Method for monitoring dynamic coupling of thermal characteristics of feeding system of numerical control machine tool in all working conditions

Abstract

The application discloses a kind of CNC machine tool feeding system full working condition thermal characteristic dynamic coupling monitoring method, including acquisition machine tool cold state data;Record the steady-state temperature of each measuring point in thermal equilibrium state and screw tail elongation;Collect the temperature of each measuring point, screw tail elongation and positioning error under variable speed, variable load experiment;After shutdown, continue to monitor the cooling curve;Based on Newton cooling law fitting thermal time constant;Range analysis and variance analysis are carried out to orthogonal experiment data of four factors and three levels of temperature, speed, load and stroke, to obtain the priority of each influencing factor;Simulation model is constructed, and experimental data and simulation data are compared point by point, to calculate the average deviation of temperature, deformation and positioning error;If deviation is out of limit, reverse optimization friction coefficient and heat transfer coefficient until high-precision simulation model is obtained;Based on high-precision simulation model, extract experimental difficult-to-measure area data, track global thermal deformation transmission path, generate thermal error compensation table, and guide heat dissipation structure optimization.

Description

Technical Field

[0001] This invention belongs to the field of dynamic testing technology for CNC machine tools, and more specifically, relates to a dynamic coupling monitoring method for the thermal characteristics of a CNC machine tool feed system under all operating conditions. Background Technology

[0002] CNC machine tools are core equipment in modern manufacturing, and their machining accuracy directly determines product quality. The feed system, as a key component for achieving precise positioning in CNC machine tools, undergoes thermal deformation during operation due to internal heat sources (such as friction between the lead screw and nut pair, and bearing friction) and external heat sources (such as changes in ambient temperature), leading to positioning errors (i.e., thermal errors). Thermal errors account for 40%-70% of the total machine tool error and are a core bottleneck restricting the improvement of CNC machine tool machining accuracy. Therefore, it is necessary to reveal the laws of thermal characteristics by monitoring temperature, thermal deformation, and positioning errors, laying the foundation for thermal error compensation.

[0003] Currently, research on thermal errors in feed systems primarily employs temperature sensors for monitoring. These sensors are typically mounted on the bearing housing, near the lead screw and nut, or on the outer wall of the machine tool via adhesive or brackets. Non-contact displacement sensors are used to acquire positioning errors, and data is stored through a signal acquisition platform. However, existing temperature sensor installation methods have significant drawbacks: they are too far from core heat sources such as bearings and nuts, or the bracket mounting method interferes with the lead screw's movement, resulting in insufficient accuracy of temperature data and difficulty in accurately reflecting the true thermal state of the heat sources. Furthermore, traditional monitoring methods struggle to cover critical areas such as the inner side of the lead screw's helical groove and the contact interface between the nut and the lead screw, failing to capture the complete temperature field distribution across the entire lead screw system.

[0004] In terms of experimental design, existing studies mostly use fixed-condition experiments to obtain data, which can only reflect the thermal characteristics under single speed and load conditions. It is difficult to simulate the complex dynamic conditions of variable speed, variable load, and variable stroke in actual processing, resulting in significant deviations between experimental results and real processing scenarios. In addition, existing experiments lack systematic research on thermal inertia during the shutdown phase, making it impossible to quantify key parameters such as the thermal time constant, and thus difficult to reveal the dynamic laws of heat storage and release in the system.

[0005] In terms of data utilization and model building, existing technologies have failed to form a closed-loop system of "experimental acquisition - simulation verification - parameter optimization". Key thermal parameters such as friction coefficient and convective heat transfer coefficient in simulation models rely heavily on empirical values, which deviate from actual operating conditions. This results in insufficient accuracy of temperature field and thermal deformation simulation results, failing to effectively compensate for the blind spots of experimental measurements. At the same time, there is a lack of quantitative analysis on the priority of various influencing factors (such as speed, load, and temperature), making it difficult to guide the accurate formulation of thermal error compensation strategies. Summary of the Invention

[0006] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a dynamic coupling monitoring method and system for the full-condition thermal characteristics of CNC machine tool feed systems. By constructing an experimental scheme covering the complete machining cycle of "heat engine operation - speed change process - shutdown cooling," and incorporating orthogonal experimental design, it solves the problems of existing technologies where temperature sensor installation affects data accuracy and the single experimental condition fails to reflect the complex actual machining conditions. A closed-loop thermal-structural coupling simulation and parameter inversion is established, consisting of "experimental data acquisition → reverse identification of friction coefficient / convection coefficient → simulation result verification." Key thermal parameters are optimized inversely using experimental temperature data, overcoming the limitations of physical detection and compensating for the shortcomings of existing technologies in comprehensively capturing the entire temperature field of the lead screw system and covering local temperature characteristics. Based on continuous temperature monitoring data during the shutdown phase, an exponential cooling curve is fitted to accurately quantify the thermal time constant, achieving precise characterization of the system's thermal inertia characteristics. This improves upon the limitations of existing technologies in the fundamental research of thermal characteristic law mining and thermal error compensation, systematically enhancing the accuracy, comprehensiveness, and depth of full-condition thermal characteristic monitoring of CNC machine tool feed systems.

[0007] To achieve the above objectives, one aspect of the present invention provides a dynamic coupling monitoring method for the thermal characteristics of a CNC machine tool feed system under all operating conditions, comprising the following steps:

[0008] 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;

[0009] 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;

[0010] 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".

[0011] 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.

[0012] 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.

[0013] 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.

[0014] Further, step S1 includes:

[0015] 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.

[0016] 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.

[0017] 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.

[0018] Further, step S2 includes:

[0019] 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.

[0020] 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.

[0021] 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;

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

[0023] Furthermore, the expression for the thermal time constant τ of the cooling process is: in,

[0024]

[0025] 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.

[0026] Further, step S3 includes:

[0027] 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;

[0028] 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;

[0029] The influence of each factor on the temperature rise, tail elongation and positioning error of the ball screw was determined by range analysis.

[0030] The significance of each influencing factor was verified by analysis of variance, and the influence priority was obtained as "speed > load > stroke > temperature".

[0031] Furthermore, step S4 specifically includes:

[0032] 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.

[0033] 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.

[0034] S43: Import the simplified 3D assembly model into the integrated simulation platform ANSYS Workbench, set material properties, apply thermal loads, set thermal boundaries, and perform mesh generation to obtain the 3D simulation model.

[0035] 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.

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

[0037] 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;

[0038] Nut-related heat rate μ ′ η represents 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 assembly in ANSYS; solve the temperature field distribution under thermal equilibrium; extract the temperature of key measuring points such as bearings and nuts;

[0041] Set the time step, and the total duration covers the experimental cycle; 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.

[0042] The steady-state / transient temperature field is imported into the structural module as a volume load, the thermal expansion coefficient of the material is defined, and the elongation at the tail end of the screw is calculated.

[0043] 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.

[0044] Further, step S5 includes:

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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 aforementioned dynamic coupling monitoring method for the thermal characteristics of a CNC machine tool feed system under all operating conditions, comprising:

[0049] 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.

[0050] 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.

[0051] 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".

[0052] 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.

[0053] 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.

[0054] 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.

[0055] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0056] (1) The CNC machine tool feed system full-condition dynamic coupling monitoring method and system of the present invention uses a threaded temperature sensor close to the heat source at the bearing cover, and a magnetic sensor in other areas. It also arranges a variety of sensors to accurately collect data such as temperature and elongation. The laser interferometer ensures that the optical path is coaxial and the positioning error is collected. Compared with the traditional method, the accuracy of the monitoring data is greatly improved, and the problems of temperature sensor installation interference and distance are solved, so that the thermal characteristic monitoring is more in line with the actual heat source state.

[0057] (2) The dynamic coupling monitoring method and system for thermal characteristics of CNC machine tool feed system under all working conditions of the present invention is designed to cover the complete machining cycle experiment of "heat engine operation - speed change process - shutdown and cooling", and incorporates orthogonal experiments of speed change (20-64m / min), load change (0-100kg), and stroke change. It can simulate the actual machining composite working conditions, make up for the single working condition defects of the existing technology, fully capture the thermal characteristic changes of the entire life cycle, and reflect the real machining thermal behavior.

[0058] (3) The dynamic coupling monitoring method and system for thermal characteristics of CNC machine tool feed system under all working conditions of the present invention is based on continuous temperature monitoring during the shutdown stage, fitting an exponential cooling curve, accurately quantifying the thermal time constant to characterize the thermal inertia of the system; constructing an experimental-simulation mutual verification closed loop, using simulation analysis of the whole-domain temperature field and thermal deformation transmission path, supplementing the difficult-to-measure details in the experiment, breaking through the limitations of physical detection, and deepening the mining of thermal characteristic laws.

[0059] (4) The dynamic coupling monitoring method and system for thermal characteristics of CNC machine tool feed system under all working conditions of the present invention constructs a closed loop of "experimental acquisition-simulation verification-parameter reverse optimization", identifies key thermal parameters such as friction coefficient and convection coefficient in reverse based on experimental data, and corrects the simulation model; through orthogonal experiments, the influence priority of "speed > load > stroke > temperature" is clarified, which guides thermal error compensation, improves the universality and accuracy of the model, and solves the problems of inaccurate parameters and unclear factor priority in the existing model. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a dynamic coupling monitoring method for the thermal characteristics of a CNC machine tool feed system under all operating conditions, according to an embodiment of the present invention.

[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 t on 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 during the 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 3 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... Figure 1 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, and compare them with the simulation results point by point 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 the experimentally difficult-to-measure areas, trace the global thermal deformation transmission path, generate a thermal error compensation table, output the heat flux density distribution of key areas, and guide the optimization of the heat dissipation structure. 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 and the average speed 30 minutes before shutdown ,load ; Based on Newton's law of cooling, an exponential decay curve is fitted to calculate the thermal time constant of the cooling process. Thermal time constant of cooling process The expression is: ,in, in, This represents the number of data sets collected during the shutdown phase. For the first The downtime corresponding to the group of data. For the first The system average temperature of the data set; The ambient temperature is considered a constant value. The initial system temperature at the moment of shutdown.

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. A dynamic coupling monitoring method for the thermal characteristics of a CNC machine tool feed system under all operating conditions, as described in claim 1 or 2, is characterized in that: Step S3 includes: Select four factors and three levels: temperature, speed, load, and stroke, according to... An orthogonal array was used to arrange 9 groups of experiments. Observe and calculate the temperature rise of the ball screw. Change in elongation at the end of the lead screw Positioning error change Ball screw temperature rise Change in elongation at the end of the lead screw Positioning error change ; 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.

4. A dynamic coupling monitoring method for the thermal characteristics of a CNC machine tool feed system under all operating conditions, as described in claim 1 or 2, 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 ANSYS Workbench, 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.

5. The method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to claim 4, 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 The calculation formula is: ,in, The bearing friction coefficient; For load; For speed; Nut-related heat rate , The coefficient of friction of the thread; Indicates transmission efficiency.

6. The method for dynamic coupling monitoring of thermal characteristics of a CNC machine tool feed system under all operating conditions according to claim 5, 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 of the bearing and nut. 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.

7. 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 2-4, 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.

8. 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 1-7, 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 to conduct 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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