Numerical control machine tool spindle system thermal design method and system fusing energy consumption control

Abstract

The invention relates to the technical field of machine tool design, and provides a numerical control machine tool spindle system thermal design method and system fusing energy consumption control, and thermal state characteristics, Z-direction deformation and stress distribution data are obtained by establishing a thermal state characteristic energy consumption response model through finite element analysis and thermal structure field coupling analysis. And actually measured data verification is carried out by combining a magnetic temperature sensor. The main shaft size parameters are used as design variables, the thermal state and energy consumption characteristics are used as optimization targets, the design variables are reduced through sensitivity analysis, and the calculation amount is reduced through BBD-RSM conversion into a response surface. A pelican optimization algorithm is adopted for solving, an optimal solution is obtained in combination with an entropy weight-G1 combination weighting method, a Pareto optimal solution giving consideration to temperature rise and energy consumption reduction can be output, the defect of parameter adjustment through traditional experience is avoided, precision improvement, energy conservation and emission reduction in the design stage are achieved, and reference is provided for multi-performance optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of machine tool design, and particularly relates to a numerical control machine tool spindle system thermal design method and system fusing energy consumption control. BACKGROUND

[0002] In the process of the vigorous development of modern industry, improving processing efficiency and precision has become an important goal of the equipment manufacturing industry. Among the many factors affecting machine tool machining precision, thermal error caused by machine tool thermal deformation accounts for 40%-70% of the total error, which highlights its "error culprit" position. As a key means of machine tool thermal error control, thermal design can give the machine tool excellent thermal characteristics if reasonably used in the design stage, which not only can effectively reduce thermal error and improve machining precision, but also can greatly reduce experimental research cost and prototype manufacturing cost. The energy consumption of machine tool throughout its life cycle presents a situation of large total amount and low utilization rate, which means that machine tool energy saving potential is huge, and it is imminent to include energy consumption into the optimization target of machine tool design.

[0003] CN118789354A discloses a numerical control machine tool electric spindle cooling structure based on dot matrix structure. At least including a motor sleeve, the motor sleeve is provided with a spindle, a motor rotor and a motor stator, the spindle is concentrically fixed in the motor rotor, the motor sleeve is a whole annular cylindrical structure extending along the axial direction, the cylindrical side wall is integrally formed into an annular interlayer space extending along the axial direction for circulating flow of cooling medium, at least one cooling medium inlet and two cooling medium outlets are provided on the outer wall of the annular interlayer space and communicated with the annular interlayer space, wherein the cooling medium inlet is arranged near the center position of the outer wall in the axial direction, the two cooling medium outlets are arranged at the two ends of the outer wall in the axial direction, the cooling medium inlet is communicated with the outlet of an external cold source providing cooling medium through a pipeline, the cooling medium outlets are communicated with the inlet of the external cold source through a pipeline, thereby forming a cooling medium circulating flow path; a plurality of annular baffles are arranged around the annular interlayer space in the axial direction, the baffles are designed to separate the annular interlayer space into a plurality of independent subspaces in the axial direction, wherein the subspace at the center position in the axial direction is communicated with the cooling medium inlet, the two subspaces at the two ends in the axial direction are respectively communicated with the cooling medium outlets, and each baffle is provided with an opening section for cooling medium to pass through, and the opening sections of adjacent two baffles are arranged in an up-down staggered manner in the circumferential direction to form a circulating flow path of the cooling medium; a dot matrix structure is arranged in each subspace of the annular interlayer space, and in each subspace, the porosity of the dot matrix structure gradually changes along the flow direction of the cooling medium, the dot matrix structure near the initial position has a larger porosity to reduce the initial flow resistance, and the dot matrix structure near the terminal position has a smaller porosity to increase the heat exchange area and obtain a higher Reynolds number.

[0004] CN117289652A discloses a multi-universe optimization-based numerical control machine tool spindle thermal error modeling method. It includes the following steps: step one: experiment on the spindle system of the numerical control machine tool under idling, and use temperature sensors and displacement sensors to obtain temperature field data and thermal deformation data of the numerical control machine tool spindle system; step two: preprocess the data obtained from the experiment to obtain a dataset about temperature and thermal deformation data; step three: divide the preprocessed dataset and select the training set and the test set; step four: build an NARX thermal error model; step five: further build an MVO-NARX thermal error prediction model and use the training set for model training; step six: use the trained model to predict the z-direction thermal deformation value of the machine tool spindle.

[0005] CN108763682B discloses a machine tool spindle thermal optimization method and device based on the Taguchi method. The thermal optimization method includes: determining the quality parameters of the machine tool spindle; determining the key parameters of the machine tool spindle based on the quality parameters; determining the test combination based on the key parameters, and screening the test combination through the Taguchi method to obtain the screened test combination; performing machine tool spindle test based on the screened test combination and obtaining the test results; analyzing the test results to obtain the best thermal performance parameters of the machine tool spindle; and performing thermal optimization on the machine tool spindle based on the best thermal performance parameters; the quality parameters include: maximum thermal deformation, maximum temperature, and total mass; determining the key parameters of the machine tool spindle based on the quality parameters includes: obtaining the parameter properties of the quality parameters; in the case of small characteristic properties, determining the key parameters of the machine tool spindle as: spindle support span, cone section length, cooling channel relative spindle distance, and spindle box side groove depth.

[0006] The spindle system, as the main heat-generating and energy-consuming component of the numerical control machine tool, directly affects the machining precision of the machine tool and is a key factor restricting the improvement of precision. Its energy consumption accounts for about 15% of the total energy consumption of the machine tool. However, at the design stage, there is a lack of thermal design scheme that can consider energy consumption control, making it difficult to simultaneously achieve the improvement of machine tool machining precision and energy saving and emission reduction. SUMMARY

[0007] Long-term practice has revealed that the spindle system, as a core functional component of CNC machine tools, is both a major heat source and a critical energy-consuming unit. Its operational performance is directly and closely related to the machining accuracy of the machine tool. The spindle's thermal deformation and vibration stability directly affect the workpiece's machining accuracy, becoming a key bottleneck restricting the breakthrough of machine tool precision to higher levels. Simultaneously, from an energy consumption perspective, the spindle system's energy consumption accounts for a significant proportion, approximately 15% of the total machine tool energy consumption. In the current pursuit of green manufacturing, its energy consumption optimization potential is receiving considerable attention. However, current design solutions for spindle systems have significant limitations. Existing solutions often struggle to find a balance between energy consumption control and thermal performance optimization. Some designs overemphasize improving the spindle's thermal stability to ensure machining accuracy, neglecting energy consumption indicators, resulting in persistently high energy consumption during operation. Other designs, while attempting to reduce energy consumption, suffer from insufficient thermal design considerations, making it difficult to effectively control spindle thermal deformation, which in turn affects machining accuracy. This current state of thermal design, which makes it difficult to balance energy consumption control, hinders the coordinated achievement of the two major goals of improving machining accuracy and energy conservation and emission reduction in machine tools, and fails to meet the dual requirements of high precision and low energy consumption in modern high-end manufacturing.

[0008] In view of this, the present invention aims to propose a thermal design method for CNC machine tool spindle systems that integrates energy consumption control, comprising:

[0009] Step S1: Based on the heat exchange process between the CNC machine tool spindle system and the environment, establish a thermal characteristic energy consumption response model of the spindle system, and obtain the heat generation and heat transfer coefficient of the spindle system.

[0010] Step S2: Use the heat generation and heat transfer coefficient as boundary conditions to perform finite element analysis and obtain the thermal characteristic data of the machine tool spindle system; import the temperature field data in the thermal characteristic data as load, and input the gravity value, rotational speed value, bearing preload value, and synchronous belt axial force value of the spindle system to perform thermal-structural field coupling analysis to obtain the Z-axis deformation data and stress distribution data of the spindle system;

[0011] Step S3: Temperature data of the spindle end face, flange, spindle body, spindle box and motor are obtained by multiple magnetic temperature sensors, so as to obtain the temperature field distribution, temperature rise and thermal deformation data of the spindle system and compare and verify them with the thermal characteristic data obtained by finite element analysis.

[0012] Step S4: Based on the mechanical structure of the spindle, the dimensional parameters of the spindle are initially determined as design variables. The thermal characteristic data and energy consumption characteristic data are used as optimization targets. After sensitivity analysis is performed one by one, an optimization design model is established. BBD experimental design is used to generate experimental design points and obtain actual data to determine the response surface analysis fitting optimization target.

[0013] Step S5: Determine the constraints of the optimization design model; use the multi-objective pelican optimization algorithm to solve the optimization design model to obtain the solution set, and then use the entropy weight-G1 combination weighting method to obtain the optimal solution from the solution set.

[0014] Preferably, in the entropy weight-G1 combined weighting method, the weight w of the j-th index calculated by the entropy weight method is... j The combined weight I is obtained by combining the weight gi of the j-th index obtained by the G1 weighting method. j for,

[0015]

[0016] Where m represents the number of indicators.

[0017] Preferably, the weight w of the j-th index calculated by the entropy weight method j for,

[0018]

[0019] Where, p ij It represents the relative importance of the i-th sample in the j-th indicator, where n is the number of experimental samples, and e j d is the information entropy value of the j-th indicator. j w is the information utility value of the j-th indicator. j is the weight of the j-th indicator, and m is the number of indicators.

[0020] Preferably, a unique order relationship is determined for the indicators, representing the ranking of the importance of each indicator.

[0021]

[0022] The weight g of the j-th index obtained by the G1 weighting method i for,

[0023]

[0024] Among them, g j This represents the weight of the j-th indicator, where n is the number of experimental samples, and r is the weight of the j-th indicator. i Indicates adjacent index y p-1 and y p The assigned value after comparison is m, where m is the number of indicators.

[0025] Preferably, in step S5, the constraints of the optimized design model are determined to include deflection constraints, strength constraints, rotation constraints, torsional deformation constraints, and dimensional constraints.

[0026] Preferably, in the process of determining the dimensional parameters of the spindle as design variables, sensitivity analysis is performed on different dimensional parameters, and design variables whose sensitivity S to the optimization target exceeds a predetermined value are used as decision variables.

[0027]

[0028] Where, f(x) i ) represents the optimization objective, x i Represents design variables.

[0029] Preferably, the thermal characteristic data in the optimization target includes the maximum temperature T. max Temperature difference T d Thermal distortion D t The energy consumption characteristic data includes energy consumption E.

[0030] Preferably, the response surface analysis fits the optimization objective using a second-order RSM model.

[0031]

[0032] Where y represents different optimization objectives (T) max T d (D, E), x i and x j Let β0 and β represent the design variables involved in the fitting. i ,β ij and β ii ε represents the coefficient of each term, and ε is the residual.

[0033] The present invention also provides a system for the thermal design method of a CNC machine tool spindle system with integrated energy consumption control as described above, the system comprising,

[0034] The model building unit is used to establish a thermal characteristic energy consumption response model of the spindle system based on the heat exchange process between the spindle system and the environment of the CNC machine tool, and to obtain the heat generation and heat transfer coefficient of the spindle system.

[0035] Finite element units are used to perform finite element analysis with heat generation and heat transfer coefficient as boundary conditions to obtain thermal characteristic data of the machine tool spindle system. The temperature field data in the thermal characteristic data is imported as a load, and the gravity value, rotational speed value, bearing preload value, and synchronous belt axial force value of the spindle system are input to perform thermo-structural field coupling analysis to obtain Z-axis deformation data and stress distribution data of the spindle system.

[0036] The verification unit is used to acquire temperature data of the spindle end face, flange, spindle body, spindle box and motor through multiple magnetic temperature sensors, thereby obtaining temperature field distribution, temperature rise and thermal deformation data of the spindle system and comparing them with the thermal characteristic data obtained by finite element analysis for verification.

[0037] The optimization target building unit is used to initially determine the spindle's dimensional parameters as design variables based on the spindle's mechanical structure, and to use thermal characteristic data and energy consumption characteristic data as optimization targets. After performing sensitivity analysis on each target, an optimization design model is established. BBD experimental design is used to generate experimental design points and obtain actual data to determine the response surface analysis fitting optimization target.

[0038] The solution unit is used to determine the constraints of the optimization design model. The multi-objective pelican optimization algorithm is used to solve the optimization design model to obtain the solution set, and then the optimal solution is obtained from the solution set by the entropy weight-G1 combination weighting method.

[0039] The present invention also discloses an electronic device, comprising at least one processor; and

[0040] A memory communicatively connected to the at least one processor; wherein,

[0041] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the above-described thermal design method for CNC machine tool spindle systems with integrated energy consumption control.

[0042] The present invention also discloses a machine-readable storage medium storing instructions for causing a machine to execute the thermal design method for a CNC machine tool spindle system with integrated energy consumption control as described above.

[0043] This invention discloses a thermal design method for a CNC machine tool spindle system integrating energy consumption control. Through steps S1-S5, based on the heat exchange process between the CNC machine tool spindle system and the environment, a thermal characteristic energy consumption response model of the spindle system is established. The heat generation and heat transfer coefficient of the spindle system are used as boundary conditions for finite element analysis to obtain thermal characteristic data of the machine tool spindle system. The temperature field data from the thermal characteristic data is imported as a load, and the gravity value, rotational speed value, bearing preload value, and synchronous belt axial force value of the spindle system are input for thermo-structural field coupling analysis to obtain Z-axis deformation data and stress distribution data of the spindle system. Temperature data from the spindle end face, flange, spindle body, spindle box, and motor are acquired using multiple magnetic temperature sensors, thereby obtaining the temperature field distribution, temperature rise, and thermal deformation data of the spindle system, which are then compared and verified with the thermal characteristic data obtained from the finite element analysis. Based on the spindle's mechanical structure, the spindle's dimensional parameters are initially determined as design variables. Thermal characteristic data and energy consumption characteristic data are used as optimization objectives. After sensitivity analysis, an optimization design model is established. BBD experimental design is used to generate experimental design points and obtain actual data, determining the response surface analysis and optimization objectives. Constraints on the optimization design model are determined. The multi-objective pelican optimization algorithm is used to solve the optimization design model to obtain a solution set, and then the optimal solution is obtained from the solution set using the entropy-weighted G1 combined weighting method. This invention also provides a system for thermal design of CNC machine tool spindle systems with integrated energy consumption control. The method and system can couple temperature rise, Z-axis deformation, and stress distribution in simulation. After verification with multi-point magnetic sensors, the error is less than 4%, ensuring the model's reliability. Sensitivity analysis is used to screen key dimensional parameters, reducing design variables by more than 40%. Furthermore, BBD-RSM transforms the multi-objective thermal-energy consumption model into a continuously differentiable response surface, significantly reducing computational load. The Multi-Objective Pelican Optimization Algorithm (MOPOA) is introduced in conjunction with entropy-weighted G1, resulting in fast convergence. A single run can output a Pareto optimal solution that balances temperature rise reduction and energy consumption reduction, avoiding the need for repeated trial and error in traditional empirical parameter tuning. Energy-saving thermal design is incorporated into the spindle system during the design phase to achieve improved accuracy and energy conservation, providing a reference for performance optimization and improvement of the spindle system and other components.

[0044] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0045] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0046] In the attached diagram:

[0047] Figure 1 A schematic diagram of a thermal design method for a CNC machine tool spindle system with integrated energy consumption control according to an embodiment of the present invention;

[0048] Figure 2 This is a simulation calculation result cloud map of the spindle system of a CNC machine tool spindle system thermal design method that integrates energy consumption control according to one embodiment of the present invention.

[0049] Figure 3 A simplified diagram of spindle system design variables for a thermal design method for CNC machine tool spindle systems that integrates energy consumption control, according to one embodiment of the present invention;

[0050] Figure 4 The simulation results cloud map of Group A of the thermal design method for CNC machine tool spindle system integrating energy consumption control according to one embodiment of the present invention;

[0051] Figure 5 A simulation result cloud diagram of Group B of the thermal design method for CNC machine tool spindle system integrating energy consumption control according to one embodiment of the present invention;

[0052] Figure 6 A cloud map of simulation results for a thermal design method for a CNC machine tool spindle system that integrates energy consumption control, according to one embodiment of the present invention;

[0053] Figure 7 The graph shows a comparison of power and energy consumption over time in a thermal design method for a CNC machine tool spindle system that integrates energy consumption control, according to one embodiment of the present invention. Detailed Implementation

[0054] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0055] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0056] It should be noted that the terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] As a core functional component of CNC machine tools, the spindle system is both a major heat source and a key energy-consuming unit, and its operating performance is directly and closely related to machining accuracy. The spindle's thermal deformation and vibration stability directly affect workpiece machining accuracy, becoming a key bottleneck restricting the breakthrough of machine tool accuracy to higher levels. From an energy consumption perspective, the spindle system accounts for 15% of the total energy consumption of a machine tool, and its energy consumption optimization potential has attracted much attention under the trend of green manufacturing. However, current spindle system thermal design schemes have significant limitations. Some designs overemphasize improving thermal stability to ensure accuracy, neglecting energy consumption indicators, resulting in persistently high operating energy consumption. Others attempt to reduce energy consumption, but due to insufficient thermal design considerations, spindle thermal deformation is difficult to control effectively, thus affecting machining accuracy. This current state of thermal design, which struggles to balance energy consumption control, hinders the synergistic achievement of machine tool goals in improving machining accuracy and energy conservation and emission reduction, failing to meet the dual demands of modern high-end manufacturing for high precision and low energy consumption. This invention provides a thermal design method for CNC machine tool spindle systems that integrates energy consumption control, such as... Figure 1 The diagram illustrates a thermal design method for a CNC machine tool spindle system integrating energy consumption control according to an embodiment of the present invention. The thermal design method for a CNC machine tool spindle system integrating energy consumption control includes...

[0058] Step S1: Based on the heat exchange process between the CNC machine tool spindle system and the environment, establish a thermal characteristic energy consumption response model of the spindle system, and obtain the heat generation and heat transfer coefficient of the spindle system.

[0059] Step S2: Use the heat generation and heat transfer coefficient as boundary conditions to perform finite element analysis and obtain the thermal characteristic data of the machine tool spindle system; import the temperature field data in the thermal characteristic data as load, and input the gravity value, rotational speed value, bearing preload value, and synchronous belt axial force value of the spindle system to perform thermal-structural field coupling analysis to obtain the Z-axis deformation data and stress distribution data of the spindle system;

[0060] Step S3: Temperature data of the spindle end face, flange, spindle body, spindle box and motor are obtained by multiple magnetic temperature sensors, so as to obtain the temperature field distribution, temperature rise and thermal deformation data of the spindle system and compare and verify them with the thermal characteristic data obtained by finite element analysis.

[0061] Step S4: Based on the mechanical structure of the spindle, the dimensional parameters of the spindle are initially determined as design variables. The thermal characteristic data and energy consumption characteristic data are used as optimization targets. After sensitivity analysis is performed one by one, an optimization design model is established. BBD experimental design is used to generate experimental design points and obtain actual data to determine the response surface analysis fitting optimization target.

[0062] Step S5: Determine the constraints of the optimization design model; use the multi-objective pelican optimization algorithm to solve the optimization design model to obtain the solution set, and then use the entropy weight-G1 combination weighting method to obtain the optimal solution from the solution set.

[0063] This invention discloses a thermal design method for a CNC machine tool spindle system integrating energy consumption control. Through steps S1-S5, based on the heat exchange process between the CNC machine tool spindle system and the environment, a thermal characteristic energy consumption response model of the spindle system is established. The heat generation and heat transfer coefficient of the spindle system are used as boundary conditions for finite element analysis to obtain thermal characteristic data of the machine tool spindle system. The temperature field data from the thermal characteristic data is imported as a load, and the gravity value, rotational speed value, bearing preload value, and synchronous belt axial force value of the spindle system are input for thermo-structural field coupling analysis to obtain Z-axis deformation data and stress distribution data of the spindle system. Temperature data from the spindle end face, flange, spindle body, spindle box, and motor are acquired using multiple magnetic temperature sensors, thereby obtaining the temperature field distribution, temperature rise, and thermal deformation data of the spindle system, which are then compared and verified with the thermal characteristic data obtained from the finite element analysis. Based on the spindle's mechanical structure, the spindle's dimensional parameters were initially determined as design variables. Thermal and energy consumption characteristics were used as optimization objectives. Sensitivity analysis was performed on each parameter to establish an optimization design model. BBD experimental design was employed to generate experimental design points and acquire actual data, determining the response surface analysis and optimization objectives. Constraints on the optimization design model were determined. The multi-objective pelican optimization algorithm was used to solve the optimization design model, obtaining a solution set. The optimal solution was then obtained from the solution set using the entropy-weighted G1 combined weighting method. The thermal design method for the CNC machine tool spindle system, integrating energy consumption control, can couple temperature rise, Z-axis deformation, and stress distribution in simulation. After verification with multi-point magnetic sensors, the error is less than 4%, ensuring the model's reliability. Sensitivity analysis was used to screen key dimensional parameters, reducing design variables by more than 40%. Furthermore, BBD-RSM was used to transform the multi-objective thermal-energy consumption model into a continuously differentiable response surface, significantly reducing computational load. By introducing a combination of MOPOA and entropy-weighted G1, the convergence speed is fast, and a single run can output the Pareto optimal solution that balances temperature rise reduction and energy consumption reduction, avoiding the repeated trial and error of traditional empirical parameter tuning. Energy consumption control is considered in the thermal design of the spindle system during the design phase to achieve improved accuracy and energy saving and emission reduction, providing a reference for the multi-performance optimization and improvement of the spindle system and even other components.

[0064] The heat exchange process between a CNC machine tool and its external environment is actually a mixture of three modes: heat conduction, convection, and heat radiation. Heat radiation has a smaller impact compared to the other two modes of heat conduction. The motor's power loss can be calculated using the following formula:

[0065]

[0066] In the formula, N m Let M be the power of the motor at a given input torque and speed, and η be the efficiency of the motor. m n represents the output torque, and n represents the motor speed.

[0067] The bearing power loss can be calculated using the following formula:

[0068] H = M × n z ×1.047×10 -4

[0069] M = M1 + M v

[0070] M1=f1F β d m

[0071]

[0072] In the formula, M is the total frictional torque, and n z M is the bearing speed, M1 is the frictional torque caused by the external load, M v Frictional torque caused by hydrodynamic losses, f1 is the load coefficient, d m F is the bearing's mean diameter. β v is the bearing friction torque. o f is the kinematic viscosity of the lubricant. o This is an empirical constant for bearings.

[0073] The convective heat transfer coefficient between the main shaft and the air can be calculated using the Nusselt criterion equation:

[0074]

[0075] In the formula, h is the heat transfer coefficient, and d s N is the diameter of the cylindrical surface during convection. u λ is the Nusselt number, and λ is the thermal conductivity of the fluid.

[0076] During machine tool operation, the high-speed rotation of the spindle drives the surrounding air to form forced turbulence. The Nusselt coefficient for convective heat transfer in turbulent flow can be calculated using the following formula:

[0077]

[0078] In the formula, R e Let P be the Reynolds number. r The Prandtl number is N = 0.4 when the fluid is heated and N = 0.3 when the fluid is cooled.

[0079] The heat exchange between the remaining components of the spindle system and the air occurs through laminar convection. The Nusselt coefficient for laminar convective heat transfer can be calculated using the following formula:

[0080]

[0081] The main convective heat transfer coefficient of the spindle system can be obtained from the above equations, while the other heat transfer modes in the spindle system have little impact on the overall thermal characteristics of the spindle system.

[0082] The spindle system is a key component of CNC machine tools. The cutting accuracy and performance of CNC machine tools are directly affected by the spindle system, and the energy consumption of the spindle system accounts for 30% of the machine tool's fixed energy consumption.

[0083] The calculation of thermal deformation is shown in the following formula:

[0084] ΔL t =α t ·L·ΔT

[0085] In the formula, ΔL t Indicates thermal deformation, α t ΔT represents the coefficient of thermal expansion, L represents the original size, and ΔT represents the temperature rise.

[0086] The inherent energy consumption of the spindle system can be divided into two parts: no-load power and starting energy consumption. The no-load power is calculated as follows:

[0087]

[0088] In the formula, P u The no-load power of the spindle system is represented by Se, and the output torque of the motor is represented by p. s I represents the generalized momentum along the principal axis. s L represents the moment of inertia of the spindle, ρ represents the density of the spindle material, and L represents the moment of inertia of the spindle. i Let D be the axial length of the i-th stepped shaft. i Let d be the outer diameter of the i-th stepped shaft, d be the inner diameter of the main shaft, and J be the outer diameter of the i-th stepped shaft. a Let be the moment of inertia of the pulley.

[0089] Start-up energy consumption is calculated as follows:

[0090]

[0091] In the formula, E s Main spindle system startup energy consumption, P max The peak power during the spindle system startup process, P sb It is standby power, P u For no-load power, t s The duration for the power to accelerate from standby power to peak power, t a The duration during which the power output smoothly transitions from standby power to output power.

[0092] For example, the spindle model is ABG0100M1-B40.B.(S), the cooling channel adopts a traditional spiral channel, and it is equipped with a 7014c angular contact ball bearing. The motor drives the spindle rotation via a synchronous belt. A 3D model of the spindle system is created using 3D modeling software and imported into CAE software. Through mesh independence analysis, different sized parts are divided using reasonable mesh sizes and meshing methods, generating a total of 701,192 nodes and 410,527 meshes. The heat generation and heat transfer coefficient of the spindle system are calculated, and the calculation results are used as boundary conditions for finite element analysis to obtain the thermal characteristics of the machine tool spindle system, as shown below. Figure 2 As shown in (a), the temperature field results are imported as a load, and a thermo-structural field coupling analysis is performed considering the gravity, rotational speed, bearing preload, and synchronous belt axial force of the spindle system to obtain the Z-axis deformation of the spindle system as follows: Figure 2 As shown in (b), the stress is as follows: Figure 2 As shown in (c).

[0093] An experimental platform was established to obtain the temperature field distribution, temperature rise, and thermal deformation characteristics of the spindle system. The effectiveness of the established model was verified by comparing simulation data with experimental data.

[0094] The experiment used an intelligent thermal characteristic detection and compensation instrument for CNC machine tool spindles to detect data of the spindle system. The spindle speed was 8000 r / min, the measurement frequency was 5 s / time, and the duration was 5.5 hours. The temperature sensor selected was a PT100 magnetic platinum resistance temperature sensor with a measurement accuracy of 0.4% and a temperature range of 0 to 300℃. The displacement sensor selected was a CPL230, a non-contact capacitive displacement sensor with a measurement range of 0-250 μm, a nonlinearity of ±0.2%FS, and a maximum temperature drift coefficient of 0.04%FS / ℃.

[0095] Considering the main heat-generating locations of the machine tool spindle system during operation and the ease of installation of the magnetic temperature sensor, the magnetic temperature sensor primarily detects the temperature data of the spindle end face, flange, spindle body, spindle box, and motor. A temperature sensor is also installed on the machine tool indicator light to detect the ambient temperature during the experiment. For displacement measurement, the displacement sensor probe is aligned with the axis of the test bar, and the displacement sensor position is fixed using a special fixture to collect thermal displacement data of the spindle system in the Z direction.

[0096] To verify the effectiveness of the thermal characteristic analysis model, six thermally sensitive points were selected as the main analysis points based on experimental data from temperature sensors. These included one on the spindle, one on the flange, and four in different directions on the spindle box. The experimental data collected by the temperature sensors after reaching thermal equilibrium were compared with the steady-state temperature field simulation results at the corresponding points in the model. The maximum relative error was found to be 4.67%. Simultaneously, the maximum thermal deformation obtained from the displacement sensor was 72.8125 mm, while the simulated thermal deformation was 75.3372 mm, with a relative error of 3.467%. This demonstrates that the simulation results are in good agreement with the experimental results, and therefore, these simulation results can serve as a basis for analyzing the internal thermal characteristics of the spindle system.

[0097] The spindle is a hollow, multi-segment stepped shaft. To reduce the computational load of finite element analysis, some structural features of the spindle that do not affect its use are simplified. The initial design variables are the lengths of the stepped segments (L1, L2, L3, L4), the outer diameters (D1, D2, D3, D4), and the inner diameter (d). The outer diameter (D3) of the spindle is in direct contact with the small sleeve, bearing, and retaining ring. The outer diameter (D4) is in direct contact with the lower end cover. The dimensions of these components are set to change with the design variables of the spindle body, thereby achieving optimized design of the spindle system. A simplified diagram of the spindle system design variables is shown below. Figure 3 As shown.

[0098] When optimizing the thermal design of a spindle system for energy consumption control, both thermal and energy characteristics must be considered simultaneously. The selection of thermal characteristics determines the maximum temperature (T0) that limits the material's safety and functional limits. max The temperature difference (T) that affects the internal thermal stress of the spindle system d And the core parameter that directly affects machining accuracy, thermal deformation (D) t In terms of energy consumption characteristics, the no-load power (P) that best represents the inherent energy consumption characteristics of the spindle system should be selected. u ) and startup energy consumption (E s To visually simulate the energy consumption during machine tool operation, the energy consumption generated by the machine tool starting up and idling for one minute (E=E) is used. s +60P u () as the optimization objective.

[0099] The global variable method is used to determine the decision variables and complete the sensitivity analysis. Regardless of the interactions between variables, their sensitivity will show their impact on the optimization objective. To reduce the complexity of simulation calculations, in a more preferred embodiment of this invention, during the process of determining the spindle size parameters as design variables, sensitivity analysis is performed for different size parameters. Design variables whose sensitivity S to the optimization objective exceeds a predetermined value are used as decision variables.

[0100]

[0101] Where, f(x) i ) represents the optimization objective, x i These represent design variables. For example, in structural optimization, nine variables are considered: the step lengths of the main shaft (L1, L2, L3, L4), the outer diameter (D1, D2, D3, D4), and the inner diameter (d). However, each variable has a different degree of influence on the optimization objective. Based on the six Sigma principle, the sensitivity value of each variable to the optimization objective is calculated, taking into account the maximum temperature T. max The dimensional variables that have a significant impact are D3, d, and L3, and the temperature difference T. d The dimensional variables that have a significant impact are D3, d, and L3, which affect thermal deformation D. t The size variables that have a significant impact are D3, D4, and L4, and the size variables that have a significant impact on energy consumption E are D3, D4, and L3. Based on the above analysis, the size variables D3, D4, d, L3, and L4 have a significant impact on the four optimization objectives, and therefore were selected as decision variables for structural optimization.

[0102] Box-Behnken (BBD) experimental design was used to generate experimental design points. This provided data sampling points for constructing the RSM mathematical model, offering advantages such as simple design and strong predictability. The maximum temperature T was calculated using simulation. max Temperature difference T d and thermal distortion D t The energy consumption E is calculated using simulation. In a more preferred embodiment of the invention, the thermal characteristic data in the optimization objective includes the maximum temperature T. max Temperature difference T d Thermal distortion D t The energy consumption characteristic data includes energy consumption E. To fit the complex relationship between the thermal characteristics of the machine tool spindle system components, energy consumption, and various dimensional variables, the heat-energy consumption multi-objective is transformed into a continuously differentiable response surface. This accurately captures the nonlinear correlations and interactions between variables, overcoming the limitations of traditional linear models in characterizing complex coupling relationships. In a more preferred embodiment of this invention, the response surface analysis fitting optimization objective uses a second-order RSM model for fitting.

[0103]

[0104] Where y represents different optimization objectives (T) max T d (D, E), x i and x j Let β0 and β represent the design variables involved in the fitting. i ,β ij and β iiThis represents the coefficient of each term, with ε being the residual. It significantly reduces computational load, eliminating the need for repeated, costly physical experiments or full-scale finite element simulations. A high-precision fitting model can be constructed using a finite number of sample points, providing an efficient computational foundation for subsequent optimization algorithms. It simplifies the multi-objective optimization process, enabling the optimization objectives of thermal and energy consumption characteristics to be expressed as quantifiable and solvable mathematical expressions.

[0105] Entropy weighting is an objective weighting method that determines indicator weights based on information entropy theory. Information entropy reflects data uncertainty; a higher entropy value indicates lower information utility and a higher probability of the corresponding event occurring. In a more preferred embodiment of this invention, the weight w of the j-th indicator calculated by entropy weighting is... j for,

[0106]

[0107] Where, p ij It represents the relative importance of the i-th sample in the j-th indicator, where n is the number of experimental samples, and e j d is the information entropy value of the j-th indicator. j w is the information utility value of the j-th indicator. j is the weight of the j-th indicator, and m is the number of indicators.

[0108] The G1 weighting method is an order-based weighting method based on expert subjective judgment. It objectively derives the weights of each indicator by constructing an importance order relationship between indicators and the relative importance of adjacent indicators. In a more preferred embodiment of this invention, a unique order relationship is determined, representing the ranking of the importance of each indicator as follows:

[0109]

[0110] The weight g of the j-th index obtained by the G1 weighting method i for,

[0111]

[0112] Among them, g j This represents the weight of the j-th indicator, where n is the number of experimental samples, and r is the weight of the j-th indicator. i Indicates adjacent index y p-1 and y p The assigned value after comparison is m, where m is the number of indicators.

[0113] The G1 method uses an adjacent scale to evaluate the relative importance of each indicator, as shown in Table 1.

[0114] Table 1. Meaning of Adjacency Scale Method

[0115]

[0116] The multiplicative composition method is suitable for scenarios where both subjective and objective weights need to contribute effectively. It couples the weights of the subjective weighting method (G1 method) and the objective weighting method (entropy weighting method) in a multiplicative manner.

[0117] The Spearman consistency coefficient ρ is used to reflect the correlation between the two weighting methods.

[0118]

[0119] Where m is 4 and ρ = 0.9995 ∈ (0, 1], the two weighting methods above are consistent and can be combined by multiplication. To balance expert experience and data patterns and avoid bias from a single method, in a more preferred embodiment of this invention, the entropy-weight-G1 combined weighting method uses the weight w of the j-th index calculated by the entropy-weight method. j The weight g of the j-th index obtained by the G1 weighting method i The combined weights I are obtained by combining them. j for,

[0120]

[0121] Where m represents the number of indicators.

[0122] Feasible boundaries were defined for the optimization process to ensure that the optimized spindle system met the thermal characteristics and energy consumption optimization targets without exceeding the core performance limits. In a more preferred embodiment of the invention, step S5 defines the constraints of the optimized design model, including deflection constraints, strength constraints, rotation constraints, torsional deformation constraints, and dimensional constraints. Deflection, rotation, and torsional deformation constraints directly ensure the accuracy stability of the spindle, preventing a decrease in machining accuracy due to over-optimization. Strength constraints ensure that the spindle does not undergo plastic deformation or fracture under working loads, ensuring operational safety. Dimensional constraints, combined with mechanical structural characteristics, prevent the optimization results from exceeding the actual machining and assembly space limitations, enhancing the manufacturability of the design.

[0123] The deflection constraint is based on the machine tool performance design requirements, and the deflection y of the spindle extension end must not exceed the allowable value [y].

[0124]

[0125] In the formula, F = 20000N is the cutting force, a = 80mm is the cantilever length of the shaft, L = 583mm is the support span, and E = 2.12 × 10 5 MPa is the elastic modulus along the principal axis, and I is the moment of inertia along the principal axis.

[0126] Strength constraint is a strength constraint based on the allowable cutting stress [τT] under a given rotational speed and output power.

[0127]

[0128] In the formula, P = 11000W is the rated power, and D... m =55mm is the minimum outer diameter of the main spindle, n m =10000r / min is the maximum spindle speed, and d=33mm is the spindle inner diameter.

[0129] Angle constraint means that the allowable deflection angle θ of the machine tool spindle should be less than its allowable value [θ].

[0130]

[0131] In the formula, F m =1200N is the maximum torsional force, D e =82mm is the equivalent outer diameter of the front bearing, L a =484mm is the length from the front bearing to the end of the spindle, E = 2.12 × 10 5 MPa is the elastic modulus along the principal axis, and I is the moment of inertia along the principal axis.

[0132] Torsional deformation constraint is the maximum torsional angle of the machine tool spindle to ensure machining accuracy. It shall not exceed its design allowable torsion angle

[0133]

[0134] In the formula, T = 400 Nm is the torque, and G = 8.25 × 10 4 MPa is the shear modulus, I P Principal axis polar moment of inertia.

[0135] Dimensional constraints are applied to ensure that the optimized spindle structure dimensions are within a reasonable range by imposing appropriate constraints on various design variables.

[0136] dv min ≤dv≤dv max

[0137] dv = {L3, L4, D3, D4, d}

[0138] For example, the 46 sets of experimental data, along with the index data consisting of maximum temperature, temperature difference, thermal deformation, and energy consumption, are represented as matrix U. (46×4) The range standardization of the indicator data U yields the standardized matrix Z = [Z1, Z2, Z3, Z4]. The overall optimization objective is:

[0139] min f=0.159Z1+0.168Z2-0.36Z3+0.313Z4

[0140]

[0141] The multi-objective pelican optimization algorithm, by integrating the natural inspiration mechanism of pelican behavior with multi-objective optimization theory, exhibits excellent performance in solution set quality, convergence speed, and computational efficiency. It is particularly suitable for complex optimization problems with high dimensions and multiple constraints. The key steps are as follows:

[0142] (1) Initialize parameters: set the number of variables m, population size N, maximum number of iterations T, facilitator probability p, step size factor a, attack strength b, and perception coefficient g. Generate an initial population within the range of the main axis size variable, and calculate the corresponding target value based on the variables.

[0143] (2) Randomly generated prey: During the exploration phase, the pelican determines the location of the prey and moves towards that area. This behavior is simulated and calculated. And by comparing X i The Pelicans' positions have been updated.

[0144]

[0145] In the formula, Let Pj be the new state of the i-th pelican in the j-th dimension during stage 1, and F be the position of the prey in the j-th dimension. p It is its objective function value. This is the new state of the i-th Pelicans, F i P1 It is based on the objective function value of stage 1.

[0146] (3) During the development phase, pelicans approached the water to hunt, and simulations of this behavior yielded the following results. And by comparing X i The Pelicans' positions have been updated.

[0147]

[0148] In the formula, R is the new state of the i-th pelican in the j-th dimension in stage 2, where R is a constant, t is the iteration counter, and T is the maximum number of iterations. This is the new state of the i-th Pelicans, F i P2 It is the objective function value based on stage 2.

[0149] (4) Perform fast non-dominated sorting on the population and select non-dominated solutions. After reaching the maximum number of iterations, output the Pareto optimal solution set and calculate the size-optimal solution by combining weights.

[0150] The model was solved using the multi-objective pelican optimization algorithm, with the objectives of minimizing maximum temperature, temperature difference, thermal deformation, and energy consumption. After obtaining the Pareto solution set, the top two optimal solutions were selected: the one with optimal energy consumption (group A) and the one with optimal thermal deformation (group B). The Pareto solution set was then combined and weighted to obtain the comprehensive optimal result (group C). Finally, the optimized dimensional parameters were rounded, and simulation calculations were performed. The results are shown in Table 2.

[0151] Table 2 Optimization Results

[0152]

[0153] As shown in Table 1, Group A can reduce the maximum temperature from 61.047℃ to 57.077℃, a reduction of 6.50%; the temperature difference from 42.218℃ to 38.170℃, a reduction of 9.59%; the thermal deformation from 75.337μm to 73.842μm, a reduction of 0.24%; and the energy consumption from 111478J to 99144J, a reduction of 11.06%. The simulation results are illustrated in the cloud map below. Figure 4 As shown in the figure. Group B reduced the maximum temperature from 61.047℃ to 56.573℃, a decrease of 7.33%; the temperature difference from 42.218℃ to 37.736℃, a decrease of 10.62%; the thermal deformation from 75.337μm to 67.406μm, a decrease of 10.53%; and the energy consumption from 111478J to 106857J, a decrease of 4.15%. The simulation results are shown in the cloud plot. Figure 5 As shown in the figure. Group C reduced the maximum temperature from 61.047℃ to 56.709℃, a decrease of 7.11%; the temperature difference from 42.218℃ to 37.814℃, a decrease of 10.43%; the thermal deformation from 75.337μm to 68.787μm, a decrease of 8.70%; and the energy consumption from 111478J to 104373J, a decrease of 6.37%. The simulation results are shown in the cloud plot. Figure 6 As shown, the thermal stress of all three optimization schemes was improved compared to the original scheme. Compared to group A, group C reduced thermal deformation by 6.85% and increased energy consumption by 5.27%; compared to group B, group C increased thermal deformation by 2.1% and reduced energy consumption by 2.32%.

[0154] The curves showing the power and energy consumption of each group after optimization and their changes over time are as follows: Figure 7As shown, the no-load power of group C decreased from 1670W to 1592W, a reduction of 4.7%, and the starting energy consumption decreased from 11249J to 8875J, a reduction of 21.1%. Comparing the optimization before and after, each machine tool saves approximately 7105J of energy per minute. In conclusion, the proposed entropy weight-G1 combined weighting method for filtering the Pareto solution set yields a comprehensive optimal solution that achieves better results in both thermal deformation and energy consumption optimization.

[0155] The present invention also provides a system for the thermal design method of a CNC machine tool spindle system with integrated energy consumption control as described above, the system comprising,

[0156] The model building unit is used to establish a thermal characteristic energy consumption response model of the spindle system based on the heat exchange process between the spindle system and the environment of the CNC machine tool, and to obtain the heat generation and heat transfer coefficient of the spindle system.

[0157] Finite element units are used to perform finite element analysis with heat generation and heat transfer coefficient as boundary conditions to obtain thermal characteristic data of the machine tool spindle system. The temperature field data in the thermal characteristic data is imported as a load, and the gravity value, rotational speed value, bearing preload value, and synchronous belt axial force value of the spindle system are input to perform thermo-structural field coupling analysis to obtain Z-axis deformation data and stress distribution data of the spindle system.

[0158] The verification unit is used to acquire temperature data of the spindle end face, flange, spindle body, spindle box and motor through multiple magnetic temperature sensors, thereby obtaining temperature field distribution, temperature rise and thermal deformation data of the spindle system and comparing them with the thermal characteristic data obtained by finite element analysis for verification.

[0159] The optimization target building unit is used to initially determine the spindle's dimensional parameters as design variables based on the spindle's mechanical structure, and to use thermal characteristic data and energy consumption characteristic data as optimization targets. After performing sensitivity analysis on each target, an optimization design model is established. BBD experimental design is used to generate experimental design points and obtain actual data to determine the response surface analysis fitting optimization target.

[0160] The solution unit is used to determine the constraints of the optimization design model. The multi-objective pelican optimization algorithm is used to solve the optimization design model to obtain the solution set, and then the optimal solution is obtained from the solution set by the entropy weight-G1 combination weighting method.

[0161] The present invention also discloses an electronic device, comprising at least one processor; and

[0162] A memory communicatively connected to the at least one processor; wherein,

[0163] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the above-described thermal design method for CNC machine tool spindle systems with integrated energy consumption control.

[0164] The present invention also discloses a machine-readable storage medium storing instructions for causing a machine to execute the thermal design method for a CNC machine tool spindle system with integrated energy consumption control as described above.

[0165] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0166] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0167] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0168] The method and apparatus for providing service information provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and concept of the present invention; furthermore, those skilled in the art will recognize that, based on the concept of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A thermal design method for a CNC machine tool spindle system integrating energy consumption control, characterized in that, The thermal design method for CNC machine tool spindle systems that integrates energy consumption control includes: Step S1: Based on the heat exchange process between the CNC machine tool spindle system and the environment, establish a thermal characteristic energy consumption response model of the spindle system, and obtain the heat generation and heat transfer coefficient of the spindle system. Step S2: Use the heat generation and heat transfer coefficient as boundary conditions to perform finite element analysis and obtain the thermal characteristic data of the machine tool spindle system; import the temperature field data in the thermal characteristic data as load, and input the gravity value, rotational speed value, bearing preload value, and synchronous belt axial force value of the spindle system to perform thermal-structural field coupling analysis to obtain the Z-axis deformation data and stress distribution data of the spindle system; Step S3: Temperature data of the spindle end face, flange, spindle body, spindle box and motor are obtained by multiple magnetic temperature sensors, so as to obtain the temperature field distribution, temperature rise and thermal deformation data of the spindle system and compare and verify them with the thermal characteristic data obtained by finite element analysis. Step S4: Based on the mechanical structure of the spindle, the dimensional parameters of the spindle are initially determined as design variables. The thermal characteristic data and energy consumption characteristic data are used as optimization targets. After sensitivity analysis is performed one by one, an optimization design model is established. BBD experimental design is used to generate experimental design points and obtain actual data to determine the response surface analysis fitting optimization target. Step S5: Determine the constraints of the optimization design model; use the multi-objective pelican optimization algorithm to solve the optimization design model to obtain the solution set, and then use the entropy weight-G1 combination weighting method to obtain the optimal solution from the solution set.

2. The thermal design method for CNC machine tool spindle system with integrated energy consumption control according to claim 1, characterized in that, In the entropy-weighted G1 combined weighting method, the weight w of the j-th index calculated by the entropy-weighted method is... j The weight g of the j-th index obtained by the G1 weighting method i The combined weights I are obtained by combining them. j for, Where m represents the number of indicators.

3. The thermal design method for CNC machine tool spindle system with integrated energy consumption control according to claim 2, characterized in that, The weight w of the j-th index calculated by the entropy weight method j for, Where, p ij It represents the relative importance of the i-th sample in the j-th indicator, where n is the number of experimental samples, and e j d is the information entropy value of the j-th indicator. j w is the information utility value of the j-th indicator. j is the weight of the j-th indicator, and m is the number of indicators.

4. The thermal design method for CNC machine tool spindle system with integrated energy consumption control according to claim 2, characterized in that, Determine the unique order relationship of the indicators, representing the ranking of the importance of each indicator. The weight g of the j-th index obtained by the G1 weighting method i for, Among them, g j This represents the weight of the j-th indicator, where n is the number of experimental samples, and r is the weight of the j-th indicator. i Indicates adjacent index y p-1 and y p The assigned value after comparison is m, where m is the number of indicators.

5. The thermal design method for CNC machine tool spindle system with integrated energy consumption control according to claim 1, characterized in that, In step S5, the constraints of the optimized design model are determined, including deflection constraints, strength constraints, rotation constraints, torsional deformation constraints, and dimensional constraints.

6. The thermal design method for CNC machine tool spindle system with integrated energy consumption control according to claim 1, characterized in that, In determining the dimensional parameters of the spindle as design variables, sensitivity analysis is performed for different dimensional parameters. Design variables whose sensitivity S to the optimization objective exceeds a predetermined value are used as decision variables. Where, f(x) i ) represents the optimization objective, x i Represents design variables.

7. The thermal design method for CNC machine tool spindle system with integrated energy consumption control according to claim 6, characterized in that, The thermal characteristic data in the optimization target include the maximum temperature T. max Temperature difference T d Thermal distortion D t The energy consumption characteristic data includes energy consumption E.

8. The thermal design method for CNC machine tool spindle system with integrated energy consumption control according to claim 7, characterized in that, The second-order RSM model was used to fit the optimization objective in response surface methodology. Where y represents different optimization objectives (T) max T d (D, E), x i and x j Let β0 and β represent the design variables involved in the fitting. i ,β ij and β ii ε represents the coefficient of each term, and ε is the residual.

9. A system for the thermal design method of CNC machine tool spindle system with integrated energy consumption control as described in any one of claims 1-8, characterized in that, The system includes, The model building unit is used to establish a thermal characteristic energy consumption response model of the spindle system based on the heat exchange process between the spindle system and the environment of the CNC machine tool, and to obtain the heat generation and heat transfer coefficient of the spindle system. Finite element units are used to perform finite element analysis with heat generation and heat transfer coefficient as boundary conditions to obtain thermal characteristic data of the machine tool spindle system. The temperature field data in the thermal characteristic data is imported as a load, and the gravity value, rotational speed value, bearing preload value, and synchronous belt axial force value of the spindle system are input to perform thermo-structural field coupling analysis to obtain Z-axis deformation data and stress distribution data of the spindle system. The verification unit is used to acquire temperature data of the spindle end face, flange, spindle body, spindle box and motor through multiple magnetic temperature sensors, thereby obtaining temperature field distribution, temperature rise and thermal deformation data of the spindle system and comparing them with the thermal characteristic data obtained by finite element analysis for verification. The optimization target building unit is used to initially determine the spindle's dimensional parameters as design variables based on the spindle's mechanical structure, and to use thermal characteristic data and energy consumption characteristic data as optimization targets. After performing sensitivity analysis on each target, an optimization design model is established. BBD experimental design is used to generate experimental design points and obtain actual data to determine the response surface analysis fitting optimization target. Solver elements are used to determine the constraints of the optimization design model; The multi-objective pelican optimization algorithm is used to solve the optimization design model to obtain the solution set, and then the optimal solution is obtained from the solution set by the entropy weight-G1 combination weighting method.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to execute the thermal design method for a CNC machine tool spindle system with integrated energy consumption control as described in any one of claims 1-8.

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