1886 results about "Cutting force" 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 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 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 relates to a digital twinning-based bogie numerical control machining error compensation method and system, and the method comprises the steps: constructing an initial digital twinning model comprising material characteristics and geometric constraints through fusing the historical machining data of a bogie and the structural characteristics of a workpiece; according to the real-time cutting force and temperature data dynamic correction model, time-varying stiffness parameters are updated, and a deformation prediction result is generated; identifying an error exceeding area based on the precision requirement of the bearing seat assembly surface; and the feed rate compensation amount is calculated according to the thin-wall area deformation gradient, and a pre-compensation parameter scheme used for adjusting the cutting path is generated. The method overcomes the industrial problems that a traditional compensation technology cannot dynamically suppress instantaneous deformation, multi-source error coupling is difficult to decouple, and parameter adjustment depends on trial cutting verification, and the machining precision and efficiency of the bogie are remarkably improved.

The invention relates to the technical field of program control, in particular to a servo position control method and system applied to an engraving and milling machine. The method comprises the following steps that real-time temperature data and real-time load data of lead screws of all shafts in the engraving and milling machine are obtained; collecting multi-node temperature distribution data of the engraving and milling machine; obtaining cutting force data and resonant frequency characteristic values in the engraving and milling machine; constructing a screw rod reverse clearance prediction model based on the real-time temperature data and the real-time load data of the screw rod, and generating a clearance compensation value; performing pre-compensation control before the servo position direction of each axis is changed based on the clearance compensation value to obtain a correction position instruction; and performing thermal deformation simulation on the engraving and milling machine based on the multi-node temperature distribution data to generate thermal deformation compensation amounts of all shafts. Through multi-source data fusion and intelligent control, comprehensive optimization of the servo position system of the engraving and milling machine is achieved, the machining precision, dynamic response and stability are effectively improved, and efficient and reliable operation of high-speed and high-precision machining is guaranteed.

The invention relates to the technical field of motion control, in particular to a CNC machine tool motion control system which comprises a cutting force feedback monitoring module, a multi-axis load coordination module, a spindle vibration suppression module and a dynamic path optimization module. According to the method, the cutting force fluctuation of each axle can be accurately mastered by monitoring the axle cutting force in real time and analyzing the load change, the load and the feeding speed of the axle are adjusted according to real-time feedback, machining errors caused by uneven loads are reduced, and the vibration amplitude and frequency can be calculated and the rotating speed of the main shaft can be adjusted by monitoring the vibration data of the main shaft in real time. The resonance phenomenon is effectively restrained, the machining stability is improved, meanwhile, dynamic path optimization is conducted, the dynamic response problem caused by improper path setting is avoided by evaluating the dynamic response of a machine tool and adjusting the machining path, the cutting direction and the feeding speed in real time, the machining precision and stability are remarkably improved, and the machining efficiency is improved. And the complex machining environment adaptability of the machine tool is improved.

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 provides a mobile phone middle frame CNC machining optimization method based on artificial intelligence. The mobile phone middle frame CNC machining optimization method comprises the steps that the surface of a mobile phone middle frame workpiece is scanned in real time, initial data of material hardness distribution are obtained, a hardness distribution mapping graph is formed, and a hardness change region division result is obtained; the hardness grade distribution and the cutting force sudden change amplitude of the cutting force sudden change specific position are recognized, adjustment parameters of the cutter feeding speed are obtained through calculation according to the hardness grade distribution and the cutting force sudden change amplitude, and a speed adjustment instruction is generated according to the adjustment parameters; and dynamically matching the cutting depth parameters according to the speed adjusting instruction, determining target cutting depth configuration, and matching the cutting path deviation monitoring value, the vibration amplitude index and the machining precision deviation value in real time through the target cutting depth configuration.

The invention discloses a dynamic control method for a complex curved surface five-axis numerical control machining tool vector, which comprises the following steps of: scanning the surface of a target workpiece by using a high-precision three-dimensional scanner to obtain point cloud data; based on the point cloud data, establishing a mathematical model S (u, v) of the target complex curved surface; a mathematical model based on the target complex curved surface; establishing a comprehensive dynamic model of the five-axis numerical control system; optimizing the initial tool posture in combination with the kinetic model; collecting dynamic operation data of the cutter and the workpiece; on the basis of the dynamic operation data, a model predictive control (MPC) algorithm is used for adjusting the machining path and the cutter direction in real time; according to the method, a comprehensive dynamic model of a five-axis numerical control system is established, dynamic coupling effects of a machine tool kinematic chain, cutter flexibility, cutting force, a temperature field and a stress field are brought into a unified framework, and complex dynamic behaviors are accurately described and controlled through multi-body dynamic modeling and an optimization algorithm.

The invention relates to the technical field of high-quality machining, in particular to a valve body high-quality machining method based on a numerical control machining center, and the method comprises the steps that power data, cutting force data, feeding speed data and rotating speed data are collected in real time when a numerical control machine tool machines a valve body; determining an operation matching index of the cutting force and the rotating speed at any acquisition moment; obtaining a machine tool operation matching index of the numerical control machine tool at any acquisition moment by combining the cutting force and the operation matching index of the rotating speed at any acquisition moment; determining a feeding speed abnormal factor of the numerical control machine tool at any acquisition moment; and on the basis of the feed speed abnormal factor at the current acquisition moment, adjusting the linear speed in the main shaft linear speed control instruction at the current acquisition moment. The feeding speed in the valve body machining process is adjusted, the surface roughness and tool abrasion are reduced, and therefore high-quality machining of the valve body is achieved.

The invention 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 a milling cutter machining method and system based on a CAM simulation technology, and the method comprises the steps: obtaining three-dimensional data of a workpiece mold and milling cutter parameters, and carrying out the process planning, and obtaining an initial workpiece machining strategy; cutter path planning and contact point calculation are conducted on the initial workpiece machining strategy, and a corresponding cutter movement path is obtained; carrying out CAM simulation analysis on the initial workpiece machining strategy to obtain corresponding real-time simulation data, and carrying out collision interference detection on the tool movement track according to the real-time simulation data to obtain a corresponding safe movement path; carrying out cutting force and cutting temperature analysis on the safe motion path to obtain a corresponding process verification result; and carrying out machining analysis on the safe motion path and the cutter predicted use data, and carrying out NC machining code optimization on the initial workpiece machining strategy to obtain a corresponding optimized workpiece machining scheme. The machining strategy can be flexibly adjusted according to the characteristics and machining requirements of different workpieces.

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 discrete point-based cutting force prediction method supporting any cutting edge shape, and relates to the technical field of milling machining.The discrete point-based cutting force prediction method comprises the steps that firstly, a workpiece and a tool are both expressed in a discrete model, a milling track is defined as a series of linear segments through NC codes, and a tool and cutting edge model and a Tri-Dexel workpiece model are built; secondly, a tool swept volume is obtained, and material removal is calculated; then subdividing the tool motion into smaller analysis steps to obtain the cutting condition of each turn, determining the cutting meshing area of each analysis step and blade elements participating in cutting, and calculating the nominal instantaneous undeformed chip thickness of each blade element based on the actual feeding direction, the normal vector and the compensation coefficient; then, discretizing the cutting condition into a plurality of orthogonal cutting units, and predicting the cutting force of each participating blade element by adopting a neural network trained based on simulation data; and finally, integrating the cutting forces of all participating blade elements to obtain a synthetic cutting force field of each analysis step. According to the method provided by the invention, the force field prediction capability in a variable geometry processing scene is effectively improved.

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 multi-axis PCB milling synchronous control method, device and equipment and a storage medium. The method comprises the steps that geometric modeling is conducted on a PCB and a tool, a PCB machining model is generated, a tool path is generated based on the PCB machining model, the meshing state of the tool and the PCB is extracted, and a meshing geometric entity is obtained. And then, calculating the instantaneous undeformed chip thickness according to the meshing geometric entity, and predicting the cutting force of the cutter acting on the PCB. Based on the cutting force prediction result, the tool path data and the PCB machining model, modeling is conducted on multi-axis motion of the PCB milling machine tool, and a multi-axis motion synchronous control model is generated. Finally, according to the synchronous control model and the cutting force prediction result, self-adaptive synchronous control is conducted on multi-axis motion. According to the PCB milling method, the cutting force is predicted in real time, the multi-axis machine tool is subjected to self-adaptive synchronous control, the machining precision is improved, the service life of the tool is prolonged, and the stability and consistency of the PCB machining process are ensured.

The invention provides a robot milling surface roughness prediction method and system based on parallel convolution and coordinate attention mechanism feature fusion. According to the method, firstly, multi-source heterogeneous high-frequency signals in the milling process of a robot are synchronously collected, empty slice segments are eliminated and environmental noise is filtered out by utilizing a self-adaptive threshold value and a robust statistical method, and a standardized time sequence sample set is constructed; secondly, establishing a multi-source heterogeneous feature fusion roughness prediction model, and independently extracting deep features of cutting force and vibration signals by adopting a double-branch parallel convolution architecture; a coordinate attention mechanism is introduced, heterogeneous modal features are mapped into a virtual two-dimensional topological structure, the dynamic relative contribution degree of cutting force and vibration signals changing along with the machining state is captured through pooling aggregation along the modal dimension, and an attention weight map is generated to conduct dynamic weighting on the features. And finally, outputting a surface roughness predicted value through a regression network. According to the method, the problem that an existing fusion method neglects the dynamic dependency relationship between physical quantities is effectively solved, the anti-interference capability of the robot in the weak rigidity machining process is enhanced, and the precision and robustness of surface roughness prediction are remarkably improved. The method can be widely applied to robot complex component machining quality analysis and intelligent optimization, and has high efficiency and reliability.

The invention discloses a three-dimensional jitter compensation method and device for laser cutting and a storage medium, and relates to the technical field of lasers, and the method comprises the steps that fast Fourier transform is carried out based on a vibration signal of a to-be-machined thin plate in the Z-axis direction, and a frequency spectrum feature is obtained; according to the frequency spectrum characteristics, combining a preset jitter filtering parameter set to carry out frequency domain filtering processing, and generating a filtering signal; reconstructing the filtering signal into a time domain compensation amount through inverse Fourier transform; based on the time domain compensation quantity, performing weighted fusion by combining the cutting acceleration and the cutting displacement deviation in the current cutting direction, and generating a three-dimensional compensation vector; and superposing the three-dimensional compensation vector to an original cutting path coordinate through a motion controller so as to correct the following state of the cutting head. The technical problem that the thin plate shakes due to nonlinear cutting force caused by interaction between high-speed movement of the cutting head and laser and materials is solved, and the stability of the thin plate during laser cutting is improved.

The invention relates to the technical field of machine tool control, in particular to a numerical control machine tool intelligent control method and system for aviation titanium alloy structural part machining. According to the method, firstly, three kinds of working condition data of cutting force, temperature and vibration are collected, then, working condition feature vectors are extracted and input into a pre-training model, an optimized combination value of the feeding speed and the main shaft rotating speed is obtained, and finally, the machine tool track is adjusted in real time based on optimized parameters. And through continuous optimization of the deep learning model, the adaptive capacity to complex working conditions is improved, and the machining precision and the surface quality of the aviation titanium alloy structural part are improved.

The invention relates to the technical field of spline machining, in particular to a spline machining device and method for an eccentric shaft of a speed reducer, and the method comprises the steps that the amplitude-frequency difference between all cutting force data and all vibration data in a frequency domain and the correlation between all cutting force data and all temperature data within a preset duration before all moments are analyzed; determining a cutting risk value; comprehensively analyzing a cutting risk value, an energy ratio, a dominant frequency amplitude difference between all vibration data and all cutting force data in a frequency domain and a change relation of a cutting related index along with the machining quality in a preset period before the current moment, predicting a machining quality score to determine a machining instability index, and combining the energy ratio and the machining quality score to determine the machining instability index. And determining a main shaft rotating speed correction value and a cutter feeding amount. The problem of compensation failure of traditional PID control is solved, the spline machining quality is improved, and the service life of a workpiece is prolonged.

The invention relates to the technical field of precision machining, in particular to a cutting force signal synchronous calibration method based on multi-sensor data fusion, which comprises the following steps of: 1, dynamically preprocessing acquired signals; step 2, constructing a reference time axis based on the spindle current signal, and realizing time domain alignment of multichannel signals by adopting a dynamic time warping algorithm; 3, establishing a feature incidence matrix containing a force-vibration-temperature coupling relation, and generating a fusion cutting force feature vector through frequency domain energy weight distribution; 4, a dynamic transfer function model is constructed, model parameters are updated in real time, and the fusion feature vector is mapped into a calibration cutting force signal; and 5, carrying out dynamic compensation on the calibration signal by adopting a nonlinear inverse compensation and residual modal screening strategy. According to the method, the accuracy of cutting force signals can be remarkably improved, errors and uncertainty in the machining process are reduced, and the production quality and efficiency are optimized.

The utility model discloses a cleaning rolling brush, cleaning equipment and a cleaning system, and belongs to the technical field of electrical equipment. The cleaning rolling brush comprises a shell, a cleaning strip, a first cutting piece and a second cutting piece, and the first cutting piece and the second cutting piece extend on the outer surface of the shell; the cleaning strip protrudes out of the outer surface of the shell; wherein a cleaning strip is arranged in a gap between the first cutting piece and the second cutting piece, so that the cutting force on entanglements wound on the cleaning strip can be improved, the entanglements can be cut off, the entanglements are prevented from being wound on the shell as much as possible, the influence of the entanglements on the cleaning rolling brush is reduced, and the working effect of the cleaning rolling brush is improved; and the service life of the cleaning rolling brush is prolonged.

The invention discloses a real-time precise balance mechanism of a numerical control cradle rotary table, which comprises a base, the base is rotatably connected with the cradle rotary table, the end part of a rotating shaft of the cradle rotary table is fixedly provided with a connecting plate, the base is detachably provided with a track close to the connecting plate, the track is slidably connected with a telescopic piece, and the telescopic piece is fixedly connected with the rotating shaft of the cradle rotary table. The output end of the telescopic piece is rotationally connected with the connecting plate, a control valve is further arranged on the base, and the control valve is electrically connected with the telescopic piece and the system control end. When the balancing mechanism is used, the balancing mechanism can change along with the change of the gravity center of the rotating part of the rotary table, the cutting force and the weight of a machined workpiece, and the torque borne by the rotating part of the numerical control cradle rotary table is accurately balanced in real time. When the device is used, the expansibility is high, the precision of the numerical control cradle rotary table can be improved or the bearing capacity of the numerical control cradle rotary table can be improved by replacing configurations, and effective technical support is provided for the numerical control cradle rotary table in the aspect of product serialization type spectrum expansion.

The invention provides a dynamic cooperative speed regulation control system and method for a machine tool spindle and a feed shaft, and the method comprises the steps: building a spindle energy transmission model and a feed shaft load-motion model, and constructing a strong correlation coupling relation between the two through cutting power; collecting main shaft power, torque and rotating speed and feed shaft cutting force, acceleration and displacement data in real time, and fusing to generate a combined state vector; calculating spindle margin and time correction based on spindle actual power and torque, and identifying working condition abrupt change according to the joint state vector; the feeding speed and the cutting depth are dynamically adjusted with the spindle margin sum as the constraint condition, and a cooperative adjustment strategy is generated; and converting the cooperative adjustment strategy into a feed shaft motion instruction and executing the feed shaft motion instruction. By constructing a real-time coupling closed loop of'spindle power-torque-rotating speed 'and'feed shaft speed-acceleration-position', the spindle is always locked to operate in a maximum power-torque envelope efficient area, the speed of the feed shaft is dynamically adjusted according to the spindle margin, locked rotor is completely eradicated, and no-load is eliminated.

The invention relates to the technical field of numerical control system fault pre-diagnosis, and particularly discloses a fault monitoring method and system for a numerical control machine tool. The method comprises the steps that a spindle vibration signal, a cutting force signal and a spindle current signal are collected; outputting an alignment signal set; respectively executing adaptive filtering processing according to the alignment signal set; constructing a multi-dimensional feature vector; constructing a health index under the processing behavior period; then accumulated trend deviation analysis is executed through a CUSUM algorithm; and judging whether the tool wear trend is abnormal. The technical problem that in the prior art, static threshold judgment is carried out based on a single signal source, and especially in a numerical control machining scene with signal noise interference, accurate early warning of the early wear trend of a tool cannot be achieved is solved. Due to the fact that a period calibration mechanism fusing spindle vibration, cutting force and current signals, feature vector entropy weight optimization and a CUSUM trend detection mechanism are adopted, the pre-diagnosis level of the numerical control machine tool on cutter state changes is improved.

The invention relates to the technical field of fastener process production, in particular to a process parameter control method and system applied to fastener production. The method comprises the following steps: acquiring three-dimensional cutting force of a tool-workpiece contact area in a fastener thread machining process in real time, and generating cutting force dynamic fluctuation characteristic data; the real-time abnormal situation of the current machining process is recognized according to the cutting force dynamic fluctuation characteristic data, a tool wear mode is judged, thread ring machining deviation prediction is conducted, and a predicted thread geometric deviation value is generated; and adjusting multiple machining control parameter values according to the predicted thread geometric deviation value, and carrying out real-time machining feedback monitoring so as to realize fastener production self-adaptive control. According to the method, intelligent recognition of the tool wear mode and accurate prediction of the machining deviation are achieved through cutting force sensing, and it is ensured that the thread machining precision of the fastener is stable and controllable through multi-parameter cooperative self-adaptive regulation and control.

The invention provides a lead screw precision milling closed-loop optimization and real-time control process based on multi-axis linkage, which comprises the following steps of: acquiring geometric parameters of a workpiece, material attributes and kinetic parameters of a machine tool, designing a cutter and clamp scheme, and setting initial processing parameters based on a cutting database; constructing a lead screw spiral groove high-precision curved surface model by using an NURBS curve, and optimizing a path through curvature adaptive discretization and quintic spline interpolation; an idle stroke path is iteratively optimized by adopting a real number coding genetic algorithm, and the total time of the path and the reversing frequency are shortened; multi-axis synchronous control is achieved through inverse kinematics solution, and the cutter inclination angle is dynamically adjusted in real time based on the contact angle; and virtual simulation and finite element analysis are utilized to predict cutting force, thermal deformation and interference risks. According to the method, the continuous and smooth multi-axis synchronization instruction is generated, the positioning error of each axis is effectively reduced, meanwhile, the cutter inclination angle is dynamically corrected through force feedback of the contact angle, cutting force fluctuation is effectively restrained, and it is ensured that the tooth surface roughness of the spiral groove meets the requirement.