Dynamic coupling monitoring method for full-working-condition thermal characteristics of numerical control machine tool feeding system

Abstract

The invention discloses a dynamic coupling monitoring method for full-working-condition thermal characteristics of a numerical control machine tool feeding system. The method comprises the steps of collecting machine tool cold state data; recording the steady-state temperature of each measuring point and the elongation of the tail end of the screw rod in the heat balance state; collecting the temperature of each measuring point, the elongation of the tail end of the screw rod and the positioning error under variable speed and variable load experiments; continuously monitoring a cooling curve after shutdown; fitting a thermal time constant based on the Newton's cooling law; performing range analysis and variance analysis on temperature, speed, load and stroke four-factor three-level orthogonal experiment data to obtain the priority of each influence factor; a simulation model is constructed, experimental data and simulation data are compared point by point, and the average deviation of temperature, deformation and positioning errors is calculated; if the deviation exceeds the limit, reversely optimizing the friction coefficient and the heat exchange coefficient until a high-precision simulation model is obtained; and extracting data of an experimental difficult-to-measure region based on a high-precision simulation model, tracking a global thermal deformation transfer path, generating a thermal error compensation table, and guiding optimization of a heat dissipation structure.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of dynamic testing of numerical control machine tools, and more particularly relates to a full-working-condition thermal characteristic dynamic coupling monitoring method for a feed system of a numerical control machine tool. BACKGROUND

[0002] A numerical control machine tool is the core equipment of modern manufacturing industry, and its machining precision directly determines the product quality. As a key component for realizing precise positioning of a numerical control machine tool, a feed system will generate thermal deformation in the running process due to internal heat sources (such as friction of a screw nut pair and bearing friction) and external heat sources (such as environmental temperature change), and then cause positioning errors (i.e. thermal errors). Thermal errors account for 40%-70% of the total errors of a machine tool, and are the core bottleneck restricting the improvement of machining precision of a numerical control machine tool. Therefore, the thermal characteristic law needs to be revealed by monitoring temperature, thermal deformation and positioning errors, so as to lay a foundation for thermal error compensation.

[0003] At present, in the research on thermal errors of a feed system, in the monitoring aspect, a temperature sensor is mostly used, which is installed on a bearing seat, near a screw nut, and on a machine tool outer wall through pasting and bracket fixing, a non-contact displacement sensor is used to obtain positioning errors, and a signal acquisition platform is used to realize data storage; however, the existing temperature sensor installation method has obvious defects: the distance from the temperature sensor to the core heat sources such as bearings and nuts is far, or the bracket fixing method interferes with the movement of the screw, which leads to insufficient accuracy of temperature data and makes it difficult to accurately reflect the real thermal state of the heat source; at the same time, the traditional monitoring method cannot cover the key areas such as the inner side of the screw helical groove and the contact interface between the nut and the screw, and cannot capture the complete distribution of the temperature field of the screw system.

[0004] In the experimental design aspect, the existing research mostly uses fixed working condition experiments to obtain data, which can only reflect the thermal characteristics under a single speed and load condition, and is difficult to simulate the compound dynamic working conditions of variable speed, variable load and variable stroke in actual machining, which leads to significant deviation between the experimental results and the real machining scene. In addition, the existing experiments lack systematic research on thermal inertia in the shutdown stage, and cannot quantify key parameters such as thermal time constant, which makes it difficult to reveal the dynamic law of heat storage and release of the system.

[0005] In the data utilization and model construction aspect, the existing technology fails to form a closed-loop system of "experimental acquisition-simulation verification-parameter optimization", and the key thermal parameters such as friction coefficient and convection heat transfer coefficient in the simulation model mostly rely on empirical assignment, which deviates from the actual working condition, leading to insufficient precision of the simulation results of temperature field and thermal deformation, and making it difficult to effectively supplement the blind area of experimental measurement. At the same time, the priority of various influencing factors (such as speed, load and temperature) lacks quantitative analysis, which makes it difficult to guide the accurate formulation of thermal error compensation strategies. SUMMARY

[0006] In view of the above defects or improvement needs of the prior art, the present application provides a numerical control machine tool feeding system full working condition thermal characteristic dynamic coupling monitoring method and system, through constructing an experimental scheme covering the complete machining cycle of "thermal machine operation-variable speed process-shutdown cooling", integrating orthogonal experimental design, the problems of existing technology that temperature sensor installation affects data accuracy and single experimental working condition is difficult to reflect actual machining composite working condition are solved; a thermal-structure coupling simulation and parameter inversion closed loop of "experimental data acquisition-friction coefficient / convection coefficient reverse identification-simulation result verification" is established, key thermal parameters are optimized in reverse using experimental temperature data, the limitation of physical detection is broken through, and the shortcomings of the prior art that it is difficult to comprehensively capture the global temperature field of the screw system and cover the local area temperature characteristics are made up; based on the fitting exponential type cooling curve of the continuous temperature monitoring data in the shutdown stage, the thermal time constant is accurately quantified, the accurate characterization of the system thermal inertia characteristic is realized, the limitations of the prior art in the aspects of thermal characteristic law mining and thermal error compensation basic research are improved, and the accuracy, comprehensiveness and depth of the numerical control machine tool feeding system full working condition thermal characteristic monitoring are systematically improved.

[0007] In order to achieve the above-mentioned purpose, one aspect of the present application provides a numerical control machine tool feeding system full working condition thermal characteristic dynamic coupling monitoring method, comprising the following steps:

[0008] S1, acquiring machine tool cold state data according to physical sensors and laser interferometer to obtain the reference state data of the numerical control machine tool feeding system not disturbed by heat;

[0009] S2, recording the steady-state temperature of each measuring point and the elongation of the screw tail end by controlling the full stroke reciprocating motion of the numerical control machine tool feeding system to reach thermal equilibrium; performing variable speed and variable load experiments, synchronously collecting the temperature, screw tail end elongation and positioning error of each measuring point; continuously monitoring the cooling curve after shutdown; based on Newton's cooling law, fitting the thermal time constant to quantify the system thermal inertia characteristic;

[0010] S3, selecting temperature, speed, load and stroke for four-factor three-level orthogonal experiment, performing range analysis and variance analysis on the orthogonal experimental data to obtain the influence priority of "speed> load> stroke> temperature";

[0011] S4, constructing a simulation model, carrying out steady-state temperature field, transient temperature field under variable speed and variable load working condition, and thermal-structure coupling simulation analysis, supplementing the temperature-deformation law of non-orthogonal speed condition simulation;

[0012] S5, compare the temperature curve, the elongation change amount and the positioning error change amount data obtained by the experiment with the simulation result data point by point, calculate the average deviation of the temperature, the deformation and the positioning error, if the deviation is out of limit, optimize the friction coefficient and the heat transfer coefficient reversely, repeat the iteration process of "experiment-simulation-parameter correction-reverification" until the precision meets the requirement, and output the high-precision simulation model;

[0013] S6, analyze the global temperature field based on the high-precision simulation model, extract the experimental difficult-to-measure region data, track the global thermal deformation transmission path, generate a thermal error compensation table, output the heat flux density distribution of the key region, and guide the optimization of the heat dissipation structure.

[0014] Further, step S1 comprises:

[0015] Pt100 temperature sensors are arranged at the motor end bearing, the floating end bearing, the nut contact area, the shaft coupling, the X-axis base outer wall, the machine tool outer wall and the environment of the ball screw;

[0016] The mirror of the laser interferometer is fixed on the workbench, and the interference mirror is installed on the spindle box to ensure that the light path is coaxial with the X-axis feeding direction, and the positioning error of the ball screw feeding system is collected by the laser interferometer.

[0017] The reference state data of the feeding system in step S1 which is not disturbed by heat includes the initial temperature of each measuring point of the machine tool in cold state, the initial elongation of the screw rod tail end and the positioning error in full stroke.

[0018] Further, step S2 comprises:

[0019] The maximum feed speed is set, the feed shaft is controlled to reciprocate at the maximum feed speed in the full stroke, and the temperature and the elongation of the screw rod tail end in the thermal equilibrium state are recorded after the continuous running until the thermal equilibrium.

[0020] After stopping and cooling to the environment temperature ± 2℃, the speed, the load and the stroke are dynamically switched according to the preset curve, and the temperature, the elongation of the screw rod tail end and the positioning error of each measuring point are synchronously collected.

[0021] After stopping, the data collection is continued, the temperature is continuously recorded until the temperature difference with the environment is less than or equal to 1℃, and the cooling curve is recorded; the initial temperature of each measuring point at the stopping moment is synchronously recorded The environment temperature T a and the average speed V0 and the load F0 in the last 30 minutes before stopping.

[0022] Based on Newton's cooling law, an exponential decay curve is fitted, and the thermal time constant of the cooling process is calculated.

[0023] Further, the expression of the thermal time constant τ of the cooling process is: wherein,

[0024]

[0025] wherein, n is the number of data groups collected in the shutdown phase, t i is the shutdown time corresponding to the ith data group, T(t i ) is the system average temperature of the ith data group; T a is the ambient temperature, which is regarded as a constant value; is the initial temperature of the system at the shutdown moment.

[0026] Further, the step S3 comprises:

[0027] Selecting temperature, speed, load, stroke as four factors, three levels, arranging 9 groups of experiments according to L9(3 4 ) orthogonal table;

[0028] Observing and calculating the temperature rise ΔT of the ball screw, the elongation change ΔL of the screw tail end, and the positioning error change ΔP; the temperature rise ΔT of the ball screw = T 实 -T0; the elongation change ΔL of the screw tail end = L 实 -L0; the positioning error change ΔP = P 实 -P0;

[0029] Determine the influence degree of each influencing factor on the temperature rise of the ball screw, the elongation of the screw tail end, and the positioning error through range analysis;

[0030] Verify the significance of each influencing factor through variance analysis, and obtain the influence priority of "speed > load > stroke > temperature".

[0031] Further, the step S4 specifically comprises:

[0032] S41: Deriving the heat flux density of the bearing and the nut pair from the experimental friction force; assigning the convective heat transfer coefficient through the empirical formula;

[0033] S42: Drawing a simplified model of the screw, nut, and bearing seat in a three-dimensional drawing software, retaining the contact interface, using equivalent volume modeling for complex structures, replacing the actual geometry with solid blocks, and outputting a lightweight but equivalent three-dimensional assembly model in terms of thermal conduction characteristics;

[0034] S43: Importing the simplified three-dimensional assembly model into the integrated simulation platform ANSYS Workbench, setting material properties, loading thermal load, setting thermal boundary, and dividing the grid to obtain a three-dimensional simulation model;

[0035] S44: Solve the steady-state temperature field through the three-dimensional simulation model, verify the accuracy of the heat source parameter setting; perform transient temperature field simulation, reproduce the dynamic thermal behavior under variable speed / variable load working conditions; predict thermal deformation through thermal-structure coupling analysis and verify the three-dimensional simulation model; simulate the temperature-deformation law through multi-speed extended working conditions, and verify the prediction ability of the model under untested working conditions.

[0036] Further, the heat flux density in step S41 includes bearing heat generation rate and nut pair heat generation rate;

[0037] Bearing heat generation rate q bear The calculation formula is: Wherein, μ is the bearing friction coefficient; F is the load; v is the speed;

[0038] Nut pair heat generation rate μ ′ The thread friction coefficient; η represents the transmission efficiency.

[0039] Further, step S44 includes:

[0040] Load the heat flux density and thermal boundary conditions of the bearing and nut pair in ANSYS; solve the temperature field distribution under thermal equilibrium state; extract the key measurement point temperature of the bearing, nut and the like;

[0041] Set the time step, and the total time length covers the experimental period; load the working condition according to the speed-load curve of step S2, simulate the transient temperature field under variable speed and variable load working conditions, and output the temperature-time curve;

[0042] Import the steady-state / transient temperature field as a body load into the structure module, define the material thermal expansion coefficient, and calculate the extension of the screw tail end;

[0043] Supplement the speed not covered in the orthogonal experiment table, simulate the temperature-deformation law under these working conditions, and verify the prediction ability of the model under untested working conditions.

[0044] Further, step S5 includes:

[0045] Extract the temperature time series data of the ball screw bearing and nut measurement points from the thermal engine operation and dynamic working condition experiment of step S2 and the orthogonal experiment of step S3; obtain the extension change amount of the screw tail end from the eddy current sensor data of the shutdown stage of step S2; combine the laser interferometer data of S1 cold state benchmark and S2 dynamic experiment to obtain the positioning error change amount;

[0046] If the deviation of the thermal equilibrium temperature distribution compared with the steady-state temperature field is ≤5%; the deviation of the temperature change trend under variable speed / variable load conditions compared with the transient temperature curve is ≤8%; the deviation of the axial elongation of the screw and thermal deformation in the shutdown stage and dynamic stage is ≤10%; the deviation of the full stroke error distribution compared with the positioning error change is ≤15%, then it is accepted;

[0047] If any of the deviations exceeds the limit, the DesignXplorer module of ANSYS Workbench is used to automatically invert the friction coefficient and heat transfer coefficient based on the experimental data, the response surface method is used to quickly locate the optimal parameter combination, and the iteration process of "experiment-simulation-parameter correction-reverification" is repeated, if the temperature / deformation deviation fluctuation amplitude is <1% in the last 3 iterations; the change rate of the parameters friction coefficient and heat transfer coefficient is <2%, then it is accepted.

[0048] The second aspect of the application provides a full-condition thermal characteristic dynamic coupling monitoring system for a numerical control machine tool feeding system, which is used to realize the numerical control machine tool feeding system full-condition thermal characteristic dynamic coupling monitoring method, comprising:

[0049] A first main module is used to collect machine tool cold state data according to physical sensors and laser interferometers, and obtain reference state data of the numerical control machine tool feeding system not disturbed by heat;

[0050] A second main module is used to record the steady-state temperature and the elongation of the tail end of the screw by controlling the full stroke reciprocating motion of the numerical control machine tool feeding system to reach thermal equilibrium, and to collect the temperature, the elongation of the tail end of the screw and the positioning error of each measuring point synchronously by conducting variable speed and variable load experiments; the cooling curve is continuously monitored after shutdown; the thermal time constant is fitted based on Newton's cooling law to quantify the thermal inertia characteristic of the system;

[0051] A third main module is used to select temperature, speed, load and stroke for four-factor three-level orthogonal experiment, and to obtain the influence priority of "speed> load> stroke> temperature" by range analysis and variance analysis on the orthogonal experiment data;

[0052] A fourth main module is used to construct a simulation model, and to carry out steady-state temperature field, transient temperature field under variable speed and variable load conditions, and thermal-structure coupling simulation analysis to supplement the temperature-deformation law under non-orthogonal speed condition simulation;

[0053] A fifth main module is used to extract the temperature curve of the ball screw, the elongation change amount of the tail end of the screw and the positioning error change amount data obtained by the experiment, and to compare them with the simulation result data point by point to calculate the average deviation of temperature, deformation and positioning error; if the deviation exceeds the limit, the friction coefficient and the heat transfer coefficient are optimized in reverse, the iteration process of "experiment-simulation-parameter correction-reverification" is repeated until the precision meets the requirements, and the high-precision simulation model is output;

[0054] The sixth main module is used for analyzing a global temperature field and a thermal deformation transmission path based on the high-precision simulation model, obtaining a thermal characteristic microscopic mechanism that cannot be directly observed by experiments, generating a thermal error compensation table, and outputting a thermal flow density distribution of a key area to guide optimization of a heat dissipation structure.

[0055] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0056] (1) The numerical control machine tool feeding system full-condition thermal characteristic dynamic coupling monitoring method and system of the present application uses a threaded temperature sensor close to the heat source at the bearing cover, and other areas use a magnetic type, and various sensors are reasonably arranged to accurately collect temperature, elongation and other data; the laser interferometer ensures that the light path is coaxial to collect positioning errors, which greatly improves the accuracy of monitoring data compared with the traditional method, solves the installation interference and distance problems of the temperature sensor, and makes the thermal characteristic monitoring more in line with the actual heat source state.

[0057] (2) The numerical control machine tool feeding system full-condition thermal characteristic dynamic coupling monitoring method and system of the present application designs to cover the complete machining cycle experiment of "thermal machine operation - variable speed process - shutdown cooling", integrates variable speed (20-64 m / min), variable load (0-100 kg), and variable stroke orthogonal experiment, can simulate the actual machining composite working condition, make up for the single working condition defect of the prior art, capture the thermal characteristic change of the whole life cycle, and reflect the real machining thermal behavior.

[0058] (3) The numerical control machine tool feeding system full-condition thermal characteristic dynamic coupling monitoring method and system of the present application is based on continuous temperature monitoring during the shutdown stage, fits an exponential cooling curve, accurately quantifies the thermal time constant to represent the thermal inertia of the system, constructs an experiment-simulation mutual verification closed loop, uses simulation to analyze the global temperature field and the thermal deformation transmission path, supplements the experimental details that are difficult to measure, breaks through the physical detection limit, and deepens the mining of thermal characteristic rules.

[0059] (4) The numerical control machine tool feeding system full-condition thermal characteristic dynamic coupling monitoring method and system of the present application constructs an "experimental collection - simulation verification - parameter reverse optimization" closed loop, inversely identifies key thermal parameters such as friction coefficient and convection coefficient according to experimental data, and corrects the simulation model; through orthogonal experiment, the influence priority of "speed > load > stroke > temperature" is determined to guide thermal error compensation, improve the universality and precision of the model, and solve the problems of inaccurate model parameters and unclear factor priority. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 Fig. 1 is a flowchart of a numerical control machine tool feeding system full-condition thermal characteristic dynamic coupling monitoring method according to an embodiment of the present application;

[0061] Figure 2This is a schematic diagram of the structure of a dynamic coupling monitoring system for the thermal characteristics of a CNC machine tool feed system under all operating conditions, according to an embodiment of the present invention.

[0062] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0064] like Figure 1 As shown, one aspect of the present invention provides a method for dynamic coupling monitoring of the thermal characteristics of a CNC machine tool feed system under all operating conditions, comprising the following steps:

[0065] S1. Initial calibration and cold benchmark establishment: Using high-precision sensors and laser interferometers, collect basic data of the CNC machine tool in a fully cooled state (without thermal interference), including the initial temperature T0 of each measuring point of the machine tool in the cold state, the initial elongation L0 of the lead screw tail end, and the full stroke positioning error P0.

[0066] S2. Hot-engine operation, dynamic working condition experiment and shutdown data acquisition: By controlling the full-stroke reciprocating motion of the CNC machine tool feed system to achieve thermal equilibrium, the steady-state temperature and lead screw tail elongation at each measuring point are recorded; variable speed (20-64m / min) and variable load (0-100kg) experiments are conducted, and the temperature, lead screw tail elongation and positioning error at each measuring point are collected simultaneously; the cooling curve is continuously monitored after shutdown; the thermal time constant is fitted based on Newton's law of cooling to quantify the thermal inertia characteristics of the system;

[0067] S3. Orthogonal Experimental Design and Data Analysis: A four-factor, three-level orthogonal experiment was conducted using temperature, speed, load, and stroke. Range analysis was performed on the orthogonal experimental data to obtain the influence of temperature, feed rate, load, and stroke on the ball screw temperature rise, screw tail elongation, and positioning error. The significance of each influencing factor was verified through analysis of variance to obtain the influence priority of "speed > load > stroke > temperature" to guide the optimization of thermal error compensation strategy.

[0068] S4. Simulation Model Construction and Multi-condition Analysis: Determine the initial values ​​of heat flux density and convective heat transfer coefficient of the bearings on both sides and the lead screw and nut pair, construct a simulation model, and carry out steady-state temperature field, transient temperature field under variable speed and load conditions, and thermal-structure coupling simulation analysis to obtain steady-state temperature field, transient temperature field, and thermal deformation simulation data. Supplement the simulation temperature-deformation law under non-orthogonal speed conditions and expand the universality of the model.

[0069] S5. Experiment-Simulation Mutual Verification and Model Optimization: Extract the ball screw temperature curve, the change in the elongation of the screw tail end, and the change in the positioning error data obtained from the experiment, and compare them point by point with the simulation results data from step S4. Calculate the average deviation of temperature, deformation, and positioning error. If the deviation exceeds the limit, optimize the friction coefficient and heat transfer coefficient in reverse, and repeat the iterative process of "experiment → simulation → parameter correction → re-verification" until the accuracy meets the requirements, and output a high-precision simulation model.

[0070] S6. Based on the high-precision simulation model, analyze the global temperature field and thermal deformation transmission path to obtain the microscopic mechanism of thermal characteristics that cannot be directly observed in experiments, generate a thermal error compensation table, output the heat flux density distribution in key areas, and guide the optimization of heat dissipation structure.

[0071] This invention significantly improves the accuracy of thermal error prediction and compensation for CNC machine tools through a closed-loop technology of "experimental data → simulation modeling → parameter optimization → engineering application".

[0072] Further, step S1 includes: equipment preparation, placing Pt100 temperature sensors (accuracy ±0.1℃) on the motor end bearing, floating end bearing, nut contact area, coupling, X-axis base outer wall, machine tool outer wall, and environment of the ball screw, especially using threaded temperature sensors instead of bolted connections at the bearing cover, close to the heat source, and using magnetic temperature sensors attached to the surface of the components in other areas; installing an eddy current displacement sensor (accuracy ±0.5μm) at the tail end of the screw for collecting temperature and elongation;

[0073] Laser interferometer setup: The reflector is fixed to the worktable, and the interferometer is installed in the spindle box to ensure that the optical path is coaxial with the X-axis feed direction, which is used to collect the positioning error of the ball screw feed system;

[0074] After setting up the laser interferometer as required, perform cold-state measurements: the machine tool is stopped for ≥10 hours until the ambient temperature stabilizes, and the initial temperature T0 of the key point and the initial elongation L0 of the lead screw end are recorded; after the laser interferometer returns to zero on the X-axis, it is zeroed at the start of the stroke, and the positioning error P0 of the entire stroke is collected as the cold-state reference.

[0075] Furthermore, step S2 obtains dynamic thermal response data by simulating the combined working conditions of variable speed and variable load in actual processing; quantifies the thermal inertia characteristics of the system through cooling process data to reflect the rate of heat storage and release; covers the entire processing cycle of "steady state-dynamic-shutdown" and quantifies the coupling effect between the system's thermal response speed and dynamic working conditions.

[0076] Step S2 includes:

[0077] S21: Warm-up operation (full stroke reciprocating): Set the maximum feed speed, control the feed axis (X-axis) to reciprocate within the full stroke at the maximum feed speed, and continue to run until the temperature fluctuation is ≤0.5℃ / 10min (thermal balance judgment). Record the temperature of each measuring point (such as bearing and nut temperature) and the elongation of the lead screw tail end at the end of the warm-up operation (thermal balance).

[0078] S22: Variable Speed ​​+ Variable Load Test (Dynamic Operating Condition): After shutdown and cooling to ambient temperature ±2℃, the speed, load, and stroke are dynamically switched according to a preset curve, and the temperature, lead screw elongation, and positioning error at each measuring point are collected simultaneously; specifically including:

[0079] Cooling interval: After the machine is warmed up, stop it for 30 minutes and wait for the temperature to drop to the ambient temperature ±2℃ before starting the dynamic experiment;

[0080] Operating conditions: The feed speed (20-64 m / min), load (0-100 kg), and stroke are switched according to a preset curve. The temperature, lead screw elongation, and positioning error at each measuring point are collected in real time. Among them, the temperature of key points is sampled at 1 Hz, covering bearings, nuts, couplings, X-axis bases, machine tool outer walls, and the environment; the lead screw elongation is sampled every 1 minute; and the positioning error is collected every 6 minutes using a laser interferometer.

[0081] S23: Shutdown data acquisition for thermal inertia analysis:

[0082] After each shutdown, continue to collect data and record the temperature until the temperature difference with the environment is ≤1℃, and record the cooling curve (time-temperature data pair);

[0083] Synchronously record the initial temperature of each measuring point at the moment of shutdown. Ambient temperature T a and the average speed V0 and load F0 30 minutes before shutdown;

[0084] S24: Based on Newton's law of cooling, fit the exponential decay curve and calculate the thermal time constant of the cooling process (the time required for the temperature to decay to 36.8% of the ambient temperature).

[0085] After shutdown, the system temperature changes over time according to an exponential decay law:

[0086]

[0087] Where T(t) is the system temperature (°C) at time t after shutdown; T represents the initial system temperature (°C) at the time of shutdown. a The ambient temperature (°C, considered a constant) is represented by t; the time after shutdown (min) is represented by τ; and the thermal time constant of the cooling process (min) is represented by τ, obtained through data fitting. t is the horizontal axis. The vertical axis is used as the reference axis.

[0088] The expression for the thermal time constant τ during the cooling process is: in,

[0089]

[0090] Where n is the number of data sets collected during the shutdown phase, and t i Let T(t) be the downtime corresponding to the i-th data set. i Let be the system average temperature of the i-th data set;

[0091] The thermal time constant τ of the cooling process characterizes the hysteresis characteristics of the screw system cooling process, that is, the time required for the system temperature to decay from the initial value of shutdown to 63.2% (1-1 / e) of the ambient temperature, reflecting the release rate of the stored heat in the system;

[0092] Furthermore, step S3 uses orthogonal experiments to isolate the coupling effects of multiple factors and identify key driving factors for thermal properties; specifically including:

[0093] Selecting four factors and three levels—temperature (20℃, 25℃, 30℃), speed (20m / min, 40m / min, 64m / min), load (0kg, 50kg, 100kg), and stroke (0-166mm, 166-332mm, 332-500mm)—according to L9(3 4 An orthogonal array was used to arrange 9 groups of experiments;

[0094] Observe and calculate the ball screw temperature rise ΔT, the change in screw tail elongation ΔL, and the change in positioning error ΔP; ball screw temperature rise ΔT = T 实 -T0; Change in elongation at the tail end of the lead screw ΔL=L 实 -L0; Positioning error change ΔP=P 实 -P0;

[0095] The influence of each factor on the temperature rise, tail elongation and positioning error of the ball screw was determined by range analysis.

[0096] The significance of each influencing factor was verified by analysis of variance (P<0.05 was considered significant), and the influence priority was obtained as "speed > load > stroke > temperature".

[0097] Furthermore, step S4 drives the simulation parameter setting based on experimental data, and realizes the visualization of global thermal characteristics through multi-physics coupling; step S4 specifically includes:

[0098] S41: The heat flux density of the bearing and nut assembly is derived from experimental friction; the convective heat transfer coefficient is assigned a value through empirical formula, and then the convective heat transfer coefficient is corrected by experimental inversion.

[0099] S42: Draw simplified models of lead screws, nuts, and bearing housings in 3D modeling software, retaining contact interfaces (such as bearing raceways and nut-lead screw meshing surfaces), and using equivalent volume modeling for complex structures (such as the internal ball circulation channel of the nut), replacing the actual geometry with solid blocks to ensure consistent heat conduction paths; output a lightweight but thermally equivalent 3D assembly model.

[0100] S43: Import the simplified 3D assembly model into the integrated simulation platform ANSYSWorkbench, set material properties, apply thermal loads (bearing heat flux density, nut assembly heat flux density), set thermal boundaries (environmental convection), and perform mesh generation (hexahedral mesh for the lead screw and bearing, size 2mm; tetrahedral mesh for the nut, size 1.5mm; with finer mesh in the contact area) to obtain the 3D simulation model.

[0101] S44: Solve the steady-state temperature field through a 3D simulation model to verify the accuracy of the heat source parameter settings; perform transient temperature field simulation to reproduce the dynamic thermal behavior under variable speed / variable load conditions; predict thermal deformation and verify the 3D simulation model through thermal-structure coupling analysis; and simulate temperature-deformation laws through multi-speed extended operating conditions to verify the model's predictive ability under untested operating conditions.

[0102] Furthermore, the heat flux density in step S41 includes the bearing heat generation rate and the nut assembly heat generation rate; the bearing heat generation rate q bear The calculation formula is: Where μ = 0.1, is the bearing friction coefficient; F is the load; and v is the speed.

[0103] Nut-related heat rate μ ′ =0.08, which is the coefficient of thread friction; η =0.9, which represents the transmission efficiency;

[0104] Furthermore, in step S43, ANSYS Workbench is an integrated simulation platform that provides a unified modeling, solving, and post-processing environment for multiphysics engineering simulations. It integrates simulation tools from disciplines such as structure, fluid, thermal, and electromagnetic into a single interface, supporting full-process automation from geometric modeling to result analysis.

[0105] Further, step S44 includes:

[0106] Load the heat flux density and thermal boundary conditions of the bearing and nut pair in ANSYS; solve the temperature field distribution under thermal equilibrium; extract the temperature of key measuring points such as bearing and nut; compare with the experimental thermodynamic data in step S2; take the temperature deviation between simulation and experimental temperature ≤5% as the acceptance criterion, otherwise the friction coefficient or heat flux density needs to be adjusted.

[0107] Set the time step to 1 minute, and the total duration to cover the experimental cycle; load the load according to the speed-load curve in step S2 (e.g., a step change from 20 to 64 m / min) to simulate the transient temperature field under variable speed and load conditions, output the temperature change curve over time, and compare it synchronously with the experimental dynamic data; the acceptance standard is that the transient temperature curve deviation is ≤8%, and if it exceeds the limit, the time step or convection coefficient needs to be checked.

[0108] The steady-state / transient temperature field is imported into the structural module as a body load. The thermal expansion coefficient of the material is defined, and the elongation (thermal deformation) of the lead screw tail end is calculated. The elongation of the lead screw tail end measured by the S2 experiment is compared with the thermal deformation deviation of ≤10%. If the deviation exceeds the limit, the material parameters or constraint conditions need to be corrected.

[0109] Supplement the orthogonal experimental table with speeds not covered (such as 30m / min and 50m / min), simulate the temperature-deformation law under these conditions, verify the model's predictive ability under untested conditions, and improve the model's universality.

[0110] Further, step S5 includes:

[0111] Temperature time-series data of ball screw bearings, nuts, and other measuring points were extracted from the S2 thermal engine operation and dynamic condition experiments and the S3 orthogonal experiment; the change in the elongation of the screw tail end was obtained from the eddy current sensor data during the S2 shutdown phase; the change in positioning error was obtained by combining the S1 cold state reference and the laser interferometer data from the S2 dynamic experiment; and the experimental timestamps were matched with the simulation time steps to ensure time sequence consistency.

[0112] If the deviation between the thermal equilibrium temperature distribution (bearing / nut measuring point) and the steady-state temperature field is ≤5%; the deviation between the temperature change trend under variable speed / variable load conditions and the transient temperature curve is ≤8%; the deviation between the axial elongation of the lead screw (stopping stage and dynamic stage) and the thermal deformation (elongation change at the tail end of the lead screw) is ≤10%; and the deviation between the full stroke error distribution and the positioning error change is ≤15%, then the acceptance is qualified. If any deviation exceeds the limit, the DesignXplorer module of ANSYS Workbench will be used to automatically invert the friction coefficient and heat transfer coefficient based on the experimental data. The optimal parameter combination will be quickly located using the response surface method. The iterative process of "experiment → simulation → parameter correction → re-verification" will be repeated. If the temperature / deformation deviation fluctuation is <1% and the change rate of the parameter friction coefficient and heat transfer coefficient is <2% in three consecutive iterations, then the acceptance is qualified.

[0113] Furthermore, step S6 includes: extracting data from difficult-to-measure areas of the experiment, such as the temperature gradient inside the spiral groove and the heat flow distribution at the nut-lead screw contact interface; tracing the heat deformation transmission path across the entire domain, such as the deformation contribution ratio of bearing → lead screw → worktable; generating a thermal error compensation table; and outputting the heat flow density distribution of key areas (such as the nut contact surface) to guide the optimization of the heat dissipation structure.

[0114] like Figure 2 As shown, a second aspect of the present invention provides a dynamic coupling monitoring system for the thermal characteristics of a CNC machine tool feed system under all operating conditions, for implementing the above-mentioned design method, comprising:

[0115] The first main module is used to collect cold-state data of the machine tool based on physical sensors and laser interferometers to obtain the reference state data of the CNC machine tool feed system that is not affected by thermal interference.

[0116] The second main module is used to control the full-stroke reciprocating motion of the CNC machine tool's feed system to achieve thermal equilibrium, record the steady-state temperature and lead screw tail elongation at each measuring point; conduct variable speed and variable load experiments, and simultaneously collect the temperature, lead screw tail elongation, and positioning error at each measuring point; continuously monitor the cooling curve after shutdown; and fit the thermal time constant based on Newton's law of cooling to quantify the system's thermal inertia characteristics.

[0117] The third main module is used to select temperature, speed, load, and stroke for a four-factor, three-level orthogonal experiment, and to perform range analysis and variance analysis on the orthogonal experiment data to obtain the influence priority of "speed > load > stroke > temperature".

[0118] The fourth main module is used to build simulation models and carry out steady-state temperature field, transient temperature field under variable speed and load conditions, and thermal-structural coupling simulation analysis, supplementing the simulation of temperature-deformation laws under non-orthogonal speed conditions.

[0119] The fifth main module is used to extract the ball screw temperature curve, the change in the elongation of the screw tail end, and the change in the positioning error obtained from the experiment. It compares the data with the simulation results point by point and calculates the average deviation of temperature, deformation, and positioning error. If the deviation exceeds the limit, it optimizes the friction coefficient and heat transfer coefficient in reverse and repeats the iterative process of "experiment → simulation → parameter correction → re-verification" until the accuracy meets the requirements and outputs a high-precision simulation model.

[0120] The sixth main module is used to analyze the global temperature field and thermal deformation transmission path based on the high-precision simulation model, obtain the microscopic mechanism of thermal characteristics that cannot be directly observed in experiments, generate a thermal error compensation table, output the heat flux density distribution in key areas, and guide the optimization of heat dissipation structure.

[0121] It should be noted that the CNC machine tool feed system full-condition thermal characteristic dynamic coupling monitoring system provided in this embodiment can be a computer program (including program code) running on a computer device. For example, the CNC machine tool feed system full-condition thermal characteristic dynamic coupling monitoring system is an application software; the CNC machine tool feed system full-condition thermal characteristic dynamic coupling monitoring system can be used to execute the corresponding steps in the above-mentioned method provided in the embodiments of this application.

[0122] In some feasible implementations, the dynamic coupling monitoring system for the thermal characteristics of a CNC machine tool feed system under all operating conditions provided in this embodiment can be implemented using a combination of hardware and software. As an example, the dynamic coupling monitoring system for the thermal characteristics of a CNC machine tool feed system under all operating conditions provided in this application embodiment can be a processor in the form of a hardware decoding processor, which is programmed to execute the dynamic coupling monitoring method for the thermal characteristics of a CNC machine tool feed system under all operating conditions provided in this application embodiment. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0123] In some feasible implementations, the dynamic coupling monitoring system for the thermal characteristics of the CNC machine tool feed system under all working conditions provided in this embodiment can be implemented in software. It can be software in the form of programs and plug-ins, and includes a series of modules to realize the dynamic coupling monitoring method for the thermal characteristics of the CNC machine tool feed system under all working conditions provided in this embodiment of the invention.

[0124] A third aspect of the present invention also provides an electronic device, Figure 3 This is a schematic diagram of the electronic device in this embodiment, as shown below. Figure 3 As shown, the electronic device 1000 in this embodiment may include: a processor 1001, a network interface 1004, and a memory 1005. Furthermore, the electronic device 1000 may also include: a user interface 1003, and at least one communication bus 1002. The communication bus 1002 is used to implement communication between these components. The user interface 1003 may include a display screen and a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 3 As shown, the memory 1005, which is a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a device control application.

[0125] like Figure 1 In the electronic device 1000 shown, the network interface 1004 provides network communication functions; the user interface 1003 is mainly used to provide an input interface for users; and the processor 1001 can be used to call the device control application stored in the memory 1005 to implement each step of the dynamic coupling monitoring method for the thermal characteristics of the CNC machine tool feed system under all working conditions.

[0126] It should be understood that in some feasible implementations, the processor 1001 described above may be a central processing unit (CPU), which may also be other general-purpose processors, DSPs, ASICs, FPGAs, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0127] In specific implementation, the aforementioned electronic device 1000 can perform the above-described actions through its built-in functional modules. Figure 1The implementation methods provided for each step are detailed in the above-mentioned implementation methods, and will not be repeated here.

[0128] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement... ​ The methods provided in each step are detailed in the implementation methods provided in the above steps, and will not be repeated here.

[0129] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0130] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions, characterized in that, Includes the following steps: S1. Collect cold-state data of the machine tool using physical sensors and laser interferometers to obtain baseline state data of the CNC machine tool feed system that is not affected by thermal interference; S2. Control the CNC machine tool's feed system to achieve thermal equilibrium through full-stroke reciprocating motion, and record the steady-state temperature and lead screw tail elongation at each measuring point; conduct variable speed and variable load experiments, and simultaneously collect the temperature, lead screw tail elongation, and positioning error at each measuring point; continuously monitor the cooling curve after shutdown; fit the thermal time constant based on Newton's law of cooling to quantify the system's thermal inertia characteristics; S3. Select temperature, speed, load, and stroke to conduct a four-factor, three-level orthogonal experiment. Perform range analysis and variance analysis on the orthogonal experiment data to obtain the influence priority of "speed > load > stroke > temperature". S4. Construct simulation models and conduct steady-state temperature field, transient temperature field under variable speed and load conditions, and thermal-structural coupling simulation analysis to supplement the temperature-deformation law of non-orthogonal speed conditions. S5. Extract the ball screw temperature curve, the change in the elongation of the screw tail end, and the change in the positioning error obtained from the experiment. Compare them point by point with the simulation results to calculate the average deviation of temperature, deformation, and positioning error. If the deviation exceeds the limit, optimize the friction coefficient and heat transfer coefficient in reverse and repeat the iterative process of "experiment → simulation → parameter correction → re-verification" until the accuracy meets the requirements and output a high-precision simulation model. S6. Based on the high-precision simulation model, analyze the global temperature field, extract data from areas difficult to measure in the experiment, trace the global thermal deformation transmission path, generate a thermal error compensation table, output the heat flux density distribution in key areas, and guide the optimization of the heat dissipation structure.

2. The method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to claim 1, characterized in that: Step S1 includes: Pt100 temperature sensors are placed on the motor end bearing, floating end bearing, nut contact area, coupling, outer wall of X-axis base, outer wall of machine tool, and in the environment of the ball screw; threaded temperature sensors are used at the bearing cover; and eddy current displacement sensors are installed at the tail end of the screw. Fix the reflector of the laser interferometer to the worktable and install the interferometer on the spindle box to ensure that the optical path is coaxial with the X-axis feed direction. Collect the positioning error of the ball screw feed system through the laser interferometer. The baseline data of the feed system in step S1, which is not affected by thermal interference, includes the initial temperature of each measuring point of the machine tool in cold state, the initial elongation of the lead screw tail end, and the full stroke positioning error.

3. The method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to claim 2, characterized in that: Step S2 includes: Set the maximum feed rate, control the feed axis to reciprocate within the full stroke at the maximum feed rate, and continue running until thermal equilibrium is reached. Record the temperature at each measuring point and the elongation at the end of the lead screw in the thermal equilibrium state. After the machine is stopped and cooled to within ±2℃ of the ambient temperature, the speed, load and stroke are dynamically switched according to the preset curve, and the temperature, lead screw elongation and positioning error of each measuring point are collected simultaneously. After shutdown, continue collecting data and recording the temperature until the temperature difference with the environment is ≤1℃, and record the cooling curve; simultaneously record the initial temperature of each measuring point at the time of shutdown. Ambient temperature T a and the average speed V0 and load F0 30 minutes before shutdown; Based on Newton's law of cooling, an exponential decay curve is fitted to calculate the thermal time constant of the cooling process.

4. The method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to claim 3, characterized in that: The expression for the thermal time constant τ during the cooling process is: in, Where n is the number of data sets collected during the shutdown phase, and t i Let T(t) be the downtime corresponding to the i-th data set. i T represents the system average temperature of the i-th data set; a The ambient temperature is considered a constant value. The initial system temperature at the moment of shutdown.

5. A method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to any one of claims 1-4, characterized in that: Step S3 includes: Select four factors (temperature, speed, load, and stroke) at three levels, according to L9(3) 4 An orthogonal array was used to arrange 9 groups of experiments; Observe and calculate the ball screw temperature rise ΔT, the change in screw tail elongation ΔL, and the change in positioning error ΔP; ball screw temperature rise ΔT = T 实 -T0; Change in elongation at the tail end of the lead screw ΔL=L 实 -L0; Positioning error change ΔP=P 实 -P0; The influence of each factor on the temperature rise, tail elongation and positioning error of the ball screw was determined by range analysis. The significance of each influencing factor was verified by analysis of variance, and the influence priority of "speed > load > stroke > temperature" was obtained.

6. A method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to any one of claims 1-4, characterized in that: Step S4 specifically includes: S41: The heat flux density of the bearing and nut assembly is derived from experimental friction; the convective heat transfer coefficient is assigned a value using empirical formulas. S42: Draw simplified models of lead screws, nuts, and bearing seats in 3D modeling software, retain the contact interface, use equivalent volume modeling for complex structures, replace the actual geometry with solid blocks, and output a lightweight but thermally equivalent 3D assembly model. S43: Import the simplified 3D assembly model into the integrated simulation platform ANSYSWorkbench, set material properties, apply thermal loads, set thermal boundaries, and perform mesh generation to obtain the 3D simulation model. S44: Solve the steady-state temperature field through a 3D simulation model to verify the accuracy of the heat source parameter settings; perform transient temperature field simulation to reproduce the dynamic thermal behavior under variable speed / variable load conditions; predict thermal deformation and verify the 3D simulation model through thermal-structure coupling analysis; and verify the model's predictive ability under untested conditions by simulating temperature-deformation laws through multi-speed extended operating conditions.

7. The method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to claim 6, characterized in that: In step S41, the heat flux density includes the bearing heat generation rate and the nut assembly heat generation rate; Bearing heat generation rate q bear The calculation formula is: Where μ is the bearing friction coefficient; F is the load; and v is the speed; Nut-related heat rate μ ′ η represents the thread friction coefficient; η represents the transmission efficiency.

8. The method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to claim 7, characterized in that: Step S44 includes: Load the heat flux density and thermal boundary conditions of the bearing and nut assembly in ANSYS; solve the temperature field distribution under thermal equilibrium; extract the temperature of key measuring points such as bearings and nuts; Set the time step to cover the experimental cycle with the total duration; load the working condition according to the speed-load curve in step S2, simulate the transient temperature field under variable speed and variable load conditions, and output the temperature change curve over time. The steady-state / transient temperature field is imported into the structural module as a body load, the thermal expansion coefficient of the material is defined, and the elongation at the tail end of the screw is calculated. To supplement the velocities not covered in the orthogonal experimental table, we simulated the temperature-deformation characteristics under these operating conditions and verified the model's predictive ability under untested operating conditions.

9. A method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to any one of claims 1-4, characterized in that: Step S5 includes: Temperature time series data of ball screw bearing and nut measuring points are extracted from the thermal operation and dynamic working condition experiment in step S2 and the orthogonal experiment in step S3; the change in elongation of the screw tail end is obtained from the eddy current sensor data during the shutdown stage in step S2; and the change in positioning error is obtained by combining the cold reference in S1 and the laser interferometer data from the dynamic experiment in S2. If the deviation between the thermal equilibrium temperature distribution and the steady-state temperature field is ≤5%; the deviation between the temperature change trend under variable speed / variable load conditions and the transient temperature curve is ≤8%; the deviation between the axial elongation of the screw and the thermal deformation during the shutdown and dynamic stages is ≤10%; and the deviation between the full stroke error distribution and the change in positioning error is ≤15%, then the acceptance is qualified. If any deviation exceeds the limit, the DesignXplorer module of ANSYS Workbench will be used to automatically invert the friction coefficient and heat transfer coefficient based on the experimental data. The optimal parameter combination will be quickly located using the response surface methodology. The iterative process of "experiment → simulation → parameter correction → re-verification" will be repeated. If the temperature / deformation deviation fluctuation is less than 1% and the change rate of the parameters friction coefficient and heat transfer coefficient is less than 2% in three consecutive iterations, then the acceptance is qualified.

10. A dynamic coupling monitoring system for the thermal characteristics of a CNC machine tool feed system under all operating conditions, characterized in that, The method for dynamic coupling monitoring of the thermal characteristics of a CNC machine tool feed system under all operating conditions, as described in any one of claims 1-9, includes: The first main module is used to collect cold-state data of the machine tool based on physical sensors and laser interferometers to obtain the reference state data of the CNC machine tool feed system that is not affected by thermal interference. The second main module is used to control the full-stroke reciprocating motion of the CNC machine tool's feed system to achieve thermal equilibrium, record the steady-state temperature and lead screw tail elongation at each measuring point; conduct variable speed and variable load experiments, and simultaneously collect the temperature, lead screw tail elongation, and positioning error at each measuring point; continuously monitor the cooling curve after shutdown; and fit the thermal time constant based on Newton's law of cooling to quantify the system's thermal inertia characteristics. The third main module is used to select temperature, speed, load, and stroke for a four-factor, three-level orthogonal experiment, and to perform range analysis and variance analysis on the orthogonal experiment data to obtain the influence priority of "speed > load > stroke > temperature". The fourth main module is used to build simulation models and carry out steady-state temperature field, transient temperature field under variable speed and load conditions, and thermal-structural coupling simulation analysis, supplementing the simulation of temperature-deformation laws under non-orthogonal speed conditions. The fifth main module is used to extract the ball screw temperature curve, the change in the elongation of the screw tail end, and the change in the positioning error obtained from the experiment. It compares the data with the simulation results point by point and calculates the average deviation of temperature, deformation, and positioning error. If the deviation exceeds the limit, it optimizes the friction coefficient and heat transfer coefficient in reverse and repeats the iterative process of "experiment → simulation → parameter correction → re-verification" until the accuracy meets the requirements and outputs a high-precision simulation model. The sixth main module is used to analyze the global temperature field based on the high-precision simulation model, extract data from areas that are difficult to measure in experiments, track the global thermal deformation transmission path, generate a thermal error compensation table, output the heat flux density distribution in key areas, and guide the optimization of the heat dissipation structure.

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