24 results about "Cutting force model" patented technology

The invention discloses a round bar processing equipment multi-parameter cooperative control method based on artificial intelligence, and particularly relates to the field of round bar processing equipment control, which comprises the steps of data acquisition, data processing, data analysis and result judgment. According to data acquisition, a force sensor, a temperature sensor, a displacement sensor and a vibration sensor of specific models are deployed, data are acquired according to different set frequencies of a high-speed cutting process and a common cutting process, abnormal values of the acquired data are eliminated according to the Pauta criterion in data processing, and Kalman filtering denoising and normalization operation are performed. And in the data analysis step, processed data are substituted into a turning and milling cutting force model, an improved Archard tool wear model and a multiple regression machining precision model to be solved, and according to result judgment, the machining quality is evaluated according to a solving result comparison standard, and the equipment state is judged in combination with a threshold value. According to the method, multi-parameter collaborative optimization is realized, the processing quality stability and efficiency are improved, the dependence on artificial experience is reduced, and the downtime is shortened.

The high-efficiency low-deformation machining method suitable for the aviation blade disc comprises the following steps that S1, a cutting force model is constructed based on a ball-head spiral conical milling cutter, a cutting test is carried out, and therefore a cutting force coefficient is determined; s2, performing numerical control simulation on the milling cutter by adopting special software, determining a machining path according to a principle of maximum rigidity of a cutting system, extracting a cutting area and a cutting edge length through the special software on the basis of the machining path so as to calculate real-time cutting force in the machining path, and then constructing a finite element model of the blade disc, carrying out transient mechanical analysis in the blade curved surface coordinate system, adjusting cutting parameters and the inclination angle of a workbench when cutting deformation exceeds a set range, and determining a parameter interval I of the feeding amount and the cutting depth; and S3, modal testing is carried out to obtain modal parameters, then calculation software is adopted to sequentially calculate parameter intervals of different machining parameters and the rotating speed of the main shaft, and effective machining parameters are obtained.

The application discloses a kind of cutting force vector regulation and control methods based on micro-element blade method, it is related to metal processing field.The main implementation method includes: first, milling coefficient is solved, fixed tool parameters and the rest milling parameters except every tooth feed rate, carries out multiple sets of orthogonal experiments and records the average milling force value of each group of experiments and corresponding every tooth feed rate, and obtains milling coefficient by linear regression of least square method;Then based on micro-element blade method, first, milling cutter is divided into multiple discrete micro-elements along the axial direction, then the actual spiral contact angle of each micro-element is calculated and whether it is actually involved in milling is judged, then the milling force model of each micro-element is established, every blade of milling cutter is integrated along the axial direction, then the milling force of each blade is summed to obtain the magnitude of the cutting force of the whole milling cutter;Finally, milling parameters are optimized by NSGA-II algorithm, aiming at studying the influence of every tooth feed rate and axial depth of cut on cutting force, the constructed milling force model is used as objective function, the input value is axial depth of cut and every tooth feed rate, the target is the average axial milling force of a period, the maximum axial milling force and material one rate, a group of optimized Pareto solution is obtained by multiple crossover mutation.The method can quickly obtain a group of more optimal milling parameters according to the cutting force model and optimization algorithm, and can effectively regulate and control the size of cutting force vector and the part relief deformation, to provide guidance for optimizing processing parameters.

The present invention provides a fault detection method and system based on an adapter tool shank and relates to the technical field of fault diagnosis of mechanical equipment. The method includes: obtaining a displacement signal of the adapter tool shank; based on the displacement signal, determining a feed rate; based on the feed rate, building a cutting force model; based on the cutting force model, building a disturbance model; building an observer model; based on the disturbance model and the observer model, determining a cutting force error; based on the observer model, determining a wear rate of the adapter tool shank; combining the cutting force error and the wear rate to perform fault detection; and based on a fault detection result, judging whether the adapter tool shank is faulted. The present invention improves the efficiency of fault diagnosis, reduces the maintenance cost, and improves the production efficiency.

The invention relates to the technical field of milling force prediction, and provides a variable helical angle milling cutter milling force model calibration method based on maximization of a force lobe area, and the method comprises the steps: building a linear average milling force model according to a cutting mechanics principle; and minimizing a loss function of an error between the simulated cutting force FS and the experimental cutting force FM to obtain each group of cutting force coefficients of the variable helical angle milling cutter. The radial run-out effect of the cutter is integrated, and the accuracy of the cutting force model of the variable helical angle cutter is improved; on the basis of a force lobe area maximization principle, cut-in phase matching of experimental force and simulation force is completed, and the cutting force coefficient identification accuracy is improved; in addition, the stability and rationality of a cutting force coefficient are further ensured by introducing an L2 regularization parameter, and occurrence of a ill-conditioned solution is avoided. The method is used for accurately identifying multiple groups of cutting force coefficients and cutter run-out parameters of the variable helical angle milling cutter so as to improve the cutting force forecasting precision of the variable helical angle milling cutter, and a basis is provided for accurate stability judgment in the machining process.

The present invention discloses a process reliability evaluation method for a spiral bevel gear milling machine tool, comprising the following steps: Step 1: establishing an instantaneous cutting force model and a milling dynamics model for processing spiral bevel gears based on a forming method, and calculating the cutting vibration characteristics of the spiral bevel gear milling machine tool during the milling process; Step 2: based on the cutting vibration characteristics in Step 1, evaluating the sensitivity of various influencing factor parameters to the cutting vibration; and calculating the amplitude eigenvalue distribution type; Step 3: based on the amplitude eigenvalue distribution type in Step 2, using the Bayesian formula, establishing a BP neural network model, and using the Garson algorithm to update the BP neural network model; using the updated BP neural network model to calculate the amplitude eigenvalue under extreme working conditions, and combining the vibration trajectory curve of the milling tool and the process reliability formula to complete the machine tool process reliability evaluation. The present invention also discloses an optimization method for tuning machine tool process parameters using the above evaluation method.

The application provides a micro-milling machining parameter identification method considering the influence of random tool wear, comprising the following steps: establishing a cutting force model under the influence of tool wear by considering tool runout and chip separation mechanism; updating the tool rotation radius according to the influence of runout and wear on the tool edge radius value, and obtaining the tool tip trajectory equation; collecting tool wear data in the actual machining process, training the neural network model, and optimizing the hyperparameters in the neural network model by using a bidirectional long short-term memory network; identifying the machining parameter values in the neural network model by using a particle filtering algorithm; simulating and calculating the cutting force and wear values under different working conditions by using the neural network model, comparing the experimental results, and evaluating the accuracy of the machining parameter identification. In the modeling process, the randomness of tool wear is fully considered, the prediction accuracy is significantly improved, and the application value is stronger.

The application discloses a method and system for optimizing the arrangement of cutters of a slurry shield cutter head, and relates to the technical field of shield cutter heads.A method for optimizing the arrangement of cutters on the front face of a slurry shield cutter head is provided.The method comprises the following steps: obtaining the structural parameters of the front face cutters and working conditions to determine the cutter arrangement parameters;establishing a soil breaking amount model based on the cutting process of the cutters and the cutter arrangement parameters;establishing a cutting force model according to the Rankine passive earth pressure theory, in combination with the cutting resistance model of the tillage cutters and the synergistic effect and cutting mechanism of the cutters;then, establishing a cutting efficiency model based on the soil breaking amount model and the cutting force model;finally, evaluating the cutting efficiency under different cutter combination arrangement parameters through the cutting efficiency model, and selecting the cutter arrangement parameters with the highest cutting efficiency as the optimal arrangement scheme.The application can improve the accuracy of the cutter arrangement parameters of the slurry shield cutter head, and thus improve the tunneling efficiency of the slurry shield cutter head.

The invention discloses a numerical control agent execution device driven by a large model, which belongs to the technical field of intelligent manufacturing and numerical control and comprises an agent subsystem, a real-time actuator subsystem, a virtual machine tool subsystem and a physical machine tool. The agent subsystem generates a verified instruction sequence through simulation interaction with the virtual machine tool subsystem; the real-time actuator subsystem is responsible for analyzing and executing the instruction generated by the intelligent agent and driving the physical machine tool to move; the virtual machine tool subsystem is used for verifying and optimizing instructions of the intelligent agent subsystem and providing simulation verification support; and the physical machine tool is driven by the real-time actuator subsystem to execute final machining operation. According to the numerical control agent execution device driven by the large model, intelligent optimization of a machine tool machining process is achieved through driving of the cutting force model, the control model and the error model in the agent, and the machining efficiency and the machining quality are improved under the condition that the machining dynamic precision is guaranteed.

A method for modeling cutting forces in side milling of end mills considering the deformation of thin-walled parts is disclosed. This invention belongs to the field of metal processing, specifically relating to a method for modeling cutting forces in side milling of end mills. The purpose of this invention is to solve the problems of low machining accuracy, poor machining quality, and low machining efficiency in existing cutting force models. The process is as follows: 1. Establish a computational coordinate system; 2. Set parameters; 3. Calculate the theoretical cutting thickness; 4. Obtain the actual cutting thickness; 5. Calculate the tangential force, radial force, and axial force; 6. Obtain the cutting force in the Cartesian coordinate system; 7. Let l = l + 1, repeat steps 3 to 6 until the cutting force on all cutting micro-elements of tooth j is obtained; 8. Let j = j + 1, repeat steps 3 to 7 until the cutting force on all cutting micro-elements of all teeth is obtained; 9. Let φ = φ + Δφ, repeat steps 3 to 8 until φ = 2π, obtaining the cutting force at all tool rotation angles of all cutting micro-elements of all teeth.

The invention discloses an intelligent auxiliary method for autologous bone transplantation, which comprises the following steps: constructing a three-dimensional jaw bone model, a three-dimensional tooth model and a three-dimensional defect area model based on CBCT (cone beam computed tomography) image data of a target object; according to the three-dimensional defect area model, obtaining defect area three-dimensional form data, inputting the defect area three-dimensional form data into the trained bone increment prediction model, and outputting to-be-supplemented autologous bone block design parameters; according to the design parameters of the to-be-supplemented bone block, in combination with the three-dimensional tooth model, determining a bone taking position of the to-be-supplemented bone block in the three-dimensional jaw bone model, and automatically marking the bone taking position so as to plan a bone block cutting path; according to the bone block cutting path, a predicted cutting force is obtained by combining a cutting force model; planning the motion track of the mechanical arm according to the predicted cutting force to realize auxiliary cutting of the autogenous bone block; in the cutting process, the actual cutting force is monitored in real time, and the movement track of the mechanical arm is dynamically adjusted according to the actual cutting force. According to the method, the preoperative planning is optimized, the operation precision in the operation is improved, and the operation risk is reduced.

The invention belongs to the field of numerical control machining, and particularly relates to a CFRT inclined hole drilling damage model building method which comprises the following steps: S1, building an inclined hole drilling three-dimensional space coordinate system, and defining a space fiber cutting angle; s2, establishing a macroscopic cutting force model of inclined hole drilling, and analyzing a cutting angle damage formation mechanism based on space fibers from a macroscopic level; and S3, establishing a single-fiber cutting model under a microscopic size, and analyzing an influence rule of cutting force on outlet area damage. According to the method, the action principle of three-way cutting force on the macroscopic level in the inclined hole drilling process is analyzed, an outlet side single fiber cutting mechanical model is established from the microscopic level based on the elastic foundation beam theory, a forming mechanism of defects such as layering, tearing and burrs of CFRT inclined hole outlet side machining is provided, and a basis is provided for formulating a CFRT inclined hole machining damage inhibition strategy.

The present application relates to a kind of time-varying chatter prediction methods in thin-walled part milling process, comprising: at initial time, un-milled thin-walled part is divided into multiple units, each unit is the same eight-node hexahedron of length-width-height;At the t time, the unit node information of each unit in the machined, to-be-cut and target machining forming part of thin-walled part is obtained, and the stiffness matrix of each unit is obtained according to the unit node information of each unit and the unit stiffness matrix of eight-node hexahedron;The stiffness matrix of each unit is assembled, and the time-varying stiffness matrix of thin-walled part is obtained;And the time-varying stiffness matrix of thin-walled part is corrected;Cutting force model in thin-walled part milling process is constructed;According to cutting force model and the time-varying deformation of corrected time-varying stiffness matrix, the time-varying deformation of thin-walled part is calculated;Finally, the time-varying chatter of thin-walled part is predicted according to time-varying deformation.This method can significantly improve the efficiency and accuracy of thin-walled part machining stability prediction.

The invention discloses a non-iteration thin-wall part side milling error compensation method considering chip thickness updating, and belongs to the technical field of thin-wall part machining deformation. The method comprises the steps that firstly, an infinitesimal cutting force model is established and used for deformation solving of a rigidity equation; then realizing stiffness equation reduction based on a node classification method, and realizing stiffness updating by using a time-varying stiffness matrix; the radial cutting depth and cutting thickness are updated by introducing a multi-element coupling effect, and an iterative deformation calculation model is established; thirdly, reconstructing the cutting force model to obtain a deformation closed-form solution, and constructing a non-iteration deformation prediction model; based on the deformation prediction model, establishing a non-iterative error compensation model by using a mirror image compensation principle; and finally, proving the effectiveness of the non-iterative compensation model through simulation and experiments. The method has good prediction precision and calculation efficiency, can be widely applied to the high-precision machining process of thin-walled parts in aerospace and the like, and provides theoretical and technical support for improving the machining precision.

The present application relates to the technical field of milling force prediction, and provides a variable helix angle milling cutter milling force model calibration method based on force pocket area maximization, which comprises the following steps: according to the principle of cutting mechanics, a linear average milling force model can be established; a loss function of minimizing the simulation cutting force F S and the experimental cutting force F M error is obtained, and each group of cutting force coefficients of the variable helix angle milling cutter is obtained. The tool radial runout effect is integrated, the accuracy of the variable helix angle cutter cutting force model is improved, the cutting-in phase matching of the experimental force and the simulation force is completed based on the force pocket area maximization principle, the cutting force coefficient identification accuracy is improved, in addition, the L2 regularization parameter is introduced to further ensure the stability and reasonableness of the cutting force coefficients, and the occurrence of the ill-conditioned solution is avoided. The present application is used for accurately identifying the multiple groups of cutting force coefficients and the tool runout parameters of the variable helix angle milling cutter, so as to improve the cutting force prediction accuracy of the variable helix angle milling cutter, and provides a basis for accurately judging the stability of the machining process.

The invention discloses a method, a device and equipment for quickly collecting complex surface processing data based on secondary development of CAM software. The method comprises the following steps: executing batch generation of data sets based on secondary development of UG / NX software; calling a cutting force model through a self-development module to verify each group of parameter combination, and performing primary optimization on the current parameter combination through effectiveness analysis of a cutting force value; cross-software connection of data between the UG / NX software and the VERICUT software is established through environment configuration, simulation and secondary optimization are carried out, and a machining quality simulation result is obtained; a target data set subjected to cross-software interaction is normalized and then subjected to multi-target optimization by adopting neural network training and a multi-target optimization algorithm, multiple targets are converted into single targets through a variable weight strategy, and optimal process parameter combinations meeting requirements of different processing stages are output. According to the method, the collection efficiency and accuracy of the industrial data set in the processing and manufacturing industry can be remarkably improved, and a targeted process parameter optimization scheme can be provided.

The invention belongs to the technical field of intelligent manufacturing twin modeling, and discloses a five-axis numerical control machine tool energy consumption modeling method and system based on energy flow footprint, and the method comprises the steps: building a five-axis numerical control machine tool three-dimensional geometric model, and obtaining part parameters; an electromechanical coupling dynamic model of the five-axis numerical control machine tool is built based on an SVPWM algorithm, and a machine tool machining process energy consumption model is built according to the electromechanical coupling dynamic model; a theoretical cutting force model is established based on an Altintas mechanical model, cutting loads of all axes are obtained according to a kinematical equation of the five-axis numerical control machine tool and applied to the load end of the machine tool machining process energy consumption model, and machine tool machining process energy consumption modeling is completed. According to the method, the cutting load model of each shaft of the machine tool is established, the cutting load can be estimated before actual machining, proper machining parameters are selected to meet the performance requirements of the machine tool, the energy consumption of the machine tool is reduced, and the problem that a method for obtaining the machine tool energy consumption model according to experimental measurement data is not applicable due to equipment change is solved.

The invention relates to the field of intelligent machining, in particular to a modeling and cutting force coefficient and run-out parameter identification method for an annular cutter, which comprises the following steps of: S1, performing dynamic cutting force accurate modeling of the annular cutter; the method specifically comprises the steps of geometric modeling of the annular cutter, modeling of infinitesimal contact, modeling of instantaneous undeformed cutting thickness, modeling of dynamic cutting thickness and modeling of dynamic cutting force. S2, accurately and efficiently identifying a cutting force coefficient and a run-out parameter of the annular cutter; the method specifically comprises the steps of implementing a mechanical identification method and optimizing an identification method. According to the method, the variable geometry annular cutter with a more complex geometric structure and higher universality is selected as a modeling object, complex working conditions on site are fully considered, multiple regeneration effects are introduced into the modeling process, a cutting force model can be more accurate, and theoretical support is provided for site process parameter selection.

The present invention discloses a universal multi-stress field coupling analysis method for connecting rod hole processing. The method focuses on the processing of the connecting rod hole system, analyzes the structure and processing technology of the connecting rod, establishes a finite element simulation model of the connecting rod, and obtains finite element simulation models of the connecting rod cover and the connecting rod body respectively. The cutting edge motion model and the cutting force model are determined according to the boring and milling processing processes respectively, and a processing physical process simulation analysis program is established to convert the complex processing physical process into a dynamic force distribution, thereby simplifying the finite element simulation of the cutting process. By utilizing the processing physical process simulation analysis program, combined with the assembly constraints and boundary constraints in the connecting rod processing process, the process cycle is performed according to the processing technology to realize multi-process superposition and multi-stress field coupling analysis. This method can effectively associate the residual stress field of the entire connecting rod machining process, and more realistically and reasonably analyze the deformation law of the key feature position of the connecting rod.

This invention discloses a dynamic control method and system for cutting force in CNC machine tools for machining integral bladed disks, comprising: a geometric mapping module between the tool and the blade surface, which considers the complex surface features of the blade and solves in real time the contact point position and surface normal of the tool and the blade surface; a cutting force model construction module, which constructs a theoretical model of infinitesimal cutting force using the infinitesimal method and calculates the total cutting force of the CNC machine tool using integral summation; and a dynamic closed-loop control module for cutting force, which projects the elastic displacement of the blade and the vibration displacement of the tool tip onto the normal of the surface contact point to form a chip thickness correction amount, thereby correcting the undeformed chip thickness in the next time step and realizing the dynamic correction of the theoretical calculation model of cutting force. This invention effectively reduces the prediction error of chip thickness in the machining of thin-walled blades with complex surfaces by introducing the surface contact point normal vector, dynamic coupling, and closed-loop correction mechanism, thus improving the machining stability of CNC machine tools.

This application discloses a machining control method, device, system, and medium based on a five-axis machining center. The method includes obtaining geometric feature parameters and tool feature information of a workpiece to be machined, establishing a cutting force model based on the obtained geometric feature parameters, and calculating the cutting force distribution under different paths; in the cutting force model, using a path optimization algorithm to generate an optimal tool path corresponding to the tool feature information based on the cutting force distribution and geometric feature parameters; establishing a simulation environment for the five-axis machining center based on the generated tool path and tool feature information to simulate the motion state of the tool in different paths; setting a reward function including path smoothness and cutting force stability in the simulation environment; using a reinforcement learning algorithm to optimize the smoothness of the tool path and generate a smooth tool path; verifying the generated smooth tool path in the simulation environment; and triggering a machining execution instruction. This application improves machining accuracy and surface quality.

The application belongs to the technical field of cutting force prediction, and discloses a physical information embedded instantaneous cutting force prediction method and system. The method comprises the following steps: inputting machining time series data into a feature extraction network of a trained instantaneous cutting force prediction model to obtain features of the machining time series data; inputting static machining parameters into a theoretical mechanical cutting force model to calculate corresponding theoretical cutting forces; and obtaining a mechanism feature vector by splicing the theoretical cutting forces and the static machining parameters, inputting the mechanism feature vector into a full connection layer of the instantaneous cutting force prediction model; in the full connection layer, the mechanism feature vector is spliced with the obtained machining time series data features and is regressed to obtain an instantaneous cutting force prediction result. The application can improve the accuracy of cutting force prediction, improve the model generalization ability, improve the real-time performance of prediction, and increase the model interpretability.

The present application relates to a kind of digital twin tool wear life prediction method and system based on physical information neural network, comprising:s1: tool data processing;S2: resultant force calculation;S3: cutting speed calculation;S4: sliding distance calculation;S5: load calculation;S6: neural network establishment;Physical information neural network is established based on main cutting force model and Archard wear model;S7: loss function establishment;S8: feedback and iteration;S9: calculate tool wear;Tool wear is calculated by physical information neural network.The present application integrates the physical formula related to the tool wear stage of numerical control machine tool into physical information neural network as part of loss function, can guarantee that data is iteratively trained under the premise of relevant physical law, and the process is enhanced Explainability.

The application provides a cavity spiral milling process cutting force prediction and stability discrimination method and system, comprising: selecting a machining path parameter according to the size of a cavity to be machined, and generating a spiral machining tool path; based on the tooth track arc assumption, geometric modeling is performed on the machining engagement area at each tool point, and the tooth cutting in and out angle is calculated; a cavity spiral milling cutting force model is established based on the geometric modeling results, and accurate prediction of transient cutting force in the cavity spiral milling process is realized; considering the dynamic cutting thickness regeneration effect, a spiral milling dynamics equation dependent on the feed direction is established, and stability analysis is performed on the representative feed direction along the spiral tool path, so as to determine the stability of the cavity spiral milling process; and a time domain method based on the discontinuous Galerkin method is proposed to improve the calculation accuracy and efficiency of the stability analysis. The application realizes accurate prediction of cutting force and stability determination in the cavity spiral milling process.