Lead screw precision milling closed-loop optimization and real-time control process based on multi-axis linkage

Abstract

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.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial production operation and control technology, in particular to a kind of closed loop optimization and real-time control process based on multi-axis linkage's screw precision milling. BACKGROUND

[0002] In the field of screw precision milling processing, traditional process generally adopts equal interval scattered linear interpolation path planning and fixed tool tilt angle processing strategy, and there are still some limitations in traditional process at present.

[0003] In the processing, acceleration mutation is easy to occur in the area where the curvature of screw changes sharply, which leads to machine tool vibration and surface vibration lines; The existing processing path changes frequently and has high path redundancy, which leads to the extension of processing time; Traditional inverse kinematics solution relies on approximate linearization model, ignores the nonlinear coupling effect of rotary shaft (B / C shaft), which leads to tool posture error accumulation, especially in variable pitch spiral groove machining, which is easy to cause tooth surface interference; And, the real-time monitoring and feedback mechanism of cutting contact angle in the current processing is not perfect, and the fixed tool tilt angle leads to cutting force fluctuation, accelerates tool wear and induces thermal deformation.

[0004] Therefore, a screw milling process integrating high-precision path planning, multi-axis dynamic cooperative control and data closed-loop optimization is needed to break through the comprehensive bottleneck of processing precision, efficiency and stability. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a kind of closed loop optimization and real-time control process based on multi-axis linkage's screw precision milling, to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme: a kind of closed loop optimization and real-time control process based on multi-axis linkage's screw precision milling, comprising the following steps:

[0007] Collecting workpiece geometric parameters, material properties and machine tool dynamics parameters, designing tool and fixture scheme, and setting initial processing parameters based on cutting database;

[0008] Use NURBS curve to construct high-precision curved surface model of screw spiral groove, and optimize path through curvature adaptive discretization and quintic spline interpolation;

[0009] Iterative optimization of idle stroke path is carried out by using real number coding genetic algorithm, which shortens the total path time and the number of reversals;

[0010] Multi-axis synchronous control is realized through inverse kinematics solution, and tool tilt angle is adjusted in real time based on contact angle;

[0011] Cutting force, thermal deformation and interference risk are predicted by using virtual simulation and finite element analysis, and process parameters are corrected;

[0012] Performing multi-axis linkage machining and monitoring load in real time, through online measurement and three-coordinate machine detection of lead, roughness and tooth profile accuracy;

[0013] Optimizing path smoothing coefficient and genetic algorithm weight combined with machining data feedback, updating process knowledge base.

[0014] As a preferred embodiment of the present application, the specific process of constructing high-precision curved surface model of screw helical groove by NURBS curve includes:

[0015] Based on screw radius, lead and helix angle, generate helical groove geometric model through NURBS curve parameter equation, define control points, weights and node vectors to accurately represent the curved surface;

[0016] Calculate the curvature of each point on the NURBS curve, dynamically adjust the discrete step length, and generate a high-density discrete point set to match the geometric features;

[0017] Fit the discrete points into quintic spline curves by segmentation, force the adjacent segments to meet the continuity of position, velocity, acceleration and jerk, and the optimization goal is to minimize the total acceleration variation of the path;

[0018] Solve the spline coefficients and generate the smoothed G-code path, which contains coordinate, velocity and acceleration instructions.

[0019] As a preferred embodiment of the present application, the curvature k(u) of the curve at parameter u is calculated as follows:

[0020] ;

[0021] Wherein, represents, and represents the first derivative and the second derivative of the curve at u respectively;

[0022] Adjust the discrete step length Δu according to the curvature, the step length calculation formula is:

[0023] ;

[0024] Wherein, is the maximum allowed step length, represents the sensitivity coefficient of the curve, represents the current discrete point parameter value.

[0025] As a preferred embodiment of the present application, the detailed process of using real number coding genetic algorithm to iteratively optimize the idle stroke path includes:

[0026] The three-dimensional coordinate sequence of the idle stroke path is encoded into a real number vector, and an initial population satisfying the machine tool motion range constraint is randomly generated;

[0027] The path performance is quantified based on the weighted sum of the total path time and the number of reversals;

[0028] High-quality individuals are retained through tournament selection, diversity is introduced through arithmetic crossover and Gaussian mutation, and out-of-bound coordinates are corrected to ensure path feasibility;

[0029] When the number of iterations reaches a preset threshold or the optimal fitness is continuously stable, the optimization process is stopped;

[0030] The real number sequence of the individual with the optimal fitness is decoded to generate a three-dimensional path and transmitted to the actuator.

[0031] As a preferred embodiment of the present embodiment, the process of quantifying the path performance based on the weighted sum of the total path time and the number of reversals includes:

[0032] A fitness function is constructed based on the total path time and the number of reversals, and the idle stroke performance is quantified by weighted sum;

[0033] The calculation formula of the fitness function is:

[0034] ;

[0035] Where F represents the weighted sum, T represents the total path time, and Ndir represents the number of reversals.

[0036] As a preferred embodiment of the present embodiment, the specific process of realizing multi-axis synchronous control through inverse kinematics solution and dynamically adjusting the tool inclination angle in real time based on the contact angle includes:

[0037] A geometric kinematics model of the machine tool is constructed, and the spatial mapping relationship and constraint conditions of the tool coordinate system and each motion axis are defined;

[0038] According to the target path point coordinates and the tool attitude angle, the real-time displacement parameters of each motion axis are derived through inverse kinematics solution;

[0039] Multi-axis synchronous control instructions are generated based on the displacement parameters, and an interpolation algorithm is used to realize dynamic matching and cooperation of the motion trajectories of each axis

[0040] The tool-workpiece contact angle is obtained in real time through a force sensor or a visual measurement device, and compared with the process allowable threshold to determine the inclination angle deviation

[0041] If the contact angle exceeds the threshold, the tool inclination angle parameter is dynamically corrected and fed back to the inverse kinematics model.

[0042] As a preferred embodiment of the present embodiment, the process of deriving the real-time displacement parameters of each motion axis through inverse kinematics solution includes:

[0043] a target pose of a tool end in a base coordinate system given by a machining path plan;

[0044] a differential relationship Jacobian matrix between tool pose changes and axis displacement changes is established through a machine tool geometry model;

[0045] an axis displacement increment is solved through pseudo-inverse of the Jacobian matrix;

[0046] a calculation formula of the displacement increment is:

[0047]

[0048] wherein, ,

[0049] in the formula, a vector of each axis displacement increment is denoted by, a pseudo-inverse of the Jacobian matrix is denoted by, a deviation vector of the target pose and a current actual pose is denoted by, a current tool actual pose is denoted by;

[0050] an axis displacement parameter is updated through a formula:

[0051] wherein, an axis displacement parameter after kth iteration is denoted by, and an update termination condition is: .

[0052] As a preferred embodiment of the present embodiment, a process of correcting process parameters by predicting cutting force, thermal deformation and interference risk by using virtual simulation and finite element analysis includes:

[0053] a finite element multi-physical field coupling simulation model including cutting force, heat conduction and contact behavior is constructed based on a workpiece CAD model, tool geometry parameters and material constitutive relationship;

[0054] a virtual simulation platform is driven to perform dynamic simulation of a cutting process, and cutting force time domain waveform, thermal deformation gradient field and tool-workpiece interference region spatial distribution are synchronously analyzed;

[0055] cutting force peak value, temperature extreme value and minimum interference distance output by simulation are compared with preset process safety threshold to generate a risk level matrix and a key sensitive parameter list;

[0056] a process parameter correction scheme under multi-objective constraints is generated by optimizing spindle speed curve, feed acceleration curve and cooling liquid injection strategy based on the risk matrix.

[0057] As a preferred embodiment of the present embodiment, a process of updating a process knowledge base by combining machining data feedback to optimize path smoothing coefficients and genetic algorithm weights includes:​​​

[0058] Based on the lead deviation distribution of online measurement, the tooth profile error spectrum and the path optimization parameters generated by step three genetic algorithm, a feature matrix containing path fairing coefficients, time-reversing weight ratio and process parameter coupling degree is constructed;

[0059] Through partial least squares regression analysis of the nonlinear relationship between lead error and λ, and the interactive effect of tooth profile accuracy and time-reversing weight ratio, a correlation response surface model of error source-process parameter is established;

[0060] Based on the response surface model, the fairing coefficients of quintic spline path and the genetic algorithm time-reversing weight ratio are simultaneously optimized by using multi-objective particle swarm optimization algorithm;

[0061] The optimized parameter combination is encoded as a rule entry with the corresponding machining quality data, and is stored in a relational database according to the material type-tool combination-machine tool type ternary classification;

[0062] When the new workpiece batch variation coefficient CV is greater than or equal to 5%, the historical optimal parameter combination is automatically matched based on the updated knowledge base, and the re-optimization process is triggered.

[0063] The present application provides a kind of based on multi-axis linkage's screw precision milling closed loop optimization and real-time control process, with the following beneficial effects: based on inverse kinematics solution and Jacobian matrix pseudo-inverse iteration algorithm, real-time mapping model of tool pose-axis displacement is constructed, combined with quintic spline interpolation algorithm generates continuous smooth multi-axis synchronous instruction, effectively reduces the positioning error of each axis, simultaneously through the dynamic correction of contact angle force feedback tool tilt angle, effectively suppresses cutting force fluctuation, ensures that spiral groove tooth surface roughness meets the requirements;In the path planning aspect, NURBS curve curvature adaptive discretization technology and quintic spline fairing optimization are used, and combined with real number coding genetic algorithm optimization idle stroke path, significantly improve machining efficiency and machine movement stability;In closed loop optimization, based on cutting simulation-online measurement data, response surface model is constructed, and path fairing coefficient is simultaneously optimized by multi-objective particle swarm optimization algorithm, realize high lead precision, low tooth profile error precision machining, while relying on process knowledge base to automatically match historical optimal parameter combination for new batch workpiece, reduce process debugging time, improve machining quality stability and production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 The process flow chart of the present application based on multi-axis linkage's screw precision milling closed loop optimization and real-time control process. DETAILED DESCRIPTION

[0065] Embodiments of the present application are described below in the detailed description and illustrated in the accompanying drawings by using example embodiments by which the present application can be understood. The embodiments described below are examples for explaining the present application and should not be understood as limiting the present application.

[0066] The following disclosure provides many different embodiments, or examples, for implementing different structures of the present application. For the purpose of simplification, the elements of the particular examples in the following description are described and illustrated in the order in which they appear in the figures. Of course, they are merely examples and are presented for the purpose of explanation and are not intended to be limiting of the application. In addition, the present application provides examples of various specific processes and materials. However, one of ordinary skill in the art will recognize that other processes and / or materials can be used.

[0067] As shown in the drawings, Figure 1 An embodiment of the present application provides a multi-axis linkage-based precision ball screw milling closed-loop optimization and real-time control process, including the following steps:

[0068] Step one: collect workpiece geometric parameters, material properties and machine tool dynamics parameters, design tool and fixture scheme, and set initial machining parameters based on the cutting database;

[0069] Specifically, the geometric parameters (lead, thread angle, length, etc.), material properties (hardness, thermal expansion coefficient, etc.) of the ball screw workpiece, and the dynamics parameters (maximum speed, acceleration, precision range) of the multi-axis CNC machine tool are obtained.

[0070] Select a ball end mill or a profile milling cutter, determine the tool diameter, edge length and coating type, design a special fixture to ensure the stability of the workpiece clamping and coaxiality ≤0.005 mm.

[0071] Based on the material cutting database, preset the spindle speed, feed speed, cutting depth and cooling liquid parameters.

[0072] Step two: construct a high-precision curved surface model of the ball screw helical groove using NURBS curve, and optimize the path through curvature adaptive discretization and quintic spline interpolation;

[0073] In this embodiment, the specific process of step two includes: based on the ball screw radius, lead and helix angle, generate a helical groove geometric model through the NURBS curve parameter equation, define the control points, weights and node vectors to accurately represent the curved surface;

[0074] Specifically, the helix is discretized into m control points {Pi|i=0, 1, 2, …, m-1} to construct a non-uniform rational B-spline (NURBS) curve;

[0075] Through the curve equation:

[0076] ;

[0077] wherein, represents the curve point coordinate corresponding to the parameter u∈[0, 1], represents the i-th p-order B-spline basis function, represents the control point coordinate, represents the control point weight, and p represents the order of the curve.

[0078] The curvature of each point on the NURBS curve is calculated, the discrete step is dynamically adjusted (the step is small when the curvature is large, and the step is large when the curvature is small), and a high-density discrete point set is generated to match the geometric features;

[0079] In the embodiment, the curvature k(u) of the curve at the parameter u is calculated according to the following formula:

[0080] ;

[0081] wherein, represents, and represent the first derivative and the second derivative of the curve at u, respectively;

[0082] The discrete step Δu is dynamically adjusted according to the curvature, and the step calculation formula is:

[0083] ;

[0084] wherein, is the maximum allowed step, represents the curve sensitivity coefficient, represents the current discrete point parameter value;

[0085] The discrete points are fitted into a quintic spline curve in sections, and the position, velocity, acceleration and jerk are forced to be continuous between adjacent sections, and the optimization objective is to minimize the total acceleration variation of the path;

[0086] The spline coefficients are solved and a smoothed G-code path is generated, which includes coordinate, velocity and acceleration instructions.

[0087] It should be noted that the path also includes geometric accuracy verification, motion stability verification and machining effect verification after output, and the specific verification method is a prior art means, which will not be described here.

[0088] Step three: Iterative optimization of idle stroke path using real number coding genetic algorithm to shorten the total path time and the number of reversals;

[0089] Specifically, the detailed process of using real number coding genetic algorithm to iteratively optimize the idle stroke path to shorten the total path time and the number of reversals includes:

[0090] Encode the three-dimensional coordinate sequence of the idle stroke path into a real number vector, and randomly generate an initial population that satisfies the machine tool motion range constraint;

[0091] Quantify the path performance based on the weighted sum of the total path time (cumulative distance and speed) and the number of reversals (number of times the angle between direction vectors exceeds the threshold);

[0092] Retain high-quality individuals through tournament selection, introduce diversity through arithmetic crossover and Gaussian mutation, and correct out-of-bound coordinates to ensure path feasibility;

[0093] When the number of iterations reaches the preset threshold or the optimal fitness is stable for a continuous period, stop the optimization process;

[0094] Decode the real number sequence of the individual with the optimal fitness, generate a three-dimensional path, and transmit it to the actuator;

[0095] Specifically, the process of quantifying the path performance based on the weighted sum of the total path time and the number of reversals includes:

[0096] Construct a fitness function based on the total path time and the number of reversals, and quantify the idle stroke performance through weighted sum;

[0097] The calculation formula of the fitness function is:

[0098] ;

[0099] Where F represents the weighted sum, T represents the total path time, and Ndir represents the number of reversals.

[0100] Step four: Realize multi-axis synchronous control through inverse kinematics solution, and dynamically adjust the tool inclination angle based on the contact angle in real time;

[0101] Specifically, the detailed process of realizing multi-axis synchronous control through inverse kinematics solution, and dynamically adjusting the tool inclination angle based on the contact angle in real time includes:

[0102] Construct a geometric kinematics model of the machine tool, define the spatial mapping relationship and constraint conditions of the tool coordinate system and each motion axis;

[0103] According to the target path point coordinates and tool attitude angle, derive the real-time displacement parameters of each motion axis through inverse kinematics solution;

[0104] The multi-axis synchronous control instruction is generated based on displacement parameters, and an interpolation algorithm is used to realize dynamic matching and cooperation of the motion trajectories of the axes

[0105] The tool-workpiece contact angle is acquired in real time through a force sensor or a vision measurement device, and compared with a process allowable threshold to determine the inclination angle deviation

[0106] If the contact angle exceeds the threshold, the tool inclination angle parameter is dynamically corrected and fed back to the inverse kinematics model.

[0107] Specifically, the process of deriving real-time displacement parameters of each motion axis through inverse kinematics calculation includes:

[0108] The target pose of the tool end given by the machining path planning in the base coordinate system;

[0109] Through the machine tool geometric model, a differential relationship Jacobian matrix between tool pose changes and axis displacement changes is established;

[0110] The axis displacement increment is calculated through the pseudo-inverse of the Jacobian matrix;

[0111] The calculation formula of the displacement increment is:

[0112] ;

[0113] Wherein, , ;

[0114] In the formula, represents the axis displacement increment vector, represents the pseudo-inverse of the Jacobian matrix, the deviation vector of the target pose and the current actual pose, represents the current tool actual pose;

[0115] The axis displacement parameters are updated through the formula:

[0116] Wherein, represents the axis displacement parameters after the kth iteration, and the update termination condition is:

[0117] It should be noted that, is an allowable error threshold.

[0118] Step five: predict the cutting force, thermal deformation and interference risk by using virtual simulation and finite element analysis, and correct the process parameters;

[0119] In the embodiment, the process of predicting the cutting force, thermal deformation and interference risk by using virtual simulation and finite element analysis, and correcting the process parameters includes:

[0120] ​Based on the CAD model of workpiece, tool geometry parameters and material constitutive relation, a finite element multi-physical field coupling simulation model including cutting force, heat conduction and contact behavior is constructed.

[0121] The driving virtual simulation platform performs dynamic simulation of the cutting process, and synchronously analyzes the time-domain waveform of cutting force, thermal deformation gradient field and spatial distribution of tool-workpiece interference area.

[0122] The simulation output of cutting force peak value, temperature extreme value and minimum interference distance is compared with the preset process safety threshold to generate a risk level matrix and a list of key sensitive parameters.

[0123] Based on the risk matrix, the spindle speed curve, feed acceleration curve and cooling liquid injection strategy are optimized to generate a process parameter correction scheme under multi-objective constraints.

[0124] Step six: Perform multi-axis linkage machining and real-time monitoring of load, and measure lead, roughness and tooth profile accuracy through online measurement and three-coordinate machine detection.

[0125] Step seven: Combine the machining data feedback to optimize the path smoothing coefficient and genetic algorithm weight, and update the process knowledge base.

[0126] Extract the lead deviation distribution, tooth profile error spectrum of online measurement in step six and the path optimization parameters generated by genetic algorithm in step three, and construct a feature matrix including path smoothing coefficient, time-reversing weight ratio and process parameter coupling degree.

[0127] Through partial least squares regression analysis of the nonlinear relationship between lead error and λ, and the interactive effect of tooth profile accuracy and time-reversing weight ratio, an associated response surface model of error source-process parameters is established.

[0128] Based on the response surface model, the smoothing coefficient of quintic spline path and the time-reversing weight ratio of genetic algorithm are simultaneously optimized by multi-objective particle swarm optimization algorithm.

[0129] The optimized parameter combination is encoded as a rule entry with corresponding machining quality data, and stored in a relational database according to the material type-tool combination-machine model ternary classification.

[0130] When the new workpiece batch variation coefficient CV≥5%, the historical optimal parameter combination is automatically matched based on the updated knowledge base, and the re-optimization process from step three to step six is triggered.

[0131] The multi-axis linkage based precision screw milling closed-loop optimization and real-time control process of the application is based on inverse kinematics calculation and Jacobian matrix pseudo-inverse iteration algorithm, constructs a real-time mapping model of tool pose-axis displacement, generates continuous and smooth multi-axis synchronous instructions combined with quintic spline interpolation algorithm, effectively reduces the positioning error of each axis, simultaneously corrects the tool inclination angle through the force feedback of the contact angle γ, effectively suppresses the cutting force fluctuation, ensures that the roughness of the helical groove tooth surface meets the requirements; in the path planning aspect, the NURBS curve curvature adaptive discretization technology and quintic spline fairing optimization are adopted, and the real number coding genetic algorithm is combined to optimize the idle stroke path, which significantly improves the machining efficiency and machine tool motion stability; in the closed-loop optimization aspect, the response surface model is constructed based on cutting simulation-online measurement data, the path smoothing coefficient is optimized synchronously through the multi-objective particle swarm algorithm, the precision machining with high lead accuracy and low tooth profile error is realized, and the historical optimal parameter combination is automatically matched for the new batch of workpieces relying on the process knowledge base, which reduces the process debugging time and comprehensively improves the machining quality stability and production efficiency.

[0132] Although embodiments of the application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the application, the scope of the application being defined by the appended claims and their equivalents.

Claims

1. A closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage, characterized in that: The following steps are involved: Collect workpiece geometry, material properties, and machine tool dynamics, design tool and fixture solutions, and set initial machining parameters based on the cutting database; A high-precision surface model of the screw spiral groove is constructed using NURBS curves, and the path is optimized through curvature adaptive discretization and quintic spline interpolation; A real-coded genetic algorithm is used to iteratively optimize the empty travel path, shortening the total path time and the number of reversals. Multi-axis synchronous control is achieved through inverse kinematics solution, and the tool inclination angle is adjusted dynamically in real time based on the contact angle; Use virtual simulation and finite element analysis to predict cutting forces, thermal deformation and interference risks, and modify process parameters; Perform multi-axis machining and monitor loads in real time, using online measurement and CMM to check lead, roughness, and tooth profile accuracy; The path smoothing coefficient and genetic algorithm weight are optimized by combining processing data feedback to update the process knowledge base.

2. The closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage according to claim 1, characterized in that: The specific process of constructing a high-precision surface model of the screw spiral groove using NURBS curves and optimizing the path through curvature adaptive discretization and quintic spline interpolation includes: Based on the screw radius, lead and helix angle, the spiral groove geometric model is generated through the NURBS curve parametric equation, and the control points, weights and node vectors are defined to accurately represent the surface; Calculate the curvature of each point on the NURBS curve, dynamically adjust the discrete step length, and generate a high-density discrete point set to match the geometric features; The discrete points are segmented and fitted into quintic spline curves, forcing the continuity of position, velocity, acceleration and jerk between adjacent segments. The optimization goal is to minimize the total acceleration change of the path. Solve for the spline coefficients and generate a smooth G-code path containing coordinate, velocity, and acceleration instructions.

3. The closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage according to claim 2, characterized in that: The curvature k(u) of the curve at parameter u is calculated as follows: ； in, express, and Represent the first-order derivative and second-order derivative of the curve at u respectively; The discrete step length Δu is dynamically adjusted according to the curvature. The step length calculation formula is: ； in, is the maximum allowed step length, represents the curve sensitivity coefficient, Indicates the current discrete point parameter value.

4. The closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage according to claim 1 is characterized in that: The detailed process of using the real-coded genetic algorithm to iteratively optimize the idle travel path and shorten the total path time and the number of reversals includes: The three-dimensional coordinate sequence of the empty travel path is encoded into a real vector, and an initial population that satisfies the motion range constraints of the machine tool is randomly generated; Quantify path performance based on the weighted sum of total path time and number of turns; High-quality individuals are retained through tournament selection, diversity is introduced through arithmetic crossover and Gaussian mutation, and out-of-bounds coordinates are corrected to ensure path feasibility; When the number of iterations reaches the preset threshold or the optimal fitness is continuously stable, the optimization process is stopped; Decode the real number sequence of the individual with the best fitness, generate a three-dimensional path and transmit it to the actuator.

5. The closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage according to claim 4, characterized in that: The process of quantifying path performance based on the weighted sum of the total path time and the number of turns includes: A fitness function is constructed based on the total path time and the number of reversals, and the empty trip performance is quantified by weighted sum; The calculation formula of the fitness function is: ； Where F represents the weighted sum, T represents the total path time, and Ndir represents the number of direction changes.

6. The closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage according to claim 4, characterized in that: The specific process of achieving multi-axis synchronous control through inverse kinematics solution and dynamically adjusting the tool inclination angle in real time based on the contact angle includes: Construct a geometric kinematic model of the machine tool and define the spatial mapping relationship and constraint conditions between the tool coordinate system and each motion axis; According to the target path point coordinates and tool posture angle, the real-time displacement parameters of each motion axis are derived through inverse kinematics solution; Generate multi-axis synchronous control instructions based on displacement parameters, and use interpolation algorithms to achieve dynamic matching and coordination of the motion trajectories of each axis The tool-workpiece contact angle is acquired in real time by a force sensor or visual measuring device, and the inclination deviation is determined by comparing it with the process allowable threshold. If the contact angle exceeds the threshold, the tool inclination parameters are dynamically corrected and fed back to the inverse kinematics model.

7. The closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage according to claim 6, characterized in that: The process of deriving the real-time displacement parameters of each motion axis through inverse kinematics solution includes: The target position of the tool end in the base coordinate system given by the machining path planning; Through the machine tool geometry model, the Jacobian matrix of the differential relationship between the tool posture change and the displacement change of each axis is established; The axis displacement increment is solved by pseudo-inverse Jacobian matrix; The calculation formula for displacement increment is: ； in, , ; Where, represents the displacement increment vector of each axis, represents the pseudo-inverse of the Jacobian matrix, The deviation vector between the target pose and the current actual pose, Indicates the actual position of the current tool; By formula: Update axis displacement parameters; in, represents the axis displacement parameter after the kth iteration, and the update termination condition is: .

8. The closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage according to claim 1, characterized in that: The process of using virtual simulation and finite element analysis to predict cutting forces, thermal deformation, and interference risks and to modify process parameters includes: Based on the workpiece CAD model, tool geometry parameters and material constitutive relations, a finite element multi-physics coupling simulation model including cutting force, heat conduction and contact behavior is constructed; Drive the virtual simulation platform to perform dynamic simulation of the cutting process, and simultaneously analyze the cutting force time domain waveform, thermal deformation gradient field, and spatial distribution of the tool-workpiece interference area; Compare the cutting force peak, temperature extremes, and minimum interference distance output by the simulation with the preset process safety threshold to generate a risk level matrix and a list of key sensitive parameters; The spindle speed curve, feed acceleration curve and coolant injection strategy are optimized based on the risk matrix to generate a process parameter correction plan under multi-objective constraints.

9. The closed-loop optimization and real-time control process for precision milling of a lead screw based on multi-axis linkage according to claim 1, characterized in that: The process of optimizing the path smoothing coefficient and the genetic algorithm weight by combining processing data feedback and updating the process knowledge base includes: Based on the lead deviation distribution and tooth profile error spectrum measured online and the path optimization parameters generated by the genetic algorithm in step 3, a characteristic matrix including the path smoothing coefficient, time-commutation weight ratio and process parameter coupling degree is constructed; The nonlinear relationship between lead error and λ, and the interactive effect between tooth profile accuracy and time-commutation weight ratio were analyzed by partial least squares regression, and a response surface model of error source-process parameters was established. Based on the response surface model, a multi-objective particle swarm optimization algorithm is used to simultaneously optimize the smoothing coefficient of the quintic spline path and the time-commutation weight ratio of the genetic algorithm. The optimized parameter combinations and corresponding machining quality data are encoded as rule entries and stored in a relational database according to the material type-tool combination-machine model triples. When the coefficient of variation CV of a new workpiece batch is ≥5%, the historical optimal parameter combination is automatically matched based on the updated knowledge base, and the re-optimization process is triggered.

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