20112 results about "Machining process" patented technology

The invention discloses a numerical control tool machining monitoring method. The method comprises the following steps that firstly, data collection and primary processing are conducted; 2, constructing a digital twinborn model; 3, adjusting a self-adaptive threshold value; 4, performing prediction maintenance and fault early warning; and 5, optimizing machining parameters in real time. Multi-dimensional data such as temperature, vibration, stress and cutting force are collected in real time through a multi-sensor system, high-frequency sampling and signal denoising technologies are combined, comprehensiveness and accuracy of monitoring data are ensured, and a foundation is laid for follow-up analysis; the virtual machining model is constructed through the digital twinning technology, real-time simulation and synchronization of the machining process are achieved, the current state of the tool can be comprehensively reflected, anomaly detection and pre-judgment are conducted in combination with historical data and real-time data, and the fault monitoring accuracy is improved.

The invention relates to the technical field of multi-axis linkage numerical control machining, and discloses a multi-axis linkage numerical control machining state monitoring method and system.The method comprises the steps that vibration, acoustic emission and cutting force signals in the multi-axis linkage numerical control machining process are collected and preprocessed, and first multi-source sensor data are obtained; performing feature extraction and compressed sensing to obtain a low-dimensional feature subset; performing fusion judgment to obtain an abnormal state detection result; training a deep learning model comprising a backbone network and a plurality of branch networks to obtain a state evaluation model; and inputting second multi-source sensor data collected in real time into the state evaluation model for state evaluation to obtain a processing state classification result and a parameter estimation value. According to the method, cooperative state evaluation and prediction simulation of multiple machine tools are realized.

The invention provides a multi-axis linkage numerical control machining control method and device, equipment and a storage medium, and the method comprises the steps: carrying out the modeling analysis of the kinematic characteristics and static stiffness characteristics of a multi-axis numerical control machine tool, and generating a kinematic model and a static stiffness analysis model; and performing multi-objective optimization on the attitude of the target workpiece based on the models to obtain an optimal workpiece attitude parameter, and re-planning an original tool path of the machine tool according to the optimal workpiece attitude parameter to generate an optimized tool path. And according to the static stiffness analysis model and the optimized path, a static deformation compensation strategy in the machining process is formulated, and a self-adaptive stiffness compensation scheme is obtained. And real-time control and self-adaptive adjustment are performed on the machining process of the machine tool by optimizing a path and a self-adaptive rigidity compensation scheme, so that closed-loop control is realized. According to the method, the kinematics characteristic and the static rigidity characteristic of the machine tool are considered, the workpiece posture and the tool path are optimized, static deformation in the machining process is compensated in real time, and therefore efficient and high-precision numerical control machining is achieved.

The invention relates to the technical field of machining control, and discloses a forging part machining process control method and system based on digital twinning. The method comprises the following steps: acquiring temperature field and stress field data streams of large oil press equipment and forgings at multiple points in different areas; performing organization-defect association mode recognition on the data stream to generate a forging process database; a constitutive unit nesting method is adopted to construct a digital twinborn model, and a microstructure evolution unit is embedded in a macroscopic unit; performing nonlinear progressive simulation to obtain a critical deformation condition and a defect threshold value; and carrying out reverse solution on the process window to obtain an optimal process parameter combination. According to the method, the forging digital twin model is established through the constitutive unit nesting method, bidirectional coupling of macroscopic deformation and microstructure evolution is achieved, a stable process parameter combination is determined through the process window reverse solution technology, and therefore the quality stability and production efficiency of forged parts 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 invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The present invention relates to the technical field of machine tool structure optimized design. Disclosed is a method for the optimized design of a headstock structure of a vertical machining center. The method mainly comprises: parameterizing to establish a headstock three-dimensional model; establishing a finite element model of a headstock structure; according to limit working conditions in an actual machining process, performing static and dynamic characteristic analysis on the headstock structure; performing modal test testing on a headstock, carrying out comparative analysis with a simulation result, and correcting a finite element model of the headstock; using a sensitivity analysis method to select a key dimension of the headstock as an optimized design input parameter, while stress strain, low-order inherent frequency, and quality are target output parameters, and establishing a mathematical model for the optimized design of the headstock; and on the basis of a response surface model constructed by a second-order polynomial and a genetic algorithm, performing optimized design of the headstock until an optimal headstock structure which has higher performance and is lighter is obtained. According to the present invention, design efficiency is improved, prototype testing costs are reduced, and a better headstock structure is obtained.

The invention relates to a shaft linkage curved surface component machining path real-time control method, system and device, and the method comprises the steps: obtaining geometric data and machining process parameters of a shaft linkage curved surface component, and carrying out the shaft linkage decomposition to obtain a multi-shaft linkage machining path; real-time operation parameters of machining equipment and tool workpiece contact data are obtained, deviation matching is conducted on the multi-axis linkage machining path, and a real-time axis control instruction set and an axis position compensation trend curve are obtained; performing path optimization on the shaft position compensation trend curve according to the real-time shaft control instruction group to obtain an initial shaft linkage adjustment scheme; performing curved surface precision evaluation on the tool workpiece contact data, and performing curved surface prediction in combination with the axis position compensation trend curve to obtain a curved surface quality prediction area; and performing coordinated optimization on the initial axis linkage adjustment scheme according to the curved surface quality prediction area to obtain an axis linkage optimization processing strategy. It can be guaranteed that efficient operation can be achieved in different machining states, and path deviation in the machining process 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 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 provides a calculation task scheduling method and system applied to a numerical control machining system, and the method comprises the steps: obtaining a task request set containing a machining process type and a real-time constraint identifier, extracting a processing complexity feature and a resource demand feature, calling a task priority evaluation model to generate a priority sequence, and carrying out the calculation of a task. Generating an initial scheduling scheme according to the sequence and current available resource information of the system, adaptively adjusting the initial scheduling scheme based on task execution state information fed back in real time, and generating a final scheduling scheme including a task execution sequence and resource allocation parameters, thereby comprehensively considering task complexity, resource requirements and real-time constraints, and improving the scheduling efficiency. Dynamic matching and optimal scheduling of tasks and resources are achieved, and the machining efficiency and the resource utilization rate of the numerical control machining system are effectively improved.

The invention discloses a ceramic bearing roller crack control method, system and device based on artificial intelligence. The method comprises the following steps: acquiring material physical parameters of a ceramic bearing roller; a three-dimensional microstructure digital twin model is constructed, and then an initial finite element model of the ceramic bearing roller is obtained; applying a mechanical boundary condition and a thermal boundary condition to the initial finite element model to obtain a comprehensive finite element model, and further obtaining thermal-mechanical coupling simulation data; initial crack parameters are obtained based on the crack initiation model, and then crack propagation simulation information is obtained; obtaining optimal processing parameters based on the crack propagation simulation information and the equipment processing parameters; and obtaining a crack risk prediction value of the current time period based on the crack risk prediction model, obtaining and adjusting actual optimal processing parameters of the current time period, and realizing control on the cracks of the ceramic bearing roller. The crack risk of the ceramic bearing roller in the machining process is effectively reduced, and the machining quality and production efficiency of the ceramic bearing roller are improved.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention discloses full-component food processing equipment and processing technology based on a high-energy fluid mill, and the equipment comprises a wet crushing assembly which is used for preliminarily crushing food to form slurry; the high-pressure gas mixing assembly is communicated with the wet crushing assembly and is used for filtering large-particle impurities in the slurry and inputting high-pressure gas into the slurry; the high-energy fluid mill is communicated with the high-pressure gas mixing assembly, and the slurry collides with one another in the high-energy fluid mill; and the ultrasonic assembly is connected with the high-energy fluid mill, and the high-energy fluid mill is located in the ultrasonic range of the ultrasonic assembly. Supercritical gas, high-speed fluid collision and ultrasound jointly act on a mixture, so that emulsification homogenization, superfine grinding and instantaneous sterilization of the mixture are achieved, the mixture is sterilized in the grinding process, the grinding effect is good, a full-component food finished product material can be formed, use of independent sterilization equipment is avoided, and the production cost is reduced. The processing technology is simplified; and the occupied area of equipment is reduced.

The invention relates to the technical field of model construction, in particular to a method and a system for constructing a sheet metal working three-dimensional process model. The method comprises the following steps: acquiring equipment specification data and material characteristic data; the method comprises the following steps: acquiring geometric parameters and motion parameters of sheet metal processing equipment by using a three-dimensional scanner, and integrating equipment specification data and material characteristic data to construct a sheet metal three-dimensional initial process model; analyzing a material plastic deformation characteristic of the material characteristic data and setting a processing parameter threshold range; the machining parameter threshold range is input into the three-dimensional initial process model for stage machining process simulation, the flange area strain rate, the dangerous section thinning rate and the rebound angle variable quantity are output, and stage machining process simulation comprises plate inflow stage simulation, forming stage simulation and shaping stage simulation. According to the method, three-dimensional scanning, comprehensive machining process simulation, topological optimization and a real-time error feedback mechanism are introduced, so that the construction accuracy of the sheet metal machining process model is improved.

The invention discloses a light spot real-time monitoring and feedback focusing system and method for laser processing. The system comprises a laser processing module, an emission light source module, a power monitoring module, a light spot monitoring module and an automatic control module. The emitting light source module is used for emitting laser beams; the power monitoring module is used for monitoring the laser power emitted by the emitting light source module; the light spot monitoring module is used for monitoring light spots of diffuse reflection light after the laser is emitted to the surface of the workpiece; the automatic control module sets reference signals of the following parameters: processing laser power and shape, position and size of a processing light spot; and according to the reference signal and the corresponding feedback signal, the laser emission parameter and the laser incident workpiece surface parameter are automatically adjusted, so that the error between the reference signal and the corresponding feedback signal is within a set precision range. According to the method, the light spot form and the light intensity in the machining process are dynamically monitored, and the light spot form and the energy in the laser machining process are adjusted in real time through feedback.

The invention relates to the technical field of numerical control machining, in particular to a CAM post-optimization method and system for geometric error compensation of a multi-axis linkage numerical control machine tool. The method comprises the following steps: firstly, carrying out kinematics analysis and geometric error modeling on a target machine tool, and deploying the target machine tool in a CAM post-optimization module; then actual state parameters of the target machine tool in the machining process are collected in real time through a laser interferometer; a motion instruction of a target machine tool and a detection result of a laser interferometer are synchronously obtained through a CAM post-optimization module, and then geometric errors of a linear axis and a rotating axis in the operation process of the target machine tool are calculated in real time through a kinematic model and a geometric error model. According to the method, different linear axis compensation modes are adopted for different instructions; and a cutter shaft attitude vector error and a cutter cutting point position error are calculated in combination with inverse kinematics, and are corrected step by step. The problem that the error compensation precision of a controller in an existing numerical control machine tool is insufficient can be solved.

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 machining parameter optimization, in particular to a machining parameter self-adaptive optimization method and device based on workpiece recognition, and the method comprises the steps: obtaining the multi-source feature information of a to-be-machined workpiece when a machining instruction is received; matching a preset process knowledge base according to the multi-source feature information, and generating initial processing parameters and process target data; dynamic state data in the machining process are obtained in real time, wherein the dynamic state data comprise change parameters of the workpiece and feedback parameters of machining equipment; based on the dynamic state data, whether deviation exists between the dynamic state data and the process target data is analyzed; and if deviation exists between the dynamic state data and the preset process target, adjusting parameters are generated according to the dynamic state data. By obtaining workpiece change parameters and equipment feedback parameters in real time, the machining state can be dynamically monitored, and deviation can be found and recognized in time. Through comparison with preset process target data, existence of deviation can be accurately identified, and then necessary adjustment measures are taken.

The invention relates to an automatic control method and system for milling cutter machining, and the method comprises the steps: obtaining geometric feature data and technological parameter information of a milling cutter, and carrying out the machining analysis of the geometric feature data based on the technological parameter information, and obtaining the corresponding original machining flow information; acquiring machining flow sensing data of the milling cutter, and performing state association on the machining flow sensing data and the original machining flow information to obtain a corresponding real-time machining state group; real-time cutting parameters of the milling cutter are obtained, machining prediction is conducted on the original machining process information according to the real-time cutting parameters, and a corresponding machining quality trend is obtained; performing intelligent control optimization on the real-time processing state group and the processing quality trend to obtain a corresponding initial control scheme; and performing global parameter optimization on the initial control scheme according to the compensation control parameters to obtain a corresponding global processing optimization scheme. According to the invention, the real-time processing state group and the processing quality trend can be intelligently controlled and optimized.

The invention relates to the technical field of image processing, and discloses a production management method and device for a flat curved surface product. The method comprises the following steps: carrying out feature extraction processing on the surface morphology of a flat curved surface product material to obtain a curved surface feature topological graph; performing non-uniform curvature partition processing on the curved surface feature topological graph to obtain a flat curved surface product processing area division scheme, and performing variable curvature compensation path planning processing to obtain a self-adaptive processing track sequence; performing real-time curved surface error detection processing on the self-adaptive processing track sequence to obtain local morphology deviation data; performing curved surface incremental forming compensation processing on the local morphology deviation data to obtain dynamic correction parameters; and performing curved surface quality feature correlation tracing processing on the dynamic correction parameters to obtain a full-cycle quality evaluation report of the flat curved surface product. Real-time error detection, dynamic compensation and quality tracing in the machining process of the flat-curved-surface product are achieved, and therefore the machining precision and quality stability of the flat-curved-surface product are improved.

The invention belongs to the field of machining, and relates to a machining process quality online monitoring and feedback adjusting system and method. The system comprises a multi-modal data acquisition module used for acquiring a mechanical vibration signal and a high-frequency micro-damage signal; the feature preprocessing and cross-modal fusion module is used for carrying out time domain, frequency domain and time-frequency domain feature extraction on the collected mechanical vibration signals and high-frequency micro-damage signals, carrying out edge, texture and shape feature extraction on workpiece machining surface image signals, and introducing a cross-modal attention mechanism to achieve weighted fusion of heterogeneous signals. And finally generating a fusion feature vector with high discrimination. The machine tool / cutter state and workpiece quality intelligent analysis module is used for constructing an intelligent analysis model for the equipment operation state and the workpiece machining quality; inputting the fusion feature vector into an intelligent analysis model to obtain a quality evaluation result; and the quality judgment and feedback adjustment module is used for adopting a dynamic threshold adjustment mechanism to realize adaptive comparison with a process standard according to a quality evaluation result.

The invention relates to a metal cutting machining path planning system, and aims to optimize the efficiency and precision of a metal machining process through advanced technical means. The system comprises a plurality of functional modules: an image acquisition module captures a multi-angle high-resolution image of a to-be-processed metal workpiece; the three-dimensional reconstruction module reconstructs a three-dimensional model by using the images; the cutting simulation module simulates the three-dimensional model in a virtual environment and records stress and heat distribution conditions in the cutting process in real time; the intelligent segmentation module divides the machining process into a plurality of stages according to the stress and heat data; the comprehensive optimization module is combined with a genetic algorithm and a simulated annealing algorithm to optimize the cutting path, depth and feeding speed of each machining stage; the feedback adjusting module monitors stress and heat changes in actual machining, and when the stress and heat changes exceed threshold values, cutting parameters are automatically adjusted or machining is paused. According to the system, through comprehensive application of image processing, analog simulation and optimization algorithms, the cutting efficiency and the product quality are remarkably improved.

The invention provides a numerical control machine tool dynamic precision control method and system based on multi-axis linkage error compensation, and relates to the technical field of mechanical manufacturing, and the method comprises the steps: collecting shaft system motion parameters and real-time cutting load parameters of a multi-axis numerical control machine tool in a linkage machining process in real time; shafting motion parameters comprise displacement, speed and acceleration of each feed shaft, and cutting load parameters are obtained through inversion of main shaft current and servo driving torque; calculating a dynamic following error of each feed shaft in a linkage state based on the shaft system motion parameters; and carrying out load coupling correction on the dynamic following error by fusing real-time cutting load parameters to obtain an initial multi-axis linkage dynamic error. According to the invention, real-time precision control in the high-dynamic machining process can be realized.

The invention discloses a laser-assisted machining path thermal error pre-compensation method, which comprises the following steps of: establishing a force-thermal coupling analysis model for a laser-assisted machining system, accurately calculating the temperature field change and deformation condition of a tool rest structural part in the laser-assisted machining process, and accurately correcting the parameter precision of the force-thermal coupling analysis model through model calibration. Thermal deformation epsilon t of a system tool nose point, a tool placement pose theta and an uncompensated tool path (xt ', zt') are obtained based on model calculation, and a processing tool path (xt, zt) is obtained through pre-compensation tool nose thermal deformation calculation, so that high-precision laser-assisted ultra-precision processing is realized under the condition that a tool rest has a thermal drift error.

The invention discloses a control method and equipment suitable for a rotary turn-over clamp of a numerical control milling center, and relates to the technical field of numerical control machining. The clamping force is optimized through the multi-source sensing and digital twinning technology, and workpiece deformation is prevented; thermal compensation and an error transfer chain model are integrated, the turnover angle is accurately corrected, and error accumulation is restrained; real-time comparison, traceability and self-adaptive adjustment in the machining process are achieved through digital twin pre-verification and multi-sensor closed-loop monitoring; the problems of out-of-tolerance of precision and batch consistency caused by unstable clamping, thermal deformation and error transmission in multi-surface milling of high-precision parts are solved, and the precision, reliability and process adaptability of complex part machining are remarkably improved.

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 discloses a system and a method for optimizing and improving the performance of a manufacturing and processing material based on aluminum alloy parts, and relates to the technical field, the system comprises the following components: a data acquisition and storage module, a machine learning model construction module, a user interaction module and a scheme generation and optimization module; related data of an aluminum alloy material are widely collected through the data acquisition and storage module, training is carried out based on a deep learning algorithm by utilizing the machine learning model building module, an aluminum alloy material component-process-performance relation model is built, a user only needs to input target performance parameters through the user interaction module, and the aluminum alloy material component-process-performance relation model is built. The system can generate aluminum alloy material components and machining process schemes meeting the requirements through reverse calculation, optimization screening is carried out, the period of material performance optimization is greatly shortened through the process, and optimization efficiency and accuracy are improved.

The invention discloses a hard and brittle material ultra-precision turning damage monitoring method based on multi-sensor fusion, and relates to the technical field of precision / ultra-precision machining state monitoring, a multi-sensor signal acquisition device is built on an ultra-precision turning machine tool to capture different types of sensor signals generated in the turning process in real time; performing signal preprocessing operation of minimum entropy deconvolution filtering on the acquired sensor signal; feature extraction is carried out on the signals subjected to minimum entropy deconvolution filtering, and 95 time domain and frequency domain statistical features reflecting turning damage are extracted; by integrating data from different sensors, the precision and reliability of monitoring are remarkably improved, more comprehensive and more accurate information can be obtained by capturing and fusing acoustic characteristics, cutting force and mechanical vibration information in the machining process, so that the abrasion degree of a tool and the damage condition of a workpiece are judged more accurately, and the machining accuracy is improved. And false alarm and missing alarm can be reduced, and the monitoring reliability can be improved.