1533 results about "Tool wear" patented technology

The invention relates to the technical field of numerical control machining, in particular to a tool cutting path machining error compensation method based on online monitoring, which comprises the following steps: establishing a cutting path data model; constructing a multi-modal sensor assembly, and collecting error data in real time; establishing an error state equation to carry out error calculation; constructing a double-layer compensation mechanism; and executing the compensation instruction by adopting a real-time control system. Through real-time synchronous monitoring of the multi-mode sensor, a mixed error resolving framework and a double-layer intelligent compensation mechanism, the influence of dynamic error sources such as cutter abrasion and machine tool thermal deformation can be effectively overcome, the machining precision and the surface quality are remarkably improved, and the high-precision machining requirements in the fields of aerospace, precise instruments and the like are met.

The invention belongs to the technical field of intelligent machining path control, and discloses an intelligent numerical control machine tool automatic programming path optimization method based on workpiece modeling, which comprises the following steps: acquiring a CAD model, machine tool sensor data, tool wear data and historical machining logs, generating a workpiece characteristic parameter set, and fusing a three-level compensation mechanism to generate a dynamic error parameter set; then, dividing a preliminary risk level of the processing area, and performing secondary risk assessment to generate a comprehensive risk level; extracting a risk level conflict area, and determining a final risk level; constructing a static / dynamic cost matrix to obtain a path priority map; thirdly, generating an initial path, smoothing an optimized path trajectory, and performing multi-objective optimization to generate an optimized path planning table; cutting parameters are adjusted in real time, the path feasibility is verified, and a real-time control instruction set is generated; and finally, constructing a quality-process correlation model, generating a global strategy packet, forming closed-loop iteration, and completing system self-evolution.

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention discloses a precise numerical control machine tool operation state intelligent control method and system, and particularly relates to the technical field of numerical control machine tools. Multi-dimensional operation data, including working temperature, load, current, pressure, cutting force and tool wear data, of a machine tool are collected in real time, a Z-score algorithm is adopted for data preprocessing, abnormal values are extracted, a prediction model is constructed and trained based on a machine learning algorithm, abnormal data are analyzed in real time, and the abnormal values of the machine tool are analyzed in real time. And accurately identifying the operation state of the machine tool and evaluating the health condition. According to the health state evaluation result, an intelligent control strategy is automatically generated, and the feeding speed, the spindle rotating speed and the cutting depth of the machine tool are dynamically adjusted, so that the operation state of the machine tool is optimized, potential faults are found and dealt with in time, the machining quality is improved, the faults are reduced, the production efficiency and the equipment stability are improved, and the maintenance cost is reduced.

The invention relates to the technical field of multi-axis linkage machining, and discloses a multi-axis linkage machining path generation method, device and equipment and a storage medium, and the method comprises the steps that modeling is conducted on a multi-axis machine tool and a workpiece, and a machine tool kinematics model and a workpiece obstacle avoidance scene mathematical model are obtained; a state space and an action space are constructed, and a machining path double-delay depth deterministic strategy gradient model is obtained; performing time deviation calibration to obtain a time synchronization sensing data set; performing path generation to obtain an initial processing path; the initial machining path is optimized, and an optimized machining path is obtained; according to the method, a directed acyclic graph is constructed, and task division and parallel optimization are performed to obtain a multi-axis linkage machining path, so that multi-objective optimization of machining time, cutting force fluctuation and tool wear is realized, and the machining efficiency and the machining quality are improved.

The invention relates to the technical field of information, in particular to an error compensation method for multi-axis linkage machining of a mold blank hole system, which comprises the following steps: judging the time range of a tool changing window based on an error accumulation trend, if error accumulation reaches a preset threshold value, triggering a compensation action, otherwise, continuously monitoring error change; aiming at the complexity of a motion track, a nonlinear optimization algorithm is adopted, an execution path of a compensation action is optimized, and conflicts with a processing action are reduced; according to the optimized compensation path, the control parameters of the servo motor are adjusted in real time, and it is ensured that the compensation action is stably executed in the machining process; dynamically updating an error model through data of a main shaft temperature sensor and a tool wear monitoring device, and continuously optimizing a time point and an execution path of a compensation action; in the machining process, the error accumulation condition is monitored in real time, if the error exceeds the preset range, the compensation action is triggered again, and the machining precision is ensured.

The present invention provides a tool wear state monitoring method and system based on multiple types of signals, and relates to the technical field of data processing. The method includes: obtaining data of a cutting force, an acoustic emission signal and a vibration signal, and extracting a plurality of statistical features from data of the cutting force and the acoustic emission signal; extracting a singularity feature from the vibration signal by combining a singularity analysis with a wavelet transform; building a tool wear state monitoring model based on a random forest, using an obtained feature to perform preliminary training, and outputting a wear prediction result; and based on the real-time data of the cutting force, the acoustic emission signal and the vibration signal, monitoring the wear state of the tool through the refined model.

The invention discloses a metal cutting process parameter optimization analysis method based on machine learning, and particularly relates to the field of machine learning. Comprising multi-dimensional process parameter feature extraction and preprocessing, cutting state intelligent identification based on integrated learning, dynamic process parameter sensitivity analysis and weight calculation, process parameter intelligent optimization under a multi-target constraint condition, and adaptive parameter adjustment and real-time control strategy. According to the method, the interaction relationship between complex nonlinear features and process parameters in the cutting process is comprehensively captured, and accurate and intelligent recognition of different cutting states such as normal cutting, tool abrasion and abnormal flutter is achieved through a three-layer integrated learning architecture; the technical bottlenecks that an existing system lacks real-time self-adaptive adjustment capacity and is low in process optimization efficiency are overcome, pertinence and effectiveness of parameter adjustment are ensured, and the technical current situation that machining quality fluctuates and repeatability is poor due to traditional fixed parameters is changed.

The invention relates to the technical field of machine tool monitoring, in particular to a numerical control boring and milling tool cutting force data intelligent monitoring method which comprises the following steps: positioning a high-speed camera in a cutting area of a numerical control boring and milling machine, continuously capturing instantaneous images of particle splashing and sparks, and collecting and analyzing particle and spark distribution of each frame in real time; a cutting site image data stream is obtained. According to the method, images generated by particle splashing and sparks in the cutting area of the boring and milling machine are captured in real time by using the high-speed camera, the prediction accuracy of tool wear and potential faults is improved by analyzing image data streams, data processing is optimized by adopting a deep learning network, and the accuracy of tool wear and potential fault prediction is improved. Particle and spark features extracted from the image provide quantitative indexes for the tool wear state, cutting force and vibration sensor data are integrated, the accuracy of abnormal point analysis is enhanced, machine tool maintenance is more predictable, and cutting parameters are adjusted in real time, so that production continuity and high efficiency are ensured, and the fault shutdown cost is reduced.

The invention discloses a numerical control machine tool machining environment monitoring system, and belongs to the technical field of intelligent monitoring. Comprising the following modules: a multi-dimensional sensing monitoring module for realizing omnibearing data acquisition of an interaction state of a cutter and a workpiece in a machining process; the feature extraction module is used for converting the time sequence data into a key feature set for representing the state of the cutter; the wear type identification module is used for accurately identifying and classifying the wear type of the cutter based on a deep learning classifier and judging the specific wear type; the wear progress prediction module calls a corresponding special prediction model according to the identified specific wear type, quantifies the wear progress rate and estimates the residual life of the cutter; the decision support module is used for integrating the tool wear state and the prediction result, balancing the production efficiency, the machining quality and the tool cost, and providing optimization suggestions of parameter adjustment and tool changing opportunities; and the self-learning optimization module is used for continuously collecting actual production data to carry out model evaluation and incremental learning so as to realize multi-machine knowledge sharing.

The invention provides a production line product quality traceability method based on industrial vision, and the method comprises the steps: extracting key feature parameters, including a texture orientation angle, a texture period and a surface roughness parameter, capable of reflecting the wear degree of a tool from the preprocessed texture image data, and constructing a multi-dimensional feature vector of the surface micro texture of a product; according to the multi-dimensional feature vector of the product surface micro-texture, in combination with the corresponding relation between the tool wear degree and the surface texture feature, obtaining a wear degree estimated value of the current tool, and comparing the wear degree estimated value with a wear early warning threshold value; and for the identified suspected defective products, identifying defect types by analyzing surface textures, comprehensively analyzing material attributes and designing dimensional tolerance factors, and judging whether surface texture abnormity is caused by tool wear or product defects.

The invention discloses a tool wear loss prediction method based on physical information self-supervised learning under the condition of few marks, and belongs to the technical field of numerical control machining center tool wear state prediction. Comprising the steps of collecting monitoring signals such as cutting force, vibration and acoustic emission in the machining process; carrying out preprocessing operation of interception and Z score standardization on each sample; a mixed residual convolutional neural network tool wear monitoring model is designed, and information fusion and high-dimensional representation extraction are carried out on multi-source sensor signals; pre-training the tool wear monitoring model by using the auxiliary task I, and guiding the model to extract the invariance feature of the signal; according to the method, under the condition of scarce marked data, high-precision prediction and excellent generalization ability of tool wear are realized, the fluctuation amplitude of prediction errors can be effectively reduced, and the stability of a prediction result is kept. According to the method, a small amount of mark data is effectively utilized in model training, the stability and reliability of tool wear monitoring are ensured, and the requirements for monitoring precision and adaptability in actual production are met.

The invention discloses a numerical control machining equipment state monitoring system based on big data analysis, and relates to the technical field of intelligent manufacturing. The method is used for solving the problems of correlation analysis and real-time feedback between tool wear and machining quality in the machining process. A time sequence correlation feature vector is extracted through a vibration signal, an acoustic emission signal and main shaft axial micro-displacement data, a machining quality degradation index is calculated in combination with a nonlinear regression model, and the machining quality is monitored in real time. And on the basis of adaptive feature weight adjustment of the types of the processing materials, feature fusion during processing of different materials is enhanced, and the precision of quality evaluation is improved. And establishing a nonlinear mapping relation between the tool wear and the surface roughness by utilizing a gradient lifting tree model, and realizing accurate prediction of the tool wear and the machining quality. And finally, through a closed-loop dynamic adjustment mechanism, the cutting speed and the feeding amount are automatically adjusted according to the machining quality degradation index and the fault positioning result, and the stability of the machining process and the product quality are ensured.

The invention relates to a shield tunneling machine tunneling error compensation control method and system, and relates to the field of tunneling operation, and the method comprises the steps: carrying out the time domain alignment and cutting region mapping alignment of a predicted soil feature distribution sequence and a predicted tool wear distribution sequence, and carrying out the integration to obtain a predicted operation state data sequence; and performing iterative optimization analysis on tunneling control parameters of the shield tunneling machine on the basis of the predicted operation state data sequence by taking the minimum overall tunneling operation error of the cutterhead as an expected target until a preset optimization scale is met, outputting an optimal tunneling parameter sequence, and controlling the shield tunneling machine to execute tunnel construction in a future time zone. According to the method, the technical problems of insufficient tunneling precision and low operation efficiency caused by the fact that a traditional method lacks a dynamic adjustment mechanism and cannot adjust tunneling control parameters in a targeted manner according to soil texture changes and cutter wear conditions can be solved; by effectively reducing the tunneling operation error, the accuracy and the operation efficiency of the shield tunneling machine in the tunneling process are remarkably improved.

The invention relates to the field of tool wear in the numerical control machining process, in particular to a tool wear monitoring and predicting method which comprises the steps of signal collection, multi-domain feature extraction, multi-modal feature fusion, tool wear prediction model construction, real-time monitoring and wear prediction. And a multi-signal fusion monitoring and prediction model is constructed, so that the precision of tool wear monitoring and the reliability of prediction are remarkably improved, and the defects of a traditional method are effectively overcome.

The invention relates to the technical field of high-quality machining, in particular to a valve body high-quality machining method based on a numerical control machining center, and the method comprises the steps that power data, cutting force data, feeding speed data and rotating speed data are collected in real time when a numerical control machine tool machines a valve body; determining an operation matching index of the cutting force and the rotating speed at any acquisition moment; obtaining a machine tool operation matching index of the numerical control machine tool at any acquisition moment by combining the cutting force and the operation matching index of the rotating speed at any acquisition moment; determining a feeding speed abnormal factor of the numerical control machine tool at any acquisition moment; and on the basis of the feed speed abnormal factor at the current acquisition moment, adjusting the linear speed in the main shaft linear speed control instruction at the current acquisition moment. The feeding speed in the valve body machining process is adjusted, the surface roughness and tool abrasion are reduced, and therefore high-quality machining of the valve body is achieved.

The invention discloses a tool wear monitoring and repairing method based on artificial intelligence. The method comprises the following steps that a terminal layer collects original machining data and uploads the original machining data to a cloud computing layer; the cloud computing layer trains a power increment diagnosis model; the terminal layer collects processing data in the processing process in real time as monitoring data; the cloud computing layer selects a deep learning algorithm or a transfer learning algorithm to train a final processing precision fault diagnosis model; constructing an edge calculation distribution optimization algorithm and performing task scheduling; and based on the machining precision fault diagnosis result, a laser surface strengthening path is constructed, and the abraded tool is repaired through laser surface strengthening treatment. The numerical control machining process can be diagnosed in real time based on the transfer learning mechanism, the laser surface strengthening path is planned based on the diagnosis result, the laser surface strengthening operation is guided in real time, the abnormal state of the tool is repaired in time through the laser surface strengthening technology, and the machining precision is ensured.

The invention relates to the field of milling, in particular to a modeling method and system for a morphology model of a milling surface, and the method comprises the steps of data collection, data preprocessing, feature extraction, model construction and model verification and optimization. According to the modeling method and system, various actual factors in milling are fully considered, including tool abrasion, cutting force vibration, workpiece material non-uniformity and the like, the fitting degree of the model and the actual milling surface appearance is improved, and the microscopic geometrical characteristics of the machining surface can be reflected more accurately; a machine learning algorithm is adopted to construct a model, so that a complex milling process parameter combination relationship can be flexibly processed, the applicability and accuracy of the model under different milling process parameters are improved, and accurate evaluation of part performance and optimization of a machining process are facilitated; the system is complete in structure, all the modules are clear in division of labor, and the construction, verification and optimization process of the morphology model of the milling surface can be efficiently achieved.

The invention relates to the technical field of production line control, and discloses an intelligent control method and system for production line mold machining. According to the method, a pressure distribution state is determined by obtaining initial cutting parameters of a cutter and surface pressure data of a mold, mold node stress distribution is calculated by utilizing a three-dimensional finite element model, and a stress concentration position is identified. And predicting a mold deformation trend through a linear regression model according to the thermal expansion coefficient and the environment temperature data. Meanwhile, the initial cutting parameters are optimized by combining a cutting parameter optimization objective function, and the objective function comprehensively considers factors of tool wear, surface quality and residual stress. And on the basis of a die deformation prediction result, a dynamic compensation algorithm is adopted to adjust supporting structure parameters and optimize cutting parameters, and finally optimal cutting parameters and a target tool path are generated. According to the method, precise coordination control over pressure distribution and mold deformation under the complex working condition is achieved, the residual deformation risk is reduced, and the mold machining precision is improved.

The invention relates to the field of machining, in particular to the field of tool face abrasion prediction, and particularly discloses a tool face abrasion loss prediction method and system and a digital twin system. Inputting the preprocessed real-time machine tool spindle current data into the trained blunt state classification model to obtain a blunt state classification result; if the blunt state classification result is a non-blunt state, inputting the preprocessed real-time machine tool spindle current data into a trained first tool face abrasion loss prediction model to obtain a tool face abrasion loss prediction value; and if the blunt state classification result is a blunt state, inputting the preprocessed real-time machine tool spindle current data into the trained second tool face abrasion loss prediction model to obtain a tool face abrasion loss prediction value. According to the method, the tool wear states are classified, wear loss prediction models are trained for different wear states, wear state information is fully utilized, and higher prediction precision is achieved.

The invention relates to the technical field of numerical control machine tool operation, in particular to a machining data acquisition method and system applied to a numerical control machine tool, and the method comprises the steps: obtaining a machining precision error value according to the difference between cutting parameters of different dimensions and initial cutting parameters; according to the difference change condition of the cutting parameter and the initial cutting parameter, a tool wear characteristic value is obtained; obtaining a tool wear influence value according to the tool wear characteristic value and the cutting force change influence value; obtaining a machining precision error influence value according to the tool wear influence value and the machining precision error value; acquiring an acquisition frequency regulation factor at the current moment according to the variation trend of the processing precision error influence values of different target error historical time periods; and acquiring the boring and milling data acquisition frequency at the current moment based on the acquisition frequency regulation factor. The boring and milling precision of the numerical control machine tool is improved.

The invention discloses a five-axis numerical control machine tool thermal error compensation method considering tool wear. The method comprises the steps that temperature data of all monitoring points are collected, position changes between a tool cutting point and a workpiece are measured to obtain thermal error data, acceleration signals are collected, and a tool wear value is measured; (2) carrying out standardization processing on the collected temperature data, and screening out temperature sensitive characteristics highly related to thermal errors; (3) establishing a nonlinear mapping relation between the temperature sensitive characteristics and the thermal error by using an SVM (Support Vector Machine) model; (4) preprocessing the acceleration signal, and training a tool wear monitoring model; (5) introducing a Sigmoid correction function on the basis of the SVM model to obtain a thermal error compensation model; (6) optimizing parameters in the SVM model and the Sigmoid correction function by adopting a Bayesian optimization algorithm; and performing real-time compensation on the thermal error of the machine tool through the optimized thermal error compensation model. According to the invention, the requirement of high-precision thermal error compensation in a long-time processing environment can be met.

The invention belongs to the related technical field of numerical control machining, and discloses a tool wear real-time monitoring and prediction method and system based on multi-sensor information fusion, and the method comprises the steps: S1, collecting a monitoring signal and a tool wear image in a machining experiment process; s2, carrying out wear region segmentation on the tool wear image by adopting a wear region segmentation model, and calculating to obtain a real wear value; s3, establishing a monitoring training sample set by using the monitoring signal and the real wear value, and training the wear monitoring model; establishing a prediction training sample set by using the sequence of the real wear values, and training a wear prediction model; s4, the real-time monitoring signal and the wear monitoring model are used for monitoring the wear value of the tool in real time; and performing online prediction on the wear value of the cutter by using the wear prediction model. According to the invention, the wear value of the cutter can be monitored in real time according to the real-time monitoring signal, online wear value prediction can be realized, and the production efficiency can be greatly improved.

The invention discloses a visual precise plastic mold surface milling system and method, and the method comprises the steps: obtaining and analyzing the surface topography data of a milling region and a tool displacement track in real time through an optical sensor, and extracting wear-related micro-deformation features through multi-band filtering processing; generating a deviation vector matrix through three-dimensional mapping of the deviation vector matrix and a reference curved surface, and realizing dynamic calculation and path correction of the cutter offset; the high efficiency and stability of the compensation cutting process are ensured by combining the self-adaptive adjustment of the rotating speed and the feeding rate of the main shaft; meanwhile, the compensation effect is projected to a visual interface in real time to form a comparison layer, hardware acceleration is started to maintain the system response speed when the frame rate fluctuates, and finally the material removal volume is evaluated according to the deviation vector matrix to intelligently control the machining cycle termination time. Therefore, the problem of microtopography deviation caused by tool abrasion in the milling process of the complex curved surface mold is effectively solved, and the machining precision and efficiency as well as the robustness and the automation level of the system are remarkably improved.

The embodiment of the invention discloses a shield tunneling machine cutter abrasion loss prediction method and device and a medium. Belongs to the technical field of shield tunneling machines, and solves the problems of poor prediction precision and robustness of the abrasion loss of a shield tunneling machine tool. Generating sample data from the historical working data of the shield tunneling machine; performing feature extraction on the wavelet transform image and the geological cross-section diagram of the main shaft vibration signal in the sample data through a dual-channel fusion shield tunneling machine lightweight feature extraction network to obtain a feature vector; constructing a multi-view information fusion feature vector based on the feature vector corresponding to the sample data; constructing a time sequence feature vector based on the multi-view information fusion feature vector, so as to construct an improved VIT shield machine cutter wear loss prediction model through the time sequence feature vector; and performing parameter optimization on the improved VIT shield tunneling machine cutter abrasion loss prediction model, and predicting the shield tunneling machine cutter abrasion loss under the condition that a parameter optimization result meets a preset error condition.

The invention provides an alloy processing workshop scheduling method based on real-time monitoring of a physical neural network, and belongs to the technical field of intelligent scheduling of alloy processing workshops. A deep operator network combining physical information and a deep learning model is provided and used for predicting the service life of the tool, and the network not only can learn the dynamic change of tool wear from data, but also can enhance the interpretability and prediction capability of the model through a physical signal; according to the intelligent scheduling method based on the cutter residual life, cutter life prediction and workshop production scheduling are deeply combined, the cutter state is monitored in real time, distribution of machining tasks and a cutter replacement plan are dynamically adjusted, and the cutter is prevented from being excessively used or replaced too early; the invention also provides a hybrid optimization strategy combining an adaptive genetic algorithm and reinforcement learning, global optimization is carried out through the genetic algorithm, local optimization is carried out in combination with reinforcement learning, the change of a workshop environment is dynamically responded, and the real-time performance and high efficiency of a scheduling scheme are ensured.

The invention relates to the technical field of numerical control machine tools, in particular to a numerical control machine tool digital twin model updating method and system. The method comprises the following steps: acquiring actual processing technology data; performing cutting process simulation according to the actual machining process data and the digital twin model of the numerical control machine tool to obtain cutting process data; performing tool wear state feature analysis on the cutting process data to obtain tool wear state data; updating a tool model according to the tool wear state data to obtain an updated tool model; according to the updated cutter model, dynamic contact state solving is carried out, and a dynamic contact solving result is obtained; and performing contact key data analysis on a dynamic contact solving result to obtain cutter-workpiece dynamic contact information. According to the method, the modeling precision and the simulation reliability are greatly improved, so that the digital twin model can reflect complex processing conditions more truly, and a reliable decision basis is provided for process optimization.

The invention discloses a shield tunneling machine or TBM hob associated characteristic abrasion analysis method and system, and belongs to the technical field of associated characteristic analys.The method comprises the steps that parameter data related to cutter abrasion are collected in real time, and a parameter time sequence is constructed; calculating a change curve of the statistical characteristics, the derivative characteristics and the rock-soil layer, and updating a parameter sequential sequence; analyzing a dynamic change condition of a parameter time sequence, calculating a feature importance score by using a random forest algorithm, avoiding interference by unimportant information in an analysis process, and constructing an associated feature model; according to the method, three-dimensional point cloud data of a rock-soil layer is constructed, fused point cloud data containing a tool wear label is generated, rich dimension data is provided, a point cloud model used for judging the state of the tool is constructed, the risk of misjudgment and missed judgment is reduced, and the state of the tool is judged; once it is judged that the cutter is damaged, a cutter damage signal is generated, and a powerful guarantee is provided for safe and efficient operation of the shield tunneling machine or the TBM.

The invention relates to the technical field of tool switching, and discloses a tool switching method of a CNC tool magazine. The method comprises the following steps: performing three-dimensional laser scanning on the surface of a cutter in a CNC machine tool to obtain a wear tiny heterogeneity index; performing association mapping on the geometric features of the workpiece and the tool wear mode to obtain feature data of a cutting boundary layer; executing cutter state evaluation calculation to obtain cutter health state indexes and inter-cutter collaborative wear relation data; switching evaluation function calculation is executed in the tool state space, and a first switching time sequence decision sequence is obtained; and the first switching time sequence decision sequence is input into a CNC tool magazine management system, and automatic tool switching operation is executed by controlling a tool replacement mechanical arm according to the calculated optimal intervention angle. The cutter switching time is greatly shortened, the machining efficiency is improved, and meanwhile the consistency and stability of the machining quality are ensured.

The invention discloses a blisk finish machining tool path compensation method, a medium and a program product, and relates to the technical field of tool path compensation, and the method comprises the steps: collecting historical blisk machining tool data, building a tool wear prediction model, estimating wear prediction values of a tool in different time periods according to the built wear model, fitting the predicted wear value with time, and drawing a tool wear time curve; and according to the influence of the tool wear on the finish machining of the blisk, the tool wear is divided into a plurality of time periods in combination with the tool wear time curve, machining compensation models of different time periods are established, and meanwhile, the current blisk is subjected to blade grouping. According to the method, the tool wear is accurately predicted and compensated, the tool compensation is corrected in combination with the product detection result, and the machining time period is reasonably divided, so that the machining precision and efficiency of the blisk can be effectively controlled and improved in different stages of the tool wear.