Automatic input system and method for geometric parameters of numerical control tool

Abstract

The invention relates to the technical field of numerical control machining, in particular to an automatic input system and method for geometric parameters of a numerical control tool. The method comprises the following steps of obtaining point cloud data and material characteristics of a tool, removing abnormal points to clean the data, extracting the edge contour of the tool, performing geometric parameter mapping through the edge contour, determining geometric characteristics of the tool, reconstructing a tool framework, obtaining a numerical control machining task, and performing cutting load simulation on the reconstructed tool. Analyzing the initial wear data, performing heat effect prediction based on cutting load, calculating the thermal deformation degree of the cutter, performing deformation compensation on the initial wear data, generating geometric deformation data, drawing a deformation-time curve, performing time sequence tracking parameter compensation according to the change process of the cutter, and realizing real-time recording and updating. According to the method, the management efficiency and the machining precision of the numerical control tool are integrally improved, and the stability and predictability of the production process are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical control machining, and in particular to a system and method for automatically inputting geometric parameters of numerical control tools. Background Art

[0002] Traditional methods for entering tool geometry parameters often rely on manual operation and static measurement, resulting in insufficient accuracy and real-time performance of data entry. Manual operation is not only prone to human errors, but also unable to promptly reflect changes in tool status. This problem is particularly prominent in high-precision machining and large-scale production environments. Traditional methods are relatively weak in data processing and analysis capabilities, and it is difficult to meet the needs of modern manufacturing for intelligence and automation. Tool wear and deformation are key factors affecting machining quality and efficiency. Existing technologies for monitoring tool wear rely more on regular inspections and fail to achieve real-time monitoring and dynamic adjustment. Especially under complex machining conditions, the thermal deformation and wear characteristics of the tool change rapidly. Traditional methods are unable to capture these changes in a timely manner, resulting in delayed evaluation of tool performance, which in turn affects the stability and reliability of the overall machining process. The lack of an effective compensation mechanism makes it impossible to effectively guarantee the tool life and machining quality. Summary of the invention

[0003] Based on this, it is necessary to provide a system and method for automatically entering the geometric parameters of CNC tools to solve at least one of the above technical problems.

[0004] To achieve the above purpose, the method for automatically entering the geometric parameters of a CNC tool comprises the following steps:

[0005] Step S1: Acquire tool point cloud data and tool material properties; remove abnormal points in the tool point cloud data to obtain cleaning tool point cloud data; extract tool edge contour based on the cleaning tool point cloud data;

[0006] Step S2: mapping geometric parameters through the tool edge profile to determine the tool geometric features; reconstructing the tool frame based on the tool geometric features;

[0007] Step S3: Acquire a numerical control machining task; simulate the machining cutting load of the reconstructed tool frame based on the numerical control machining task to obtain simulated machining cutting load data; and analyze the initial tool wear data according to the simulated machining cutting load data;

[0008] Step S4: Predicting the thermal effect based on the simulated machining cutting load data to obtain the predicted machining thermal effect; calculating the thermal deformation degree of the tool according to the tool material characteristics and the predicted machining thermal effect;

[0009] Step S5: Perform deformation compensation on the initial tool wear data based on the degree of tool thermal deformation to generate tool geometric deformation data; calibrate the corresponding deformation timestamps for the tool geometric deformation data, plot the deformation-time curve, and simultaneously determine the tool change process based on the deformation-time curve;

[0010] Step S6: Perform sequential tracking parameter compensation according to the tool change process to generate tracked compensated tool parameters; perform real-time entry and update based on the tracked compensated tool parameters.

[0011] The present invention ensures the accuracy and integrity of data by obtaining tool point cloud data and tool material characteristics. The elimination of outliers improves the data quality, enabling the more accurate extraction of the tool edge contour from the cleaned point cloud data, thereby ensuring the accuracy of geometric parameter mapping. The process of reconstructing the tool framework enhances the visualization and practicality of tool design, ensuring the stability and reliability of the tool during machining. The combination of obtaining the CNC machining task and simulating the cutting load provides in-depth analysis of tool wear, providing a scientific basis for tool performance evaluation. The simulated machining cutting load data can reflect the force condition of the tool during actual machining, laying a foundation for subsequent wear analysis. The prediction of the thermal effect combined with material characteristics can accurately calculate the degree of tool thermal deformation during machining, forming a comprehensive understanding of the tool thermal stress. The prediction of thermal deformation provides a basis for optimizing tool material selection and machining plans. The geometric deformation data generated by deformation compensation provides a reliable reference for the actual use of the tool, effectively reducing machining errors caused by thermal deformation. The addition of timestamps and the plotting of the deformation-time curve enable the real-time monitoring of tool state changes. The analysis of the deformation-time curve helps technicians better understand the performance of the tool during machining. The tracked compensated tool parameters generated by sequential tracking parameter compensation ensure the real-time update and accuracy of tool parameters. The implementation of tracked compensation improves the machining accuracy of the tool, reduces production fluctuations caused by tool parameter changes, overall improves the management efficiency and machining accuracy of CNC tools, promotes the development and application of intelligent manufacturing, and ultimately realizes the optimization of tool life cycle management, making the production process more efficient and economical, enhancing the competitiveness of enterprises. Especially in the context of the increasing demand for high-precision manufacturing, this method provides a new solution for the intelligent management of CNC tools, promotes the transformation of the manufacturing industry towards digitalization and intelligentization, ensures the tool performance in complex machining environments, improves the stability and predictability of the production process, and provides strong technical support and guarantee for the development of modern manufacturing.

[0012] The present invention also provides an automatic entry system for CNC tool geometric parameters, which is used to execute the automatic entry method for CNC tool geometric parameters as described above. The automatic entry system for CNC tool geometric parameters includes:

[0013] A point cloud acquisition module, which is used to acquire tool point cloud data and tool material characteristics; eliminate abnormal points in the tool point cloud data to obtain cleaned tool point cloud data; extract the tool edge contour according to the cleaned tool point cloud data;

[0014] A geometric mapping module, which is used to perform geometric parameter mapping through the tool edge contour to determine the tool geometric characteristics; reconstruct the tool framework based on the tool geometric characteristics;

[0015] A cutting simulation module, which is used to obtain a numerical control machining task; perform a machining cutting load simulation on the reconstructed tool framework based on the numerical control machining task to obtain simulated machining cutting load data; analyze the initial tool wear data according to the simulated machining cutting load data;

[0016] A thermal effect prediction module, which is used to perform a thermal effect prediction based on the simulated machining cutting load data to obtain a predicted machining thermal effect; calculate the tool thermal deformation degree according to the tool material characteristics and the predicted machining thermal effect;

[0017] A deformation compensation module, which is used to perform deformation compensation on the initial tool wear data based on the tool thermal deformation degree to generate tool geometric deformation data; calibrate a corresponding deformation timestamp for the tool geometric deformation data, draw a deformation-time curve, and determine the tool change process based on the deformation-time curve;

[0018] A parameter tracking module, which is used to perform sequential tracking parameter compensation according to the tool change process to generate tracked compensated tool parameters; perform real-time entry and update based on the tracked compensated tool parameters.

[0019] Through the application of the point cloud acquisition module, the present invention realizes the comprehensive acquisition of the tool point cloud data and material characteristics, eliminates abnormal points to improve the accuracy of the data, and the cleaned point cloud data ensures the accurate extraction of the tool edge contour. The introduction of the geometric mapping module makes the determination of the tool geometric characteristics more reliable. The ability to reconstruct the tool framework enhances the visualization and practicality of tool design. The cutting simulation module combines with the numerical control machining task to be able to perform real machining cutting load simulation, providing a deep analysis of tool wear. The simulation results provide a scientific basis for tool performance evaluation. The thermal effect prediction module can accurately predict the thermal effect of the tool during the working process by analyzing the machining cutting load data, and calculate the degree of tool thermal deformation, enabling the selection of tool materials and the optimization of machining plans to have theoretical support. The deformation compensation module compensates the initial tool wear data based on the degree of thermal deformation, and the generated geometric deformation data provides a reliable basis for the actual use of the tool. The addition of timestamps and the plotting of the deformation-time curve facilitate the real-time monitoring of tool state changes. The analysis of the deformation-time curve helps technicians understand the performance of the tool during machining. The parameter tracking module can perform sequential tracking according to the tool change process, generate tracking compensation tool parameters, ensuring the real-time update and accuracy of tool parameters. The implementation of tracking compensation improves the machining accuracy of the tool and reduces production fluctuations caused by tool parameter changes. The entire system improves the management efficiency and machining accuracy of numerical control tools, promotes the development and application of intelligent manufacturing, and finally realizes the optimization of tool life cycle management, making the production process more efficient and economical, enhancing the competitiveness of enterprises. Especially in the context of the increasing demand for high-precision manufacturing, it provides a new solution for the intelligent management of numerical control tools, promotes the process of the transformation of the manufacturing industry to digital and intelligent, ensures the stability and predictability of tool performance in complex machining environments, provides strong technical support and guarantee for the development of modern manufacturing, promotes the scientific and systematic management of tools, and improves production efficiency. Description of the Drawings

[0020] Figure 1 It is a schematic flow chart of the steps of an automatic input method for the geometric parameters of a numerical control tool;

[0021] Figure 2 It is a schematic detailed implementation step flow chart of step S2;

[0022] The realization, functional characteristics and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the drawings. Detailed Embodiment

[0023] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0024] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different network and / or processor methods and / or microcontroller methods.

[0025] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0026] To achieve this, please refer to Figures 1 to 2 , a method for automatically entering geometric parameters of a numerical control tool, comprising the following steps:

[0027] Step S1: Acquire tool point cloud data and tool material properties; remove abnormal points in the tool point cloud data to obtain cleaning tool point cloud data; extract tool edge contour based on the cleaning tool point cloud data;

[0028] Step S2: mapping geometric parameters through the tool edge profile to determine the tool geometric features; reconstructing the tool frame based on the tool geometric features;

[0029] Step S3: Acquire a numerical control machining task; simulate the machining cutting load of the reconstructed tool frame based on the numerical control machining task to obtain simulated machining cutting load data; and analyze the initial tool wear data according to the simulated machining cutting load data;

[0030] Step S4: Predicting the thermal effect based on the simulated machining cutting load data to obtain the predicted machining thermal effect; calculating the thermal deformation degree of the tool according to the tool material characteristics and the predicted machining thermal effect;

[0031] Step S5: Perform deformation compensation on the initial tool wear data based on the degree of tool thermal deformation to generate tool geometric deformation data; calibrate the corresponding deformation timestamp for the tool geometric deformation data, plot the deformation-time curve, and simultaneously determine the tool change process based on the deformation-time curve;

[0032] Step S6: Perform timing tracking parameter compensation according to the tool change process to generate tracking compensation tool parameters; perform real-time entry and update based on the tracking compensation tool parameters.

[0033] The present invention ensures the accuracy and integrity of the data by obtaining the tool point cloud data and the tool material characteristics. The elimination of abnormal points improves the data quality, enabling the more accurate extraction of the tool edge contour from the cleaned point cloud data, thereby ensuring the accuracy of geometric parameter mapping. The process of reconstructing the tool framework enhances the visualization and practicality of tool design, ensuring the stability and reliability of the tool during machining. The acquisition of the numerical control machining task in combination with the cutting load simulation provides an in-depth analysis of tool wear, providing a scientific basis for tool performance evaluation. The simulated machining cutting load data can reflect the force condition of the tool during actual machining, laying a foundation for subsequent wear analysis. The combination of thermal effect prediction and material characteristics can accurately calculate the degree of tool thermal deformation during machining, forming a comprehensive understanding of the tool thermal stress. The prediction of thermal deformation provides a basis for optimizing tool material selection and machining plans. The geometric deformation data generated by deformation compensation provides a reliable reference for the actual use of the tool, effectively reducing the machining error caused by thermal deformation. The addition of timestamps and the plotting of the deformation-time curve enable the real-time monitoring of tool state changes. The analysis of the deformation-time curve helps technicians better understand the performance of the tool during machining. The tracking compensation tool parameters generated by timing tracking parameter compensation ensure the real-time update and accuracy of tool parameters. The implementation of tracking compensation improves the machining accuracy of the tool, reduces production fluctuations caused by tool parameter changes, overall improves the management efficiency and machining accuracy of numerical control tools, promotes the development and application of intelligent manufacturing, and ultimately realizes the optimization of tool life cycle management, making the production process more efficient and economical, enhancing the competitiveness of the enterprise. Especially in the context of the increasing demand for high-precision manufacturing, this method provides a new solution for the intelligent management of numerical control tools, promotes the transformation of the manufacturing industry towards digitalization and intelligentization, ensures the tool performance in complex machining environments, improves the stability and predictability of the production process, and provides strong technical support and guarantee for the development of modern manufacturing.

[0034] In the embodiment of the present invention, the automatic entry method for the geometric parameters of the numerical control tool includes the following steps:

[0035] Step S1: Acquire tool point cloud data and tool material properties; remove abnormal points in the tool point cloud data to obtain cleaning tool point cloud data; extract tool edge contour based on the cleaning tool point cloud data;

[0036] In this embodiment, during the tool point cloud data acquisition stage, a three-dimensional laser scanner Leica AbsoluteScanner model LAS-20 is used, the scanning head resolution is set to 0.01 mm, the scanning frequency is 500 Hz, and the scanning distance is set within 400 mm. The standard carbide turning tool is fully scanned to obtain the tool surface point cloud data. The data format is uniformly converted into an xyz coordinate format. The tool material properties are indexed by the scan number and the preset material table in the database, and the tool material recorded by the corresponding number is extracted. The recorded attributes include material type, density, thermal expansion coefficient, Young's modulus and hardness. The material density is limited to 14.5 grams per cubic centimeter, and the thermal expansion coefficient is 4.8×10⁻ 6 Per degree Celsius, Young's modulus is set to 590GPa, hardness range is HRC85 to HRC90. In the stage of eliminating abnormal points, based on the statistical filtering method, the distance threshold is set to 0.3 mm, and the average distance of 50 neighboring points with a radius of 0.5 mm in the neighborhood of each point in the point cloud is calculated. Points exceeding the average value ± 2 times the standard deviation are eliminated as abnormal points. The cleaned tool point cloud data is subjected to edge detection. The edge detection algorithm based on the normal vector change rate is used to detect the position where the normal vector angle is greater than 45° as the edge point. The edge point cloud is extracted and fitted into a continuous contour segment, and the tool edge contour data is output.

[0037] Step S2: mapping geometric parameters through the tool edge profile to determine the tool geometric features; reconstructing the tool frame based on the tool geometric features;

[0038] In this embodiment, the edge contour of the tool is extracted and the Bezier curve interpolation method is used to fit the discrete point sequence of the edge contour into a continuous curve. The Bezier curve order is set to fourth order, and the interpolation interval is set to 0.05 mm. The geometric characteristics of the tool, such as the front angle, back angle, tool tip arc radius, cutting edge length and main deflection angle, are calculated based on the fitting curve. The front angle range is limited to 5° to 15°, the back angle is 8° to 20°, and the tool tip arc radius is set to 0.2 mm to 0.8 mm. Based on the above geometric characteristics, the parametric modeling method is used to reconstruct the three-dimensional geometric structure of the tool. The reconstruction uses the SolidWorks modeling module, and the input parameter value setting is strictly consistent with the extracted characteristic value, and the complete tool three-dimensional model STL format data file is output.

[0039] Step S3: Acquire a numerical control machining task; simulate the machining cutting load of the reconstructed tool frame based on the numerical control machining task to obtain simulated machining cutting load data; and analyze the initial tool wear data according to the simulated machining cutting load data;

[0040] In this embodiment, in the CNC machining task data acquisition stage, the machining task database in the MES system (Task Manager) is called to extract the workpiece material, machining depth, machining feed speed, spindle speed, tool type and machining environment temperature under the current machining task number. The workpiece material is 45 steel, the machining depth is 5 mm, the feed speed is 300 mm per minute, and the spindle speed is set to 2500 rpm. Based on the machining task parameters, the reconstructed tool frame is imported into the Deform-3D simulation platform to build a tool-workpiece-environment simulation model, set the interface friction factor to 0.12, the simulation step length to 0.001 second, the simulation cutting path is consistent with the task path, and the simulation outputs the three-axis components of the cutting force Fx, Fy, and Fz. After the simulation is completed, the average cutting force data on all cutting steps is extracted. According to the average cutting force distribution and combined with the wear experience model, the tool wear rate is calculated, and the wear critical point is determined based on the cutting force peak value exceeding 700 Newtons to generate an initial tool wear data table.

[0041] Step S4: Predicting the thermal effect based on the simulated machining cutting load data to obtain the predicted machining thermal effect; calculating the thermal deformation degree of the tool according to the tool material characteristics and the predicted machining thermal effect;

[0042] In this embodiment, the cutting load data obtained by simulation was imported into the COMSOL Multiphysics simulation platform to construct a tool thermal-mechanical coupling model. The initial temperature of the tool was set to 25 degrees Celsius, and the heat flux density of the workpiece contact surface was converted according to the simulated cutting force and friction power. The heat flux density was set to 5×10 5 Watts per square meter, simulation time 5 seconds, step size 0.01 seconds, output temperature field distribution, based on the thermal expansion coefficient of the tool material 4.8×10⁻ 6 For every degree Celsius, the linear expansion of the tool in the maximum hot zone is calculated. The expansion length ΔL is equal to the thermal expansion coefficient multiplied by the length multiplied by the temperature rise. The length is 50 mm from the tip to the handle. The temperature rise is the maximum temperature of 320 degrees Celsius obtained by simulation minus the initial temperature of 25 degrees Celsius. The degree of thermal deformation of the tool is calculated, and the thermal deformation vector field data of each part of the tool is output.

[0043] Step S5: performing deformation compensation on the initial tool wear data based on the degree of thermal deformation of the tool to generate tool geometric deformation data; calibrating the corresponding deformation timestamp for the tool geometric deformation data, and drawing a deformation-time curve, and determining the tool change process based on the deformation-time curve;

[0044] In this embodiment, the degree of tool thermal deformation is applied to the initial tool wear data. Based on the finite element node interpolation method, the thermal deformation vector is superimposed on the corresponding wear position coordinates to correct the wear size, and the tool geometric deformation data after deformation compensation is obtained. The data format uses a csv table, which includes three columns: node coordinates, deformation values, and wear values after compensation. All data is added with a timestamp, the timestamp accuracy is set to 0.01 seconds, the starting value is set to the machining start time, a deformation-time curve is plotted, the abscissa is time, the ordinate is the deformation values of each key node, the Matplotlib graphics library is used, the line chart mode is set, the line width is 2 pixels, the peak points of each stage are marked, and according to the curve change trend, the tool change process is segmented and marked. The change process is divided into a linear growth segment, a slow growth segment, and a sharp rise segment, and a complete tool change process data file is output.

[0045] Step S6: Perform timing tracking parameter compensation according to the tool change process to generate tracking compensation tool parameters; perform real-time entry and update based on the tracking compensation tool parameters.

[0046] In this embodiment, according to the tool change process data, a timing tracking compensation rule is set. If the tool deformation value at a certain moment is greater than 0.15 mm, it is determined that the tool wear is critical. According to the compensation rule, the tool geometric parameters at the current moment are adjusted to the state with the minimum deformation value, and all geometric parameters affected by the deformation are corrected synchronously. The corrected parameter values are written into the tracking compensation tool parameter table. The parameter table includes the current moment, tool type, corrected geometric parameter values, and wear status marks, and is real-time entered into the numerical control tool parameter library. The entry period is set to 0.01 seconds, and the entry tool uses a real-time data writing interface based on SQL Server. The interface cache queue depth is 128 items, ensuring that all tracking compensation tool parameters are accurately updated to the tool parameter database in chronological order.

[0047] Preferably, step S1 includes the following steps:

[0048] Step S11: Perform multi-angle laser scanning on the surface of the numerical control tool to obtain tool point cloud data. Among them, the scanning angle range is set to 15° to 75°, and the single-scan point distance is set to 0.01 mm to 0.05 mm;

[0049] Step S12: Calibrate the spatial coordinate system of the tool point cloud data to generate calibrated tool point cloud data; perform outlier monitoring on the calibrated tool point cloud data to obtain marked abnormal point position data;

[0050] Step S13: Perform abnormal correction on the calibrated tool point cloud data based on the marked abnormal point position data to obtain cleaned tool point cloud data. Among them, the correction range is limited to ±0.05 mm;

[0051] Step S14: Perform 3D surface fitting on the cleaned tool point cloud data and conduct edge detection to generate the tool edge contour, where the surface fitting residual is controlled within 0.01 mm.

[0052] In this embodiment, when performing surface multi-angle laser scanning on a numerical control tool, a three-dimensional laser scanning device of model FARO QuantumS is used, and it is equipped with a FARO LLP (Laser Line Probe) to perform high-density point cloud acquisition. The tool is fixed at the center of a five-axis numerical control turntable. The tool is adjusted to rotate around the Y-axis through the turntable. The rotation angle range is limited to 15° to 75°, the step angle is set to 5°, the emission wavelength of the scanning light source is set to 632.8 nm, the laser line width is limited to 0.02 mm, the single-scan point distance is set at 0.02 mm, and the point cloud density reaches more than 2,500 points per square millimeter. The point distance is jointly controlled by the acquisition rate of the laser head and the rotation speed of the turntable. The rotation speed is limited to 5° / s. The scanning path adopts a spiral progressive layer-by-layer trajectory. The obtained point cloud data is recorded in the form of three-dimensional coordinates, with the unit of millimeters, and the.ply file format is used, which contains XYZ coordinates and reflection intensity. Ensure that there is no shadow area on the tool surface during each scan. Perform a spatial coordinate calibration operation on the acquired tool point cloud data. Use a standard positioning ball group with a fixed ball center radius of 10 mm as the reference. The positioning balls are distributed around the scanning area. The three-dimensional coordinate origin is set at the center of any one of the positioning balls. Use the least squares method to fit the point cloud on the surface of the positioning ball, extract the ball center coordinates, calculate the rotation matrix and translation vector according to the fitted ball center position and the theoretical standard coordinates, perform coordinate transformation, and complete the unification of the spatial coordinate system. The calibrated tool point cloud data is recorded with a five-digit decimal precision, and the coordinate unit is millimeters. For the calibrated point cloud data, use a statistical outlier detection method. Set the Z-score threshold to 3. Calculate the mean value of each point and its 50 neighboring points, and calculate the deviation value. If the deviation value exceeds 3 times the standard deviation, it is determined as an abnormal point. Mark the abnormal point data in the additional marking column of the point cloud file. The abnormal point value is recorded as 1, and the normal point value is recorded as 0, forming a point cloud data set containing abnormal position information. When performing abnormal correction on the calibrated tool point cloud data based on the marked abnormal position data, use a weighted neighborhood average interpolation algorithm. Set the search radius to 0.1 mm, and extract no less than 30 normal positions within the search radius. Calculate the weighted average value of the neighborhood points according to the inverse distance weighting, and replace the abnormal position coordinates. The corrected value is limited within the range of ±0.05 mm above and below the original position value. If the corrected calculation result exceeds this range, the upper and lower limit values are directly taken as the interval boundary values to complete the correction operation. After the correction is completed, all abnormal point marks are cleared, and the abnormal position marking column is uniformly set to 0. The corrected cleaned tool point cloud data is retained to a five-digit decimal precision. The corrected point cloud file adopts...PLY format, including XYZ coordinates, reflection intensity, and a column for marked abnormal points after clearing. When performing three-dimensional surface fitting on the cleaned tool point cloud data, the Rational B-Spline surface modeling method is adopted. The point cloud is divided into several sub-regions according to the fitting area, with the size of each sub-region limited to 1mm×1mm. The number of points in a single sub-region shall not be less than 500. The number of fitting control points is fixed at 10×10. Global surface fitting is performed using Rational B-Spline, and the fitting residual is controlled within 0.01mm. The residual value is calculated from the position deviation value between the fitting points and the original point cloud points. After fitting, an edge detection operation is performed, and the Canny operator is used for edge detection. The upper limit of the Canny algorithm threshold is set to 0.3, and the lower limit is set to 0.1. The set of detected edge points is stored in the form of two-dimensional projection coordinates, with the unit of millimeters. The output file format is set to.csv, and the file contains the X coordinate, Y coordinate, and edge intensity value. Finally, complete and accurate tool edge contour data is obtained.

[0053] Preferably, step S2 includes the following steps:

[0054] Step S21: Perform Fourier descriptor transformation on the tool edge contour to obtain the frequency domain characteristics of the tool contour; perform principal component analysis on the frequency domain characteristics of the tool contour to generate the geometric parameters of the tool spindle;

[0055] Step S22: Determine the tool geometric parameters based on the geometric parameters of the tool spindle and the tool contour parameters; extract the tool geometric features according to the tool geometric parameters;

[0056] Step S23: Perform surface envelope fitting according to the tool geometric features to generate the tool shape envelope; perform axial cross-section tomography on the tool shape envelope and dissect it into equal cross-section slices;

[0057] Step S24: Perform geometric projection fusion based on the equal cross-section slices to obtain the contour feature fusion data; reconstruct the tool framework according to the contour feature fusion data and the tool geometric features.

[0058] In this embodiment, when performing Fourier Descriptor transformation on the tool edge profile data, the two-dimensional contour projection data in.csv format obtained in the steps is used as the input. The X coordinate and the Y coordinate are extracted to form a complex number form point sequence. The X coordinate is used as the real part and the Y coordinate is used as the imaginary part. The total number of data points is not less than 2048. The Fast Fourier Transform (FFT) algorithm is used to perform Fourier transform on the complex number point sequence, and the complex coefficients in the transformation result are extracted as the frequency domain features of the tool contour. The first 64 complex coefficients are retained as the frequency domain components mainly describing the contour shape, and the high-frequency coefficients after the 64th order are discarded. The components with the amplitude of the high-frequency coefficients lower than 0.001 mm are directly set to zero. The frequency domain feature data is recorded in the form of a two-column array. The first column is the coefficient modulus value with the unit of millimeter, and the second column is the coefficient phase with the unit of radian. Principal Component Analysis (PCA) is performed on the obtained 64-order frequency domain feature data, and the first 3 principal components with the variance contribution rate exceeding 95% are extracted. The eigenvectors of these 3 principal components are multiplied by the original frequency domain feature matrix to obtain a low-dimensional tool contour feature vector. This feature vector corresponds to the geometric parameters in the tool spindle direction, and the number of parameters is fixed at 3, which respectively represent the spindle diameter, the eccentricity, and the yaw angle. The numerical units are unified as millimeters. Based on the above tool spindle geometric parameters and the original tool contour frequency domain features, the complete tool geometric parameters are determined. First, the 64-order frequency domain feature data is inverse-transformed back to the spatial contour curve to restore the contour two-dimensional coordinate points. The number of restored points is the same as the original number of points, and the point distance error does not exceed 0.005 mm. The maximum contour diameter value is extracted in combination with the spindle diameter parameter, the eccentricity parameter determines the offset position of the contour center, and the yaw angle parameter defines the angle between the contour spindle and the coordinate system Z axis. Combining the above data, the outer diameter, the center offset, the spindle tilt direction angle, the end face thickness, and the edge fillet radius of the tool are determined. In the extraction method, quantitative geometric measurement is adopted for each parameter. The outer diameter is directly taken as the maximum diameter, the end face thickness is calculated according to the difference between the maximum and minimum values of the projection of the contour points along the Z axis, and the edge fillet radius is calculated by fitting the edge arc segment and calculating the radius value. The number of points for fitting the arc segment is not less than 128, and the fitting residual does not exceed 0.005 mm. When extracting the tool geometric features according to the complete tool geometric parameters, based on parameters such as the outer diameter, the spindle tilt direction angle, and the eccentricity, according to the envelope surface generation rule, the outer circle part of the tool is defined as the reference surface, the end face part is defined as the normal surface, and the edge fillet area is defined as the transition surface. The surface joint points are generated based on the contact points of the three. The envelope contour is fitted with multi-segment Bezier curves. The number of control points for each segment of Bezier curve is 4, the length of the curve segment does not exceed 0.5 mm, and the difference between the fitting residuals of adjacent curves does not exceed 0.003 mm. The fitting curve records coordinates with five - decimal - place precision. All control points and curvature continuity of the fitting curve are recorded in the geometric feature database, and a tool geometric feature code is generated. The code includes the outer - circle diameter, eccentricity, tilt - direction angle, end - face thickness, edge - radius, positions of curvature extreme points, and corresponding curvature values. According to the above - mentioned tool geometric features, a tool - shape envelope is generated using the two - way surface - envelope fitting method. The reference outer - circle curve and end - face curve are used as fitting boundary conditions, and two - way Bezier surface fitting is used. The surface order is set to the third order, the control - point array is set to 16×16, and the fitting residual is controlled within 0.01 mm. After the envelope fitting is completed, an axial - section tomography operation is performed. Along the Z - axis direction of the tool axis, the surface is cut at a step of 0.1 mm to generate equal - cross - section slices. The number of contour points in each slice is not less than 512, the recording precision of contour - point coordinates is 0.001 mm, and each slice is stored as separate two - dimensional contour data in.csv format, including the X - coordinate, Y - coordinate, and Z - axis coordinate where the slice is located. Based on all equal - cross - section slices, geometric - projection fusion is performed. All slice contour points are projected onto the XZ plane and YZ plane along the Z - axis direction to generate the XZ - projection contour and YZ - projection contour respectively. Edge detection is performed on the projection contours using the Sobel operator with a threshold set to 0.2. The contour - boundary coordinate points are extracted, and the average width, maximum width, minimum width, and edge - curvature distribution of each projection contour are calculated. The contour - characteristic data are recorded in the form of an array with the unit of millimeters. According to all projection - contour characteristic data and the aforementioned tool geometric features, a tool - space framework is reconstructed using two - way Bezier surface. The number of surface control points is set to 32×32, and the residual of the reconstructed surface is controlled within 0.015 mm. The surface control points correspond to the original geometric - feature points, and the coordinate precision of the control points is maintained to five decimal places, including complete tool - geometric - space - structure data.

[0059] Preferably, step S3 includes the following steps:

[0060] Step S31: Obtain a numerical - control machining task; extract task - process parameters according to the numerical - control machining task;

[0061] Step S32: Predict the cutting - force distribution based on the task - process parameters; perform a cutting - force response simulation on the reconstructed tool framework according to the predicted cutting - force distribution to generate simulation - cutting - response data;

[0062] Step S33: Perform a cutting - load mapping according to the simulation - cutting - response data to generate simulation - machining - cutting - load data;

[0063] Step S34: Calculate the cumulative - load sequence based on the simulation - machining - cutting - load data; analyze the theoretical wear rate according to the cumulative - load sequence;

[0064] Step S35: Estimate the tool wear of the reconstructed tool frame according to the theoretical wear rate to generate initial tool wear data.

[0065] In this embodiment, when obtaining a numerical control machining task, the task scheduling module in the machine tool control system is called to extract the current machining work order to be executed from the machining task scheduling table built into the numerical control machine (NC machine). Through parsing the G-code program file stored in the machine tool controller, process parameter information such as machining process number, feed rate, spindle speed, machining path trajectory coordinates, feed direction, tool number, and cutting depth is extracted. All process parameters are in a unified structured format and are extracted sequentially according to the machining order. The set range of the feed rate is from 80 mm / min to 3000 mm / min, the set range of the spindle speed is from 500 rpm to 20000 rpm, and the cutting depth range is from 0.1 mm to 5 mm. After all parameter extractions are completed, they are organized into a standardized JSON data format and stored in the intermediate buffer for subsequent calls in the link of predicting the cutting force distribution. When predicting the cutting force distribution based on the task process parameters, a cutting force prediction model constructed based on the BP neural network (Back Propagation Neural Network) is used. The input parameters include spindle speed, feed rate, cutting depth, tool geometric parameters, and material mechanical property parameters. The tool geometric parameters include rake angle, clearance angle, helix angle, and nose radius. The material mechanical property parameters include Young's modulus, Poisson's ratio, and yield strength. The specific value ranges are as follows: rake angle from 6° to 20°, clearance angle from 6° to 15°, helix angle from 30° to 60°, nose radius from 0.2 mm to 1.2 mm, Young's modulus from 160 GPa to 210 GPa, Poisson's ratio from 0.28 to 0.34, and yield strength from 400 MPa to 900 MPa. The BP neural network adopts a three-layer structure, with 12 nodes in the input layer, 24 nodes in the hidden layer, and 3 nodes in the output layer, corresponding to the predicted values of the cutting force distribution in the X, Y, and Z directions. The neural network training samples are from the measured cutting force data under existing machining conditions. The mean square error minimization is used as the error function, and the number of iterations is 5000. After the prediction is completed, the cutting force distribution field data in the three-dimensional coordinate system is generated. According to the predicted cutting force distribution, a cutting dynamic response simulation is carried out on the reconstructed tool structure. Using the ANSYS Mechanical module of the finite element analysis software, the tool structure is established as a three-dimensional solid model and divided into tetrahedral elements, with the side length of each element not greater than 0.2 mm. The predicted cutting force distribution data is applied as an external load to the main cutting edge area of the tool, and the fixed constraint condition of the tool holder is set. The tool material property is defined as high-speed steel, with a density of 7800 kg / m³, Young's modulus of 210 GPa, and Poisson's ratio of 0.3. The static analysis module is used to solve the displacement, stress, and strain responses of each node of the tool, and the simulation cutting response data file is output, recording the node number, coordinate position, displacement vector, maximum principal stress, and equivalent strain value. The simulation step size is 0.0.01 seconds, cumulative simulation time is 1 second. Perform cutting load mapping based on the simulated cutting response data. Using grid mapping technology, project the node load results of the simulation model to the corresponding machining path position points. The path position points are generated according to the machining path G-code, with a path step distance of 0.5 mm. During the mapping process, based on the nearest neighbor interpolation method, assign the cutting load values of each simulation node to the machining path points, and record the simulated machining cutting load data after the mapping is completed. The data format is CSV, including path point number, three-dimensional coordinates, and load values in the X, Y, and Z directions. Calculate the cumulative load sequence based on the simulated machining cutting load data. Using the cumulative superposition method, sequentially add the loads in the X, Y, and Z directions point by point along the path from the starting point to the ending point of the machining path, and record the cumulative values in the corresponding path point numbers. The update step of the cumulative sequence is 0.5 mm. The generated cumulative load sequence has a unit of N·mm, and the sequence length is equal to the number of machining path points. Analyze the theoretical wear rate based on the cumulative load sequence. Using the Archard wear model, the input parameters are the cumulative normal force, cutting path length, tool hardness, and wear coefficient. The tool hardness is taken as 62 HRC, and the wear coefficient is taken as 0.0000025 mm³ / N·mm. Calculate the wear rate per unit length of each path point and summarize to obtain the theoretical wear rate data. Estimate the tool wear of the reconstructed tool framework based on the theoretical wear rate. Apply the corresponding wear rate to the position of each cutting path point based on the three-dimensional solid model. Using the geometric reduction method, reduce the corresponding wear depth along the cutting contact normal direction according to the position of the path point. The wear depth calculation method is the wear rate multiplied by the path step distance. Generate the initial tool wear data file for the updated three-dimensional tool model, recording the node numbers, coordinate positions, and wear depths of each surface of the tool.

[0066] Particularly importantly, step S32 includes:

[0067] Perform force field load mapping conversion on the task process parameter data to obtain cutting load mapping data;

[0068] Subdivide the contact area action weights of the cutting load mapping data to generate regional load distribution data;

[0069] Perform load gradient matrix fitting based on the regional load distribution data to obtain the gradient load matrix;

[0070] Perform tool framework load mapping coupling on the gradient load matrix data to obtain tool load action data;

[0071] Perform cutting contact surface effect mapping based on the tool load action data to obtain cutting response mapping data;

[0072] Perform response trajectory reconstruction simulation on the cutting response mapping data to obtain simulated cutting response data.

[0073] In this embodiment, when performing a force field load mapping conversion on the task process parameter data, a cutting force calculation engine is called. The input includes task process parameter data such as spindle speed, feed rate, cutting depth, tool rake angle, clearance angle, helix angle, tool nose radius, Young's modulus of the material, yield strength, and Poisson's ratio. This data is used as the input quantity for the cutting force empirical prediction model. The prediction model adopts a method of superimposing a multivariable regression and a physical model. The regression model coefficients are fitted based on existing cutting force measurement data. The physical model is calculated based on the relationship between the main cutting edge length, cutting area, and load per unit area. The load per unit area of cutting ranges from 500 N / mm² to 3500 N / mm². Combining the G-code trajectory information of the machining path, the prediction result is mapped to the corresponding coordinates of the path points in the form of a three-dimensional vector. The load components are calculated separately in the X, Y, and Z directions, and the cutting load mapping data is output and stored as a data table in a three-column vector format. The fields of the data table include path point number, three-dimensional coordinates, X-direction load, Y-direction load, and Z-direction load. When dividing the action weight of the contact area of the segmented cutting load mapping data, according to the position of the path points on the machining path and in accordance with the principle of dividing the cutting area based on the contact area of the tool cutting edge surface, the cutting area is divided into three types of areas: the rake face contact area, the flank face contact area, and the tool nose transition area. The division of the contact area is determined by the nearest neighbor relationship between the tool model grid node positions and the machining path points. The grid node density is set to 30 nodes per millimeter. According to the cutting contact theory, the action weight of the rake face contact area is set to 0.5, the action weight of the flank face contact area is set to 0.35, and the action weight of the tool nose transition area is set to 0.15. Multiply each path point load value by the corresponding action weight and output the regional load distribution data. The data table structure maintains four fields: path point number, regional type, X-direction load, Y-direction load, and Z-direction load. When fitting the load gradient matrix based on the regional load distribution data, a three-dimensional quadratic polynomial surface fitting method is used. The fitting equation form is the quadratic polynomial relationship of the regional load distribution in the X, Y, and Z directions with respect to the machining path coordinate points X, Y, and Z. The least squares method is used for fitting. All fitting sample points are provided by the regional load distribution data table. The fitting result outputs a three-dimensional gradient matrix, and the matrix dimension is set to the number of points of the machining path length / stepping distance. Each matrix element records the fitted gradient value, and the unit of the gradient value is N / mm. Perform maximum, minimum, and mean statistics on the three-dimensional gradient matrix respectively and record them in the load gradient matrix data table. The data table contains four columns: matrix number, three-dimensional coordinate position, X gradient, Y gradient, and Z gradient. When performing tool frame load mapping coupling on the gradient load matrix data, import the tool three-dimensional finite element mesh model and use the ANSYS APDL script module. Take the gradient load matrix data as the external load input and apply it to the tool model mesh nodes. The node matching method is based on the nearest neighbor interpolation method of the path point coordinates and the tool mesh node positions. Each tool model node applies the X, Y, and Z three-direction coupling loads according to the interpolation results of the gradient loads of adjacent path points. The load value is calculated by linear interpolation. The boundary condition of the tool model is set to fix the tool handle and release other degrees of freedom. After the load application is completed, output the tool load action data. The data format is the ANSYS standard RST result file, which records the force condition, displacement, and stress response values of each node. The node number corresponds one-to-one with the load application point number. When performing cutting contact surface effect mapping based on the tool load action data, call the ANSYS Workbench Mechanical module, read the RST result file, and extract the stress, strain, and displacement response values at the surface mesh nodes of the tool model. According to the definition of the tool-workpiece contact area, only retain the surface node response data located in the rake face, flank face, and tool tip area of the tool. The node screening condition is that the angle between the surface normal and the cutting direction is less than 30°. After screening, map the stress, strain, and displacement values of each surface node to the corresponding machining path point. The mapping method uses the weighted average method based on the Euclidean distance. Each path point takes the response values of the 5 surface nodes closest to it, with weight coefficients 0.4, 0.3, 0.15, 0.1, 0.Perform weighted summation, and use the calculated cutting contact surface response data as cutting response mapping data, which is saved as a seven-column data table with path point number, X, Y, Z coordinates, normal stress, normal strain, and normal displacement. When performing response trajectory reconstruction simulation on the cutting response mapping data, based on the ABAQUS Explicit dynamics solver, establish a discrete point trajectory model of the machining path, use the cutting response mapping data as the external force input for the path points, define the normal stress, normal strain, and normal displacement of the path points as loading conditions, with a step spacing of 0.5 mm for the trajectory points, a total path length of 100 mm, a time step of 0.0001 seconds, and a total simulation time of 0.1 seconds. The applied external force is loaded in a linearly increasing manner with time. The simulation outputs the displacement trajectory of the path points, the velocity change, and the path deformation response, records the simulation cutting response data, and saves it as a TXT format file. The file content includes five fields: time step number, path point number, displacement vector, velocity vector, and deformation amount.

[0074] Preferably, the thermal effect prediction based on the simulated machining cutting load data in step S4 includes:

[0075] Perform power structure separation based on the simulated machining cutting load data to obtain the power data in each direction;

[0076] Perform cutting plastic zone power mapping based on the power data in each direction and the tool material characteristics to generate cutting deformation power;

[0077] Determine the tool-workpiece contact unit according to the reconstructed tool framework; perform contact zone thermal response simulation based on the power data in each direction and the tool-workpiece contact unit to obtain the simulated contact zone response;

[0078] Perform thermal power backtracking mapping based on the simulated contact zone response to generate contact friction power;

[0079] Perform thermal conversion rate conversion on the cutting deformation power and the contact friction power to generate deformation heat power data and friction heat power data respectively;

[0080] Perform thermal accumulation processing on the deformation heat power data and the friction heat power data according to the simulated machining cutting load data to obtain the accumulated deformation heat and the accumulated friction heat respectively;

[0081] Perform heat source spatial mapping based on the accumulated deformation heat and the accumulated friction heat to generate heat source distribution data;

[0082] Perform heat source superposition coupling on the accumulated deformation heat and the accumulated friction heat according to the heat source distribution data, and perform thermal diffusion response prediction to obtain the predicted machining thermal effect.

[0083] In this embodiment, power structure separation is performed based on the cutting load data of simulation machining. First, the cutting process is simulated through simulation machining software (such as ANSYS, Abaqus, etc.) to obtain cutting load data, including the mechanical responses in different cutting stages. According to the simulation results, the cutting load is separated into power according to different cutting directions. Specifically, by decomposing the cutting force data, the cutting force is respectively converted into axial power, cutting force power, and radial power data according to different cutting directions. Based on mechanical calculations, the power data range in each direction is set. Usually, the power data is separated according to the X-axis (axial), Y-axis (radial), and Z-axis (cutting force). The power data in each direction will be processed separately through a mechanical calculation model and normalized according to cutting parameters such as cutting speed, feed rate, and cutting depth to ensure accurate calculation of the power data in different directions. Finally, the power data in each direction is obtained. Based on the power data in each direction and the tool material characteristics, power mapping of the cutting plastic zone is performed. First, the characteristics of the tool material are analyzed, such as hardness, plasticity, etc. The material characteristics of the tool will affect the formation and power consumption of the plastic zone during the cutting process. After obtaining the power data in each direction, based on the characteristics of the tool material, power mapping of the cutting plastic zone is performed. The tool material models used in the mapping process include a shear strength model and a plastic deformation model. Assuming that the contact area between the tool and the workpiece is the cutting plastic zone, the power of the cutting plastic zone is calculated through the correlation between the cutting force and the cutting angle. This power is manifested as the generation and consumption of heat during the plastic deformation process. Combining the actual process parameters, the value of the cutting deformation power is obtained. According to the reconstructed tool framework, the tool-workpiece contact unit is determined. When reconstructing the tool framework, based on the geometric shape of the tool and the initial state of the workpiece, the contact surface between the tool and the workpiece is calculated through numerical simulation. First, it is necessary to model through the contact situation between the cutting edge of the tool and the workpiece surface. Using the finite element analysis (FEA) method, the contact area between the tool and the workpiece is divided into multiple small units. The cutting edge of the tool serves as the boundary of the contact unit. By analyzing the stress and strain in the contact area, the tool-workpiece contact unit is determined. On this basis, further calculations are performed using the contact model to ensure the accurate modeling and reconstruction of the contact unit. Based on the power data in each direction and the tool-workpiece contact unit, a thermally induced response simulation of the contact area is performed. First, based on the power data in each direction obtained previously, the power data is used as the input condition to perform the thermally induced response simulation. During the thermal response simulation, considering the influence of different power directions on the temperature change in the contact area, the heat change in the contact area is calculated through heat conduction and heat convection models. The heat conduction equation and the temperature field distribution in the contact unit are used for simulation. In the simulation, parameters such as cutting temperature, material thermal conductivity, and contact pressure need to be input. Finally, the simulated response data of the contact area is obtained, where the response data includes temperature distribution, heat generation, etc.During the simulation process, ensure the matching of the heat source and power to obtain an accurate thermally induced response in the contact area. Based on the simulated contact area response, perform a thermal power backtracking mapping. First, use the temperature data obtained from the simulation of the thermally induced response in the contact area and combine it with the power data for backtracking analysis. During the analysis process, use a thermodynamic backtracking model to compare the response in the contact area with the initial input power, and inversely calculate the thermal power generation situation of each contact unit. The process of thermal power backtracking mapping needs to be adjusted according to the thermal physical properties of the material and the thermal effects during the cutting process. In particular, the influence of the contact force between the tool and the workpiece needs to be taken into account. Finally, obtain the contact friction power, which is the heat generated by friction during the contact between the tool and the workpiece. Perform a thermal conversion rate conversion on the cutting deformation power and the contact friction power. First, combine the material properties of the tool and the workpiece to calculate the thermal conversion rate of the cutting deformation power and the contact friction power. Specifically, parameters such as the friction coefficient between the tool and the workpiece, the specific heat capacity and thermal conductivity of the material will affect the thermal conversion rate. Based on these thermal physical properties, use a thermal conversion rate model for conversion and calculate the thermal conversion rates of the cutting deformation power and the contact friction power respectively. During this process, the thermal conversion rate is usually set within a fixed range and adjusted according to different material properties to ensure that the converted data accurately reflects the heat distribution during the machining process. Perform a thermal accumulation process on the deformed heat power data and the frictional heat power data according to the simulated machining cutting load data. During the accumulation process, accumulate the thermal power data for each time period to obtain the accumulated heat. The time range of the accumulation process is set according to the machining time, usually with a processing time span of 30 seconds to 5 minutes. At each time step, perform heat accumulation based on the change of the thermal power data. The accumulation process of the cutting deformation power data and the frictional heat power data will perform data fusion based on the corresponding temperature change curves to ensure that the finally obtained accumulated deformation heat and frictional heat data can accurately represent the heat energy consumption during the machining process. Based on the accumulated deformation heat and the accumulated frictional heat, perform a heat source spatial mapping. First, use the accumulated deformed heat and frictional heat data for spatial mapping. Use a heat source distribution model to distribute the accumulated heat in the contact area between the tool and the workpiece. During the mapping, considering the spatial distribution characteristics and temperature gradient of the heat source, use the finite element method to numerically simulate the heat source and distribute the accumulated heat according to the spatial coordinates. Finally, obtain the heat source distribution data, which describes the distribution of heat energy between the tool and the workpiece during the machining process. According to the heat source distribution data, perform a heat source superposition coupling on the accumulated deformation heat and the accumulated frictional heat, and predict the thermal diffusion response. First, couple the heat source distribution data with the accumulated heat. Combine the contact situation between the tool and the workpiece in the actual machining process, perform a superposition process on the accumulated heat and the heat source, and simulate the diffusion of heat in the workpiece and the tool through a thermal diffusion model. The coupled data will be input into the thermal diffusion equation for calculation, and predict the propagation and diffusion of heat through a heat conduction model. Finally,Obtain the predicted machining thermal effect during the machining process. The predicted data includes the distribution of heat, the diffusion path, and the temperature change, etc., to ensure that the final prediction of the thermal effect conforms to the actual cutting conditions.

[0084] Preferably, calculating the tool thermal deformation degree according to the tool material characteristics and the predicted machining thermal effect in step S4 includes:

[0085] Conduct material group decomposition and thermal elastic coefficient mapping on the tool material characteristics to generate material thermodynamics parameters;

[0086] Determine the tool thermal response characteristics based on the material thermodynamics parameters;

[0087] Conduct tool surface heat load distribution mapping based on the predicted machining thermal effect to generate tool surface heat load data;

[0088] Conduct thermally induced strain driving treatment according to the tool surface heat load data and the tool thermal response characteristics to obtain the local strain response of the tool;

[0089] Infer the geometric deformation of the edge contact based on the local strain response of the tool; conduct non-contact edge thermal expansion simulation of the tool according to the local strain response of the tool to generate non-edge thermal expansion geometric deformation;

[0090] Conduct geometric shape migration and reconstruction according to the edge contact geometric deformation and the non-edge thermal expansion geometric deformation to obtain the thermal deformation geometric shape; construct the tool thermal deformation field based on the thermal deformation geometric shape;

[0091] Infer the tool thermal deformation of the reconstructed tool framework through the tool thermal deformation field and evaluate the tool thermal deformation degree.

[0092] In this embodiment, the material characteristics of the cutting tool are decomposed into material groups and the thermal elastic coefficient is mapped. First, by obtaining the chemical composition data of the cutting tool material and analyzing the proportion of each component in the cutting tool material. For example, the proportion of alloy elements such as carbon, tungsten, cobalt, etc. The microstructure of the cutting tool material is analyzed by scanning electron microscopy (SEM) or X-ray diffraction (XRD). Combining with the standard material mechanics database, the initial thermal elastic coefficient of the cutting tool material is determined. For each component, according to its thermal expansion coefficient, specific heat capacity, thermal conductivity and other thermal performance parameters, the material thermodynamics model is used for calculation to obtain the thermodynamics parameters of the cutting tool material. Usually, these parameters include the thermal diffusivity, thermal stress constant, etc. of the material. Through the thermal strain test at different temperatures, the mapping relationship between temperature and thermal elastic coefficient is established, so as to obtain the detailed thermal elastic characteristic data of the cutting tool material. Based on the material thermodynamics parameters, the thermal response characteristics of the cutting tool are determined. First, according to the obtained thermodynamics parameters of the cutting tool material and combined with the temperature distribution of the cutting tool during the cutting process, the thermal response characteristics of the cutting tool are simulated. During the simulation, it is assumed that the heat source during the cutting process of the cutting tool is the main influencing factor, and the thermodynamics model is used to calculate the thermal response of the cutting tool. The specific process is as follows: the heat generated by cutting is distributed to each area of the cutting tool according to different material characteristics, such as the cutting edge, flank face, etc. of the cutting tool, and the temperature gradient and thermal stress distribution are calculated according to the thermophysical characteristics of the cutting tool. Through the numerical simulation of the temperature field, the thermal response characteristic data of the cutting tool are obtained. This data includes the temperature change, thermal stress value and thermal strain distribution of the cutting tool surface, cutting edge and back surface, etc. Finally, a thermal response characteristic model of the cutting tool is formed. Based on the prediction of the machining thermal effect, the thermal load distribution on the cutting tool surface is mapped. First, according to the prediction of the cutting force and heat distribution during the machining process, the thermal load distribution model is used to map the thermal load on the cutting tool surface. The finite element analysis (FEA) is used to simulate the heat flow situation of the cutting tool during the cutting process. Specifically, the cutting force data and the heat source distribution are combined to calculate the thermal load generated on the cutting tool surface during the cutting process. Further, according to the position and size of the heat source, the thermal load density of different areas on the cutting tool surface is determined. In the thermal load distribution mapping, the thermal conductivity of the cutting tool material and the dynamic changes during the cutting process need to be considered. Finally, the accurate distribution of the thermal load on the cutting tool surface during the machining process is obtained. According to the thermal load data on the cutting tool surface and the thermal response characteristics of the cutting tool, the thermal strain-driven treatment is carried out to obtain the local strain response of the cutting tool. First, the thermal load data on the cutting tool surface obtained in the previous step is used as the input, and it is combined with the thermal response characteristics of the cutting tool to calculate the thermal strain. In this process, it is assumed that when the cutting tool surface is driven by the thermal load, the strain of the material will respond to the change of the thermal load. The thermal strain model is used to carry out the thermal strain analysis on the cutting tool surface in combination with the thermodynamics parameters. During the specific operation, by setting a certain time step, the thermal strain of each part of the cutting tool is calculated step by step. Finally, the local strain response of the cutting tool surface is obtained. This strain response reflects the geometric deformation of the cutting tool due to the action of the thermal load.Infer the geometric deformation of the cutting edge contact based on the local strain response of the tool. Combine the tool surface heat load and thermally induced strain response data to infer the geometric deformation of the cutting edge contact area of the tool. First, determine the thermal strain value of the cutting edge contact area according to the geometric shape of the tool. Analyze the geometric deformation of the cutting edge based on the strain distribution. Usually, calculate the temperature distribution and thermal strain value of the contact area to deduce the geometric deformation of this area. For example, the cutting edge of the tool may undergo deformations such as bending and expansion. Combine the plastic properties of the material and use the finite element method to simulate the geometric deformation of the cutting edge, and finally obtain the deformation shape and deformation amount of this area. Conduct non-contact cutting edge thermal expansion simulation of the tool based on the local strain response of the tool to generate non-cutting-edge thermal expansion geometric deformation. First, according to the local strain response data of the tool obtained in the previous step, conduct thermal expansion simulation for the non-contact cutting edge of the tool. In this process, the geometric deformation of the non-contact cutting edge is mainly generated by the thermal expansion effect. The non-contact cutting edge of the tool expands to a greater extent because it is heated differently from the contact cutting edge. Through numerical simulation, use the thermal expansion model to conduct thermal expansion analysis on the non-contact cutting edge, specifically analyze the relationship between the temperature change and expansion coefficient of the non-cutting edge, so as to calculate the geometric deformation of the non-cutting edge, and finally obtain the non-cutting-edge thermal expansion geometric deformation result. Conduct geometric shape migration and reconstruction based on the cutting edge contact geometric deformation and non-cutting-edge thermal expansion geometric deformation to obtain the thermally deformed geometric shape. First, merge the data of the cutting edge contact geometric deformation and non-cutting-edge thermal expansion geometric deformation to form the overall thermally deformed geometric shape model of the tool. In the specific operation, use the geometric shape migration algorithm to combine the geometric deformation data of the two to conduct the overall thermal deformation reconstruction of the tool. During the reconstruction process, based on the thermal expansion coefficient and strain data of each part of the tool, deduce the overall deformation result of the tool. Finally, obtain the thermally deformed geometric shape data of the tool, which reflects the overall geometric deformation of the tool due to temperature changes during the machining process. Construct the tool thermal deformation field based on the thermally deformed geometric shape. First, based on the thermally deformed geometric shape data obtained in the previous step, combine the temperature distribution data of the tool to construct the tool thermal deformation field. The specific steps are as follows: Analyze the thermal deformation degree of each part of the tool during the cutting process through the thermodynamic model. Based on the heat and strain data of different regions, use the numerical simulation method to construct the tool thermal deformation field. Usually, the establishment of the thermal deformation field needs to consider factors such as the material properties, temperature distribution, and thermal strain response of the tool. Finally, obtain the thermal deformation field data of each part of the tool during the machining process, which provides a complete picture of the deformation process experienced by the tool during the cutting process. Conduct tool thermal deformation inference on the reconstructed tool structure through the tool thermal deformation field and evaluate the degree of tool thermal deformation. First, according to the constructed tool thermal deformation field, conduct thermal deformation inference on the tool. Combine the geometric structure of the tool and the thermal deformation field data, and use the finite element analysis (FEA) method to analyze the thermal deformation of each part of the tool and deduce the degree of tool thermal deformation. During the inference process,It is necessary to combine the stress, strain of each part of the tool with the influence of the heat source to evaluate the thermal deformation degree of the tool during the actual cutting process, and finally obtain the thermal deformation amount generated by the tool during the machining process. The inferred results provide a basis for the tool design optimization and machining accuracy evaluation.

[0093] Especially importantly, perform thermally induced strain-driven processing based on the tool surface heat load data and the tool thermal response characteristics to obtain the tool local strain response, including:

[0094] Perform zoning reduction of the heat load density on the tool surface heat load data to obtain the zoned heat load density;

[0095] Perform thermally coupled zoning coupling mapping on the zoned heat load density data combined with the tool thermal response characteristics to obtain the heat zone coupling response data;

[0096] Perform thermal gradient flow fitting based on the heat zone coupling response data to obtain the gradient thermal stress distribution;

[0097] Perform thermal effect hierarchical stacking reconstruction based on the gradient thermal stress distribution data to obtain the thermal stress superposition field;

[0098] Perform strain-driven function conversion on the thermal stress superposition field to obtain the driving strain response;

[0099] Determine the tool local strain response based on the driving strain response.

[0100] In this embodiment, when performing heat load density zoning reduction on the heat load data of the tool surface, the tool surface in the numerical control machining area is divided into rectangular units of 5 mm × 5 mm. The surface temperature values of each unit are collected in real time by a thermocouple array measurement device. The measurement device uses K-type thermocouples with a resolution of up to 0.1 °C and a sampling frequency set to 1000 Hz. According to the surface temperature values, the heat load density of each unit is calculated through the established heat conduction model. In the model, the thermal conductivity of the tool material is taken as 42 W / (m·K). The heat load density values of all units are divided into 5×5 blocks, and the average heat load density in each block is calculated to obtain the zoned heat load density data. All the zoned heat load density data are stored in a 64×64 two-dimensional array matrix with the unit of W / m². When performing the thermal coupling zoning coupling mapping operation by combining the zoned heat load density data with the tool heat response characteristics, a preset tool heat response library is called. This library contains the thermoelastic parameter data obtained by decomposing based on different material groups. The thermoelastic parameters include the coefficient of thermal expansion set to 13.5×10^-6 1 / K, the Young's modulus set to 215 GPa, and the Poisson's ratio set to 0.29. The data in the heat response library is sourced from the test results of a high-temperature dynamic mechanical analyzer DMA. The heat load density value of each block is calculated point by point with the corresponding heat response parameters to obtain the heat zone coupling response data. The coupling method is to multiply the average heat load density within the block by the combined weight value of the coefficient of thermal expansion and thermal conductivity, and finally form a heat zone coupling response matrix. The matrix dimension is consistent with the heat load density matrix. When performing the heat gradient flow fitting based on the heat zone coupling response data, the finite difference method is used to calculate the gradient of adjacent unit points in the heat zone coupling response matrix. The gradient direction is determined by the temperature difference between adjacent points. The gradient value is obtained by taking the difference between the coupling response values of adjacent two points divided by the distance using the central difference method. The distance is fixed at 5 mm. All the heat gradient values within the blocks are mapped to a two-dimensional coordinate thermal stress distribution map. Each pixel point in the map represents a 5 mm × 5 mm area, and the color intensity corresponds to the gradient thermal stress value with the unit of MPa. The thermal stress value is obtained by multiplying the heat gradient by the thermoelastic parameters. The thermal stress distribution map is smoothed by the kernel density estimation method to improve the continuity of the thermal stress gradient. When performing the thermal effect hierarchical stacking and reconstruction operation based on the gradient thermal stress distribution data, the thermal stress distribution map is divided along the tool radius direction into 0.A 5-mm equidistant annular region is used. The average value of the thermal stress values of all the units within each annular region is taken, and they are successively superimposed to form a thermal stress superposition field. When superimposing, the sum of the thermal stress value of each layer and the upper layer is used as the cumulative thermal stress value of this layer. The cumulative value is iteratively accumulated using a recurrence formula. The data of the superposition field is stored in the form of a three-dimensional matrix, with the dimensions being the number of layers stratified in the radial direction × the number of angular partitions × the thermal stress value. The unit of the thermal stress value is MPa. After the superposition is completed, a thermal stress superposition field map is generated. The color intensity within the map corresponds to the cumulative thermal stress intensity value of each region. When performing a strain-driven function conversion on the thermal stress superposition field, a linear thermal strain relationship is selected. The thermal stress value of each unit within the superposition field is divided by the Young's modulus of the corresponding region of the tool. The Young's modulus is taken as 215 GPa to obtain the thermal-induced strain value of this unit, with the unit being dimensionless. The strain value of each unit is associated with its spatial coordinates and is stored in a three-dimensional strain response matrix, whose dimensions are the same as those of the superposition field matrix. When determining the local strain response of the tool based on the driving strain response, the strain values in the three-dimensional strain response matrix are clustered according to their spatial positions. The clustering method uses the density-based spatial clustering algorithm DBSCAN. The neighborhood radius is set to 5 mm, and the minimum number of points is set to 10. After clustering, the high-density strain response region is extracted as the local strain response region of the tool, and the average value, maximum value, and coordinate distribution information of the strain within this region are output. All the output results are recorded in a table. The table fields include the partition number, strain mean value, strain extreme value, and position coordinates. Among them, the strain response unit is dimensionless, and the position coordinate unit is mm.

[0101] Preferably, the deformation compensation of the initial tool wear data based on the tool thermal deformation degree in step S5 includes:

[0102] Performing a mapping of the heat flux density distribution on the initial tool wear data to obtain the tool thermal gradient distribution;

[0103] Constructing a three-dimensional deformation vector field of the tool based on the tool thermal gradient distribution;

[0104] Optimizing the boundary constraint conditions of the three-dimensional deformation vector field of the tool according to the tool thermal deformation degree and constructing an actual thermal deformation model of the tool;

[0105] Determining the geometric deformation data of the tool based on the actual thermal deformation model of the tool.

[0106] In this embodiment, for the mapping of the heat flux density distribution of the initial tool wear data, first, a high-precision infrared thermal imager is used to measure the surface temperature distribution of the tool. The temperature measurement accuracy of the instrument is 0.1 °C, and the measurement range covers the temperature range from 100 °C to 1000 °C. The temperature distribution data of the tool under different cutting conditions are obtained. Based on the temperature distribution data, the heat flux density on the surface and inside of the tool is calculated. A heat flux density model based on thermal conductivity and cutting force is adopted. The thermal conductivity parameter is selected according to the tool material. For example, the thermal conductivity of a high-speed steel tool is 25 W / (m·K), and the thermal conductivity of a cemented carbide tool is 150 W / (m·K). The distribution data of the tool heat flux density are obtained. Further, this data is used to construct the heat gradient distribution on the tool surface. The heat gradient reflects the rate of change of the tool temperature and provides basic data for the subsequent deformation model. The unit of the heat gradient distribution is usually K / m. By combining the high-precision heat flux density distribution with the temperature gradient, the accuracy of the heat gradient data is ensured to reach 0.1 K / m. Based on the tool heat gradient distribution, a three-dimensional deformation vector field of the tool is constructed. First, according to the heat gradient data and in combination with the geometric model of the tool, a three-dimensional space grid of the tool is established. The size of the grid is set according to the geometric complexity of the tool, and the grid size is between 0.1 mm and 1 mm. A refined grid is selected to ensure the deformation accuracy. The deformation vector field of the tool is numerically calculated by the finite element method (FEM). The calculation process is based on the thermal expansion coefficient of the tool, the stress-strain relationship, and the temperature field and force field during the cutting process. The range of the thermal expansion coefficient generally ranges from 5×10⁻ 6 1 / °C to 15×10⁻ 61 / °C, select a specific value according to the tool material. Using a computer simulation tool, construct a three-dimensional deformation vector field of the tool under different cutting states. This vector field describes the deformation of each grid node of the tool, with an accuracy of up to 0.001 mm. Optimize the boundary constraint conditions of the tool's three-dimensional deformation vector field according to the degree of tool thermal deformation, and construct an actual thermal deformation model of the tool. First, set the boundary conditions according to the contact conditions between the tool and the workpiece. The contact force is calculated using the data of the force sensor, and the range of the contact force is from 10 N to 500 N. The specific value is determined according to the actual loading situation during the cutting process. The boundary conditions include the fixing method of the tool and the fixture, the contact surface between the tool and the workpiece, and the temperature distribution data of the tool itself. The optimization algorithm uses the least squares method or the genetic algorithm. During the optimization process, by calibrating and adjusting the thermal deformation model, ensure that the model more accurately reflects the actual thermal deformation of the tool. Finally, obtain the actual thermal deformation model of the tool. This model provides the geometric change information of each point of the tool, and the calculation accuracy is within the range of 0.001 mm. Determine the geometric deformation data of the tool based on the actual thermal deformation model of the tool. Use a three-dimensional measuring instrument (such as a coordinate measuring machine) to accurately measure the geometric shape of the tool, with a measurement accuracy of 0.001 mm. By comparing the thermal deformation model of the tool during the actual machining process with the actual geometric shape, extract the geometric deformation data of the tool. The deformation data of the tool usually includes the wear of the tool edge, the deformed tool shape, and the geometric changes of each surface and cutting edge of the tool. During the measurement process, combined with the actual machining conditions, record the tool shape data before and after cutting, and determine the deformation amount through calculation. This deformation amount is used to adjust the tool geometric parameters in the numerical control system to ensure the geometric compensation effect of the tool. Finally, obtain the accurate geometric data of the tool.

[0107] Preferably, in step S5, assign a deformation timestamp corresponding to the tool geometric deformation data, and draw a deformation-time curve. At the same time, the determination of the tool change process based on the deformation-time curve includes:

[0108] Perform cutting time sequence synchronization processing on the tool geometric deformation data to generate timestamp-associated deformation data;

[0109] Map the timestamp-associated deformation data to a deformation-time discrete point set;

[0110] Fit the deformation-time discrete point set to a deformation-time curve;

[0111] Detect the curve inflection points of the deformation-time curve and mark them as deformation key nodes;

[0112] Perform an analysis of the slope change at the inflection points of the deformation-time curve based on the deformation key nodes and map it to a deformation acceleration feature;

[0113] Perform deformation node synchronous projection based on the deformation acceleration characteristics and tool geometric deformation data to generate deformation synchronization data;

[0114] Determine the tool change process based on the deformation synchronization data.

[0115] In this embodiment, the cutting time sequence synchronization process is carried out on the tool geometric deformation data. First, the starting time and ending time of each cutting process are recorded by the numerical control system, with the accuracy requirement reaching the millisecond level. The recorded data includes the tool position, tool rotation speed, feed speed, and cutting force during cutting. Through the synchronous timestamp, all the tool geometric deformation data is sorted and matched according to the time series during the cutting process. The timestamp is obtained through a high-speed data acquisition system with a sampling frequency of 1 kHz. Within each sampling period, the geometric shape and wear condition of the tool are updated once, and the corresponding timestamp is recorded and matched with the geometric deformation data. Each position of the tool is marked in detail to ensure that each data point can correspond to the accurate cutting time. The timestamp-associated deformation data is mapped into a deformation-time discrete point set. The data interpolation method is used to interpolate the geometric deformation data of the tool at different timestamps. The specific interpolation method is cubic spline interpolation to ensure the smoothness and accuracy of the data. The interpolated data is displayed through a graphical tool (such as the Matlab or Matplotlib toolbox in Python) to form a deformation-time discrete point set. Each discrete point represents the relationship between a tool deformation data and the corresponding timestamp. The abscissa of the discrete point set is time, with the unit of seconds, and the ordinate is the tool deformation amount, with the unit of millimeters or micrometers. The number of discrete point sets depends on the duration of the cutting process and the sampling frequency. Among them, the number of sample points can reach several thousand, and the distribution of points shows a regular change with the change of cutting time. The deformation-time discrete point set is fitted into a deformation-time curve. First, the least squares method is used to fit the discrete point set to generate a smooth curve of deformation and time, which can better reflect the geometric deformation change trend of the tool during the cutting process. During the fitting process, a suitable function model is first set. Commonly used models include polynomial models, exponential models, etc. The specific selection depends on the data distribution. The fitting accuracy requirement is 0.001 mm, and the fitting error range of the curve is controlled within 5%. Through the fitting of the curve, the change rule of the tool deformation is obtained, and a continuous deformation-time curve is formed to ensure that the fitted curve can truly reproduce the geometric deformation process of the tool. Detect the inflection points of the deformation-time curve and mark them as deformation key nodes. Based on the deformation-time curve, a numerical analysis tool is used to detect the inflection points of the curve. The condition of the first derivative being zero is used to determine the inflection point position. By detecting the change trend of the deformation curve, the key turning points during the tool deformation process are determined. The inflection point corresponds to an important stage of the tool geometric shape change. The change passing through the inflection point usually means that the tool wear intensifies or the deformation speed changes significantly. The detection of the inflection point needs to ensure its accuracy by setting a threshold. When the change range of the deformation amount exceeds 0.01 mm, it is regarded as a key node. The processed inflection points are marked as deformation key nodes on the deformation-time curve.Analyze the slope change at the inflection point of the deformation-time curve based on the key deformation nodes and map it to the deformation acceleration feature. First, calculate the tangent slope of the deformation data before and after each key node. The slope represents the rate of deformation. By calculating the change in the slope at the inflection point, the acceleration feature of the tool deformation is obtained. The specific calculation method is to select several time points near the inflection point and calculate the rate of change of the slope between adjacent points to obtain the acceleration data. The deformation acceleration of the tool is usually in units of millimeters per second squared. The area with a large slope change usually corresponds to the stage where the tool wear is more serious. The deformation acceleration feature is of great significance for judging the remaining life of the tool. The calculated deformation acceleration feature will be combined with the specific geometric deformation data of the tool for further analysis of the tool change process. Generate deformation synchronization data based on the deformation acceleration feature and the tool geometric deformation data through deformation node synchronous projection. Based on the deformation acceleration feature obtained above, through time series synchronous analysis, map the acceleration data of each deformation node to its corresponding tool geometric deformation data, establish a one-to-one correspondence between the acceleration feature and the time stamp, and generate deformation synchronization data. The deformation synchronization data can truly reflect the geometric changes and acceleration features of the tool in each cutting stage. The time accuracy of the deformation synchronization data is 1 ms, and the number of data points is determined by the duration of the cutting process and the synchronization accuracy. The generated synchronization data provides accurate geometric data for subsequent tool life prediction and automatic input. Determine the tool change process based on the deformation synchronization data. Finally, through the processing of the deformation synchronization data, use data analysis methods to draw a graph of the tool change process, and display the deformation of the tool during the entire cutting process through the graph. The change process graph comprehensively presents data such as time, deformation amount, and acceleration. Through this graph, the relationship between tool wear and cutting time can be intuitively understood, and the tool life cycle can be further estimated. The tool change process graph can provide accurate tool status information for the numerical control system, and then automatically input the tool geometric parameters.

[0116] Preferably, step S6 includes the following steps:

[0117] Step S61: Segment the tool change process in time series to obtain tool change time series data, where the single-segment time length is set within the range of 5 s to 30 s;

[0118] Step S62: Measure the parameter drift of the tool change time series segment data to obtain the tool drift parameter, where the parameter drift amplitude threshold is limited within ±0.05 mm;

[0119] Step S63: Perform compensation fitting processing according to the tool drift parameter data to generate tracking compensation tool parameters, where the compensation coefficient range is set to 0.8 to 1.2;

[0120] Step S64: Based on the tracking compensation tool parameters, perform real-time entry and update to obtain real-time updated tool parameters.

[0121] In this embodiment, the tool change process is segmented in time series. First, by collecting the deformation data of the tool during the machining process, the data is segmented according to the time sequence, and the time length of each segment is between 5 seconds and 30 seconds. The specific time period length is adjusted according to the cutting process and machining characteristics of the tool. Usually, the tool will experience a relatively stable working state during this time period. When segmenting, it is set according to the data volatility. If the data changes violently, a shorter time period may be selected for analysis. The key point of data segmentation is to flexibly set the cutting segment according to the real-time collected data fluctuation and cutting parameter change. Use signal processing tools or algorithms (such as Fast Fourier Transform (FFT)) to analyze the frequency characteristics of the tool deformation data, and measure the parameter drift of the tool change time series data. First, analyze the data of each time period and calculate the offset (drift amount) during the tool change process. This drift amount is defined as the difference between the geometric deformation of the tool in each time period and the theoretical value. When measuring the drift, first select the reference tool deformation value, which can be obtained from the initial geometric shape of the tool or the most recent accuracy calibration. The drift amplitude of each segment of data needs to be compared with the threshold of ±0.05 mm. If the offset exceeds this threshold, further adjustment is required. The calculation of the drift parameter is completed by the difference method. Each data point is compared with the adjacent data points before and after to obtain the change in the drift amount. If the drift amount continues to increase, it indicates that the tool may have suffered wear or other deformation. The drift amplitude threshold is limited to ±0.05 mm. Perform compensation fitting processing according to the tool drift parameter data. The compensation processing mainly corrects the tool deformation through the compensation coefficient. The compensation coefficient range is set between 0.8 and 1.2. By setting a suitable coefficient to fit the drift data of each segment, the selection of the compensation coefficient is based on the stability of the drift parameter and the size of the offset. If the drift amount is large, the compensation coefficient is close to 1.2. If the drift amount is small, it is close to 0.8. When performing compensation fitting, first calculate the compensation curve according to the drift data and the theoretical value, and the compensation coefficient is obtained through curve fitting. When performing curve fitting, methods such as polynomial regression or exponential regression can be selected. The compensated data will be mapped back to the tool geometric parameters to obtain the real-time compensated tool parameters, ensuring a high degree of matching between the tool geometry and the actual cutting state. Based on the tracked compensated tool parameters, perform real-time entry and update. First, synchronize the compensated tool parameters with the numerical control system in real time through the data input module, and update the geometric parameters of the tool in real time, including the radius of the tool, the edge angle, the length of the tool, etc. Each parameter after compensation will be automatically entered into the numerical control system to ensure that the tool always maintains the best working state during the machining process. During the entry process, the accuracy requirement for parameter update is 0.001 mm, for each compensated tool geometric parameter, the time accuracy of real-time update is 1 ms. In the numerical control system, parameter synchronization is carried out through a data interface (such as RS-232, Ethernet, etc.) so that the geometric data of the tool can be immediately fed back to the machining tool, and any subtle change of the tool can be compensated by means of real-time update to ensure that the change process of the tool can be reflected and corrected in real time.

[0122] The present invention also provides an automatic input system for the geometric parameters of a numerical control tool, which is used to execute the automatic input method for the geometric parameters of the numerical control tool as described above. The automatic input system for the geometric parameters of the numerical control tool includes:

[0123] A point cloud acquisition module, which is used to acquire tool point cloud data and tool material characteristics; eliminate abnormal points in the tool point cloud data to obtain cleaned tool point cloud data; extract the tool edge contour according to the cleaned tool point cloud data;

[0124] A geometric mapping module, which is used to perform geometric parameter mapping through the tool edge contour to determine the geometric characteristics of the tool; reconstruct the tool framework based on the geometric characteristics of the tool;

[0125] A cutting simulation module, which is used to obtain a numerical control machining task; perform a machining cutting load simulation on the reconstructed tool framework based on the numerical control machining task to obtain simulated machining cutting load data; analyze the initial tool wear data according to the simulated machining cutting load data;

[0126] A thermal effect prediction module, which is used to perform thermal effect prediction based on the simulated machining cutting load data to obtain the predicted machining thermal effect; calculate the tool thermal deformation degree according to the tool material characteristics and the predicted machining thermal effect;

[0127] A deformation compensation module, which is used to perform deformation compensation on the initial tool wear data based on the tool thermal deformation degree to generate tool geometric deformation data; calibrate a corresponding deformation timestamp for the tool geometric deformation data, draw a deformation-time curve, and at the same time determine the tool change process based on the deformation-time curve;

[0128] A parameter tracking module, which is used to perform sequential tracking parameter compensation according to the tool change process to generate tracked compensation tool parameters; perform real-time input update based on the tracked compensation tool parameters.

[0129] Through the application of the point cloud acquisition module, the present invention realizes the comprehensive acquisition of the tool point cloud data and material characteristics, eliminates abnormal points to improve the accuracy of the data, and the cleaned point cloud data ensures the accurate extraction of the tool edge contour. The introduction of the geometric mapping module makes the determination of the tool geometric features more reliable. The ability to reconstruct the tool framework enhances the visualization and practicality of tool design. The cutting simulation module combines with the numerical control machining task to perform real machining cutting load simulation, providing in-depth analysis of tool wear. The simulation results provide a scientific basis for tool performance evaluation. The thermal effect prediction module can accurately predict the thermal effect of the tool during operation and calculate the degree of tool thermal deformation through the analysis of machining cutting load data, providing theoretical support for the selection of tool materials and the optimization of machining plans. The deformation compensation module compensates the initial tool wear data based on the degree of thermal deformation, and the generated geometric deformation data provides a reliable basis for the actual use of the tool. The addition of timestamps and the plotting of the deformation-time curve facilitate the real-time monitoring of tool state changes. The analysis of the deformation-time curve helps technicians understand the performance of the tool during machining. The parameter tracking module can perform sequential tracking according to the tool change process, generate tracking compensation tool parameters, ensuring the real-time update and accuracy of tool parameters. The implementation of tracking compensation improves the machining accuracy of the tool and reduces production fluctuations caused by tool parameter changes. The entire system improves the management efficiency and machining accuracy of numerical control tools, promotes the development and application of intelligent manufacturing, and finally realizes the optimization of tool life cycle management, making the production process more efficient and economical, enhancing the competitiveness of enterprises. Especially in the context of the increasing demand for high-precision manufacturing, it provides a new solution for the intelligent management of numerical control tools, promotes the process of the transformation of the manufacturing industry to digitalization and intelligentization, ensures the stability and predictability of tool performance in complex machining environments, provides strong technical support and guarantee for the development of modern manufacturing, promotes the scientific and systematic management of tools, and improves production efficiency.

[0130] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes that fall within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0131] The above are only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An automatic input method for the geometric parameters of a numerically controlled tool, characterized in that, The following steps are involved: Step S1: Acquire tool point cloud data and tool material properties; remove abnormal points in the tool point cloud data to obtain cleaning tool point cloud data; Extract tool edge contour based on cleaning tool point cloud data; Step S2: mapping geometric parameters through the tool edge profile to determine the tool geometric features; reconstructing the tool frame based on the tool geometric features; Step S3: Acquire a numerical control machining task; simulate the machining cutting load of the reconstructed tool frame based on the numerical control machining task to obtain simulated machining cutting load data; and analyze the initial tool wear data according to the simulated machining cutting load data; Step S4: Predicting the thermal effect based on the simulated machining cutting load data to obtain the predicted machining thermal effect; calculating the thermal deformation degree of the tool according to the tool material characteristics and the predicted machining thermal effect; Step S5: performing deformation compensation on the initial tool wear data based on the degree of thermal deformation of the tool to generate tool geometric deformation data; calibrating the corresponding deformation timestamp for the tool geometric deformation data, and drawing a deformation-time curve, and determining the tool change process based on the deformation-time curve; Step S6: Perform time-series tracking parameter compensation according to the tool change process to generate tracking compensation tool parameters; and perform real-time input and update based on the tracking compensation tool parameters.

2. The automatic input method for the geometric parameters of a numerically controlled tool according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: performing multi-angle laser scanning on the surface of the CNC tool to obtain tool point cloud data, wherein the scanning angle range is set to 15°~75°, and the single scanning point distance is set to 0.01mm~0.05mm; Step S12: calibrating the tool point cloud data in a spatial coordinate system to generate corrected tool point cloud data; performing outlier monitoring on the corrected tool point cloud data to obtain marked abnormal point data; Step S13: performing abnormal correction on the calibration tool point cloud data based on the marked abnormal point data to obtain cleaning tool point cloud data, wherein the correction range is limited to ±0.05 mm; Step S14: Perform three-dimensional surface fitting on the cleaning tool point cloud data and perform edge detection to generate a tool edge profile, wherein the surface fitting residual is controlled within 0.01 mm.

3. The automatic input method for the geometric parameters of a numerically controlled tool according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Perform Fourier descriptor conversion on the tool edge profile to obtain the tool profile frequency domain characteristics; perform principal component analysis on the tool profile frequency domain characteristics to generate tool spindle geometry parameters; Step S22: determining the tool geometric parameters based on the tool spindle geometric parameters and the tool profile parameters; extracting the tool geometric features according to the tool geometric parameters; Step S23: performing curve envelope fitting according to the geometric features of the tool to generate the tool shape envelope; performing axial cross-sectional tomography on the tool shape envelope and dividing it into equal cross-sectional slices; Step S24: Perform geometric projection fusion based on the equal-section slices to obtain contour characteristic fusion data; reconstruct the tool frame according to the contour characteristic fusion data and the tool geometric features.

4. The automatic input method for the geometric parameters of a numerically controlled tool according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Acquire a numerical control machining task; extract task process parameters according to the numerical control machining task; Step S32: Predict the cutting force distribution based on the task process parameters; perform a cutting power response simulation on the reconstructed tool framework according to the predicted cutting force distribution to generate simulated cutting response data; Step S33: Perform a cutting load mapping based on the simulated cutting response data to generate simulated machining cutting load data; Step S34: Calculate the cumulative load sequence based on the simulated machining cutting load data; analyze the theoretical wear rate according to the cumulative load sequence; Step S35: Perform a tool wear estimation on the reconstructed tool framework according to the theoretical wear rate to generate initial tool wear data.

5. The automatic input method for the geometric parameters of a numerically controlled tool according to claim 1, characterized in that, The thermal effect prediction based on the simulated machining cutting load data in Step S4 includes: Perform a power structure separation based on the simulated machining cutting load data to obtain the power data in each direction; Perform a cutting plastic zone power mapping based on the power data in each direction and the tool material characteristics to generate the cutting deformation power; Determine the tool-workpiece contact elements according to the reconstructed tool framework; perform a contact zone thermally induced response simulation based on the power data in each direction and the tool-workpiece contact elements to obtain the simulated contact zone response; Perform a thermal power backtracking mapping according to the simulated contact zone response to generate the contact friction power; Perform a thermal conversion rate conversion on the cutting deformation power and the contact friction power to generate the deformation heat power data and the friction heat power data respectively; Perform a thermal accumulation process on the deformation heat power data and the friction heat power data according to the simulated machining cutting load data to obtain the cumulative deformation heat and the cumulative friction heat respectively; Perform a heat source spatial mapping based on the cumulative deformation heat and the cumulative friction heat to generate the heat source distribution data; Perform a heat source superposition coupling on the cumulative deformation heat and the cumulative friction heat according to the heat source distribution data, and perform a thermal diffusion response prediction to obtain the predicted machining thermal effect.

6. The automatic input method for the geometric parameters of a numerically controlled tool according to claim 1, wherein, The calculation of the tool thermal deformation degree according to the tool material characteristics and the predicted machining thermal effect in Step S4 includes: Perform a material group decomposition on the tool material characteristics and perform a thermal elastic coefficient mapping to generate the material thermodynamics parameters; Determine the tool thermal response characteristics based on the material thermodynamics parameters; Perform a tool surface thermal load distribution mapping based on the predicted machining thermal effect to generate the tool surface thermal load data; Perform a thermally induced strain driving process according to the tool surface thermal load data and the tool thermal response characteristics to obtain the local tool strain response; Infer the edge contact geometric deformation based on the local tool strain response; perform a non-edge thermal expansion simulation of the tool according to the local tool strain response to generate the non-edge thermal expansion geometric deformation; Perform a geometric shape migration reconstruction according to the edge contact geometric deformation and the non-edge thermal expansion geometric deformation to obtain the thermal deformation geometric shape; construct a tool thermal deformation field based on the thermal deformation geometric shape; Infer the tool thermal deformation of the reconstructed tool framework through the tool thermal deformation field and evaluate the tool thermal deformation degree.

7. The automatic input method for the geometric parameters of a numerically controlled tool according to claim 1, wherein, The deformation compensation for the initial tool wear data based on the tool thermal deformation degree in Step S5 includes: Perform a heat flux density distribution mapping on the initial tool wear data to obtain the tool thermal gradient distribution; Construct a three-dimensional deformation vector field of the tool based on the tool thermal gradient distribution; Optimize the boundary constraint conditions of the three-dimensional deformation vector field of the tool according to the tool thermal deformation degree and construct an actual tool thermal deformation model; Determine the tool geometric deformation data based on the actual tool thermal deformation model.

8. The automatic input method for the geometric parameters of a numerically controlled tool according to claim 1, wherein, Calibrate the corresponding deformation timestamp for the tool geometric deformation data in step S5, and draw the deformation-time curve. At the same time, the process of determining the tool change based on the deformation-time curve includes: Perform cutting time sequence synchronization processing on the tool geometric deformation data to generate timestamp-associated deformation data; Map the timestamp-associated deformation data into a deformation-time discrete point set; Fit the deformation-time discrete point set into a deformation-time curve; Detect the curve inflection points of the deformation-time curve and mark them as deformation key nodes; Analyze the slope change at the inflection point of the deformation-time curve based on the deformation key nodes and map it into a deformation acceleration feature; Perform deformation node synchronous projection according to the deformation acceleration feature and the tool geometric deformation data to generate deformation synchronous data; Determine the tool change process based on the deformation synchronous data.

9. The automatic input method for the geometric parameters of a numerically controlled tool according to claim 1, wherein, Step S6 includes the following steps: Step S61: Segment the tool change process in time sequence to obtain tool change time sequence data, where the single-segment time length is set in the range of 5s to 30s; Step S62: Measure the parameter drift of the tool change time sequence segment data to obtain the tool drift parameter, where the parameter drift amplitude threshold is limited within ±0.05mm; Step S63: Perform compensation fitting processing according to the tool drift parameter data to generate tracking compensation tool parameters, where the compensation coefficient range is set to 0.8 to 1.2; Step S64: Perform real-time entry update based on the tracking compensation tool parameters to obtain real-time updated tool parameters.

10. An automatic input system for the geometric parameters of a numerically controlled tool, wherein, For implementing the automatic entry method of the numerical control tool geometric parameters as described in claim 1, the automatic entry system of the numerical control tool geometric parameters includes: A point cloud acquisition module for acquiring tool point cloud data and tool material characteristics; removing abnormal points in the tool point cloud data to obtain cleaned tool point cloud data; extracting the tool edge contour according to the cleaned tool point cloud data; A geometric mapping module for performing geometric parameter mapping through the tool edge contour to determine the tool geometric characteristics; reconstructing the tool framework based on the tool geometric characteristics; A cutting simulation module for obtaining a numerical control machining task; performing machining cutting load simulation on the reconstructed tool framework based on the numerical control machining task to obtain simulation machining cutting load data; analyzing the initial tool wear data according to the simulation machining cutting load data; A thermal effect prediction module for performing thermal effect prediction based on the simulation machining cutting load data to obtain the predicted machining thermal effect; calculating the tool thermal deformation degree according to the tool material characteristics and the predicted machining thermal effect; A deformation compensation module for performing deformation compensation on the initial tool wear data based on the tool thermal deformation degree to generate tool geometric deformation data; calibrating the corresponding deformation timestamp for the tool geometric deformation data and drawing the deformation-time curve, and at the same time determining the tool change process based on the deformation-time curve; A parameter tracking module for performing time sequence tracking parameter compensation according to the tool change process to generate tracking compensation tool parameters; performing real-time entry update based on the tracking compensation tool parameters.

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