19086 results about "Numerical control" patented technology

The invention provides an intelligent maintenance decision-making method and system for a numerical control machine tool based on digital twinning, and relates to the technical field of equipment maintenance, and the method comprises the steps: obtaining the structural data and simulation precision requirements of the numerical control machine tool, determining the optimal grid size and distribution parameters of a finite element model, obtaining operation conditions, and adding the operation conditions to a multi-physics coupling modeling module, generating a machine tool digital twinborn model, fusing the machine tool digital twinborn model with the physical field, generating a machine tool operation degradation model, and optimizing to obtain a self-evolution digital twinborn model; obtaining machine tool operation parameters, performing preprocessing to obtain multi-modal perception data, extracting multi-view potential features, determining potential correlation features, and obtaining a machine tool health state evaluation result; the method comprises the following steps: generating a multi-level fault prediction result, determining a candidate maintenance scheme, adding the candidate maintenance scheme and the multi-level fault prediction result into a knowledge graph construction module, generating a semantic association network, and solving to obtain an optimal maintenance decision in combination with a multi-objective optimization algorithm.

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 numerical control machine tools, and discloses a five-axis high-precision numerical control machine tool control method, device and equipment and a storage medium, and the method comprises the steps: collecting temperature data and displacement data of a five-axis high-precision numerical control machine tool, and carrying out state space modeling to obtain a machine tool motion state model; stress data and thermal error influence factors of the feed shaft are calculated; feeding shaft stress data and the thermal error influence factors are input into the compensation model, real-time compensation parameters are calculated, compensation control is conducted on the machine tool feeding shaft, and a compensation control result is obtained; according to the compensation control result, an initial machining track optimization model is constructed, and optimized machining parameters are generated; performing segmented machining control based on the optimized machining parameters to obtain real-time machining data; the initial machining track optimization model is optimized based on the real-time machining data, a target machining track optimization model is obtained, and the dynamic response performance and the control precision of the five-axis high-precision numerical control machine tool are improved through the method.

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

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

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 discloses a numerical control machine tool operation control system and a double-station sawing and drilling integrated machine tool, and relates to the technical field of numerical control machine tool operation. A multi-axis linkage state, a gas cylinder stress state, vibration frequency, cutting force and deformation data are acquired in real time through a data acquisition module; the dynamic modeling module constructs a digital twinborn model by using finite element analysis and real-time sensing data, and accurately simulates stress dynamic change and deformation trend; the intelligent error compensation module dynamically evaluates the multi-axis linkage precision by analyzing the machining data, generates an error compensation instruction and optimizes the operation parameters of the machine tool; the closed-loop feedback control module adjusts the cutting speed, the feeding depth and the clamping force in real time through a fuzzy logic algorithm, a closed-loop adjusting mechanism is formed, the machining precision and stability are greatly improved, the machining error and the workpiece surface damage risk are reduced, and the machining quality and efficiency of complex workpieces are effectively improved.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

The invention relates to the technical field of numerical control machine tools, and discloses a self-adaptive error compensation method and system for a high-precision numerical control machine tool, and the method comprises the steps: carrying out the mathematical modeling of a multi-dimensional error of a five-axis numerical control machine tool, and obtaining a thermal deformation displacement vector, a friction force vector and a path deviation vector; inputting into a multi-dimensional expansion state observer for error observation to obtain thermal deformation, a friction force value and a path error value; performing trust domain parameter optimization to obtain a thermal deformation compensation gain matrix, a friction force pre-compensation coefficient matrix and a path error compensation coefficient matrix; performing three-stage series compensation calculation to obtain a compensation position instruction, a compensation speed instruction and a compensation acceleration instruction; and track reconstruction operation is carried out to generate a position instruction sequence, a speed instruction sequence and an acceleration instruction sequence of each axis, so that self-adaptive error compensation of the high-precision numerical control machine tool is realized, and the machining precision of the high-precision numerical control machine tool is improved.

The present invention relates to the technical field of drilling machines. Disclosed is a numerical control vertical drilling machine having an automatic chip removal function. The numerical control vertical drilling machine comprises a console. A workbench is mounted at the upper end of the console; a vertical plate is connected to the upper end of the console, and a moving frame is slidably connected to the side wall of one side of the vertical plate; a lifting / lowering assembly for driving the moving frame to move in a vertical direction is mounted on the vertical plate; a movable plate is slidably connected into the moving frame, and a moving assembly used for driving the movable plate to move horizontally is mounted in the moving frame; a machining head is mounted at the lower end of the movable plate; and a mounting bracket having an L-shaped section is connected to the side wall of one side of the console. The defects in the prior art are overcome in the present invention, and by means of mutual cooperation of structures such as the console, the workbench, the moving frame, the movable plate, the machining head, a cylinder, a moving plate, a first electric push rod, a cleaning brush, a motor, a lead screw, and a second electric push rod, chips generated after part machining can be automatically cleaned, so that the burden of cleaners is relieved, thereby improving the applicability of a device.

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.

A method of constructing a digital twin physical field of a computer numerical control machine tool with double order reduction is provided. A physical field finite element simulation sample data set in a working state sampling space has an order preliminarily reduced to sparse grid node data via an improved K Nearest Neighbor (KNN) algorithm, so that the number of point sets is kept within the real-time rendering capability range of graphic rendering software. A digital twin physical field proxy model is trained using a radial basis function (RBF) to realize reconstruction of the physical field. The proxy model is iteratively optimized using a genetic algorithm to solve the problem of deviation between a proxy physical field model and a real physical model. Finally, the proxy model is deployed to a digital twin system, and the physical field distribution is solved in real time.

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

The invention 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.

Disclosed in the present invention is a large complex ellipsoidal part machining deformation control method based on dynamic cutting planning. The method comprises: performing initial clamping on a blank part, constructing a machining coordinate system, completing reference non-contact measurement and reference surface fitting of inner and outer shape surfaces of the blank part, and completing the division of a generatrix tangent plane; on the basis of product feature decomposition, completing in the same coordinate system the comparison between each theoretical section and an actually measured section, completing cutting evaluation based on the generatrix tangent plane in a machining area, and guiding overall machining route planning and local machining route planning; on the basis of route planning and a cuttable margin, using an adjustable support device to complete part clamping based on dynamic adjustment, so as to realize stable cutting under a variable cutting thickness, and thereby solving the problem of overall deformation control of a large complex ellipsoidal part; and completing high-rib feature twist amount identification in combination with adjustable support, and completing tool path trajectory offset compensation on the basis of a relationship between a circumferential offset and an allowance, and thereby realizing precise compensation machining of a mesh after a local twist. The present invention solves the key difficulties of high-rib local twist deformation compensation and overall deformation control on the basis of ensuring the precision of the residual wall thickness during numerical control machining of a large complex thin-walled ellipsoidal part, thereby further improving the overall manufacturing reliability of a large complex ellipsoidal part.

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 invention relates to the technical field of workshop scheduling, in particular to a multi-online collaborative scheduling method and system for a flexible intelligent processing production line, and the method comprises the steps: building a numerical control parameter model for processing equipment, decomposing a processing program into basic process instruction units, combining the numerical control parameter model and the real-time operation state of the equipment to analyze the adaptation degree of the basic process instruction unit and the equipment, and generating a preliminary task allocation scheme; constructing a distributed control network among the devices, generating a local task sequence of each device based on the preliminary task allocation scheme and the adaptation degree, and obtaining a final task allocation scheme and a task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling scheme under the multi-constraint collaborative framework; and establishing a heterogeneous equipment motion cooperative control model, and performing multi-level adjustment through event-driven feedback control. According to the invention, flexible cooperative scheduling of heterogeneous equipment can be realized.

The invention belongs to the technical field of numerical control machine tool machining, and particularly discloses a numerical control machine tool machining reference point correction method and system, a terminal and a medium. Based on the three-dimensional coordinate information, the actual position of a preset reference point on the machine tool is extracted and compared with a preset theoretical reference point, and a deviation value is calculated; executing coordinate deviation aggregation analysis according to the deviation value, and determining coordinate system correction parameters including a translation vector and a rotation matrix; the corrected parameters are issued to a numerical control machine tool control system, and a current workpiece coordinate system is updated; and after the coordinate system is updated, the machining task continues to be executed, the machining state is monitored in real time, and if it is detected that the deviation exceeds a threshold value, the measurement and correction process is automatically triggered again. Automatic correction of the reference point and real-time compensation of the workpiece coordinate system can be achieved, the machining precision is improved, manual intervention is reduced, and the method is suitable for high-precision numerical control machining 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 discloses a numerical control machining path control system based on artificial intelligence, particularly relates to the field of numerical control machining, is used for solving the problems of continuity and stability of a curvature mutation area in cutter path planning, and accurately captures global and local geometric characteristics of a curved surface through a curvature constraint feature matrix and a curvature adaptive convolutional network. A machining track meeting the differential geometric continuity is generated, and the problem of path breakage of a curvature sudden change area is effectively avoided; meanwhile, energy scale criteria and curvature manifold constraints are introduced, the machining track is dynamically repaired, geometric repair and physical stability are balanced, cutting force sudden change and stress concentration are reduced, and therefore the machining reliability is enhanced; in addition, through finite element simulation of the digital twin platform and synchronous updating of servo system parameters, dynamic closed-loop matching of a control instruction and a machining state is achieved, and the control efficiency and the surface quality are further optimized. And therefore, high-precision, high-stability and high-efficiency collaborative optimization is realized in complex curved surface processing.

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

The invention discloses a numerical control lathe tool wear monitoring method and system. The method comprises the steps that numerical control lathe tool operation process data are monitored in real time; a corresponding preset operation parameter threshold value is matched according to the operation process data, and when it is monitored that the preset operation parameter threshold value is exceeded, the operation process data of the numerical control lathe tool is transmitted to an edge computing node; matching a corresponding preset operation parameter combination and finishing operation parameter screening according to the operation process data by utilizing the edge computing node; based on the screened operation parameters, key features are selected and extracted by using a feature selection algorithm, and an optimal feature set is acquired and uploaded to a cloud platform; and matching a corresponding tool wear prediction model according to the operation process data at the cloud platform, inputting the optimal feature set into the matched tool wear prediction model, obtaining a wear value, a wear degree and a life prediction result, and performing early warning decision and early warning prompt. According to the invention, efficient, accurate and real-time tool wear monitoring under complex working conditions is 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 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 relates to the field of numerical control machining, discloses a numerical control machining method and system integrating thermal expansion dynamic compensation, and aims to solve the problem that precision is out of control due to thermal deformation in high-speed cutting of motorcycle light alloy castings. The method comprises the steps that distributed temperature and displacement sensors are arranged in a key area of a workpiece, and a temperature field and thermal expansion amount are collected in real time; constructing a local thermal deformation prediction model based on a material expansion coefficient and a geometric structure, and correcting a tool path in real time through a coordinate system dynamic offset algorithm; and an on-line measurement feedback mechanism is introduced, and model parameters are adaptively optimized, so that closed-loop compensation is realized. The system comprises a sensing module, a thermal deformation modeling and compensation calculation module, a coordinate dynamic correction module, an online measurement feedback module and a parameter adaptive updating module. Through millisecond-level dynamic compensation and closed-loop self-optimization, the size precision and form and position stability of the complex thin-wall part under the high-temperature working condition are remarkably improved, the rejection rate and cooling energy consumption are reduced, and the method is suitable for a high-precision batch machining scene.

The invention is suitable for the technical field of reliability test of numerical control machine tools, and provides a ball screw pair comprehensive performance test device and method for simulating real working conditions. The device comprises a ball screw pair, a test device base, a rolling guide rail pair, a grating ruler integrated installation unit, two sets of support driving units, a loading workbench, an axial force simulation loading unit, a tested workbench and a comprehensive performance detection unit. The two sets of supporting and driving units are distributed on the test device base in a staggered alignment manner and are respectively connected with the loading workbench and the tested workbench, and synchronous driving of the loading workbench and the tested workbench is realized through synchronous control; and the axial force simulation loading unit realizes static and dynamic simulation loading of the axial force through electro-hydraulic servo control. According to the invention, a test device and technical support are provided for carrying out the reliable comprehensive performance bench test of the ball screw pair, and the problems of the ball screw pair in the aspects of high-frequency dynamic load simulation loading, comprehensive performance test and motion tracking loading are solved.

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 relates to the technical field of data processing, in particular to a high-precision numerical control machine tool operation state abnormity monitoring method and system, and the method comprises the steps: determining a plurality of initial clustering clusters when a pre-obtained target sequence is clustered through an ISODATA algorithm, and carrying out the clustering of a plurality of initial clustering clusters during the subsequent iteration clustering; taking the reliability of each sampling point in each initial clustering cluster as a weight, carrying out weighted summation on the positions of all the sampling points in the corresponding initial clustering cluster to obtain a plurality of weighted cluster centers during current clustering until a clustering result converges to obtain a plurality of clustering clusters, and carrying out clustering on the plurality of clustering clusters; and according to a comparison result of the normalized value of the distance between each sampling point in each clustering cluster and the clustering center of the corresponding clustering cluster and a preset threshold value, monitoring the abnormal operation state of the numerical control machine tool. The accuracy of monitoring the abnormal operation state of the numerical control machine tool can be improved.

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.