1134 results about "Tool wear" patented technology

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 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 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 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 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 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 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 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 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 low-temperature minimal quantity lubrication cooling method in aluminum alloy precision machining, and relates to the field of aluminum alloy precision machining. Machining parameters such as the cutting speed and the feeding amount are determined according to the aluminum alloy material, the machining requirement and the tool performance, and finite element simulation optimization is carried out; high-purity vegetable oil-based lubricating oil is selected, a nano anti-wear agent and an antioxidant are added, and cooling liquid is prepared by mixing compressed air through vortex tube refrigeration; spraying to a processing area through a special multi-channel nozzle, and dynamically adjusting the flow; multiple sensors are used for monitoring the temperature, the tool abrasion and the surface quality, and adjustment is conducted when threshold values are exceeded; and the cooling liquid is recycled after being subjected to centrifugation and membrane filtration treatment, and the metal filing is classified, recycled and reused. The cooling and lubricating effects are good, cutting heat and tool abrasion are reduced, the machining precision is improved, and the tool service life is prolonged; environment-friendly lubricating oil is recycled, so that pollution and cost are reduced; real-time monitoring and adjustment guarantee stable machining quality, machining efficiency is improved, and high-end manufacturing requirements are met.

The invention relates to the technical field of machining, and discloses a lathe tool wear self-inspection alarm system which comprises a multi-source sensing module, an intelligent analysis module, a dynamic response module, an interactive display module and a self-adaptive energy supply module. The multi-source sensing module synchronously acquires the surface abrasion loss, the vibration spectrum and the acoustic emission signal of the cutter through an optical sensor, a capacitive sensor and an MEMS triaxial accelerometer, and outputs the surface abrasion loss, the vibration spectrum and the acoustic emission signal to the intelligent analysis module; and the intelligent analysis module performs fusion analysis on the multi-source data by adopting a mixed feature extraction and double-model decision mechanism. High-precision monitoring and dynamic evaluation of lathe tool wear are realized through optical, vibration and acoustic emission sensors of the multi-source sensing module in combination with mixed feature extraction and a double-model decision mechanism of the intelligent analysis module. By means of the mode of combining multi-dimensional monitoring and intelligent analysis, machining defects and equipment failure risks caused by tool abrasion can be reduced.

The invention relates to the technical field of numerical control machine tool control, in particular to a machining parameter automatic optimization method and system for a three-axis numerical control machine tool, and the method comprises the specific steps: collecting machine tool parameter data and workpiece parameter data, the machine tool parameter data comprises the current and voltage of the three-axis numerical control machine tool and the wear state of a cutter; the workpiece parameter data comprises workpiece scale, vibration, deflection and surface roughness; based on the machine tool parameter data and the workpiece parameter data, a physical information neural network is adopted to construct a three-axis numerical control machine tool digital twin model, real-time monitoring and feedback are carried out, and the machining efficiency and energy consumption are calculated; a numerical control machine tool machining process model and an energy consumption model are constructed based on the machining efficiency and the energy consumption, the machining efficiency is maximized and the energy consumption is minimized by using an optimization algorithm, the machining process model reflects quality efficiency indexes, and the energy consumption model reflects energy consumption characteristics; and through an optimization function obtained through the optimization algorithm, a control instruction execution module is constructed, and comprehensive factory control is covered.

The invention discloses an intelligent mold part machining cutting tool control method and system. The method comprises the following steps that S1, a multi-source sensing information collection layer is established; s2, constructing a cutter health knowledge graph; s3, generating a dynamic decision control instruction; s4, executing a bimodal control response; and S5, realizing closed-loop control optimization. Through quadruple mechanism coupling of global coverage of a multi-mode sensing layer, dynamic deduction of a knowledge graph decision-making layer, risk isolation of a double-track execution layer and intelligent evolution of a closed-loop optimization layer, the method is realized in an industrial control domain for the first time: raising a sensing dimension, and converting a physical world fragmentation signal into a cutter full life cycle digital twinborn body; reconstructing decision logic, replacing traditional threshold judgment with topological correlation, and foreseeably inhibiting ill-conditioned failure; the system is ecological and self-consistent, and the control strategy continuously evolves in operation to form the anti-interference capability; and finally, normal form transition of tool wear control from passive remediation to active immunity is achieved.

The invention relates to the technical field of numerical control system fault pre-diagnosis, and particularly discloses a fault monitoring method and system for a numerical control machine tool. The method comprises the steps that a spindle vibration signal, a cutting force signal and a spindle current signal are collected; outputting an alignment signal set; respectively executing adaptive filtering processing according to the alignment signal set; constructing a multi-dimensional feature vector; constructing a health index under the processing behavior period; then accumulated trend deviation analysis is executed through a CUSUM algorithm; and judging whether the tool wear trend is abnormal. The technical problem that in the prior art, static threshold judgment is carried out based on a single signal source, and especially in a numerical control machining scene with signal noise interference, accurate early warning of the early wear trend of a tool cannot be achieved is solved. Due to the fact that a period calibration mechanism fusing spindle vibration, cutting force and current signals, feature vector entropy weight optimization and a CUSUM trend detection mechanism are adopted, the pre-diagnosis level of the numerical control machine tool on cutter state changes is improved.

The invention discloses a machine vision-based numerical control machine tool cutter wear identification method and system, and relates to the technical field of machine vision. A numerical control machine tool cutter two-dimensional image sequence is collected, high-precision point clouds are obtained through three-dimensional reconstruction and calibration, and high-precision cutter reference three-dimensional point clouds are obtained; constructing a functional region division rule, segmenting the high-precision tool reference three-dimensional point cloud to obtain a tool functional region, and generating tool region structured reference data through geometric shape quantitative description algorithm analysis; collecting regional structured data of the cutter in different machining periods, and obtaining a shape evolution track of the cutter through space-time registration; constructing a tool wear recognition model based on the time sequence convolutional network, inputting a tool morphology evolution trajectory, and outputting a tool wear type; and collecting tool historical case data to construct a wear mechanism knowledge base, performing matching mapping on a tool wear type and the knowledge base, and generating a tool wear identification result. And based on the wear identification result, realizing the identification of the wear of the numerical control machine tool cutter.

The invention provides a machining cutter wear monitoring method based on deep learning, and the method comprises the steps: obtaining multi-modal monitoring data, and carrying out the preprocessing of the multi-modal monitoring data, and obtaining a monitoring data set; performing geometric feature coding on the tool face wear image to obtain a geometric parameter set of slope wear; inputting the temperature field matrix and the geometric parameter set into a fluid dynamic model for dynamic simulation to obtain flow field velocity distribution and temperature gradient distribution data; inputting the geometric parameter set, the flow field velocity distribution data and the temperature gradient distribution data into a multi-modal fusion deep learning model, and outputting multi-physics field coupling features including a geometric feature evolution trend and a flow field and thermal field coupling coefficient; wear mechanism modeling is carried out according to the multi-physical field coupling characteristics, and tool wear mechanism data are generated. By adopting the method, the precision and real-time performance of wear monitoring of the machining tool under the complex working condition can be improved.

The invention relates to the technical field of fastener process production, in particular to a process parameter control method and system applied to fastener production. The method comprises the following steps: acquiring three-dimensional cutting force of a tool-workpiece contact area in a fastener thread machining process in real time, and generating cutting force dynamic fluctuation characteristic data; the real-time abnormal situation of the current machining process is recognized according to the cutting force dynamic fluctuation characteristic data, a tool wear mode is judged, thread ring machining deviation prediction is conducted, and a predicted thread geometric deviation value is generated; and adjusting multiple machining control parameter values according to the predicted thread geometric deviation value, and carrying out real-time machining feedback monitoring so as to realize fastener production self-adaptive control. According to the method, intelligent recognition of the tool wear mode and accurate prediction of the machining deviation are achieved through cutting force sensing, and it is ensured that the thread machining precision of the fastener is stable and controllable through multi-parameter cooperative self-adaptive regulation and control.

The invention discloses a diamond cutter path error compensation method based on cutter wear evolution, which comprises the following steps: S1, multi-modal sensor data acquisition and processing: in the machining process, key state information between a diamond cutter and a workpiece is acquired in real time through a multi-modal sensor; s2, tool wear modeling: key geometric parameters and evolution trend of tool wear are extracted from the collected and processed data, and tool wear modeling is carried out. And S3, artificial intelligence prediction of the abrasion loss: training a tool abrasion prediction model based on a deep learning network structure. And S4, tool path compensation is conducted, specifically, the predicted abrasion loss and the actual free-form surface model are combined, the tool path compensation amount and the compensated control point coordinates are calculated, a new machining path is generated, and dynamic compensation of tool cutting edge abrasion is achieved. And S5, system integration and closed-loop control: integrating the steps on processing equipment, and realizing real-time data interaction and synchronization through a unified data protocol.

The invention relates to the technical field of tool control, in particular to a tooth cutting tool parameter control system based on bevel angle cutting, which comprises a tool parameter acquisition module used for acquiring the rotating speed, the axial feeding amount and the cutting depth of a tool in real time; the force-heat prediction model is used for outputting predicted cutting force and predicted cutting tool temperature; the multi-target parameter optimization model is used for receiving constraint conditions set by a user, optimizing the rotating speed, the axial feeding amount and the cutting depth of the cutter by taking the lowest cutting force, the lowest temperature of the cutting cutter and the highest machining efficiency as targets in the constraint conditions, and integrating and outputting a regulation and control strategy; and the cutter parameter control module is used for regulating and controlling the rotating speed, the axial feeding amount and the cutting depth of the cutter based on a regulation and control strategy when the predicted cutting force and the predicted temperature of the cutting cutter exceed threshold values. According to the method, by accurately regulating and controlling the parameters of the tooth cutting tool, the machining efficiency of the tooth cutting tool can be ensured, and meanwhile the abrasion speed of the tooth cutting tool is reduced.

The invention relates to the technical field of machining automation control, and discloses an intelligent tool changing decision-making method based on tool wear perception, which comprises the following steps: acquiring real-time state data of a tool through vibration, acoustic emission, force and temperature sensors, and fusing features of a multi-modal graph neural network to obtain tool wear feature data. And inputting the parameters into a Bayesian decision network, optimizing a tool changing strategy by using a dynamic probabilistic reasoning structure, and generating decision optimization parameters. And a multi-target tool changing optimization model with the highest machining efficiency and the longest service life of the tool as targets is constructed, and an optimal tool changing strategy is determined by adopting an improved particle swarm algorithm. Based on this, a hierarchical decision control model is established and comprises a global evaluation layer, a dynamic adjustment layer and an execution control layer, and intelligent control of tool changing action is realized. In addition, a self-healing control module is embedded in the system to deal with abnormal wear of the cutter. The machining efficiency is improved, the service life of the cutter is prolonged, the machining quality is guaranteed, and intelligent development of machining is promoted.

The invention discloses a high-performance diamond cutter wear detection device and method, and relates to the technical field of wear detection, and the device comprises a structural characteristic obtaining module which obtains cutter structural characteristics; the cutting working condition characteristic acquisition module is used for acquiring cutting working condition characteristics of the cutter; the wear risk assessment module is used for carrying out wear risk assessment; the maintenance period optimization module is used for receiving the real-time cutting working condition and adaptively optimizing the tool maintenance period and the detection index priority; and the optimization module is adjusted, and the real-time cutting parameters are optimized. The technical problems that the detection frequency cannot be dynamically optimized according to different cutting working conditions, the timeliness and accuracy of wear detection are insufficient, and the service life of the tool is influenced in the existing tool wear detection are solved, and the purposes of dynamically adjusting the detection period and the index priority according to the change of the cutting working conditions, improving the timeliness and accuracy of wear detection and improving the working efficiency are achieved. The service life of the cutter is prolonged, and the technical effect of online nondestructive flaw detection of the high-performance diamond cutter is achieved.

The invention relates to the technical field of machine tool performance analysis, and provides a machine tool functional part performance analysis method and system based on big data. Multi-source operation information, including a vibration signal, a temperature signal, a motor current signal, an acoustic emission signal and working condition information, of functional parts of a machine tool is collected, and machine tool operation performance indexes are determined based on the information; and when any index exceeds a normal interval under the current working condition, the system can judge that the functional part of the machine tool is preliminarily abnormal, multi-dimensional abnormal information is constructed according to the working condition information, the vibration information and the temperature information, the fault risk level is determined according to the multi-dimensional abnormal information, and finally early warning information is sent and user feedback is acquired. Therefore, the problem of false alarm caused by non-fault factors such as tool wear in the prior art is effectively solved, and the difficulty that the credibility of an operator to early warning information is reduced is avoided.

The invention provides a micro-milling machining parameter identification method considering the random tool wear influence. The method comprises the steps that a cutting force model under the tool wear influence is established by considering tool bounce and a chip separation mechanism; updating the rotating radius of the tool according to the influence of jumping and abrasion on the edge radius value of the tool, and obtaining a tool nose trajectory equation; tool wear data in the actual machining process are collected, the neural network model is trained, and hyper-parameters in the neural network model are optimized through a bidirectional long-short-term memory network; identifying processing parameter values in the neural network model by adopting a particle filtering algorithm; and simulating and calculating cutting forces and wear values under different working conditions by using a neural network model, comparing experimental results, and evaluating the accuracy of machining parameter identification. According to the method, the randomness of tool wear is fully considered in the modeling process, the prediction precision is remarkably improved, and the method has higher practical application value.

The invention relates to the field of detection and calibration of numerical control machining equipment, and discloses a quick calibration method for a CNC machine tool cutter bit, which comprises the following steps: S1, collecting state data in the operation process of the cutter bit; s2, carrying out preprocessing on the state data; s3, inputting the preprocessed state data into a tool wear identification model, and outputting tool wear state information; s4, acquiring spatial point cloud data of the end part of the tool bit, and calculating spatial error information between the point cloud and a preset tool reference model; and S5, calculating the position offset and the attitude error of the tool bit in the three-dimensional space based on the space error information. By collecting the physical state data in the operation process of the tool bit and combining with the wear recognition model of the time sequence modeling mechanism, the wear state of the tool can be accurately recognized in real time, so that the problem of unstable recognition precision caused by environmental interference in a traditional method is solved, and the recognition accuracy and real-time performance are remarkably improved.

The invention discloses a cutting parameter optimization method and device based on wear prediction, equipment and a medium. The cutting parameter optimization method based on wear prediction comprises the steps that a real-time wear image of a target tool and real-time tool machining parameters are collected; based on a pre-trained tool wear prediction model, according to the real-time wear image and the real-time tool machining parameters, determining a predicted wear curve of the target tool; the real-time abrasion loss of the target tool under the preset machining condition is determined based on the real-time tool machining parameters; and on the basis of the real-time abrasion loss and the predicted abrasion loss of the target tool, tool machining parameters of the target tool are optimized. According to the technical scheme, the service life of the cutter is prolonged and the quality of the machined surface is improved while the cutting machining efficiency is ensured.

The invention discloses a numerical control machine tool device convenient for changing the cutting angle of a cutter, and relates to the technical field of numerical control machine tools, the numerical control machine tool device comprises a main body structure, the main body structure comprises a machine tool body, one side of the top of the machine tool body is fixedly connected with a three-jaw chuck, one side of the three-jaw chuck is provided with a cutter rest, and the bottom of the cutter rest is fixedly connected with a cutter table; the centering assembly comprises a fixing bolt connected to the top of the tool rest in a threaded mode, and the surface of the fixing bolt is rotationally connected with a connecting plate. The centering assembly is used for replacing manual visual alignment, tool positioning is achieved, the tool positioning state can be switched under the conditions that a tool rest is not aligned, and the tool is obliquely installed due to abrasion, the tool can be obliquely installed according to actual requirements, and therefore the cutting angle can be conveniently adjusted and changed; and a cutter pushing-in-dust removal linkage mechanism is achieved through the dust removal assembly, so that it is guaranteed that the fixing bolt can be tightly attached to the top of the cutter when being fastened, the cutter can be ejected out in an auxiliary mode when the cutter is disassembled, and use convenience is improved.