12732 results about "Numerical control" 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 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 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 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 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 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 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 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 relates to the technical field of numerical control machining, in particular to a CAM post-optimization method and system for geometric error compensation of a multi-axis linkage numerical control machine tool. The method comprises the following steps: firstly, carrying out kinematics analysis and geometric error modeling on a target machine tool, and deploying the target machine tool in a CAM post-optimization module; then actual state parameters of the target machine tool in the machining process are collected in real time through a laser interferometer; a motion instruction of a target machine tool and a detection result of a laser interferometer are synchronously obtained through a CAM post-optimization module, and then geometric errors of a linear axis and a rotating axis in the operation process of the target machine tool are calculated in real time through a kinematic model and a geometric error model. According to the method, different linear axis compensation modes are adopted for different instructions; and a cutter shaft attitude vector error and a cutter cutting point position error are calculated in combination with inverse kinematics, and are corrected step by step. The problem that the error compensation precision of a controller in an existing numerical control machine tool is insufficient can be solved.

The invention discloses a numerical control machining equipment state monitoring system based on big data analysis, and relates to the technical field of intelligent manufacturing. The method is used for solving the problems of correlation analysis and real-time feedback between tool wear and machining quality in the machining process. A time sequence correlation feature vector is extracted through a vibration signal, an acoustic emission signal and main shaft axial micro-displacement data, a machining quality degradation index is calculated in combination with a nonlinear regression model, and the machining quality is monitored in real time. And on the basis of adaptive feature weight adjustment of the types of the processing materials, feature fusion during processing of different materials is enhanced, and the precision of quality evaluation is improved. And establishing a nonlinear mapping relation between the tool wear and the surface roughness by utilizing a gradient lifting tree model, and realizing accurate prediction of the tool wear and the machining quality. And finally, through a closed-loop dynamic adjustment mechanism, the cutting speed and the feeding amount are automatically adjusted according to the machining quality degradation index and the fault positioning result, and the stability of the machining process and the product quality are ensured.

The invention relates to the technical field of intelligent production scheduling, and discloses a numerical control machining order priority ranking and production scheduling intelligent management system, which comprises a data acquisition and time interval division module, which is used for acquiring production basic data and equipment resource data and dividing a window locking time interval and a rolling time interval; the priority evaluation module is used for calculating an order priority score in combination with a preset weight; the initial schedule generation module is used for constructing a resource process diagram and generating an initial feasible schedule; the influence domain generation module is used for identifying a process set influenced by the production event and forming an influence domain; the scheduling optimization module is used for performing optimization in the influence domain based on the priority score and the cost matrix to generate a new schedule; and the execution issuing module is used for freezing the window locking period schedule and issuing a process execution list. According to the invention, intelligent sorting of production tasks and efficient and stable execution of feasible scheduling in a numerical control machining environment are realized.

The invention discloses a numerical control machine tool wear monitoring and intelligent compensation control method based on deep learning, and relates to the field of numerical control machine tool wear monitoring. Multi-source data are collected through a sensor, and are preprocessed and fused through edge calculation; deep features are extracted and enhanced through an improved network, the abrasion state is evaluated through a mixed expert model, and life is predicted in combination with survival analysis; based on enhanced transfer learning, generating an intelligent compensation strategy according to a processing target, and executing the intelligent compensation strategy after verification in a virtual environment; a closed-loop system is constructed, all modules are acquired, fed back and optimized, and full-process intelligent management of functions such as knowledge graph early warning and multi-machine-tool cooperation is integrated. According to the method, the wear monitoring accuracy is improved, and early wear is accurately recognized; machining parameters are intelligently compensated and optimized, precision is improved, and the service life of a tool is prolonged; closed-loop control and multiple technologies are fused, the response time is shortened, and shutdown is reduced; the operation efficiency and reliability of the numerical control machine tool are improved, and the cost is reduced.

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 relates to the field of tool wear in the numerical control machining process, in particular to a tool wear monitoring and predicting method which comprises the steps of signal collection, multi-domain feature extraction, multi-modal feature fusion, tool wear prediction model construction, real-time monitoring and wear prediction. And a multi-signal fusion monitoring and prediction model is constructed, so that the precision of tool wear monitoring and the reliability of prediction are remarkably improved, and the defects of a traditional method are effectively overcome.

The invention relates to a numerical control equipment cooperative scheduling method and system based on a heterogeneous computing architecture, and aims to solve the core problems of multi-brand numerical control equipment protocol isomerism, weak digital twin generalization ability, low multi-objective optimization efficiency, quantum era communication security and the like in the industrial internet. Real-time analysis and unified format conversion of 18 industrial protocols are realized through a PDSA-Chip hardware-level protocol conversion module; in combination with a transfer learning digital twinning technology, cross-brand equipment parameter self-calibration is completed within 72 hours; a quantum annealing and NSGA-III collaborative optimization algorithm is adopted, the machining parameter optimization period is shortened to 1 / 3 of that of a traditional method, energy consumption, precision and the service life of a tool are dynamically balanced, and the service life of the tool is prolonged by 37% in a high-load scene; an NTRU lattice encryption and chaos confusion fused anti-quantum security communication system is constructed, the G code confusion degree is larger than 98%, and SL3-level quantum attacks are defended.

The invention discloses a numerical control machining intelligent monitoring method and system based on industrial Internet of Things, and relates to the technical field of industrial Internet of Things and intelligent manufacturing. The method mainly comprises the steps of multi-source data real-time acquisition, edge side data preprocessing, cloud intelligent analysis and diagnosis, digital twin closed-loop optimization and visualization and feedback control. According to the invention, visualization and feedback control are adopted. By introducing the digital twin-driven real-time process optimization and multi-modal data fusion intelligent early warning mechanism, the machining precision and efficiency are remarkably improved.

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

The invention relates to the technical field of intelligent manufacturing equipment fault diagnosis, and particularly discloses a Meta-Transform driven multi-working-condition equipment bearing fault diagnosis method and system. The method aims at bearing fatigue damage risks caused by dynamic adjustment of technological parameters of a numerical control machine tool in the aerospace manufacturing process and challenges such as feature distribution offset and fault sample scarcity caused by variable working conditions. The diagnosis system is constructed through three core modules. The method comprises the following steps: firstly, reconstructing an original bearing signal into a multi-scale time-frequency feature space by adopting continuous wavelet transform; then designing a causal Transform architecture with a strict lower triangle attention mask, and realizing feature extraction and classification according to a physical causal law of fault propagation; and finally, integrating the mechanisms into a model-independent element learning framework, and realizing cross-working-condition rapid self-adaption through a self-adaption gradient pruning strategy. The bearing fault diagnosis accuracy under the condition of few samples is improved, the interpretability and generalization ability of the model are enhanced, and the industrial application practicability of bearing fault diagnosis is improved.

The invention relates to the technical field of numerical control machine tool operation, in particular to a machining data acquisition method and system applied to a numerical control machine tool, and the method comprises the steps: obtaining a machining precision error value according to the difference between cutting parameters of different dimensions and initial cutting parameters; according to the difference change condition of the cutting parameter and the initial cutting parameter, a tool wear characteristic value is obtained; obtaining a tool wear influence value according to the tool wear characteristic value and the cutting force change influence value; obtaining a machining precision error influence value according to the tool wear influence value and the machining precision error value; acquiring an acquisition frequency regulation factor at the current moment according to the variation trend of the processing precision error influence values of different target error historical time periods; and acquiring the boring and milling data acquisition frequency at the current moment based on the acquisition frequency regulation factor. The boring and milling precision of the numerical control machine tool is improved.

The invention relates to the technical field of numerical control, in particular to a control method and system of numerical control equipment and a medium. The method comprises the following steps: collecting workpiece surface temperature distribution data and obtaining a workpiece CAD model; importing the workpiece surface temperature distribution data into a workpiece CAD model for internal temperature conduction inversion to obtain a first workpiece internal temperature distribution diagram; identifying a non-key area of the workpiece; performing a pre-cutting test on the non-key area of the workpiece to collect the temperature in the cutting process; performing nonlinear thermal expansion modeling on the workpiece based on the cutting process temperature to obtain a workpiece thermal expansion characteristic model; and an internal temperature distribution diagram of the second workpiece is obtained, and the internal temperature distribution diagram of the second workpiece represents the initial thermal state of the workpiece before formal machining. The numerical control machining precision and the cooling system efficiency are improved, the machining cost and resource consumption are reduced, and the machining adaptability, flexibility and stability are enhanced.

The invention relates to the technical field of image processing, and discloses a visual positioning system and method for a complex box five-axis numerical control comprehensive processing machine. The system comprises a calibration module for collecting multi-angle images and utilizing mark point calibration coordinates to obtain a parameter mapping table, an extraction module for carrying out edge detection and contour extraction splicing features to obtain a three-dimensional data set, a marking module for calculating the actual position of a box and marking inner cavity machining areas, a recording module for analyzing and determining cutting parameters and deformation of all the areas, and a display module for displaying the cutting parameters and the deformation of all the areas. The processing module generates an obstacle avoidance cutter path and a thin-wall area machining strategy, and the comparison module monitors and compares actual effects to dynamically adjust parameters to obtain compensation data. According to the five-axis numerical control comprehensive processing machine, high-precision visual positioning and intelligent processing parameter optimization of the complex box on the five-axis numerical control comprehensive processing machine are realized, and the processing precision, efficiency and reliability of the complex box are improved.

The invention belongs to the related technical field of numerical control machining, and discloses a tool wear real-time monitoring and prediction method and system based on multi-sensor information fusion, and the method comprises the steps: S1, collecting a monitoring signal and a tool wear image in a machining experiment process; s2, carrying out wear region segmentation on the tool wear image by adopting a wear region segmentation model, and calculating to obtain a real wear value; s3, establishing a monitoring training sample set by using the monitoring signal and the real wear value, and training the wear monitoring model; establishing a prediction training sample set by using the sequence of the real wear values, and training a wear prediction model; s4, the real-time monitoring signal and the wear monitoring model are used for monitoring the wear value of the tool in real time; and performing online prediction on the wear value of the cutter by using the wear prediction model. According to the invention, the wear value of the cutter can be monitored in real time according to the real-time monitoring signal, online wear value prediction can be realized, and the production efficiency can be greatly improved.