TW202213004A - Precision predictive and correction system of tool machine - Google Patents

Abstract

A system for predicting and compensating machine tool accuracy. It is equipped with an Internet of Things device in the sub-system of the machine tool, and the physical parameter of the sub-system is sensed by the Internet of Things device, and when the machine tool is performing processing simulation, the instrument quantity is detected by error Measure multiple errors of the sub-system, use an industrial computer to capture and receive the parameters of the industrial computer main controller during processing simulation, and cooperate with the aforementioned physical parameters and errors to build an artificial intelligence model using the parameters and errors Installed on the industrial computer, and then when the industrial computer captures and receives multiple controller parameters and several physical parameters of each sub-system in real time, the artificial intelligence model can be used to simulate the current simulation error and input the main controller to perform Error compensation achieves the effect of predicting and compensating the accuracy of the machine tool.

Description

Machine Tool Accuracy Prediction and Compensation System

The invention relates to a compensation system for an artificial intelligence model, in particular to a machine tool precision prediction and compensation system.

Many researchers have invested in the machine tool monitoring system. The main research direction is to focus on the status monitoring of components such as spindle shaft status monitoring and bearing status monitoring. Through the status monitoring of equipment components, it can help maintenance personnel to quickly Identify components that require repair and repair damaged components.

However, although the aforementioned means of monitoring the status of equipment components can allow maintenance personnel to quickly identify components that need to be repaired, the information obtained by monitoring components cannot help on-site processing personnel to determine whether the accuracy of the machine is sufficient to apply to the workpiece. Whether it is possible to process workpieces that meet the tolerances required by the work order during processing.

Since the monitoring system of the existing machine tool mainly focuses on the monitoring of the state of the components, it cannot predict the machining accuracy of the machine tool, nor can it compensate for its error. Therefore, the present invention establishes an artificial intelligence model through the parameter collection of the main controller and the physical quantities of the components, and cooperates with the error measurement of the machine tool to monitor the precision of the machine tool and compensate for the error by means of artificial intelligence model simulation.

In order to achieve the above-mentioned creative purpose, the present invention provides a machine tool accuracy prediction and compensation system, which is applied to a machine tool. The machine tool is provided with a main controller and includes more than one sub-system, and the system includes:

More than one IoT device, each IoT device is installed in each sub-system and includes several sensors, and the several sensors are used to sense several physical parameters of each sub-system;

One or more precision error detection instruments, when the machine tool performs multiple machining simulations, each precision error detection instrument measures the multiple error amounts of the machine tool's sub-systems in an offline or online manner; and

An industrial computer, the industrial computer receives a plurality of error inputs of each sub-system, the industrial computer is connected with the main controller to capture a plurality of controller parameters of each sub-system during processing simulation, the industrial computer is connected with each object The networking device is connected with a signal to receive several physical parameters of each sub-system during processing simulation; the industrial computer is provided with a database, which stores a plurality of controller parameters, a plurality of physical parameters and a plurality of error quantities of each sub-system , and use these parameters and a plurality of error quantities to establish an artificial intelligence model corresponding to each sub-system, and install the artificial intelligence model on the industrial computer; then, when the industrial computer immediately captures and receives multiple controls of each sub-system Enter the corresponding artificial intelligence model to simulate a current simulation error, and input each simulation error into the main controller for error compensation.

Further, each Internet of Things device of the present invention is provided with an Internet of Things wireless module, which is electrically connected to the plurality of sensors through the Internet of Things wireless module; the industrial computer is provided with a wireless module, and the wireless module The group is connected to the signal of each IoT wireless module.

Furthermore, the industrial computer of the present invention is provided with an accuracy compensation control interface, and an error warning threshold is set corresponding to each sub-system on the precision compensation control interface. When each analog error exceeds the corresponding error warning threshold, the The industrial computer inputs the analog error amount to the main controller for error compensation.

Preferably, in the machine tool accuracy prediction and compensation system of the present invention, the sensors of each IoT device include a temperature sensor, a vibration sensor, a current sensor, and a deformation sensor.

Further, the sub-system of the machine tool of the present invention includes a main shaft system; the precision error detection instrument for measuring the main shaft system is a main shaft temperature rise displacement and yaw detection device; a plurality of controller parameters of the main shaft system include machining speed, Depth of cut, cutting speed, fluid supply pressure, pump motor speed, and coolant temperature; several physical parameters of the spindle system include temperature change, vibration level, motor drive current, and structural deformation.

Further, the sub-system of the machine tool of the present invention has four and is divided into a main shaft system, a feeding system, a rotating shaft system and a base leveling system. The instrument for measuring the accuracy and error of the spindle system is the spindle temperature rise, displacement and yaw detection device; the instrument for measuring the accuracy and error of the feed system is a linear positioning detection device; the instrument for measuring the accuracy and error of the rotating shaft system The detection instrument is a rotating shaft positioning detection device; the precision error detection instrument for measuring the leveling system of the base is a spirit level.

When the present invention is used, several physical quantity parameters, a plurality of controller parameters captured in the process of multiple processing simulations by the machine tool, and the error amount of each system processing are measured, and the algorithm is established on the input physical quantity parameters, After the parameters of the controller can be simulated, the artificial intelligence model of the variation of the error amount can be simulated, and the artificial intelligence model can be installed on the industrial computer.

The effect of the present invention is that when the industrial computer subsequently captures and receives the physical quantity parameters and controller parameters corresponding to each sub-system, it can simulate and calculate the corresponding simulation error through the corresponding artificial intelligence model, so as to achieve the accuracy of the machine tool. The effect of precision prediction, and when the analog error amount is input to the main controller of the machine tool, the error can be compensated for the sub-system of the machine tool, which can ensure that the machining accuracy of the machine tool can be maintained in a better state .

In order to understand the technical features and practical effects of the present invention in detail, and to implement it according to the contents of the description, the preferred embodiments shown in the drawings are further described in detail as follows.

1 to 3, the present invention provides a machine tool accuracy prediction and compensation system, which is applied to a machine tool 10. The machine tool 10 is provided with a main controller 11 and includes four The sub-system 12, the main controller 11 is a computer numerical controller (CNC controller), the aforementioned four sub-systems 12 are divided into a spindle system 121, a feed system 122, a rotary axis system 123 and a base level System 124, the spindle system 121 includes components such as a spindle and a cooling system, the feed system 122 includes components such as a motor, a screw, a linear slide, a motor seat and a bearing seat, and the rotating shaft system 123 includes a motor, a turntable, a worm and a Elements such as turbines, gears and bearings, the base horizontal system 124 includes a horizontal base and five major castings (head, column, table, saddle and base) and other elements; The system of the present invention applied to the aforementioned machine tool 10 includes:

Four IoT devices 20 matching the number of sub-systems 12, four IoT devices 20 are respectively installed on the spindle system 121, the feed system 122, the rotation axis system 123 and the base level system 124, each IoT device 20 includes a temperature sensor 21, a vibration sensor 22, a current sensor 23, a deformation sensor 24, and an IoT wireless module 25 electrically connected to these sensors. The deformation The sensor 24 may be a stress or strain sensor; the sensors of each IoT device 20 are used to sense elements of the spindle system 121 , the feed system 122 , the rotation axis system 123 or the base level system 124 Several physical quantity parameters, such as the temperature change of the main shaft of the main shaft system 121, the degree of vibration, the magnitude of the motor drive current, and the amount of structural deformation and other physical quantity parameters.

When the machine tool 10 operates, the components of each sub-system 12 will have a shape change that affects the machining accuracy due to temperature changes. The temperature sensors 21 of each IoT device 20 are used to measure the temperature changes of the components; each vibration sensor 22 is used to sense whether the components of each sub-system 12 are worn out due to long-term operation, or the joint surface is loose, resulting in abnormal vibration or shaking; when the load of the components is different, the driving current of the motor will be different, Each current sensor 23 is used to sense the motor driving current of each sub-system 12; each deformation sensor 24 is used to sense the structural deformation of the components of each sub-system 12; The sensors of each IoT device 20 of the rotating shaft system 123 and the base level system 124 measure several physical parameters related to the machining accuracy of the machine tool 10 according to the functions of the aforementioned different sensors.

There are four precision error detection instruments 30 corresponding to the number of the sub-systems 12 , and the four precision and error detection instruments 30 are respectively used to measure a plurality of errors when each sub-system 12 operates. The precision error detection instrument 30 that cooperates with the measurement of the spindle system 121 is a main shaft temperature rise displacement and deflection detection device; the precision error detection instrument 30 cooperates with the measurement of the feed system 122 is a linear positioning detection device; The precision error detection instrument 30 of the system 123 is a rotation axis positioning detection device; the precision error detection instrument 30 that cooperates with the measurement of the base leveling system 124 is a spirit level. When the machine tool 10 performs multiple machining simulations under different machining conditions, each precision error detection instrument 30 measures each sub-system 12 of the machine tool 10 in an offline or online manner, that is, the spindle system 121 , the Error quantities of the feed system 122 , the rotary axis system 123 or the base level system 124 .

An industrial computer 40, the industrial computer 40 is installed with the main controller connection software that can connect with the main controller 11 of the machine tool 10, and is connected to the machine tool 10 as a smart set-top box (SMB) of the machine tool 10. Line, the industrial computer 40 is provided with a wireless module 41, a database 42 and a precision compensation control interface 43 displayed on the display. The industrial computer 40 receives a plurality of measurements of each sub-system 12 measured by each precision error detection instrument 30 when the machine tool 10 performs multiple processing simulations in an offline file transmission mode or an online transmission mode connected by a port. For error input, the industrial computer 40 is also connected to the main controller 11 through a connection port, and the wireless module 41 is connected with the signal of the IoT wireless module 25 of each IoT device 20 via the wireless module 41 .

The industrial computer 40 is connected to the main controller 11 to capture a plurality of controller parameters of each sub-system 12 during processing simulation, and the industrial computer 40 is connected to each IoT device 20 to receive signals from each sub-system 12 during processing simulation Several physical parameters of each sub-system 12 are stored in the database 42, and a plurality of controller parameters of each sub-system 12 during processing simulation are stored in the database 42 , Several physical parameters and multiple error quantities are used as data data, using algorithms such as linear regression analysis when the data data is small, and genetic algorithm or neural algorithm when the data data is large to establish a corresponding system. The artificial intelligence model of 12 is installed on the industrial computer 40 .

Afterwards, when the industrial computer 40 instantly captures and receives a plurality of controller parameters and a plurality of physical quantity parameters of each sub-system 12, and inputs the corresponding artificial intelligence model to simulate a current simulation error, the control can be compensated prior to the accuracy. The interface 43 sets an error warning threshold corresponding to each of the sub-systems 12 . When each analog error exceeds the corresponding error warning threshold, the industrial computer 40 inputs the analog error to the main controller 11 for error compensation.

The aforementioned process of simulating the current simulation error amount of each sub-system 12 with each artificial intelligence model, taking the thermal deformation of the main shaft of the main shaft system 121 as an example: when the machine tool 10 operates, the precision error affected by heat is the most affected, and the heat will cause The extension of the spindle makes the tool nose point of the cutting tool deviate from the original setting, which affects the machining accuracy. When establishing the artificial intelligence model, the Internet of Things device 20 installed in the spindle system 121 is used to sense the physical parameters of the spindle system 121, and these physical parameters include the temperature change of the spindle of the spindle system 121, the degree of vibration, the motor drive current size, and structural deformation, and capture a number of controller parameters of the main controller 11, these controller parameters include machining speed, depth of cut, cutting speed, hydraulic pressure, pump motor speed, and pump motor current. Load, coolant flow rate, and coolant temperature (the number of controller parameters can be increased or decreased, and only some parameters that have a significant impact on the accuracy error are used), in conjunction with the accuracy error detection instrument 30 (that is, the spindle temperature rise displacement and deviation The pendulum detection device) measures the tool tip point on the machine tool 10 during multiple machining simulations, including multiple errors in the displacement changes of the X-axis, Y-axis, and Z-axis and the angular yaw change in each axis, and uses calculation The artificial intelligence model including the physical quantity parameters, controller parameters and the change of the final tool tip point is established by the method.

After the artificial intelligence model corresponding to the change of the tool nose point of the spindle system 121 is installed on the industrial computer 40, the physical quantity parameters and controller parameters related to the spindle system 121 can be captured and received in real time through the industrial computer 40, and then simulated and calculated. The simulated error amount of the current tool nose point displacement change is used to compensate the error of the spindle system 121 of the machine tool 10 . In this way, when the machine tool 10 uses the present invention and then leaves the factory, it is not necessary to use the precision error detection instrument 30 of the spindle temperature rise displacement and yaw detection device to measure the spindle system 121 of the machine tool 10 in a different place. The artificial intelligence model performs parameter simulation and infers the change of the tool nose point of the same machine tool 10, thereby compensating the machining accuracy.

In addition to the above-mentioned preferred embodiment of the present invention, in addition to installing the IoT device 20 in each sub-system 12, the IoT device 20 can also be installed in only one or other number of sub-systems 12 to establish corresponding The artificial intelligence models of the system 12 are installed on the industrial computer 40 corresponding to the artificial intelligence models of different sub-systems 12, and are used to simulate and calculate the The analog error amount is used for error compensation of the machining accuracy of each sub-system 12 of the machine tool 10 .

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the scope of rights claimed by the present invention. All other equivalent changes or modifications that do not depart from the spirit disclosed in the present invention shall be included in the present invention. within the scope of the patent application.

10: Tool machine 11: Main Controller 12: Subsystem 121: Spindle system 122: Feeding system 123: Rotary axis system 124: Pedestal Leveling System 20: IoT Devices 21: Temperature sensor 22: Vibration sensor 23: Current sensor 24: Deformation sensor 25: IoT Wireless Module 30: Precision error detection instrument 40: Industrial Computer 41: Wireless Module 42:Database 43: Precision compensation control interface

FIG. 1 is a system block diagram of a preferred embodiment of the present invention. FIG. 2 is a block diagram of an IoT device according to a preferred embodiment of the present invention. FIG. 3 is a block diagram of the detection error amount according to the preferred embodiment of the present invention.

10: Tool machine

11: Main Controller

12: Subsystem

121: Spindle system

122: Feeding system

123: Rotary axis system

124: Pedestal Leveling System

20: IoT Devices

30: Precision error detection instrument

40: Industrial Computer

41: Wireless Module

42:Database

43: Precision compensation control interface

Claims (7)

A machine tool accuracy prediction and compensation system, applied to a machine tool, the machine tool is provided with a main controller and includes more than one sub-system, the system includes: More than one IoT device, each IoT device is installed in each sub-system and includes several sensors, and the several sensors are used to sense several physical parameters of each sub-system; One or more precision error detection instruments, when the machine tool performs multiple machining simulations, each precision error detection instrument measures the multiple error amounts of the machine tool's sub-systems in an offline or online manner; and An industrial computer, the industrial computer receives a plurality of error inputs of each sub-system, the industrial computer is connected with the main controller to capture a plurality of controller parameters of each sub-system during processing simulation, the industrial computer is connected with each object The networking device is connected with a signal to receive several physical parameters of each sub-system during processing simulation; the industrial computer is provided with a database, which stores a plurality of controller parameters, a plurality of physical parameters and a plurality of error quantities of each sub-system , and use these parameters and a plurality of error quantities to establish an artificial intelligence model corresponding to each sub-system, and install the artificial intelligence model on the industrial computer; then, when the industrial computer immediately captures and receives multiple controls of each sub-system Enter the corresponding artificial intelligence model to simulate a current simulation error, and input each simulation error into the main controller for error compensation. The machine tool accuracy prediction and compensation system according to claim 1, wherein each IoT device is provided with an IoT wireless module, and the IoT wireless module is electrically connected to the plurality of sensors; the The industrial computer is provided with a wireless module, and the wireless module is used for signal connection with the wireless modules of the Internet of Things. The machine tool accuracy prediction and compensation system according to claim 2, wherein the industrial computer is provided with an accuracy compensation control interface, and an error warning threshold is set corresponding to the respective sub-systems on the accuracy compensation control interface. When the amount exceeds the corresponding error warning threshold, the industrial computer inputs the analog error amount to the main controller for error compensation. The machine tool accuracy prediction and compensation system according to any one of claims 1 to 3, wherein the sensors of each IoT device include a temperature sensor, a vibration sensor, a current sensor detector, and a deformation sensor. The machine tool accuracy prediction and compensation system according to claim 4, wherein the sub-system of the machine tool includes a main shaft system; the precision error detection instrument for measuring the main shaft system is a main shaft temperature rise displacement and deflection detection device; the Several controller parameters of the spindle system include machining speed, depth of cut, cutting speed, hydraulic pressure, pump motor speed, and coolant temperature; several physical parameters of the spindle system include temperature change, vibration level, motor drive The magnitude of the current, and the amount of structural deformation. The machine tool accuracy prediction and compensation system as claimed in claim 4, wherein the machine tool has four sub-systems and is divided into a spindle system, a feed system, a rotary axis system and a base level system. The machine tool accuracy prediction and compensation system according to claim 6, wherein the precision error detection instrument for measuring the spindle system is a spindle temperature rise displacement and yaw detection device; the precision error detection instrument for measuring the feed system is matched It is a linear positioning detection device; the precision error detection device for measuring the rotation axis system is a rotary axis positioning detection device; the precision error detection device for measuring the base level system is a spirit level.

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