Cutting product control system and method of compound machine tool

Abstract

The invention provides a machining product control system and method of a compound machine tool, relates to the technical field of product machining, and realizes real-time monitoring, intelligent control and fault prediction of a machining process by introducing a multi-sensor module, a data processing platform, an intelligent control module and a fault prediction module. The multi-sensor module can comprehensively process information from a plurality of sensors; the data processing platform can pre-process and analyze the collected data, extract processing state features and provide decision basis for intelligent control; the intelligent control module automatically sets and adjusts cutting parameters according to workpiece parameters input by a user and a machining state analysis result, and intelligent control over the machining process is achieved. And the fault prediction module predicts the operation state of the machine tool based on the historical fault data and the processing state analysis result, and triggers an early warning mechanism when a fault is predicted to occur, so that cooperative response before the fault is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of product processing, and in particular to a control system and method for cutting processing products of a compound machine tool. Background Art

[0002] In modern manufacturing, compound machine tools, as efficient and multifunctional processing equipment, are widely used in the processing and manufacturing of various precision parts. With the rapid development of intelligent manufacturing technology, higher requirements are placed on the processing accuracy, production efficiency and operation stability of compound machine tools. Traditional machine tool control systems often rely on manual experience and fixed parameter settings, and are difficult to adapt to complex and changing processing environments and workpiece requirements, resulting in problems such as low processing efficiency, waste of resources and frequent failures.

[0003] Therefore, it is necessary to provide a control system and method for cutting processing products of a composite machine tool to solve the above technical problems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a control system and method for cutting processing products of a composite machine tool, which not only improves the processing accuracy and production efficiency of the composite machine tool, but also significantly enhances the operation stability and reliability of the machine tool.

[0005] The present invention provides a cutting product control system for a composite machine tool, the control system comprising a multi-sensor module, a data processing platform, an intelligent control module and a fault prediction module, the multi-sensor module and the data processing platform transmit data with the intelligent control module via a data bus, the fault prediction module transmits data with the intelligent control module and the data processing platform via a data bus, wherein: Multi-sensor module for real-time collection of environmental parameter sets during processing; A data processing platform, used for receiving and processing the environmental parameter set from the multi-sensor module, obtaining a processing state analysis result, and sending the processing state analysis result to the intelligent control module; An intelligent control module, used to initially set cutting parameters according to workpiece parameters input by a user, and dynamically adjust the cutting parameters according to the machining state analysis results sent by the data processing platform during machining; A fault prediction module is used to predict the operating status of the compound machine tool based on the processing status analysis results provided by the data processing platform and in combination with historical fault data, and to trigger an early warning mechanism when a fault is predicted to occur, and at the same time send an early warning signal to the intelligent control module through the data bus. Finally, the intelligent control module automatically executes early warning measures according to the early warning signal to achieve a coordinated response before a fault occurs.

[0006] Preferably, the multi-sensor module includes a plurality of sensors of different types for measuring different physical quantities, and a data fusion unit for comprehensively processing information from the plurality of sensors, wherein the plurality of sensors of different types include temperature sensors, pressure sensors, vibration sensors and displacement sensors.

[0007] Preferably, the data processing platform includes a data preprocessing unit and a data analysis unit, wherein: The data preprocessing unit is used to preprocess the received environmental parameter set; The data analysis unit is used to apply a statistical analysis method to extract processing state features from the pre-processed environmental parameter set and generate processing state analysis results.

[0008] Preferably, the intelligent control module includes a parameter setting unit and a dynamic adjustment unit, wherein: The parameter setting unit is used to automatically calculate and set cutting parameters using a preset model according to workpiece parameters input by a user, wherein the cutting parameters include cutting speed, cutting depth and feed speed, and the preset model is a mathematical model based on machine learning; The dynamic adjustment unit adjusts the cutting parameters in real time according to the machining status analysis results sent by the data processing platform.

[0009] Preferably, the fault prediction module further includes a fault type identification unit and an early warning strategy formulation unit, wherein: The fault type identification unit identifies the fault type using a pre-trained neural network model based on the processing state analysis result and historical fault data; The early warning strategy formulation unit formulates and executes corresponding early warning measures according to the identified fault type, wherein the early warning measures include adjusting processing parameters, suspending processing and starting an emergency shutdown procedure.

[0010] The present invention also provides a control method for cutting processed products of a composite machine tool, which is applied to a control system for cutting processed products of a composite machine tool. The control method comprises the following steps: Collecting the environmental parameter set in the processing process in real time through the multi-sensor module, wherein the environmental parameter set includes temperature data, pressure data, vibration data and displacement data; The data processing platform performs preprocessing and feature extraction on the received environmental parameter set, generates a processing state analysis result, and sends the processing state analysis result to the intelligent control module, wherein the feature extraction result includes temperature features, pressure features, vibration features, and displacement features extracted from the preprocessed temperature data, pressure data, vibration data, and displacement data, respectively; The intelligent control module automatically sets the initial cutting parameters according to the workpiece parameters input by the user before processing, and dynamically adjusts the cutting parameters according to the processing state analysis results using an adaptive tuning algorithm during processing, wherein the cutting parameters include cutting speed, cutting depth and feed speed; The fault prediction module predicts the operation state of the composite machine tool based on the processing state analysis result and the historical fault data, and triggers the early warning mechanism when a fault is predicted to occur, and sends an early warning signal to the intelligent control module through the data bus; The intelligent control module automatically executes early warning measures according to the early warning signal to achieve coordinated response before a fault occurs.

[0011] Preferably, it also includes: receiving workpiece parameters of a workpiece, wherein the workpiece parameters include type, size and material; The received workpiece parameters are input into the intelligent control module.

[0012] Preferably, the adaptive tuning algorithm is specifically: Based on the temperature characteristics, pressure characteristics, vibration characteristics and displacement characteristics, and in combination with a preset mapping table, the current operating state of the composite machine tool is evaluated, wherein the operating state of the composite machine tool includes a stable state, an unstable state and an abnormal state, and the mapping table represents the correspondence between the characteristics extracted from the data collected by the sensor and the operating state of the composite machine tool; According to the operating state of the compound machine tool, the cutting parameters are adjusted according to the preset processing strategy.

[0013] The processing strategies include: If the operation state of the compound machine tool is stable, the feed speed is increased, the cutting speed is increased and the cutting depth is maintained, wherein: The feed rate increase formula is: ; in, is the feed speed before adjustment, is the adjusted feed speed, Increase the ratio for the preset first one; The increasing formula of the cutting speed is: ; in, is the cutting speed before adjustment, is the adjusted cutting speed, Increase the ratio for the preset second; The cutting depth maintenance formula is: ; in, and Maintain the cutting depth at the front and back respectively; If the operation state of the compound machine tool is unstable, the feed speed, cutting speed and cutting depth are reduced, wherein: The feed speed reduction formula is: ; in, is the feed speed before adjustment, is the adjusted feed speed, is a preset first reduction ratio; The increasing formula of the cutting speed is: ; in, is the cutting speed before adjustment, is the adjusted cutting speed, is a preset second reduction ratio; The reduction formula of the cutting depth is: ; in, is the cutting depth before adjustment, is the adjusted cutting depth, is a preset third reduction ratio; If the operation state of the compound machine tool is abnormal, the processing is stopped immediately.

[0014] Compared with the related art, the control system and method for cutting processing products of a composite machine tool provided by the present invention have the following beneficial effects: The present invention realizes real-time monitoring, intelligent control and fault prediction of the machining process by introducing key components such as a data fusion unit, a data preprocessing unit, a data analysis unit, an intelligent control module and a fault prediction module. Among them, the data fusion unit can comprehensively process information from multiple sensors to improve the accuracy and reliability of the data; the data preprocessing unit and the data analysis unit can preprocess and analyze the collected data, extract the machining state characteristics, and provide a decision basis for intelligent control; the intelligent control module can automatically set and adjust the cutting parameters according to the workpiece parameters and machining state analysis results input by the user, and realize intelligent control of the machining process; the fault prediction module can predict the operating status of the machine tool based on historical fault data and machining state analysis results, and trigger the early warning mechanism when a fault is predicted to occur, so as to realize a coordinated response before the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the structure of a control system for cutting products of a composite machine tool provided by the present invention; Figure 2 The present invention provides a flow chart of a method for controlling a cutting product of a composite machine tool. DETAILED DESCRIPTION

[0016] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only the parts related to the present invention, rather than all structures, are shown in the accompanying drawings. In addition, the embodiments of the present invention and the features in the embodiments may be combined with each other without conflict.

[0017] It should also be noted that, for ease of description, only the parts related to the present invention, but not all of the contents, are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. Embodiment 1

[0018] The present invention provides a cutting product control system for a composite machine tool, which aims to improve the processing quality and equipment reliability by real-time monitoring and adjusting cutting parameters. Figure 1 As shown, the system includes a multi-sensor module, a data processing platform, an intelligent control module and a fault prediction module, and data transmission is achieved between the modules through a data bus.

[0019] The multi-sensor module is responsible for real-time acquisition of environmental parameter sets during the machining process. It includes temperature sensors, pressure sensors, vibration sensors and displacement sensors, as well as a data fusion unit for comprehensive processing of information from multiple sensors. The temperature sensor measures the temperature of the machining area, the pressure sensor monitors the cutting force and hydraulic system pressure, the vibration sensor monitors the vibration of the machine tool, and the displacement sensor measures the relative position of the tool and the workpiece.

[0020] More specifically, the multi-sensor module is responsible for real-time collection of environmental parameter sets during the processing to provide comprehensive monitoring data to ensure the stability and quality of the processing. This module includes the following types of sensors and their functions: The temperature sensor is mainly used to measure the temperature of the processing area, and can include but is not limited to thermocouples and infrared temperature sensors.

[0021] Among them, the thermocouple is installed near the tool or on the surface of the workpiece to measure the temperature in direct contact; the infrared temperature sensor is installed away from the processing area to measure the temperature in a non-contact manner.

[0022] By monitoring the temperature changes in the processing area, it helps to detect overheating in time and avoid tool wear and workpiece deformation caused by high temperature.

[0023] The pressure sensor is mainly used to monitor cutting force and hydraulic system pressure, and may include but is not limited to piezoelectric pressure sensors and strain gauge pressure sensors.

[0024] Among them, piezoelectric pressure sensors are usually installed on the tool handle or machine tool spindle to directly measure the cutting force; strain gauge pressure sensors are installed on the pipes or cylinders of the hydraulic system to monitor the pressure of the hydraulic system.

[0025] Real-time monitoring of cutting forces and hydraulic system pressure helps optimize cutting parameters and prevent overload and hydraulic system failure.

[0026] The vibration sensor is mainly used to monitor the vibration of the machine tool. In this application, an accelerometer vibration sensor is used.

[0027] Accelerometers are installed on key components of machine tools, such as spindles, guide rails and work tables, to monitor vibrations in different directions. By detecting the vibration of the machine tools, abnormal vibrations can be discovered in time to avoid machining errors and equipment damage caused by vibration.

[0028] The displacement sensor is mainly used to measure the relative position of the tool and the workpiece, and can include but is not limited to laser displacement sensors and photoelectric encoders.

[0029] Among them, the laser displacement sensor is usually installed at a fixed position of the machine tool, measuring the relative distance between the tool and the workpiece through a laser beam; the photoelectric encoder is installed on the moving parts of the machine tool, such as the spindle and feed axis, to measure position changes.

[0030] By real-time monitoring of the relative position of the tool and the workpiece, the machining accuracy is ensured and machining quality problems caused by position deviation are avoided.

[0031] The data processing platform receives and processes the environmental parameter set from the multi-sensor module, generates the processing state analysis results, and sends the results to the intelligent control module. The data processing platform includes a data preprocessing unit and a data analysis unit. The data preprocessing unit performs preprocessing operations such as filtering and denoising on the collected environmental parameter set to remove noise and outliers; the data analysis unit applies statistical analysis methods to extract processing state features from the preprocessed environmental parameter set and generates processing state analysis results.

[0032] The work content of the data preprocessing unit specifically includes: Use low-pass filters, band-pass filters, and high-pass filters to remove noise at different frequencies.

[0033] Kalman filter, wavelet transform and median filter are used to remove random noise and impulse noise.

[0034] Sensors should be calibrated regularly to ensure the accuracy of measurements and to normalize data from different sensors to the same dimension.

[0035] Ensure that data from different sensors are synchronized in time to avoid data inconsistency caused by time differences.

[0036] The data analysis unit applies statistical analysis methods to extract processing state features from the pre-processed environmental parameter set and generate processing state analysis results. The specific implementation includes: Calculate statistical characteristics such as mean, variance, standard deviation, analyze the spectral characteristics and time domain characteristics of the signal, and calculate the correlation coefficient between different sensor data.

[0037] Set the threshold range of multiple features and combine multiple feature values ​​to make status judgment.

[0038] Threshold setting: For temperature characteristics, the average temperature is <60°C in a stable state, the average temperature is 60°C≤<70°C in an unstable state, and the average temperature is ≥70°C in an abnormal state. For pressure characteristics, the peak pressure in a stable state is <100 bar, in an unstable state it is 100 bar ≤ the peak pressure <120 bar, and in an abnormal state it is ≥120 bar For vibration characteristics, the average vibration intensity is <0.1g in a stable state, 0.1g≤the average vibration intensity is <0.25g in an unstable state, and the average vibration intensity is ≥0.25g in an abnormal state. For displacement characteristics, the displacement change rate is <0.05mm / s in a stable state, 0.05mm / s≤displacement change rate<0.1mm / s in an unstable state, and ≥0.1mm / s in an abnormal state. Multi-feature comprehensive evaluation: Steady State: All eigenvalues ​​are within the steady state range.

[0039] Unstable state: Any eigenvalue is within the unstable state range, and no eigenvalue is within the abnormal state range.

[0040] Abnormal state: Any characteristic value is within the abnormal state range.

[0041] The intelligent control module initially sets the cutting parameters according to the workpiece parameters input by the user, and dynamically adjusts the cutting parameters during the machining process according to the machining status analysis results sent by the data processing platform. The intelligent control module includes a parameter setting unit and a dynamic adjustment unit. The parameter setting unit automatically calculates and sets the cutting parameters, including cutting speed, cutting depth and feed speed, according to the workpiece parameters input by the user using a preset model (a mathematical model based on machine learning); the dynamic adjustment unit adjusts the cutting parameters in real time according to the machining status analysis results sent by the data processing platform to optimize the machining process.

[0042] More specifically, the parameter setting unit automatically calculates and sets cutting parameters using a preset model based on the workpiece parameters input by the user. The specific implementation includes: The user inputs the type, size, material and other information of the workpiece through the human-computer interaction interface.

[0043] Using a mathematical model based on machine learning, cutting parameters are automatically calculated and set based on the input workpiece parameters. These parameters include cutting speed, cutting depth and feed rate.

[0044] According to the model calculation results, the initial cutting parameters are set to ensure that the starting point of the machining process meets the process requirements.

[0045] The dynamic adjustment unit adjusts the cutting parameters in real time according to the processing status analysis results sent by the data processing platform to optimize the processing process. The specific implementation includes: The dynamic adjustment unit continuously receives the processing status analysis results sent by the data processing platform and monitors various status changes during the processing.

[0046] According to the results of machining state analysis, the cutting parameters are dynamically adjusted using an adaptive tuning algorithm. The specific steps are as follows: Based on the temperature characteristics, pressure characteristics, vibration characteristics and displacement characteristics, and in combination with a preset mapping table, the current operating state of the composite machine tool is evaluated, wherein the operating state of the composite machine tool includes a stable state, an unstable state and an abnormal state, and the mapping table represents the correspondence between the characteristics extracted from the data collected by the sensor and the operating state of the composite machine tool; The mapping table is a database containing multiple entries, each of which is associated with one or more feature values ​​extracted from sensor data and the corresponding operation status of the composite machine tool. These feature values ​​are usually statistical features, spectral features or time domain characteristics extracted from sensor data such as temperature, pressure, vibration and displacement.

[0047] The first part of the mapping table contains the feature values ​​extracted from the sensor data. These feature values ​​include mean, variance, standard deviation, peak value, frequency content and correlation coefficient, depending on the statistical analysis method applied by the data analysis unit and the type of extracted features.

[0048] The second part of the mapping table defines the operating status of the composite machine tool. These statuses usually include stable status, unstable status and abnormal status. The stable status means that the machine tool is operating in the expected manner without significant problems; the unstable status means that the operation of the machine tool begins to deviate from expectations, but has not yet reached an emergency or dangerous level; the abnormal status means that there is a serious problem in the operation of the machine tool and immediate action is required.

[0049] For each feature value, the mapping table also defines thresholds or ranges associated with different operating states. These thresholds or ranges are used to evaluate the current operating state of the machine tool. For example, if a feature value exceeds the threshold associated with a stable state but has not yet reached the threshold associated with an abnormal state, the machine tool may be judged to be in an unstable state.

[0050] In addition to the characteristic values, operating states, and thresholds / ranges, the mapping table also contains a detailed description of each operating state and corresponding action guidelines. These descriptions and guidelines help operators understand the current state of the machine tool and know what actions should be taken to deal with potential problems.

[0051] Exemplarily, the mapping table contains the following entries: Characteristic value: Mean vibration intensity = 0.05g (acceleration due to gravity).

[0052] Running status: stable state.

[0053] Threshold / Range: 0.01g ≤ Vibration intensity mean ≤ 0.1g.

[0054] Status description: The machine tool runs smoothly without significant vibration.

[0055] Action: No action required.

[0056] Characteristic value: Mean vibration intensity = 0.15g.

[0057] Running status: unstable state.

[0058] Threshold / Range: 0.1g< average vibration intensity<0.25g.

[0059] Status description: The machine tool vibration begins to increase, which may indicate tool wear or poor workpiece clamping.

[0060] Action Guide: Check tool and workpiece clamping conditions and consider reducing feed rate or spindle speed.

[0061] Characteristic value: Mean vibration intensity = 0.3g.

[0062] Running status: abnormal status.

[0063] Threshold / Range: Vibration intensity mean > 0.25g.

[0064] Status Description: Abnormally high machine tool vibration may indicate a serious fault such as bearing damage or tool breakage.

[0065] Action Guide: Stop processing immediately, check the machine tool and take appropriate maintenance measures.

[0066] According to the operating status of the compound machine tool, the cutting parameters are adjusted according to the preset processing strategy.

[0067] Among them, the processing strategies include: If the operation state of the compound machine tool is stable, increase the feed speed, increase the cutting speed and maintain the cutting depth, where: The feed rate increase formula is: ; in, is the feed speed before adjustment, is the adjusted feed speed, It is the preset first increase ratio, usually ranging from 0.05 to 0.10; The formula for increasing cutting speed is: ; in, is the cutting speed before adjustment, is the adjusted cutting speed, It is the preset second increase ratio, usually ranging from 0.03 to 0.05; The formula for maintaining cutting depth is: ; in, and Maintain the cutting depth at the front and back respectively; If the operation state of the compound machine tool is unstable, the feed speed, cutting speed and cutting depth are reduced, wherein: The feed rate reduction formula is: ; in, is the feed speed before adjustment, is the adjusted feed speed, is the preset first reduction ratio, which is usually set to 0.10 to 0.20; The formula for increasing cutting speed is: ; in, is the cutting speed before adjustment, is the adjusted cutting speed, is the preset second reduction ratio, which is usually set to 0.05 to 0.10; The formula for reducing cutting depth is: ; in, is the cutting depth before adjustment, is the adjusted cutting depth, is the preset third reduction ratio, which is usually set to 0.10 to 0.20; If the operation status of the compound machine tool is abnormal, the processing will be stopped immediately.

[0068] The fault prediction module predicts the operating status of the compound machine tool based on the processing status analysis results provided by the data processing platform and the historical fault data, and triggers the early warning mechanism when a potential fault is predicted. The fault prediction module includes a fault type identification unit and an early warning strategy formulation unit. The fault type identification unit uses a pre-trained neural network model to identify the fault type based on the processing status analysis results and historical fault data; the early warning strategy formulation unit formulates and executes corresponding early warning measures according to the identified fault type, including adjusting processing parameters, suspending processing, and starting the emergency shutdown procedure.

[0069] More specifically, the fault type identification unit uses a pre-trained neural network model to identify the fault type based on historical processing state analysis results and historical fault data. The specific implementation is as follows: Collect and organize the historical fault data of composite machine tools, including fault type, time of fault occurrence, environmental parameters before fault, etc., annotate the historical fault data, mark each data as a specific fault type, including but not limited to tool wear, hydraulic system leakage and motor overheating, and use it as the output label for model training.

[0070] For the historical processing state analysis results, the processing state features in the results are extracted, including but not limited to the temperature mean, pressure peak, and vibration standard deviation, and used as input parameters for model training.

[0071] Finally, the neural network-based machine learning model is trained through input and output, and the model parameters are optimized through multiple iterations to improve the model's prediction accuracy.

[0072] The real-time machining status analysis results provided by the data processing platform are used as input into the trained model, and the model outputs the current fault type prediction results, such as tool wear, hydraulic system leakage, etc.

[0073] The early warning strategy formulation unit formulates and executes corresponding early warning measures according to the identified fault type. The specific implementation is as follows: According to the identified fault type, adjust the cutting parameters, such as reducing the feed speed, reducing the spindle speed, etc.

[0074] When a serious fault is detected, the machining process is halted to prevent further damage.

[0075] When an emergency fault is identified, the emergency shutdown procedure is immediately initiated to ensure equipment safety.

[0076] Based on the fault type identification results, the cutting parameters are dynamically adjusted. For example, if tool wear is identified, the feed rate and spindle speed are reduced to extend the tool life.

[0077] When a serious fault is identified, a pause command is sent to the intelligent control module via the data bus to suspend the processing process.

[0078] When an emergency fault is identified, an emergency stop command is sent to the intelligent control module via the data bus to immediately stop processing and initiate safety protection measures.

[0079] For example, suppose the user needs to process a 100mm x 50mm x 10mm 6061-T6 aluminum alloy workpiece. The user inputs the workpiece parameters through the intelligent control module, and the parameter setting unit calculates the initial cutting parameters: cutting speed 150m / min, cutting depth 2 mm, feed rate 0.2 mm / rev. During the processing, the multi-sensor module collects the environmental parameter set in real time, the data processing platform generates the processing status analysis results, and the intelligent control module dynamically adjusts the cutting parameters. For example, if the vibration intensity is detected to exceed the threshold, the dynamic adjustment unit will reduce the feed speed and spindle speed. The fault prediction module continuously monitors the processing status, identifies potential faults and takes corresponding measures, such as reducing the cutting depth or pausing the processing and issuing an alarm. Embodiment 2

[0080] The present invention also provides a cutting product control method for a composite machine tool, which is applied to a cutting product control system for a composite machine tool, referring to Figure 2 As shown, the control method includes the following steps: S1: Collecting a set of environmental parameters in the processing process in real time through a multi-sensor module, wherein the set of environmental parameters includes temperature data, pressure data, vibration data and displacement data.

[0081] Specifically, the multi-sensor module includes a temperature sensor, a pressure sensor, a vibration sensor and a displacement sensor, which collects temperature, pressure, vibration and displacement data during the processing in real time.

[0082] Temperature sensors measure the temperature of the machining area, pressure sensors monitor cutting forces and hydraulic system pressure, vibration sensors monitor machine vibrations, and displacement sensors measure the relative position of the tool and workpiece.

[0083] S2: The data processing platform preprocesses and extracts features of the received environmental parameter set, generates a processing state analysis result, and sends the processing state analysis result to the intelligent control module, wherein the feature extraction result includes temperature features, pressure features, vibration features and displacement features extracted from the preprocessed temperature data, pressure data, vibration data and displacement data, respectively.

[0084] Specifically, the data preprocessing unit performs preprocessing operations such as filtering and denoising on the collected environmental parameter set to remove noise and abnormal values.

[0085] The data analysis unit applies statistical analysis methods to extract processing state features from the pre-processed environmental parameter set and generates processing state analysis results.

[0086] The generated processing status analysis results are sent to the intelligent control module through the data bus.

[0087] S3: The intelligent control module automatically sets the initial cutting parameters according to the workpiece parameters input by the user before processing, and dynamically adjusts the cutting parameters according to the processing state analysis results using an adaptive tuning algorithm during the processing, wherein the cutting parameters include cutting speed, cutting depth and feed speed.

[0088] Specifically, the user inputs information such as the type, size, and material of the workpiece through the human-computer interaction interface.

[0089] The parameter setting unit automatically calculates and sets the initial cutting parameters, including cutting speed, cutting depth and feed rate, based on the input workpiece parameters using a preset model.

[0090] The dynamic adjustment unit adjusts the cutting parameters in real time according to the machining status analysis results sent by the data processing platform to optimize the machining process.

[0091] The adaptive tuning algorithm includes the following steps: The temperature data, pressure data, vibration data and displacement data are collected in real time through multiple different types of sensors installed on the compound machine tool, and the data are pre-processed; Extracting temperature features, pressure features, vibration features and displacement features from the pre-processed temperature data, pressure data, vibration data and displacement data respectively; According to all the extracted features, combined with a preset mapping table, the current operating state of the composite machine tool is evaluated, wherein the operating state of the composite machine tool includes a stable state, an unstable state and an abnormal state; According to the operating status of the compound machine tool, the cutting parameters are adjusted according to the preset processing strategy.

[0092] Processing strategies include: If the operation state of the compound machine tool is stable, increase the feed speed, increase the cutting speed and maintain the cutting depth, where: The feed rate increase formula is: ; in, is the feed speed before adjustment, is the adjusted feed speed, Increase the ratio for the preset first one; The formula for increasing cutting speed is: ; in, is the cutting speed before adjustment, is the adjusted cutting speed, Increase the ratio for the preset second; The formula for maintaining cutting depth is: ; in, and Maintain the cutting depth at the front and back respectively; If the operation state of the compound machine tool is unstable, the feed speed, cutting speed and cutting depth are reduced, wherein: The feed rate reduction formula is: ; in, is the feed speed before adjustment, is the adjusted feed speed, is a preset first reduction ratio; The formula for increasing cutting speed is: ; in, is the cutting speed before adjustment, is the adjusted cutting speed, is a preset second reduction ratio; The formula for reducing cutting depth is: ; in, is the cutting depth before adjustment, is the adjusted cutting depth, is a preset third reduction ratio; If the operation status of the compound machine tool is abnormal, the processing will be stopped immediately.

[0093] S4: The fault prediction module predicts the operating state of the composite machine tool based on the processing state analysis result and the historical fault data, and triggers the early warning mechanism when a fault is predicted to occur, and sends an early warning signal to the intelligent control module through the data bus.

[0094] Specifically, the fault type identification unit identifies the fault type using a pre-trained neural network model based on the processing state analysis result and historical fault data.

[0095] The early warning strategy formulation unit formulates and executes corresponding early warning measures according to the identified fault type, including adjusting processing parameters, suspending processing and initiating emergency shutdown procedures.

[0096] When a serious fault is identified, a pause command is sent to the intelligent control module via the data bus to suspend the processing process.

[0097] When an emergency fault is identified, an emergency stop command is sent to the intelligent control module via the data bus to immediately stop processing and initiate safety protection measures.

[0098] S5: The intelligent control module automatically executes early warning measures according to the early warning signal to achieve coordinated response before failure.

[0099] Specifically, the intelligent control module automatically executes early warning measures according to the received early warning signals, such as reducing the feed speed, reducing the spindle speed, etc.

[0100] When a serious fault is detected, the machining process is halted to prevent further damage.

[0101] When an emergency fault is identified, the emergency shutdown procedure is immediately initiated to ensure equipment safety.

[0102] Additionally, it includes: receiving workpiece parameters of the workpiece, wherein the workpiece parameters include type, size and material; The received workpiece parameters are input into the intelligent control module.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0105] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or includes elements that are inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device that includes the elements.

Claims

1. A cutting product control system for a composite machine tool, characterized in that: The control system includes a multi-sensor module, a data processing platform, an intelligent control module and a fault prediction module. The multi-sensor module and the data processing platform transmit data with the intelligent control module through a data bus, and the fault prediction module transmits data with the intelligent control module and the data processing platform through a data bus, wherein: Multi-sensor module for real-time collection of environmental parameter sets during processing; A data processing platform, used for receiving and processing the environmental parameter set from the multi-sensor module, obtaining a processing state analysis result, and sending the processing state analysis result to the intelligent control module; An intelligent control module, used to initially set cutting parameters according to workpiece parameters input by a user, and dynamically adjust the cutting parameters according to the machining state analysis results sent by the data processing platform during machining; A fault prediction module is used to predict the operating status of the compound machine tool based on the processing status analysis results provided by the data processing platform and in combination with historical fault data, and to trigger an early warning mechanism when a fault is predicted to occur, and at the same time send an early warning signal to the intelligent control module through the data bus. Finally, the intelligent control module automatically executes early warning measures according to the early warning signal to achieve a coordinated response before a fault occurs.

2. The cutting product control system of a composite machine tool according to claim 1, characterized in that: The multi-sensor module includes a plurality of sensors of different types for measuring different physical quantities, and a data fusion unit for comprehensively processing information from the plurality of sensors, wherein the plurality of sensors of different types include temperature sensors, pressure sensors, vibration sensors and displacement sensors.

3. The cutting product control system of a composite machine tool according to claim 2, characterized in that: The data processing platform includes a data preprocessing unit and a data analysis unit, wherein: The data preprocessing unit is used to preprocess the received environmental parameter set; The data analysis unit is used to apply a statistical analysis method to extract processing state features from the pre-processed environmental parameter set and generate processing state analysis results.

4. The cutting product control system of a composite machine tool according to claim 3, characterized in that: The intelligent control module includes a parameter setting unit and a dynamic adjustment unit, wherein: The parameter setting unit is used to automatically calculate and set cutting parameters using a preset model according to workpiece parameters input by a user, wherein the cutting parameters include cutting speed, cutting depth and feed speed, and the preset model is a mathematical model based on machine learning; The dynamic adjustment unit adjusts the cutting parameters in real time according to the machining status analysis results sent by the data processing platform.

5. The cutting product control system of a composite machine tool according to claim 4, characterized in that: The fault prediction module also includes a fault type identification unit and an early warning strategy formulation unit, wherein: The fault type identification unit identifies the fault type using a pre-trained neural network model based on the processing state analysis result and historical fault data; The early warning strategy formulation unit formulates and executes corresponding early warning measures according to the identified fault type, wherein the early warning measures include adjusting processing parameters, suspending processing and starting an emergency shutdown procedure.

6. A method for controlling cutting products of a composite machine tool, applied to a control system for cutting products of a composite machine tool as claimed in claim 5, characterized in that: The control method comprises the following steps: Collecting the environmental parameter set in the processing process in real time through the multi-sensor module, wherein the environmental parameter set includes temperature data, pressure data, vibration data and displacement data; The data processing platform performs preprocessing and feature extraction on the received environmental parameter set, generates a processing state analysis result, and sends the processing state analysis result to the intelligent control module, wherein the feature extraction result includes temperature features, pressure features, vibration features, and displacement features extracted from the preprocessed temperature data, pressure data, vibration data, and displacement data, respectively; The intelligent control module automatically sets the initial cutting parameters according to the workpiece parameters input by the user before processing, and dynamically adjusts the cutting parameters according to the processing state analysis results using an adaptive tuning algorithm during processing, wherein the cutting parameters include cutting speed, cutting depth and feed speed; The fault prediction module predicts the operation state of the composite machine tool based on the processing state analysis result and the historical fault data, and triggers the early warning mechanism when a fault is predicted to occur, and sends an early warning signal to the intelligent control module through the data bus; The intelligent control module automatically executes early warning measures according to the early warning signal to achieve coordinated response before a fault occurs.

7. The method for controlling cutting products of a composite machine tool according to claim 6, characterized in that: Also includes: receiving workpiece parameters of a workpiece, wherein the workpiece parameters include type, size and material; The received workpiece parameters are input into the intelligent control module.

8. The method for controlling cutting products of a composite machine tool according to claim 7, characterized in that: The adaptive tuning algorithm is specifically: Based on the temperature characteristics, pressure characteristics, vibration characteristics and displacement characteristics, and in combination with a preset mapping table, the current operating state of the composite machine tool is evaluated, wherein the operating state of the composite machine tool includes a stable state, an unstable state and an abnormal state, and the mapping table represents the correspondence between the characteristics extracted from the data collected by the sensor and the operating state of the composite machine tool; According to the operating state of the compound machine tool, the cutting parameters are adjusted according to the preset processing strategy.

9. The method for controlling cutting products of a composite machine tool according to claim 8, characterized in that: The processing strategies include: If the operation state of the compound machine tool is stable, the feed speed is increased, the cutting speed is increased and the cutting depth is maintained, wherein: The feed rate increase formula is: ; in, is the feed speed before adjustment, is the adjusted feed speed, Increase the ratio for the preset first one; The increasing formula of the cutting speed is: ; in, is the cutting speed before adjustment, is the adjusted cutting speed, Increase the ratio for the preset second; The cutting depth maintenance formula is: ; in, and Maintain the cutting depth at the front and back respectively; If the operation state of the compound machine tool is unstable, the feed speed, cutting speed and cutting depth are reduced, wherein: The feed speed reduction formula is: ; in, is the feed speed before adjustment, is the adjusted feed speed, is a preset first reduction ratio; The increasing formula of the cutting speed is: ; in, is the cutting speed before adjustment, is the adjusted cutting speed, is a preset second reduction ratio; The reduction formula of the cutting depth is: ; in, is the cutting depth before adjustment, is the adjusted cutting depth, is a preset third reduction ratio; If the operation state of the compound machine tool is abnormal, the processing is stopped immediately.

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