829 results about "Tool wear" patented technology

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 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 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 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 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 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 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.

The invention provides a tool wear morphology detection method and system based on laser measurement, and relates to the technical field of tool wear detection.The method comprises the steps that a target tool is carried through a tool carrier, and a PLC controls the tool carrier to move; enabling laser emitted by a laser contour sensor to vertically irradiate on a to-be-detected surface of a target cutter, collecting multi-angle contour data of the cutter, and detecting multi-angle cutter distance data through a laser displacement sensor; performing coordinate normalization and splicing fusion based on the multi-angle tool distance data and the tool multi-angle contour data; and geometric feature extraction and wear measurement comparison are carried out on the three-dimensional shape of the tool and the initial three-dimensional shape of the target tool, and the tool shape wear extent is determined. According to the method and the device, the technical problem of low data fusion precision in the laser measurement and three-dimensional reconstruction process in the prior art can be solved, and the technical effects of reconstructing the three-dimensional shape of the cutter with high precision and accurately identifying the wear area are achieved.

The invention relates to the technical field of machine tool control, and provides a machine tool machining control method and system.The method comprises the steps that in the machine tool machining process, microenvironment parameters of a cutting area are obtained and monitored; obtaining a cutter type, a workpiece material attribute and a preset cutting parameter corresponding to the machine tool; based on the tool type, the workpiece material attributes and the preset cutting parameters, an initial judgment basis of the tool wear speed is established; according to the deviation between the microenvironment parameters and a preset standard value, the initial judgment basis is corrected, and the corrected tool wear speed is obtained; according to the actual tool abrasion loss, the rule for subsequently correcting the initial judgment basis is optimized; and based on the corrected tool wear speed, the tool remaining life is updated, and when tool wear reaches a preset threshold value, a compensation strategy of the cutting parameters or the tool path is decided and executed in combination with the value of the machined workpiece and the remaining machining task. The method has the effect of improving the prediction accuracy of the tool wear speed.

The invention discloses a tool wear real-time prediction method fusing wear and jumping mechanism-three-dimensional vibration data, and belongs to the field of machine tool state monitoring and intelligent manufacturing. According to the method, a three-dimensional acceleration sensor is arranged near a main shaft or a cutter handle, three-dimensional vibration signals in the machining process are obtained, and a cutting force mechanism model is established in combination with cutting parameters and geometric features of the cutter; and tool wear correction and bounce disturbance items are introduced into the mechanism model, and prediction calculation of the three-way cutting force is carried out. And then, a cutting force prediction result and vibration signal features are fused, a comprehensive feature vector is constructed, and real-time prediction of the tool abrasion loss is realized based on a deep learning regression model. According to the method, physical rule constraints of mechanism modeling and data driving advantages of vibration signals are fully utilized, the accuracy and real-time performance of tool wear prediction are effectively improved, and important engineering application value is provided for improving the machining quality, prolonging the service life of a tool and achieving intelligent production.

Systems and methods relating to automated machining. A machining tool is controlled by an industrial robot that guides the tool around the contour of a workpiece. As the tool traverses the estimated contour of the workpiece, the tool machines the surface while a force sensor provides force readings, the force readings being readings of the force exerted by the tool on the surface. The path followed by the tool around the contour of the workpiece is adjusted based on the force readings. The speed at which the tool traverses the workpiece may be modulated and the path may be adjusted based on estimated tool wear.

The invention discloses a shield cutter cutting load real-time monitoring and adjusting method and system. The method comprises the following steps: constructing a shield cutter distributed sensing network, and collecting strain, vibration and temperature of a cutter; a wireless transmission network is established, and the collected strain, vibration and temperature of the cutter are transmitted in real time; according to the strain, vibration and temperature, received in real time, of the cutter, cutting load parameters are obtained in a multi-source data fusion mode; based on the obtained cutting load parameters, stratum parameter identification is carried out in combination with a deep learning algorithm; in combination with the stratum parameters and the cutting load parameters, a wear state evaluation model based on fuzzy reasoning is established, and the tool wear state is evaluated; and designing a multi-objective optimization function according to the wear state of the cutter and the stratum parameters, solving the multi-objective optimization function, obtaining optimal shield tunneling machine operation parameters, and performing dynamic adjustment. The cutting load of each cutter in shield construction can be monitored in real time, and the cutter parameters can be adaptively adjusted.

The invention relates to the technical field of PCB machining, and provides a tool compensation machining method and PCB machining device.The tool compensation machining method is used for machining a plate, machining of the plate has multiple stages, and the tool compensation machining method comprises the steps that the initial tool diameter of a tool before machining is detected, and an initial tool compensation value is generated; according to the initial cutter compensation value, a cutter is driven to execute first-stage machining; detecting the actual cutter diameter of the cutter and generating a first cutter compensation value under the condition that the cutter completes the machining of the first stage and the cutter is lifted; and according to the first cutter compensation value, the cutter is driven to execute second-stage machining. According to the method, self-adaptive dynamic compensation of tool abrasion can be achieved, the machining precision is guaranteed, and the machining quality and the automation level are improved.

The invention relates to the technical field of shearing machines, in particular to aluminum veneer continuous shearing equipment which comprises a moving platform, a portal frame moving synchronously with the moving platform is assembled on the surface of the moving platform, and a cutter assembly used for shearing aluminum veneers is assembled below the portal frame; by arranging the moving platform, the moving frame and the tool rests, in the moving process of the moving frame and the moving platform, the tool rests are unlocked through the mounting assembly, the pushing assembly pushes the non-working tool rests to move, the continuously-working tool rests are extruded into the empty moving frame, and the non-working tool rests move into the mounting base; the cutter is fixed through the mounting assembly, and the conversion operation of the cutter can be realized in the moving process; meanwhile, when the abrasion loss of the tool exceeds a threshold value after the tool works for a certain time, the tool switching operation is switched into the tool changing operation in the non-stop state, and finally the continuous shearing operation of cleaning, heat dissipation and tool changing of the aluminum veneer in the non-stop state is achieved.

The invention relates to the technical field of machine tool machining parameter optimization, and provides a self-adaptive machining parameter optimization method and system for a numerical control machine tool. The method comprises the following steps: performing wear detection on a numerical control machine tool cutter to generate a wear state vector; reading the task, and retrieving a first processing parameter set matched and corresponding to the processing database; predicting and generating a state time sequence based on the wear state and the first parameter set, controlling the machine tool to execute the task by the first parameter set, monitoring the state in real time, and comparing with the predicted time sequence to identify a first deviation point; and judging whether the deviation exceeds a threshold value or not, if not, predicting deviation continuity by taking the point as a starting point, and optimizing processing parameters according to a result. The technical problems that in the machining process of an existing numerical control machine tool, machining parameters cannot be dynamically adjusted according to the tool abrasion state, so that the machining precision is reduced, and the machining efficiency is lost are solved, and the purposes of monitoring tool abrasion in real time, optimizing the machining parameters, improving the machining efficiency, reducing tool loss and improving the machining precision are achieved. And the technical effect of stable processing quality is ensured.

The invention belongs to the technical field of numerical control machine tool machining, and particularly relates to a numerical control boring and milling tool cutting force intelligent monitoring method and system, and the method comprises the steps: obtaining a real-time main force, a real-time transverse force, a real-time longitudinal force sequence, a real-time main shaft rotating speed, a signal sampling frequency and a basic statistical threshold value in a machining task of numerical control boring and milling equipment; obtaining characteristic force; calculating a historical mean value at the current moment, and recording the historical mean value as a historical mean value; calculating a current historical deviation, and determining the width of a sliding window; calculating an instantaneous deviation; obtaining the change amplitude of the characteristic force at the current moment, the severity of the change trend deviating from the historical normal state, and the maximum value of the change amplitude and the severity to obtain a risk index; and comparing whether the risk index is abnormal or not according to the basic statistical threshold, and generating a processing process digital file. The technical problems of high false alarm rate and poor reliability caused by incapability of adapting to normal wear of the cutter in the prior art are solved.

The invention relates to the technical field of image data processing, in particular to a numerical control lathe tool wear real-time detection method and system based on machine vision, and the method comprises the steps: positioning a region of interest of a tool image; for pixel points in the region, fusing gradient amplitudes, phase consistency and local structure anisotropy of the pixel points, and calculating an initial boundary probability; constructing an exploration path and performing weighted integration on an initial boundary probability on the exploration path so as to evaluate path consistency; and fusing the initial boundary probability and the path consistency score to obtain a final boundary probability so as to extract a wear contour and realize evaluation of the wear state of the tool. According to the method, the problem of inaccurate wear contour extraction caused by insufficient robustness in a complex processing environment is solved, and the accuracy and reliability of detection are improved.

The invention discloses a machine tool cutter cutting control method and system, and relates to the technical field of high-precision manufacturing, and the method comprises the steps: obtaining a first vibration signal, temperature data and a cutting force signal of a main shaft bearing; generating a spindle bearing health score according to the first vibration signal and the temperature data; according to the cutting force signal, the tool abrasion loss is calculated; according to the health score of the main shaft bearing, the tool abrasion loss is corrected; determining a cutter replacement threshold value according to the health score of the main shaft bearing; and if the corrected tool abrasion loss is larger than the tool replacement threshold value, a tool replacement instruction is generated, and cutting is carried out after tool replacement. The cutter can be replaced by combining the health score of the spindle bearing and the abrasion loss of the cutter, so that the cutting control of the cutter is realized, the reliability is improved, and the resource consumption is reduced.

The invention relates to the technical field of friction stir repair, and discloses a multi-dimensional defect friction stir solid-phase repair planning system and a defect repair method. The system comprises a defect acquisition unit for acquiring multi-dimensional defect data of a workpiece and generating a time-space correlation data set covering defect depth distribution, a material heat affected zone range, a friction tool wear state and environment temperature and humidity parameters; the feature reconstruction unit decomposes the data set and separates core and abnormal feature tensors; the repair planning unit generates a multi-region cooperation scheme containing a tool path, a pressure sequence and a rotating speed strategy in combination with the core features and the process constraints; the dynamic adjusting and optimizing unit monitors abnormal characteristics and adjusts the scheme when conditions are met; the collaborative strategy unit responds to cross-regional defect linkage abnormity, allocates a standby tool and updates a track offset; and the feature fusion unit fuses the multi-scale monitoring features to generate a repair quality evaluation feature set. The system enhances the comprehensiveness and adaptability of complex defect repair.

The invention relates to the technical field of intelligent manufacturing and precision machining, and discloses a skylight guide rail clamp clamping force intelligent regulation and control method and system.The skylight guide rail clamp clamping force intelligent regulation and control method comprises the steps that multi-dimensional sensing data of a skylight guide rail clamp is obtained and preprocessed; acquiring a force displacement data pair and identifying a flexibility coefficient of the guide rail; the elasticity modulus is inversely calculated through the flexibility coefficient, and temperature correction is carried out; calculating theoretical stress distribution and a displacement field, and fusing and correcting the theoretical stress distribution and the displacement field with measured data of the sensor; evaluating stress safety, deformation stability and vibration stability; the current clamping force distribution is adjusted; the clamping force adjustment amount is calculated and converted into a control instruction for adjustment, the guide rail is machined, and quality detection is conducted; updating the quality prediction model, predicting tool wear and calculating compensation; according to the invention, through an online learning and adaptive optimization mechanism, the system can continuously learn and evolve and automatically adapt to changes of production conditions.

The invention belongs to the technical field of tool wear measurement, and particularly relates to a machining center tool wear state early warning method based on multi-source data fusion, and the method comprises the steps: calculating a cutting power consumption characteristic through a main shaft load energy mapping model according to the original sampling current and the real-time feeding speed of a main shaft; sliding window sampling is carried out on the cutting power consumption characteristics, and the cutting state disorder degree is calculated; performing nonlinear coupling on the power consumption gradient item and the cutting state disorder degree to obtain a grinding loss stability coefficient; and establishing a dynamic statistical reference surface, and performing cutter state early warning according to the deviation degree of the grinding loss stability coefficient monitored in real time. Instantaneous interference caused by uneven material hardness is effectively recognized and eliminated, meanwhile, signal structure disorder at the initial stage of tool failure is captured, the technical problems that in die steel machining, due to material structure segregation and variable working conditions, the false alarm rate is high, and failure precursor capturing is difficult are solved, and the machining precision is improved. And the monitoring accuracy of the precision machining process is obviously improved.

The invention discloses a self-adaptive compensation system based on cutter wear of a numerical control machine tool, and particularly relates to the field of machining precision control of the numerical control machine tool, and the self-adaptive compensation system comprises a multi-dimensional data acquisition module, a progressive data processing module, a hierarchical analysis module, a multi-stage compensation output module, a data storage module and a self-diagnosis module. The multi-dimensional data acquisition module synchronously acquires cutting force, spindle dynamic state and vibration parameters; the progressive data processing module performs preprocessing, feature extraction and data fusion on the acquired parameters; the hierarchical analysis module is used for realizing wear grade evaluation through single-mode identification, multi-mode association verification and fuzzy comprehensive evaluation; the multi-stage compensation output module outputs a state snapshot and a graded compensation instruction and predicts the service life of the cutter; the data storage module adopts local and cloud dual backup to store full-process data; and the self-diagnosis module detects the state of each module and pushes a diagnosis report when the state is abnormal. According to the system, the wear evaluation precision and real-time performance are improved, the misjudgment rate is reduced, and the machining stability is guaranteed.

The invention relates to the technical field of numerical control machining, in particular to a numerical control tool wear online monitoring method and system based on multi-sensor fusion, and solves the problems that the monitoring precision is reduced and the false alarm rate is high due to working condition parameter drift. The technical bottlenecks that the discrimination adaptability of a fixed threshold value is insufficient and an offline training model cannot be continuously optimized are overcome. According to the method, the tool wear identification accuracy is effectively improved, the false alarm occurrence frequency is reduced, the residual life prediction reliability is improved, the intelligent monitoring level and the production stability in the numerical control machining process are improved, a reliable technical means is provided for precise management and preventive maintenance of the tool in the whole life cycle, and the method is suitable for popularization and application. Through a dual synchronization mechanism of unifying timestamps of a hardware clock and triggering angles of a main shaft encoder, accurate alignment of multi-sensor data with different sampling frequencies in a time axis and an angle domain is ensured.

The invention relates to the technical field of data processing, in particular to an AI large model multivariate data fusion method and system based on multiple modalities, and the method comprises the steps: collecting multi-source data; performing classification abnormity judgment; abnormal type identification; calculating severity; switching fusion modes; and outputting an alarm. According to the method, parameters such as the spindle temperature, the rotating speed fluctuation rate, the vibration acceleration, the current load, the acoustic emission energy spectrum peak value, the cutting force variance and the cooling liquid metal particle concentration are acquired and fused step by step in multiple dimensions in the machining process of the numerical control machine tool, so that the running state of the machine tool is dynamically sensed from multiple angles; furthermore, the physical essence of tool wear and material peeling is revealed through the coupling relationship between the cutting force variance and the metal particle concentration, and meanwhile, the dynamic stability change of the machine tool structure is captured by combining the spindle rotating speed fluctuation ratio; the problem that state monitoring is not accurate in the numerical control machining process due to data isolation and fusion strategy staticization is effectively solved.

The application discloses a kind of visual-based end mill flank wear detection method and its image acquisition device, method includes fixing tool on the tool holder of image acquisition device, and the wear area of tool is observed by microscope of image acquisition device and S is photographed End mill flank wear picture under multiple shooting angles;Contrast the topography of tool flank face in the picture shot before and after wear, measure the wear of tool flank face in the picture shot, obtain S group of tool wear data under different shooting angles;Establish the mathematical model of tool wear and shooting angle, solve the only fitting function describing the change of tool wear with shooting angle;Draw the image of fitting function y (x) in the shooting range K, solve image peak value.The maximum wear of tool can be obtained simply, intuitively and accurately by the application.Under the premise of image acquisition automation, automatic measurement of tool wear can be realized, and has high engineering application value.

The invention discloses a numerical control machine tool machining state evaluation method based on multi-source sensor data, and particularly relates to the field of regulation and evaluation, which comprises the following steps: acquiring and preprocessing multi-source heterogeneous data, acquiring physical sensor signals, CNC internal parameters, in-situ measurement and process parameters, and performing fusion, synchronization, purification and dynamic windowing processing; carrying out deep fusion on the features, dividing modals according to the features, carrying out exclusive coding, GRU time sequence modeling and cross attention interaction, and carrying out dimension reduction quantization to obtain core features; carrying out uncertainty quantitative evaluation, constructing an evidence deep learning model, and outputting state values and uncertainty of flutter and tool wear; the result is applied, and the reliability is improved through multi-protocol cross-end transmission, based on state and certainty degree grading decision, and in combination with closed-loop optimization and fault guarantee; the method solves the problems of asynchronous multi-source data, shallow fusion and no confidence degree of evaluation, adapts to multiple working conditions, improves the evaluation precision, and supports intelligent control.