program and the like; a RAM303 for temporary storage in each process related to machine learning; and a nonvolatile memory 304 for storing a learning model or the like. The machine learning device 300 can observe each piece of information (for example, data indicating the operation state of the servo motor 50, a temperature sensor, a humidity sensor, or the like, not shown, and the detection value of the sensor 3) that can be acquired by the control device 1 via the interface 21. The control device 1 obtains the processing result output from the machine learning device 300 via the interface 21, and stores or displays the obtained result or transmits the result to another device via the network 5 or the like.
Fig. 9 is a schematic block diagram showing functions of the control device 1 according to the second embodiment of the present invention. The functions of the control device 1 according to the present embodiment are realized by executing a system program by the CPU11 of the control device 1 and the processor 301 of the machine learning device 300 shown in fig. 5, and controlling the operations of the respective units of the control device 1 and the machine learning device 300.
The control device 1 of the present embodiment includes: the control unit 110, the data acquisition unit 120, the detection unit 130, the tension estimation unit 150, and the alarm operation command unit 160. A control program 200 for controlling the servomotor 50 provided in the injection molding machine 2 is stored in advance in the RAM13 or the nonvolatile memory 14 of the control device 1, and the data acquisition unit 120 is provided with an acquisition data storage unit 210 and an alarm condition storage unit 220, which is a region in which alarm conditions are stored in advance, as a region in which data acquired from the servomotor 50, the sensor 3, and the like are stored.
The tension estimating unit 150 of the present embodiment is mounted on the machine learning device 300. The learning model storage unit 158, which is an area for storing the learning model, is prepared in advance in the RAM303 or the nonvolatile memory 304 of the machine learning device 300.
The control unit 110, the data acquisition unit 120, the detection unit 130, and the alarm operation command unit 160 of the present embodiment have the same functions as the control unit 110, the data acquisition unit 120, the detection unit 130, and the alarm operation command unit 160 of the first embodiment.
The tension estimating unit 150 according to the present embodiment learns and estimates the tension value of the belt attached to the injection molding machine 2 based on the frequency (resonance frequency or antiresonance frequency) of the extreme point detected by the detecting unit 130. The tension estimating unit 150 includes: an estimating unit 154, a learning unit 156, and a learning model storage unit 158.
The learning unit 156 generates or updates a learning model by performing machine learning using learning data (teacher data) using the frequency set (resonance frequency or antiresonance frequency) of the extreme points detected by the detection unit 130 as input data and the actually measured belt tension as output data, and stores the generated or updated learning model in the learning model storage unit 158. Here, as input data for learning, only the primary anti-resonance frequency may be used, or a primary resonance frequency may be further added, or a secondary or subsequent resonance frequency or anti-resonance frequency may be added. The output data for learning may be measured by an operator using a tensiometer such as an acoustic belt tensiometer, and the value input to the input device 71 may be acquired by the data acquisition unit 120. For example, the data acquisition unit 120 may acquire a value obtained by the control unit 110 automatically acquiring the belt tension by a predetermined sensor 3 (tension meter) as the output data for learning.
The machine learning performed by the learning unit 156 is a known supervised learning. The learning model generated or updated by the learning unit 156 learns the correlation of the tension value of the belt as the output data with respect to the frequency group as the extreme point of the input data. Examples of the learning model created by the learning unit 156 include neural networks such as multilayer perceptron, recurrent neural network, long Short-Term Memory, convolutional neural network, and the like. Further, a learning model for estimating the belt tension using machine learning such as linear regression, lasso regression, ridge regression, elastic net regression, polynomial regression, and multiple regression may be used. The regression-based learning model has the advantage of a simple structure and a small computational load of the processor 301. On the other hand, a learning model based on deep learning such as convolutional neural network can expect an effect of improving the estimation accuracy of the belt tension.
The estimating unit 154 of the present embodiment uses the frequency set (resonance frequency or antiresonance frequency) of the extreme point detected by the detecting unit 130 as input data, performs the process of estimating the belt tension using the learning model stored in the learning model storage unit 158, and outputs the estimation result. The estimation process performed by the estimation unit 154 is an estimation process using the learning model created by the learning unit 156. For example, when the learning model stored in the learning model storage unit 158 is created as a neural network (convolutional neural network), the estimation unit 154 inputs data of the frequency group of the extreme points to the neural network, and outputs an estimated value of the belt tension output as an estimated result.
The alarm operation instructing unit 160 determines whether or not an alarm condition is satisfied based on the belt tension as the estimation result of the estimating unit 154, and instructs the control unit 110 of an alarm operation corresponding to the satisfied alarm condition.
The control device 1 having the above-described configuration estimates the belt tension from feedback data obtained from a conventional control device, without using a measuring instrument such as an acoustic belt tension meter, as in the control device 1 of the first embodiment. Further, even in a state where the belt tension is relaxed, the rotation speed and the rotation torque of the motor for driving the belt are switched to the operation command values of the safety level and the operation is continued as long as the estimated tension is within the allowable range in terms of design, and therefore, the yield and the productivity (production efficiency) can be improved. Even in a loose state of the belt, the machine continues to operate without applying an excessive load, and therefore, the belt can be prevented from being consumed and deteriorated, and the operating life of the machine can be prolonged.
The embodiments of the present invention have been described above, but the present invention is not limited to the examples of the embodiments described above, and can be implemented in various modes with appropriate modifications.
For example, in the above-described embodiment, the belt tension is subjected to the alarm operation in accordance with the belt tension value estimated based on the resonance frequency or the antiresonance frequency obtained by frequency-analyzing the physical quantity data obtained during the diagnostic operation. However, for example, when the sensor 3 for actually measuring the tension of the belt such as a tension meter is attached to the injection molding machine 2, the alarm operation may be performed in accordance with the tension value of the belt actually measured. Fig. 10 is a block diagram schematically showing the function of a control device according to another embodiment capable of actually measuring the tension of a belt attached to the injection molding machine 2. The injection molding machine equipped with the sensor 3 is expensive to measure the tension of the belt during operation, and the overall cost increases, but the alarm level can be determined and the alarm operation can be performed in accordance with the tension value of the belt actually measured without estimating the tension value of the belt.
In the above-described embodiment, the injection molding machine was described as an example of the industrial machine having the belt, but as long as the industrial machine has a structure using the belt to transmit power, other industrial machines such as a forging press, a pulverizer, and a conveying robot can be operated appropriately using the present invention.
In the case of using the machine learning technique, a plurality of models may be prepared in accordance with the attribute of the machine to be subjected to the machine learning technique. For example, a learning model is created in which learning is performed individually for each of the machine type, the belt type, and the motor type, and the learning model is used separately as appropriate when estimating the belt tension, whereby improvement of estimation accuracy can be expected.
In the above-described embodiment, the control device 1 is mounted on the control device that controls the injection molding machine 2, but may be mounted on, for example, the mist computer 6 or the cloud server 7, and data is acquired from the industrial machine 4 via the network 5, and estimation processing and learning processing are performed based on the acquired data. In the case of such a configuration, the control unit 110 instructs the industrial machine 4 to perform the frequency scanning operation in response to a request from each industrial machine 4, and performs the estimation process and the learning process based on the data acquired in the diagnostic operation performed in response to the instruction. The estimation result is transmitted to the industrial machines 4, and each industrial machine 4 performs an operation corresponding to the result.
Description of the reference numerals
1. Control device
2. Injection molding machine
3. Sensor for detecting a position of a body
4. Industrial machine
5. Network system
6. Fog computer
7. Cloud server
11CPU
12ROM
13RAM
14 non-volatile memory
15. 17, 18, 20, 21 interfaces
22. Bus line
70. Display device
71. Input device
72. External device
110. Control unit
120. Data acquisition unit
130. Detection unit
150. Tension estimating unit
152. Tension determination table storage unit
154. Estimation unit
156. Learning unit
158. Learning model storage unit
160. Alarm operation instruction unit
200. Control program
210. Acquisition data storage unit
220. Alarm condition storage unit
300. Machine learning device
301. Processor and method for controlling the same
302ROM
303RAM
304. Nonvolatile memory
401. Mold clamping unit
402. Injection unit
411. Fixed side metal mould
412. Movable side metal mold
414. Fixed pressing plate
416. Movable platen
420. Belt with a belt body
422. Belt wheel
426. Injection cylinder
436. Hopper
440. And (3) a nozzle.
Claims (14)
1. A control device for estimating tension of a belt for transmitting power of an industrial machine to control operation of the belt, characterized in that,
the control device comprises:
a control unit that controls a diagnostic operation for driving the belt;
a data acquisition unit that acquires data obtained from the diagnostic operation;
an alarm condition storage unit that stores an alarm condition table that lists alarm conditions that relate at least a range of belt tension and an alarm operation related to control of the industrial machine to an alarm level;
and an alarm operation command unit that, with reference to the alarm condition storage unit, when the tension of the belt obtained based on the data acquired by the data acquisition unit satisfies a predetermined alarm condition, commands the control unit to perform an alarm operation corresponding to the satisfied alarm condition.
2. The control device according to claim 1, wherein,
the data acquisition unit acquires at least feedback data obtained from the diagnostic operation,
the control device comprises:
a detection unit that analyzes the frequency characteristics of the feedback data, detects a plurality of extreme points in the frequency characteristics, and detects the frequency of each of the extreme points as a resonance frequency or an antiresonance frequency;
a tension estimating unit that estimates tension of the belt based on the resonance frequency or the antiresonance frequency detected by the detecting unit,
when the estimated value of the belt tension estimated by the tension estimating unit satisfies a predetermined alarm condition, the alarm operation instructing unit instructs the control unit to perform an alarm operation corresponding to the satisfied alarm condition.
3. The control device according to claim 2, wherein,
the tension estimating unit includes:
a tension determination table storage unit that stores a tension determination condition that correlates a range of resonance frequencies or a range of antiresonance frequencies with an estimated value of the tension of the belt as a tension determination table;
and an estimating unit that outputs an estimated value of the tension of the belt corresponding to the resonance frequency or the antiresonance frequency detected by the detecting unit from the tension determination conditions stored in the tension determination table.
4. The control device according to claim 2, wherein,
the tension estimating unit includes:
a learning model storage unit that stores a learning model for estimating the tension of the belt, the learning model being obtained by performing machine learning on the correlation between the resonance frequency or the antiresonance frequency detected by the detection unit and the tension of the belt;
an estimating unit that estimates and outputs an estimated value of the tension of the belt using the learning model stored in the learning model storage unit, based on the resonance frequency or the antiresonance frequency detected by the detecting unit.
5. The control device according to claim 4, wherein,
the tension estimating unit further includes: and a learning unit that performs machine learning on a correlation between the resonance frequency or the antiresonance frequency detected by the detection unit and the tension of the belt, and generates or updates a learning model that estimates the tension of the belt.
6. The control device according to claim 5, wherein,
the data acquisition unit also acquires a measurement value of the belt tension when the diagnostic operation is performed,
the learning unit generates the learning model by supervised learning based on learning data in which the resonance frequency or the antiresonance frequency is used as input data and the measured value of the belt tension acquired by the data acquisition unit is used as output data.
7. The control device according to claim 6, wherein,
the learning model generated by the supervised learning is any one of a linear regression model, a Lasso regression model, a Ridge regression model, an elastic net regression model, a polynomial regression model, a multiple regression model and a neural network model.
8. The control device according to claim 1, wherein,
the warning operation is either one of an operation of limiting the speed of the motor driving the belt to a predetermined speed upper limit value and an operation of limiting the torque of the motor driving the belt to a predetermined torque upper limit value.
9. The control device according to claim 1, wherein,
the warning operation operates a brake that brakes the movement of a motor that drives the belt.
10. The control device according to claim 1, wherein,
the alarm operation is an operation to stop the operation of the industrial machine after a predetermined elapsed time has elapsed, or an operation to stop the operation of the industrial machine after a predetermined number of productions have been produced.
11. The control device according to claim 1, wherein,
the industrial machine is also provided with a display device,
the alarm operation instruction unit displays and outputs at least one of the tension of the belt and information related to a predetermined alarm condition determined to satisfy the alarm condition on the display device.
12. The control device according to claim 2, wherein,
the diagnostic action is a frequency scanning action.
13. The control device according to claim 2, wherein,
the feedback data is any of the speed and position and torque to which the belt relates,
the frequency characteristic is any one of a frequency-gain characteristic and a frequency-phase characteristic.
14. A control method for estimating tension of a belt for transmitting power provided in an industrial machine to control the operation of the belt, characterized by,
the control method performs:
a step of performing control of a diagnostic operation for driving the belt;
a step of acquiring data obtained from the diagnostic operation;
and a step of controlling the industrial machine based on an alarm operation corresponding to the alarm condition that is satisfied when the tension of the belt obtained based on the data acquired in the step of acquiring satisfies the predetermined alarm condition, with reference to an alarm condition storage unit that stores an alarm condition table that includes alarm conditions that relate at least a range of the tension of the belt and an alarm operation related to control of the industrial machine to an alarm level.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/JP2021/031230 WO2023026419A1 (en) | 2021-08-25 | 2021-08-25 | Control device and control method |
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| CN117795308A true CN117795308A (en) | 2024-03-29 |
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|---|---|
| CN (1) | CN117795308A (en) |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11262932A (en) | 1998-03-16 | 1999-09-28 | Niigata Eng Co Ltd | Driving force transmitting apparatus for injection molding machine |
| JP4273560B2 (en) | 1999-03-23 | 2009-06-03 | パナソニック株式会社 | Motor control device |
| JP2007098260A (en) * | 2005-10-04 | 2007-04-19 | Hitachi Koki Co Ltd | Centrifuge |
| JP5813151B2 (en) | 2014-02-21 | 2015-11-17 | ファナック株式会社 | Numerical control device having function of calculating frequency characteristic of control loop |
| DE112017005650B4 (en) * | 2016-12-15 | 2023-09-07 | Mitsubishi Electric Corporation | TRANSMISSION MECHANISM ANOMALY DIAGNOSTIC DEVICE AND TRANSMISSION MECHANISM ANOMALY DIAGNOSTIC PROCEDURE |
| JP7222204B2 (en) * | 2018-09-05 | 2023-02-15 | 富士電機株式会社 | Machine diagnosis device and machine diagnosis program |
| JP7381280B2 (en) * | 2019-10-08 | 2023-11-15 | ファナック株式会社 | Diagnostic equipment and machine learning equipment |
| JP7332438B2 (en) * | 2019-11-05 | 2023-08-23 | ファナック株式会社 | diagnostic equipment |
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