21714 results about "Machine tool" patented technology

The invention provides a numerical control machine tool wear automatic detection and compensation method based on artificial intelligence, and the method comprises the steps: collecting the cutting force data of a high-curvature region in real time through multi-sensor fusion, and obtaining the cutting force fluctuation characteristics; cutting temperature data of the high-curvature area are monitored and obtained, the cutting temperature change rate is extracted, whether the temperature exceeds a preset threshold value or not is judged, and if yes, an alarm mechanism is triggered, and cutting parameters are adjusted; predicting the tool wear rate in combination with the co-evolution relationship between wear and temperature, the online monitoring data and the processed time, and generating a wear prediction curve in a preset time period; and performing trend analysis and feature extraction on the wear prediction curve to obtain wear parameter changes of the cutter in a preset time, and if the prediction curve shows that the wear parameter changes at a certain time point in the future exceed a preset critical value, adjusting the cutting parameters and generating a target cutting parameter combination.

The invention relates to the technical field of curved surface machining control, and discloses an intelligent curved surface machining method and system based on a CNC cutting machine tool, and the method comprises the steps: carrying out the laser scanning of a to-be-machined workpiece, obtaining the three-dimensional geometric data of the workpiece, activating a target clamping device, and generating an initial clamping scheme; the target clamping device is controlled to execute multi-area pressure gradient clamping operation, and target position data and clamping pressure distribution data are obtained; performing multi-field coupling dynamic analysis to generate a processing optimization parameter set; executing segmented continuous Hamiltonian path planning and self-adaptive lattice point reconstruction according to the machining optimization parameter set, and generating a tool path data set; according to the method, it is ensured that all the surfaces of the polygon prism workpiece are evenly stressed, the problem of machining deviation caused by edge stress concentration in a traditional method is solved, and the overall machining quality of the workpiece is improved.

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 numerical control machine tools, in particular to a five-axis machining center real-time thermal error compensation system based on digital twinning and a medium. Firstly, a data acquisition unit is used for acquiring physical actual measurement data in real time; then, the digital twin processing unit constructs a model set used for representing the thermal dynamic behavior of the five-axis machining center, and the model set is composed of a plurality of virtual thermal model members; thirdly, fusing physical measured data into the model set through a data assimilation algorithm to dynamically correct the state of each model member and predict the thermal error of the machine tool; the prospective compensation decision-making unit receives the thermal error prediction result output by the digital twinning processing unit and generates a prospective compensation instruction; and finally, the compensation execution unit issues the prospective compensation instruction to a numerical control system of the five-axis machining center for real-time adjustment. The control precision of real-time thermal error compensation of the five-axis machining center can be improved.

The invention relates to the technical field of intelligent manufacturing and numerical control machining control, and discloses a machine tool dynamic characteristic sensing and intelligent machining control method and system based on a knowledge graph and a large model, and the method comprises the following steps: extracting frequency domain parameters through a vibration sensor, obtaining vibration displacement in combination with laser displacement, and comparing the vibration displacement with modal data to construct a graph; processing parameters and displacement are synchronously sampled, time sequence characteristics are extracted to generate tensors, dynamic characteristics are predicted and corrected, and frequency response adjustment rotating speed is matched to generate optimized track control parameters which are converted into G code instructions. According to the method, a dynamic characteristic map is constructed by fusing vibration signals and displacement, a sliding window synchronizes processing parameters and vibration data, LSTM extracts joint characteristics, GRU predicts rigidity and damping ratio, an attention mechanism dynamically corrects weight, characteristic coupling analysis and prediction precision is improved, nonlinear modeling captures dominant frequency offset and harmonic distribution, response speed is enhanced, and the method has the advantages of being high in precision and high in precision. Cutting vibration is inhibited, and the process stability is guaranteed.

The invention relates to the technical field of machine tool casting detection, and discloses an automatic detection system for surface defects of machine tool precision castings. The system comprises a surface information acquisition core module, a first defect identification core module, a second defect identification core module and a defect type fusion core module. The surface information acquisition module is used for synchronously acquiring real-time optical images and process parameter data in production aiming at the surface of the casting part, and constructing a defect diagnosis characteristic spectrum and an auxiliary text according to the real-time optical images and the process parameter data; the first defect recognition module inputs the atlas and the auxiliary text into a pre-training double-flow convolutional neural network to generate a first classification result of defect types; a second defect identification module extracts defect mechanism characteristic values from the atlas and matches the defect mechanism characteristic values with a pre-stored defect mechanism knowledge base to obtain a second classification result; and the defect type fusion module fuses the two types of results to determine a target defect type. The system solves the problems of single detection information and identification deviation in the prior art, improves the detection accuracy and real-time performance, and meets the requirements of different production scenes.

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 relates to the technical field of collaborative optimization, in particular to a geometric parameter collaborative optimization method of a taper hole machining cutter, which comprises the following steps: by constructing a high-fidelity digital twin model, integrating multi-physics field coupling and machine tool dynamic characteristics based on a finite element method, generating a geometric parameter-performance data mapping set and training and calculating an agent model; outputting a Pareto solution set through multi-target global optimization; and constructing a constraint range based on the solution set, and calling a digital twin model to carry out local optimization to obtain an optimal geometric parameter combination. The method comprises a self-correction mechanism: correcting a material constitutive relation and a friction coefficient through experimental data; staged adaptive learning, NSGA-II and DBSCAN clustering are adopted, and the efficiency is optimized along with the method; and a Pareto stability index and transfer learning are introduced, so that the result robustness and the cross-task reusability are improved. According to the method, high-precision and high-efficiency geometric parameter collaborative optimization of the taper hole machining tool can be realized.

The invention discloses a Bayesian learning and piezoelectric ceramic driving numerical control machine tool thermal error compensation system and method. According to the system, a distributed temperature sensor array is arranged in heat sensitive areas such as a machine tool spindle, a ball screw, a guide rail and a bearing seat, whole-field temperature information is collected in combination with a thermal infrared imager, and multi-source thermal field sensing is achieved; meanwhile, a laser interferometer and a capacitive displacement sensor are used for constructing a dynamic pose monitoring network. The intelligent decision-making unit integrates a Bayesian online learning engine, fuses a physical model and a data driving model, dynamically predicts a thermal error and generates a compensation instruction. And the piezoelectric execution mechanism carries out nonlinear pre-compensation on a driving signal through a three-section Prantl-Ishlinskii hysteresis inverse model according to the instruction, so that high-precision pose adjustment is realized. The thermal error compensation precision is remarkably improved, the adaptability of the system to complex working conditions is enhanced, the service life of equipment is prolonged, and the method is suitable for various numerical control machine tools.

The invention discloses a precision compensation method of a composite numerical control machine tool, and particularly relates to the technical field of precision control of the numerical control machine tool, which comprises the following steps: synchronously acquiring temperature, vibration and force information under the driving of a unified clock through multiple types of sensors deployed at key nodes of the machine tool, and generating a synchronous multi-source sensing data set with aligned timestamps; inputting the data set into a multivariable coupling error model obtained through data driving training, and calculating and generating a comprehensive space volume error predicted value of a tool nose point of the tool; performing inverse calculation based on a machine tool kinematic chain model, and generating a multi-dimensional micro-compensation instruction set containing the compensation amount of each motion axis and the sequential relationship; and finally, dynamically writing the instruction set into a servo control ring of the numerical control system through a real-time data interface, and driving each motion shaft to execute synchronous compensation motion. According to the method, comprehensive compensation of multi-physics field coupling errors such as temperature, vibration and force load is achieved, and the machining precision and stability of the numerical control machine tool under complex working conditions 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 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 hardware part numerical control machining real-time error control system which comprises the steps that historical machining information of a target numerical control machine tool is collected, error coupling relation analysis is conducted on the basis of the data, and therefore a coupling physical error model is constructed. Meanwhile, a processing error prediction model is established by using an LSTM network, and training is carried out by using historical data, so that the trained prediction model is associated with a physical model to form a composite error analysis model. Processing monitoring information is obtained through a machine tool sensor array, and after preprocessing, the processing monitoring information is input into the composite model for error prediction; and on the basis of a prediction result, an advanced compensation amount is generated by adopting an improved GA algorithm to control the machine tool, and whether self-correction needs to be performed on the composite model is judged according to a correction effect, so that efficient and accurate error control is realized. And processing errors and machine tool part maintenance are associated through historical maintenance information to form a maintenance knowledge graph, so that the state of the numerical control machine tool can be known, and maintenance early warning is realized.

The invention discloses a dust removal system and method for a laser cutting machine and the laser cutting machine, and belongs to the field of laser machining equipment. According to the technical scheme, the dust removal system of the laser cutting machine comprises a side dust removal device capable of being arranged on the side of a machine body of the cutting machine, the side dust removal device comprises an X-axis air draft pipeline, the X-axis air draft pipeline is connected with an X-axis fan, and a follow-up component is arranged on the X-axis air draft pipeline; the upper dust removal device can be arranged on a cross beam of the cutting machine, the upper dust removal device comprises a Y-axis air draft pipeline, the Y-axis air draft pipeline is divided into at least two air draft areas, and each air draft area is connected to the transition pipeline through a valve and is communicated with the follow-up component. According to the scheme, the upper dust removal device capable of following in the Y-axis direction is arranged on the cross beam of the laser cutting machine, air draft and dust removal are directly carried out at the position, the dust removal position is located above the machining position of the laser cutting machine at any time, the dust removal position is closer to the machining position, and the dust removal effect is greatly improved.

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

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 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.

The invention discloses a metal grinding machine tool grinding wheel wear compensation type intelligent control system, and relates to the technical field of mechanical manufacturing automation, and the system comprises a contact type reference detection module which is used for obtaining the initial contour and reference wear loss of a grinding wheel through discrete point measurement in a non-machining state; the multi-parameter non-contact real-time detection module is used for collecting acoustic emission, vibration and spindle current signals in the grinding process through three types of sensors; the grinding environment sensing module collects the temperature of a grinding area and the cutting fluid concentration; the intelligent control unit receives reference data and an environment signal, performs deep learning on a wear loss prediction model after multi-source data fusion, performs periodic reference calibration, analyzes the dynamic wear loss, and synchronously transmits the data to the data storage and tracing module; the executing mechanism adjusts grinding wheel feed amount compensation according to the dynamic abrasion loss; and the data storage and tracing module stores full-process data in real time. According to the system, high-precision and real-time wear compensation is achieved, the machining precision and consistency are improved, and high-end manufacturing requirements are met.

The invention relates to the technical field of numerical control machining monitoring, in particular to a tooth deviation high-precision monitoring system in a tooth cutting machining process, which comprises an online measurement module, a dynamic compensation module, a deviation calculation module and a result output module which are in signal connection with a numerical control system of a tooth cutting machine tool, the online measurement module, the dynamic compensation module, the deviation calculation module and the result output module are integrated on the processing element; the processing element is used for obtaining workpiece design parameters and constructing a theoretical workpiece model, the online measurement module is used for collecting tooth surface point cloud data in real time in the machining process, the dynamic compensation module is used for correcting vibration errors and temperature errors of the point cloud data, and the deviation calculation module is used for quantifying tooth surface deviation values. And the result output module is used for generating an early warning signal and a dynamic correction instruction and executing system self-calibration. The device and a numerical control system form a closed loop of machining, monitoring, correction and remachining, and finally high-precision real-time monitoring and dynamic correction of tooth deviation are achieved.

The invention provides a collaborative tool servo diamond turning method and system based on track pre-filtering, and the method comprises the steps: S1, integrating a fast tool shaft to a machine tool shaft in the cutting depth direction, and constructing a master-slave collaborative tool servo system; wherein the machine tool shaft serves as a main servo shaft of the master-slave cooperative tool servo system, and a low-frequency tool path obtained after reference tool path pre-filtering is executed; the fast cutter shaft is used as a slave servo shaft of the master-slave collaborative cutter servo system, and the deviation between the actual displacement of the master servo shaft and the reference cutter path is compensated in real time; s2, implementing a collaborative tool servo machining process based on a master-slave collaborative tool servo system; and S3, after machining is completed, surface contour data are obtained, and quantitative evaluation is conducted on a machining result through surface shape errors and surface roughness indexes.

The invention relates to the technical field of machine tool equipment, in particular to a numerical control grinding machine product quality real-time monitoring optimization method based on the Internet of Things. The method comprises the following steps: acquiring multi-node real-time operation data of the numerical control grinding machine; working state parameters of key components of the grinding machine are counted according to the multi-node real-time operation data; the product machining quality fluctuation trend is determined based on the multi-node real-time operation data and the working state parameters of the key components of the grinding machine; according to the product machining quality fluctuation trend, the cooperative change rule of the grinding machine tailstock jacking force is detected; and determining the geometric accuracy limitation trend of the workpiece according to the cooperative change rule of the jacking force of the tailstock of the grinding machine. According to the method, the control parameter adjustment behavior, the system response state and the quality detection result of the numerical control grinding machine are associated and integrated, and the tracking and effect evaluation of the key control behavior are realized by constructing a historical contrast system of the adjustment behavior and the response data.

The invention relates to the technical field of machine tool cutter wear monitoring, and particularly provides an integrated multi-dimensional data monitoring system and a monitoring method.The integrated multi-dimensional data monitoring system comprises a multi-dimensional data acquisition module which can acquire multi-dimensional data of the working state of a cutter in real time; the feature information layer and the intelligent analysis layer comprise a multi-source data fusion module, an abnormal state recognition module, a residual life prediction module and a self-adaptive learning module; the decision execution layer comprises an early warning feedback module, a parameter optimization suggestion module and an intervention control module; the knowledge base system comprises a case base module, a rule base module and a model base module, different physical quantities are collected through the multi-dimensional data collection module, full-dimensional sensing of the state of the cutter is achieved in combination with machining features, material information and environment data, multi-sensor collection, physical model construction, feature information integration and intelligent algorithm analysis are combined, and the machining accuracy of the cutter is improved. And comprehensive, real-time and accurate monitoring of various abnormal states of the machine tool cutter is realized.

The invention discloses a milling path automatic generation method based on reinforcement learning, and relates to the technical field of machine tool machining, and the milling path automatic generation method comprises the following steps: step 1, defining action output; step 2, defining state input including a cutter state, a global observation space and a local observation space; 3, defining a reward function; value feedback given to the intelligent agent by the environment according to the current state and action is determined through the reward function; 4, setting deep neural networks including a feature extractor, a strategy network and a value network; 5, constructing a process autonomous generation model and a machining process world model; step 6, strategy training; step 7, outputting a strategy, and autonomously completing a machining process flow; by means of the method, the problems that in the prior art, manual experience dependence is too heavy, local optimization limitation is remarkable, and physical simulation efficiency is insufficient are solved.

The invention relates to the technical field of machine tool fault diagnosis and maintenance, and discloses a machine tool fault predictive maintenance method based on vibration analysis, which comprises the following steps: collecting vibration, temperature and acoustic emission signals and machine tool working condition parameters through a multi-modal sensor, extracting multi-domain features after preprocessing the vibration signals, and combining the working condition parameters through feature fusion and dimension reduction to obtain a machine tool fault predictive maintenance result. And establishing a fault classification model by using transfer learning, and performing hierarchical optimization. Model parameters are updated in real time based on an online learning mechanism, a fault early warning agent model is constructed to predict fault probability distribution, a dynamic threshold strategy is designed to avoid false report and missing report, and finally, related models and strategies are integrated to edge computing equipment. According to the method, multi-source data are integrated, multiple advanced algorithms are applied, machine tool faults can be accurately predicted, real-time monitoring and maintenance decision output are achieved, the machine tool operation reliability is improved, and the maintenance cost is reduced.

The invention relates to the technical field of cycloidal gear machining control, in particular to a speed reducer cycloidal gear efficient machining method and device based on a machining machine tool, and the method comprises the steps that machining data in the cycloidal gear machining process are obtained and preprocessed; preliminarily judging whether a flutter phenomenon occurs or not by analyzing the periodic change of the cutting depth and the periodic fluctuation of the cutting force under different temperature changes; vibration, current and cutting force data are combined to evaluate the chatter occurrence possibility; classifying the processing data through clustering analysis by utilizing the chatter occurrence possibility so as to screen out high-quality processing data and flaw processing data; and a dragonfly optimization algorithm is adopted, initial position distribution of the algorithm is optimized through the high-quality machining data and the defect machining data, and the optimal cutting speed is solved to serve as the cutting speed in the current cycloidal gear machining process. According to the method, the flutter phenomenon is comprehensively analyzed through the multi-dimensional machining data, so that the overall machining efficiency in the current cycloidal gear machining process is improved.

The invention provides a five-axis series-parallel numerical control machine tool machining process virtual monitoring simulation method and system based on digital twinning, and the method comprises the steps: building an information interaction mechanism between a physical machine tool and a digital twinning model, enabling a server side to collect sensor data, and transmitting the real-time data to a client side through a communication protocol; the client processes the data and drives the digital twin model. And aiming at potential errors of twin system forecast, tool setting error compensation can be carried out in real time, so that the machining precision is improved. Meanwhile, the system supports an offline simulation function, combines an inverse kinematics algorithm and an NC code, fuses a material removal model, simulates a machining process, evaluates machining performance and errors, and provides an optimization basis for machining strategies of workpieces with different geometrical shapes and materials. According to the method, bidirectional interaction between the physical entity and the virtual model is achieved, the real-time monitoring and visualization capability of the machining process is enhanced, a reliable simulation evaluation and optimization means is provided for machining of complex parts, and the method has wide industrial application prospects.

The invention relates to the technical field of precision machining, and particularly discloses a complex curved surface precision machining method based on tool path optimization, which comprises the following steps: S1, acquiring a geometric model of a curved surface to be machined and tool kinetic parameters; s2, the optimal machining step length, the cutter roll angle and the feeding speed are calculated in real time according to the geometric features of the curved surface and the dynamic parameters of the cutter; s3, generating a self-adaptive tool path based on the dynamic optimization parameters in the step S2; s4, synchronously monitoring the cutting load in the machining process, and dynamically correcting cutter path parameters according to load feedback; and S5, outputting the optimized tool path instruction to the numerical control machine tool to execute machining. By calculating the Gaussian curvature and the average curvature of the curved surface in real time, dynamically adjusting the machining step length and the feeding speed, and automatically reducing the step length and reducing the feeding speed in a curvature sudden change area, the residual height of a steep area is controlled within a precise standard range, and the surface roughness is reduced by more than 30% compared with that of a traditional method; and the consistency of machining precision of each area of the complex curved surface is ensured.

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 relates to a numerical control lathe turning process energy consumption prediction system, which belongs to the technical field of data processing and prediction, and comprises a multi-source data acquisition unit, a steady-state power prediction unit, a numerical control lathe turning process energy consumption prediction unit and a numerical control lathe turning process energy consumption prediction unit, according to the invention, the prediction precision under an abnormal working condition is improved, the limitation of an energy consumption gradual change hypothesis is broken through by fusing transient vibration information of a physical world, and the prediction precision is improved. A new abrupt change sensing type energy consumption prediction normal form is created; the conversion from passive response to active predictive maintenance is realized, and the risks of workpiece scrapping and accidental shutdown are greatly reduced.

The invention discloses an industrial equipment health state analysis method, and relates to the technical field of industrial equipment health monitoring, and the method comprises the steps: obtaining vibration data, lubricating oil pollution image data and real-time motor current data of an automobile factory punching machine; generating a dynamic weight coefficient according to the ratio of the real-time motor current data to the rated current of the equipment; performing weighted summation to calculate a real-time health score; constructing a wear-fatigue coupling model to calculate and output an alarm prediction L value; and when the alarm prediction L value exceeds a set threshold value, generating an equipment maintenance instruction and adjusting equipment operation parameters. According to the method, through multi-physics field coupling modeling, dynamic weight distribution and closed-loop self-optimization mechanisms, the precision and real-time performance problems of equipment health state analysis in an automobile manufacturing scene are solved, the method is particularly suitable for high-load equipment such as a punching machine tool and a welding robot, and compared with a traditional method, the comprehensive operation and maintenance cost is reduced, and the non-planed downtime is shortened.