A method for fast processing in CNC 3-axis machining process

Abstract

The invention discloses a method for rapid processing in a CNC 3-axis process, and relates to the technical field of CNC processing. The invention adopts corresponding rapid processing measures for different abnormal situations, including automatic shutdown, alarm, and recording of the current processing position, while automatically adjusting cutting parameters, workpiece clamping force and processing program, diagnosing and locating tool replacement, workpiece clamping adjustment and machine tool failure, and realizing timely response and processing of various abnormal situations that may occur in the processing process; and through real-time monitoring and analysis, timely discovering and processing abnormal situations in the processing process, effectively reducing workpiece damage and excessive tool wear problems caused by abnormal situations, improving processing quality and processing efficiency, realizing automatic optimization of processing parameters, and improving processing efficiency and finished product quality.

Description

Technical Field

[0001] The invention relates to the technical field of numerical control machining, in particular to a method for rapid processing in a numerical control three-axis process. Background Art

[0002] Rapid processing during CNC machining usually involves quickly resolving abnormal situations during CNC machining to restore normal machining status with minimal downtime and resource investment. Abnormal situations generally include tool breakage, workpiece clamping problems, machining program errors, improper machining parameters, machine tool failures, etc.

[0003] For example, the patent with publication number CN107728576B discloses a multi-axis CNC machining tool axis vector optimization method based on tool force deformation, including the following steps: 1) parameter collection; 2) establishing a tool force deformation model in the multi-axis CNC machining process according to the cutting force on the tool during the machining process and the stiffness of the multi-axis CNC equipment process system; 3) determining the constraints satisfied by the model; 4) establishing a tool axis vector optimization mathematical model based on the tool force deformation model and the constraints, with the goal of minimizing the tool force deformation; 5) solving the above tool axis vector optimization mathematical model to determine the tool rake angle and roll angle at the optimal tool axis vector Vc,opt; 6) converting the tool rake angle and roll angle into the tool axis vector to obtain the optimized tool axis vector. The present invention makes up for the shortcomings of the existing multi-axis CNC machining tool axis vector planning that only considers geometric constraints and the stiffness characteristics of the process system, and provides a new idea for the tool axis vector planning of multi-axis CNC machining of complex surfaces.

[0004] Although the above scheme has the above advantages, the traditional CNC 3-axis process lacks an intelligent processing parameter adjustment mechanism and cannot automatically optimize the processing parameters according to real-time monitoring data, resulting in low processing efficiency and reduced tool life. At the same time, it often faces difficulties in diagnosing and locating machine tool faults, and cannot accurately determine the cause and location of the fault, which prolongs the maintenance time and downtime. Therefore, a method for rapid processing in the CNC 3-axis process is urgently needed to solve such problems. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for rapid processing in a CNC 3-axis process, which solves the problem that the prior art cannot automatically optimize and adjust the processing parameters according to real-time monitoring data, resulting in low processing efficiency and reduced tool life. At the same time, it often faces difficulties in diagnosing and locating machine tool faults and cannot accurately determine the cause and location of the fault.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] The present invention provides a method for rapid processing in a CNC 3-axis process, comprising:

[0008] Real-time monitoring of the machining process, monitoring of the three-axis machining process parameters, including tool wear, workpiece clamping force changes, tool trajectory, machining parameters, and machine tool status;

[0009] The tool wear conditions monitored include tool length and cutting force parameters;

[0010] The monitored machining parameters include cutting force, temperature, and vibration parameters during machining;

[0011] Monitored machine tool conditions include temperature, vibration, and lubricant oil pressure;

[0012] Machining parameter adjustment: Based on real-time monitoring of machining process parameters, it can detect and warn whether the tool should be replaced, workpiece clamping, machining procedures, and basic machine tool status failures in three-axis machining;

[0013] Adjustment of machining parameters includes tool adjustment, workpiece clamping adjustment, machining program verification, and machine tool fault monitoring;

[0014] The tool replacement method is based on tool length and cutting force parameters. When the tool wear reaches the preset threshold, an alarm is automatically issued, the machine is immediately stopped and the current processing position is recorded. The spare tool is automatically called for processing. At the same time, based on tool wear and cutting requirements, it is determined whether the cutting parameters need to be adjusted and whether the tool force needs to be reduced. When adjustment is required, a feedback control algorithm is used to make corresponding adjustments based on real-time monitoring data.

[0015] The tool change steps include:

[0016] Assume the tool length is L(t) and the cutting force is F cut (t), t represents the monitoring time, and the condition that the tool wear reaches the preset threshold is set to L thresh ;

[0017] When the tool wear reaches the preset threshold, that is, L(t)≥L thresh The system automatically issues an alarm and triggers shutdown, while recording the current processing position P(t) and status;

[0018] In the shutdown state, the system automatically calls the spare tool for processing;

[0019] The way to adjust cutting parameters based on tool wear and cutting requirements is:

[0020] According to tool wear L(t) and cutting force F cut (t) Real-time monitoring data to determine whether cutting parameters need to be adjusted;

[0021] If the tool is severely worn, adjust the cutting speed V cut (t) and feed speed f feed (t), reduce tool stress and reduce cutting force;

[0022] Adaptive PID controller is used to adjust cutting speed and feed speed. The controller adjusts the cutting speed V in real time according to the real-time monitoring of tool wear and cutting force data. cut (t) and feed speed f feed (t): Among them, u(t) represents the adjusted cutting speed and feed speed, e(t) is the error signal, K p , K i , K i are the proportional, integral and derivative gain parameters of the PID controller, represents the integration of the error signal e(t), that is, the cumulative integral value from the initial time t=0 to the current time t, represents the differentiation of the rate of change of e(t), It represents the rate of change of the error signal e(t) with respect to time t, that is, the time derivative of the error signal.

[0023] The present invention is further configured to: determine whether adjustment is required, that is, whether the tool wear is serious, by:

[0024] The tool length data is differentially calculated to obtain the rate of change of tool wear rate, and then the wear of the tool is determined based on the positive or negative value of the rate of change;

[0025] Perform spectrum analysis on the real-time monitored cutting force data to obtain the cutting force fluctuation, including the mean, standard deviation and peak-to-peak value of the cutting force, to determine whether the cutting force is within the normal range;

[0026] Then, based on the monitoring data of tool wear rate and cutting force, a judgment model is established using support vector machine (SVM) to associate the monitoring data with the need for cutting parameter adjustment.

[0027] Preset thresholds. When the current tool wear rate and cutting force exceed the set thresholds, the cutting parameter adjustment is triggered;

[0028] The present invention is further configured as follows: the workpiece clamping adjustment method is based on the real-time monitoring of the workpiece clamping force changes. Once an abnormal change is detected, an alarm is immediately issued, the machine is automatically stopped and the current processing position is recorded; the clamping device is automatically corrected again and the operator is instructed to make adjustments; the workpiece is re-clamped after the adjustment and the processing is continued; the automatic correction method is based on the clamping force monitoring and uses the PID control algorithm to adjust, and based on the structural characteristics of the clamping device, the finite element analysis method is used to adjust the clamping force distribution;

[0029] The present invention is further configured such that during the workpiece clamping adjustment process, the abnormal change detection method is:

[0030] Workpiece clamping force F based on real-time monitoring of sensors clamp (t) Record the change of clamping force over time, statistically analyze the clamping force data within the recent period T, and calculate the average value of the clamping force and standard deviation σ clamp , if the clamping force F at the current time t clamp (t)Out of the preset range It is determined to be an abnormal change, where k is a preset coefficient;

[0031] Once an abnormal change is detected, an alarm is immediately sounded and the machine automatically stops, recording the current processing position P(t) when it stops;

[0032] After the shutdown is triggered, the system automatically calibrates the clamping device. The calibration process is as follows:

[0033] Finite element analysis was used to simulate the structural characteristics of the clamping device, including the clamp stiffness, workpiece surface shape, and contact area;

[0034] According to the simulation results, the parameters of the clamping device are adjusted to adjust the distribution and stability of the clamping force;

[0035] After the adjustment is completed, the system re-clamps the workpiece and resumes the machining process;

[0036] The present invention is further configured such that during the workpiece clamping adjustment process, the abnormal change detection method is:

[0037] Workpiece clamping force F based on real-time monitoring of sensors clamp (t) Record the change of clamping force over time, statistically analyze the clamping force data within the recent period T, and calculate the average value of the clamping force and standard deviation σ clamp , if the clamping force F at the current time t clamp (t)Out of the preset range It is determined to be an abnormal change, where k is a preset coefficient;

[0038] Once an abnormal change is detected, an alarm is immediately sounded and the machine automatically stops, recording the current processing position P(t) when it stops;

[0039] After the shutdown is triggered, the system automatically calibrates the clamping device. The calibration process is as follows:

[0040] Finite element analysis was used to simulate the structural characteristics of the clamping device, including the clamp stiffness, workpiece surface shape, and contact area;

[0041] According to the simulation results, the parameters of the clamping device are adjusted to adjust the distribution and stability of the clamping force;

[0042] After the adjustment is completed, the system re-clamps the workpiece and resumes the machining process;

[0043] The present invention is further configured as follows: the processing program verification method is to automatically detect whether the processing trajectory deviates from the preset path and whether the processing parameters are abnormal based on the real-time monitoring of the tool trajectory and processing parameters during the processing; when an abnormality is detected, the machine is immediately stopped and the current processing position is recorded, an alarm is issued, and an operator is instructed to adjust the program; the method for determining whether the deviation from the preset path and whether the processing parameters are abnormal is to monitor and analyze the processing process based on a machine learning algorithm to identify abnormalities;

[0044] The present invention is further configured to: extract features based on the monitored multidimensional data of the machining process, including tool trajectory, machining parameters, and workpiece status, including time series features, frequency domain features, and spatial features;

[0045] Based on the extracted features, mean variance detection, isolation forest and recurrent neural network are used to build anomaly detection models. The selected anomaly detection models are trained and optimized using historical data. Model parameters are determined through cross-validation.

[0046] Perform anomaly detection and prediction on the trained model, and determine whether the current processing state is abnormal based on the anomaly score and probability output by the model;

[0047] The present invention is further configured as follows: the processing program verification also includes automatically adjusting the processing parameters based on the cutting force, temperature and vibration parameters in the processing process monitored in real time, and immediately stopping the machine and recording the current processing position when an abnormal situation is detected; the processing parameter verification method uses a fuzzy control algorithm to adjust the processing parameters according to the real-time monitoring data, and optimizes the processing parameters based on a neural network based on a back propagation algorithm;

[0048] The present invention is further configured as follows: basic machine tool fault detection and early warning includes, based on the real-time monitoring of the machine tool status, using a machine tool status prediction model of a machine learning algorithm to predict the probability of occurrence of a machine tool fault, using a Bayesian network algorithm to diagnose and locate the machine tool fault, and issuing an alarm;

[0049] The present invention is further configured as follows: the steps of diagnosing and locating machine tool faults based on the Bayesian network algorithm include:

[0050] According to the characteristics and mechanisms of machine tool failures, a Bayesian network model is constructed to diagnose different machine tool failures;

[0051] Use historical data to train and estimate the parameters of the Bayesian network model, and use maximum likelihood to estimate the probability parameters in the model;

[0052] After the model training is completed, the machine tool status data monitored in real time is used to perform fault diagnosis and location through the Bayesian reasoning algorithm;

[0053] Based on the currently observed machine tool status, infer the possible causes of the fault and calculate the posterior probability of each fault;

[0054] The severity and urgency of a fault are determined based on the posterior probability of the fault. If the posterior probability of a certain fault exceeds a preset threshold, it is considered that the fault may occur, the system issues an alarm, and gives maintenance instructions to the operator.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention can timely detect abnormal conditions in the processing process by real-time monitoring of processing parameters and machine tool status, thereby improving the accuracy and timeliness of abnormality detection;

[0057] The present invention can realize rapid response to abnormal situations, shorten downtime and improve production efficiency by automatically issuing an alarm, immediately stopping the machine and recording the current processing position measures.

[0058] The present invention adopts machine learning algorithm and PID controller for intelligent control, automatically adjusts processing parameters according to real-time monitoring data, optimizes processing efficiency and finished product quality, and prolongs tool life;

[0059] The present invention introduces advanced algorithms such as Bayesian network algorithms, which can accurately diagnose and locate machine tool faults, shorten maintenance time, and reduce production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The present invention is a flowchart of a method for rapid processing in a CNC 3-axis process. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0062] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0063] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0064] Example

[0065] See also Figure 1 The present invention provides a method for rapid processing in a CNC 3-axis process, comprising:

[0066] Real-time monitoring of the machining process, monitoring of the three-axis machining process parameters, including tool wear, workpiece clamping force changes, tool trajectory, machining parameters, and machine tool status;

[0067] The tool wear conditions monitored include tool length and cutting force parameters;

[0068] The monitored machining parameters include cutting force, temperature, and vibration parameters during machining;

[0069] Monitored machine tool conditions include temperature, vibration, and lubricant oil pressure;

[0070] Machining parameter adjustment: Based on real-time monitoring of machining process parameters, it can detect and warn whether the tool should be replaced, workpiece clamping, machining procedures, and basic machine tool status failures in three-axis machining;

[0071] Adjustment of machining parameters includes tool adjustment, workpiece clamping adjustment, machining program verification, and machine tool fault monitoring;

[0072] The tool replacement method is based on tool length and cutting force parameters. When the tool wear reaches the preset threshold, an alarm is automatically issued, the machine is immediately stopped and the current processing position is recorded. The spare tool is automatically called for processing. At the same time, based on tool wear and cutting requirements, it is determined whether the cutting parameters need to be adjusted and whether the tool force needs to be reduced. When adjustment is required, a feedback control algorithm is used to make corresponding adjustments based on real-time monitoring data. The tool replacement steps include:

[0073] Assume the tool length is L(t) and the cutting force is Fcut (t), t represents the monitoring time, and the condition that the tool wear reaches the preset threshold is set to L thresh ;

[0074] When the tool wear reaches the preset threshold, that is, L(t)≥L thresh The system automatically issues an alarm and triggers shutdown, while recording the current processing position P(t) and status;

[0075] In the shutdown state, the system automatically calls the spare tool for processing;

[0076] The way to adjust cutting parameters based on tool wear and cutting requirements is:

[0077] According to tool wear L(t) and cutting force F cut (t) Real-time monitoring data to determine whether cutting parameters need to be adjusted;

[0078] If the tool is severely worn, adjust the cutting speed V cut (t) and feed speed f feed (t), reduce tool stress, reduce cutting force, and extend tool life;

[0079] Adaptive PID controller is used to adjust cutting speed and feed speed. The controller adjusts the cutting speed V in real time according to the real-time monitoring of tool wear and cutting force data. cut (t) and feed speed f feed (t): Among them, u(t) represents the adjusted cutting speed and feed speed, e(t) is the error signal, K p , K i , K i are the proportional, integral and derivative gain parameters of the PID controller, represents the integration of the error signal e(t), that is, the cumulative integral value from the initial time t=0 to the current time t, represents the differentiation of the rate of change of e(t), It represents the rate of change of the error signal e(t) with respect to time t, that is, the time derivative of the error signal;

[0080] The method to judge whether adjustment is needed, that is, whether the tool wear is serious, is as follows:

[0081] The tool length data is differentially calculated to obtain the rate of change of tool wear rate, and then the wear of the tool is determined based on the positive or negative value of the rate of change;

[0082] Perform spectrum analysis on the real-time monitored cutting force data to obtain the cutting force fluctuation, including the mean, standard deviation and peak-to-peak value of the cutting force, to determine whether the cutting force is within the normal range;

[0083] Then, based on the monitoring data of tool wear rate and cutting force, a judgment model is established using support vector machine (SVM) to associate the monitoring data with the need for cutting parameter adjustment.

[0084] Preset thresholds. When the current tool wear rate and cutting force exceed the set thresholds, the cutting parameter adjustment is triggered;

[0085] The workpiece clamping adjustment method is based on the real-time monitoring of the workpiece clamping force changes. Once an abnormal change is detected, an alarm is immediately issued, the machine is automatically stopped and the current processing position is recorded; the clamping device is automatically calibrated again and the operator is instructed to make adjustments; after the adjustment, the workpiece is re-clamped and processing continues; the automatic correction method is based on the clamping force monitoring and uses the PID control algorithm to adjust, and based on the structural characteristics of the clamping device, the finite element analysis method is used to adjust the clamping force distribution;

[0086] During the workpiece clamping adjustment process, the abnormal change detection method is as follows:

[0087] Workpiece clamping force F based on real-time monitoring of sensors clamp (t) Record the change of clamping force over time, statistically analyze the clamping force data within the recent period T, and calculate the average value of the clamping force and standard deviation σ clamp , if the clamping force F at the current time t clamp (t)Out of the preset range It is determined to be an abnormal change, where k is a preset coefficient;

[0088] Once an abnormal change is detected, an alarm is immediately sounded and the machine automatically stops, recording the current processing position P(t) when it stops;

[0089] After the shutdown is triggered, the system automatically calibrates the clamping device. The calibration process is as follows:

[0090] Finite element analysis was used to simulate the structural characteristics of the clamping device, including the clamp stiffness, workpiece surface shape, and contact area;

[0091] According to the simulation results, the parameters of the clamping device are adjusted to adjust the distribution and stability of the clamping force;

[0092] After the adjustment is completed, the system re-clamps the workpiece and resumes the machining process;

[0093] Adjust the clamping force distribution as follows:

[0094] The geometric model of the clamping device is established using CAD software and converted into a finite element model. The geometric model of the clamping device is defined as Ω, its boundary as Γ, and the outer surface of the clamping device as Γ. ext , the inner surface is Γ int; Define the material properties of the clamping device, including elastic modulus E and Poisson's ratio ν, and define the geometric parameters of the clamping device, including length L, width W, and thickness H;

[0095] Boundary conditions are imposed on the finite element model to simulate the actual working environment and loading conditions of the clamping device. It is assumed that the boundary Γ ext Under the action of external load, the external load is defined as F, then the boundary condition is expressed as: t·n=F, where t is the load vector on the boundary and n is the normal vector of the boundary;

[0096] Define the load applied to the clamping device during workpiece machining, i.e., the cutting force, and assume that the distribution of the cutting force on the surface of the clamping device is p(x), where x is the position vector on the surface of the clamping device;

[0097] Establish a finite element model of the clamping device and establish mechanical equations based on the theory of elastic mechanics. Then, discretize the geometric model into finite element units and apply boundary conditions and loads to the model.

[0098] The model is solved using finite element analysis software to obtain the displacement field u(x) and stress field σ(x) of the clamping device;

[0099] Calculate the clamping force distribution on the clamping device surface based on the stress field σ(x): F clamp =∫Γ ext σ(x)·ndΓwhere Γ ext represents the outer surface of the clamping device, n is the normal vector of the outer surface, and σ(x) is the stress tensor;

[0100] Define the criterion for abnormal change, if the clamping force F clamp If the distribution exceeds the preset threshold, it is determined to be an abnormal change;

[0101] According to the simulation results, the parameters of the clamping device are adjusted to optimize the distribution and stability of the clamping force;

[0102] The structural characteristics of the clamping device are simulated using the finite element analysis method, and the parameters of the clamping device are adjusted according to the simulation results to optimize the distribution and stability of the clamping force;

[0103] The processing program verification method is to automatically detect whether the processing trajectory deviates from the preset path and whether the processing parameters are abnormal based on the real-time monitoring of the tool trajectory and processing parameters during the processing; when an abnormality is detected, the machine is immediately stopped and the current processing position is recorded, an alarm is issued, and the operator is instructed to adjust the program; the method for determining whether the deviation from the preset path and whether the processing parameters are abnormal is to monitor and analyze the processing process based on a machine learning algorithm to identify abnormalities;

[0104] Based on the multi-dimensional data of the monitored machining process, including tool trajectory, machining parameters, and workpiece status, feature extraction is performed, including time series features, frequency domain features, and spatial features;

[0105] Based on the extracted features, mean variance detection, isolation forest and recurrent neural network are used to build anomaly detection models. The selected anomaly detection models are trained and optimized using historical data. Model parameters are determined through cross-validation.

[0106] Perform anomaly detection and prediction on the trained model, and determine whether the current processing state is abnormal based on the anomaly score and probability output by the model;

[0107] Processing program verification also includes automatically adjusting processing parameters based on real-time monitoring of cutting force, temperature, and vibration parameters during the processing process. When an abnormal situation is detected, the machine is immediately stopped and the current processing position is recorded. The processing parameter verification method uses fuzzy control algorithms to adjust processing parameters according to real-time monitoring data, and optimizes processing parameters based on a neural network based on a back-propagation algorithm.

[0108] Basic machine tool fault detection and early warning include: based on real-time monitoring of machine tool status, using machine tool status prediction model with machine learning algorithm to predict the probability of machine tool fault, using Bayesian network algorithm to diagnose and locate machine tool fault, and issuing alarm;

[0109] The steps for diagnosing and locating machine tool faults based on the Bayesian network algorithm include:

[0110] According to the characteristics and mechanisms of machine tool failures, a Bayesian network model is constructed to diagnose different machine tool failures;

[0111] Use historical data to train and estimate the parameters of the Bayesian network model, and use maximum likelihood to estimate the probability parameters in the model;

[0112] After the model training is completed, the machine tool status data monitored in real time is used to perform fault diagnosis and location through the Bayesian reasoning algorithm;

[0113] Based on the currently observed machine tool status, infer the possible causes of the fault and calculate the posterior probability of each fault;

[0114] The severity and urgency of a fault are determined based on the posterior probability of the fault. If the posterior probability of a certain fault exceeds a preset threshold, it is considered that the fault may occur, the system issues an alarm, and gives maintenance instructions to the operator.

[0115] The CNC 3-axis process provided by the present invention adopts corresponding rapid processing measures for different abnormal situations, including automatic shutdown, alarm, and recording of the current processing position, while automatically adjusting the cutting parameters, workpiece clamping force and processing program, and adopts the Bayesian network algorithm and finite element analysis method to diagnose and locate tool replacement, workpiece clamping adjustment and machine tool failure, so as to achieve timely response and processing of various abnormal situations that may occur during the processing, greatly improving the stability, efficiency and reliability of the CNC processing process; and through real-time monitoring and analysis, timely discovering and processing abnormal situations in the processing process, effectively reducing the problems of workpiece damage and excessive tool wear caused by abnormal situations, and improving the processing quality and processing efficiency. Finally, an adaptive PID controller and a fuzzy control algorithm are used to adjust the processing parameters, and a rapid response is made according to real-time monitoring data to achieve automatic optimization of the processing parameters, thereby improving the processing efficiency and the quality of the finished product.

[0116] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for rapid processing in a CNC 3-axis process, characterized in that: include: Real-time monitoring of the machining process, monitoring of the three-axis machining process parameters, including tool wear, workpiece clamping force changes, tool trajectory, machining parameters, machine tool status, The tool wear conditions monitored include tool length, cutting force parameters, The monitored processing parameters include cutting force, temperature, and vibration parameters during the processing. Monitored machine tool conditions include temperature, vibration, and lubricant oil pressure; Machining parameter adjustment: Based on real-time monitoring of machining process parameters, it can detect and warn whether the tool should be replaced, workpiece clamping, machining procedures, and basic machine tool status failures in three-axis machining; The method to judge whether the tool wear is serious is: The tool length data is differentially calculated to obtain the rate of change of tool wear rate, and then the wear of the tool is judged based on the positive or negative value of the rate of change. Perform spectrum analysis on the real-time monitored cutting force data to obtain the cutting force fluctuation, including the mean, standard deviation and peak-to-peak value of the cutting force, to determine whether the cutting force is within the normal range. Then, based on the monitoring data of tool wear rate and cutting force, a judgment model was established using support vector machine (SVM) to associate the monitoring data with the cutting parameter adjustment requirements. Preset thresholds. When the current tool wear rate and cutting force exceed the set thresholds, the cutting parameter adjustment is triggered; Adjustment of machining parameters includes tool adjustment, workpiece clamping adjustment, machining program verification, and machine tool fault monitoring; The tool replacement method is based on tool length and cutting force parameters. When the tool wear reaches the preset threshold, an alarm is automatically issued, the machine is immediately stopped and the current processing position is recorded. The spare tool is automatically called for processing. At the same time, based on tool wear and cutting requirements, it is determined whether the cutting parameters need to be adjusted and whether the tool force needs to be reduced. When adjustment is required, a feedback control algorithm is used to make corresponding adjustments based on real-time monitoring data. The tool replacement steps include: Assume the tool length is , cutting force is , Indicates the monitoring time, and sets the tool wear to reach the preset threshold condition. When the tool wear reaches the preset threshold, The system automatically issues an alarm and triggers shutdown, while recording the current processing position and status; In the shutdown state, the system automatically calls the spare tool for processing, and adjusts the cutting parameters based on tool wear and cutting requirements: according to the tool length and cutting force Real-time monitoring data can be used to determine whether cutting parameters need to be adjusted. If the tool is severely worn, the cutting speed can be adjusted. and feed speed , reduce the force on the tool and reduce the cutting force; Adaptive PID controller is used to adjust cutting speed and feed speed. The controller adjusts the cutting speed in real time according to the real-time monitoring of tool wear and cutting force data. and feed speed : ,in, Indicates the adjusted cutting speed and feed speed. is the error signal, , , are the proportional, integral and derivative gain parameters of the PID controller, Indicates the error signal Integrate, that is, from the initial time To current time The accumulated integral value within Express Differentiate the rate of change, Indicates the error signal About Time The rate of change of , i.e., the time derivative of the error signal; The workpiece clamping adjustment method is to monitor the clamping force changes of the workpiece in real time, issue an alarm after detecting abnormal changes, automatically stop the machine and record the current processing position; automatically calibrate the clamping device again and instruct the operator to make adjustments; re-clamp the workpiece after adjustment and continue processing; the automatic correction method uses the PID control algorithm based on the clamping force monitoring to adjust, and uses the finite element analysis method to adjust the clamping force distribution based on the structural characteristics of the clamping device; During the workpiece clamping adjustment process, the detection method for abnormal changes is: Workpiece clamping force based on real-time monitoring of sensors Record the change of clamping force over time and statistically analyze the recent period Calculate the average clamping force based on the clamping force data within and standard deviation , if the current moment Clamping force Out of preset range , it is judged as an abnormal change, where is the preset coefficient; When abnormal changes are detected, an alarm is issued and the machine automatically stops, recording the current processing position when it stops. ; After the shutdown is triggered, the system automatically calibrates the clamping device. The calibration process is as follows: Finite element analysis was used to simulate the structural characteristics of the clamping device, including the clamp stiffness, workpiece surface shape, and contact area; According to the simulation results, the parameters of the clamping device are adjusted to adjust the distribution and stability of the clamping force; After the adjustment is completed, the system re-clamps the workpiece and resumes the machining process; Adjust the clamping force distribution as follows: Use CAD software to establish the geometric model of the clamping device and convert it into a finite element model. The geometric model of the clamping device is defined as , whose boundary is , the outer surface of the clamping device is , the inner surface is ; Define the material properties of the clamping device, including the elastic modulus , Poisson's ratio , while defining the geometric parameters of the clamping device, including length ,width ,thickness ; Boundary conditions are applied to the finite element model to simulate the actual working environment and loading conditions of the clamping device. Affected by external load, the external load is defined as , then the boundary condition is expressed as: , here is the load vector on the boundary, is the normal vector of the boundary; Define the load applied to the clamping device during workpiece machining, that is, the cutting force, and assume that the distribution of the cutting force on the surface of the clamping device is ,in is the position vector on the surface of the clamping device; Establish a finite element model of the clamping device and establish mechanical equations based on the theory of elastic mechanics. Then, discretize the geometric model into finite element units and apply boundary conditions and loads to the model. The model is solved using finite element analysis software to obtain the displacement field of the clamping device. and stress field ; According to the stress field Calculate the clamping force distribution on the clamping device surface: , here represents the outer surface of the clamping device, is the normal vector of the outer surface, is the stress tensor; Define the criteria for abnormal changes if the clamping force If the distribution exceeds the preset threshold, it is determined to be an abnormal change; According to the simulation results, the parameters of the clamping device are adjusted to optimize the distribution and stability of the clamping force; The processing program verification method is to automatically detect whether the processing trajectory deviates from the preset path and whether the processing parameters are abnormal based on the real-time monitoring of the tool trajectory and processing parameters during the processing; when an abnormality is detected, the machine is immediately stopped and the current processing position is recorded, an alarm is issued, and the operator is instructed to adjust the program; the method for determining whether the deviation from the preset path and whether the processing parameters are abnormal is to monitor and analyze the processing process based on a machine learning algorithm to identify abnormalities; Based on the multi-dimensional data of the monitored machining process, including tool trajectory, machining parameters, and workpiece status, feature extraction is performed, including time series features, frequency domain features, and spatial features; Based on the extracted features, mean variance detection, isolation forest and recurrent neural network are used to build anomaly detection models. The selected anomaly detection models are trained and optimized using historical data. Model parameters are determined through cross-validation. The trained model is used for anomaly detection and prediction, and the anomaly score and probability output by the model are used to determine whether the current processing status is abnormal.

2. The method for rapid processing in a CNC 3-axis process according to claim 1, characterized in that: Processing program verification also includes automatically adjusting processing parameters based on real-time monitoring of cutting force, temperature, and vibration parameters during the processing process. When an abnormal situation is detected, the machine is immediately stopped and the current processing position is recorded. The processing parameter verification method uses a fuzzy control algorithm to adjust processing parameters according to real-time monitoring data, and optimizes processing parameters based on a neural network based on a back-propagation algorithm.

3. The method for rapid processing in a CNC 3-axis process according to claim 2, characterized in that: Basic machine tool fault detection and early warning include, based on real-time monitoring of machine tool status, using a machine tool status prediction model with a machine learning algorithm to predict the probability of machine tool failure, using a Bayesian network algorithm to diagnose and locate machine tool failures, and issuing an alarm.

4. The method for rapid processing in a CNC 3-axis machining process according to claim 3, characterized in that: The steps for diagnosing and locating machine tool faults based on the Bayesian network algorithm include: According to the characteristics and mechanisms of machine tool failures, a Bayesian network model is constructed to diagnose different machine tool failures; Use historical data to train and estimate the parameters of the Bayesian network model, and use maximum likelihood to estimate the probability parameters in the model; After the model training is completed, the machine tool status data monitored in real time is used to perform fault diagnosis and location through the Bayesian reasoning algorithm; Based on the currently observed machine tool status, infer the possible causes of the fault and calculate the posterior probability of each fault; The severity and urgency of a fault are determined based on the posterior probability of the fault. If the posterior probability of a certain fault exceeds a preset threshold, it is considered that the fault may occur, the system issues an alarm, and gives maintenance instructions to the operator.

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