Collaborative optimization strategy-based numerical control machine tool feeding system optimization design method and storage medium

Abstract

The invention belongs to the technical field of machine tool design, and particularly discloses a collaborative optimization strategy-based numerical control machine tool feeding system optimization design method and a storage medium. The method comprises the following steps: determining a design index, and completing preselection of main equipment; based on a collaborative optimization method, establishing a subdisciplinary analysis unit in an MDO framework module of the numerical control machine tool feeding system; creating an optimization model of each subject in combination with the constructed sub-subject analysis unit; establishing an agent model for the structure subject analysis unit; and determining the mutual influence and transmission relationship of each subject analysis unit, establishing a system-level optimization model and a subject optimization model under a collaborative optimization framework by combining the constructed subject optimization models and proxy models, and carrying out collaborative loop optimization of the subject-level optimization model and the system-level optimization model until a system-level optimization target is converged. And obtaining and outputting a final optimization result. The method has the advantages of being easy to operate, high in design efficiency and capable of remarkably improving reliability and flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of machine tool design, and in particular to a CNC machine tool feed system optimization design method and storage medium based on a collaborative optimization strategy, which has simple operation, high design efficiency, and significantly improved reliability and flexibility. Background Art

[0002] In the process of lean, global, collaborative, service-oriented, green and intelligent development of CNC machine tool design and manufacturing, the traditional CNC machine tool design mode dominated by experience and analogy is gradually transitioning to a design mode dominated by modeling, simulation, optimization and analysis. As the core component of CNC machine tools, the motion accuracy, response speed and stability of the feed system are directly related to the processing accuracy and processing efficiency of the machine tool. Under the new design mode, a large number of studies have been carried out at home and abroad on the design and optimization of the structure and control system of CNC machine tool feed systems: for example, in terms of structural optimization design, mainly for the moving parts and bearing parts in the feed system, using shape optimization, topology optimization, size optimization, bionic design and other methods to achieve the improvement of the static and dynamic characteristics, reliability, energy saving and other aspects of the feed system; in terms of control system design, artificial intelligence algorithms such as particle swarm optimization and genetic algorithm are used to design the optimal control parameters of the system to improve the performance indicators such as anti-interference ability, dynamic response characteristics, stability and accuracy of the feed system.

[0003] However, the above research has the following limitations: (1) From the perspective of the design object, the feed system is a typical complex system integrating mechanical, electrical, hydraulic and control. Its performance depends not only on the characteristics of each subsystem, but also on the interaction between the subsystems. This makes it impossible to achieve the maximum performance of the feed system through the design methods of components or subsystems; (2) From the perspective of the design process, the traditional CNC machine tool feed design process adopts a serial design mode, selecting different disciplines to design the feed system at different design stages, and each discipline operates relatively independently. Although its design results can meet the requirements, the design cycle of the process is long, the calculation efficiency is low, and the economic cost is also high.

[0004] As an effective optimization technology for the design of complex engineering systems with multidisciplinary attributes, multidisciplinary design optimization (MDO) has been widely used in the fields of automobiles, aircraft, ships, etc. It can shorten the design cycle by realizing modular parallel design of various disciplines, tap the design potential by considering the mutual coupling between disciplines, realize the automated design of products through high system integration, improve reliability through comprehensive consideration of various disciplines, and reduce research and development costs through multidisciplinary comprehensive design. In the prior art, although there are cases of using MDO for collaborative optimization design of machine tool bed and base, the relationship between the bed and the base is relatively simple, and its collaborative optimization design method is difficult to apply to the feed system of machine tools.

[0005] Therefore, in order to design a high-performance CNC machine tool feed system, it is necessary to utilize the latest achievements in complex engineering system design methods, start from the perspective of optimizing the overall performance of the system, fully consider the coupling relationship between various disciplines, and conduct new theoretical method research on the overall design of the feed system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a CNC machine tool feed system optimization design method based on a collaborative optimization strategy, which has simple operation, high design efficiency, and significantly improved reliability and flexibility, and also provides a storage medium.

[0007] The CNC machine tool feed system optimization design method based on the collaborative optimization strategy of the present invention is implemented as follows: including equipment selection, construction of sub-discipline analysis units, establishment of sub-discipline-level optimization models, construction of structural discipline agent models, and collaborative optimization steps. The specific process is as follows: A. Equipment selection: Determine the design indicators of the CNC machine tool feed system and complete the pre-selection of major equipment; B. Constructing sub-discipline analysis units: Based on the collaborative optimization method, sub-discipline analysis units in the MDO framework module of the CNC machine tool feed system are established, and the sub-discipline analysis units include a structure discipline analysis unit, a transmission discipline analysis unit, a fluid mechanics discipline analysis unit, and a control discipline analysis unit; C. Establishment of sub-discipline optimization model: Combine the sub-discipline analysis units constructed in step B to create optimization models for each discipline; D. Constructing a proxy model for structural disciplines: Establishing a proxy model for the structural discipline analysis unit. Establishing the proxy model includes experimental design, DOE sampling calculation, finite element calculation, judging model accuracy, and outputting the proxy model with the highest fitting accuracy. E. Collaborative optimization: Determine the mutual influence and transmission relationship between the analysis units of each discipline, combine the optimization models of each discipline constructed in step C and the agent model constructed in step D, create a system-level optimization model and a discipline optimization model under the collaborative optimization framework, and then carry out collaborative cycle optimization of the discipline-level optimization model and the system-level optimization model until the system-level optimization target converges, and obtain the final optimization result and output it.

[0008] Furthermore, in step A, the design indicators of the CNC machine tool feed system include maximum rapid moving speed, acceleration, maximum load, table size and / or moving stroke, and the main equipment pre-selection includes a motor and a multi-head pump.

[0009] Furthermore, the specific process of step B is as follows: B10, in the structural subject analysis unit, the moving parts of the feed system are analyzed using the finite element method; wherein the input parameters include the structural size, the maximum load and / or the structural material properties, and the output parameters include the mass of the moving parts and the maximum deformation; B20, in the transmission discipline analysis unit, the transmission chain motion characteristics, ball screw mechanical characteristics, and dynamic characteristics of the feed system are analyzed; wherein the input parameters include the ball screw lead, diameter, moving part mass, selected motor related parameters, maximum fast moving speed and / or acceleration; the transmission chain motion characteristics analysis uses the maximum torque, effective torque, maximum speed and / or load inertia as output parameters; the ball screw mechanical characteristics use the critical speed and maximum axial load as output parameters; the dynamic characteristics are modeled using the concentrated mass method with the transmission chain low-order response characteristics as output parameters; B30. In the fluid mechanics analysis unit, the working characteristics of the hydrostatic guide rail are analyzed; the input parameters include the oil chamber size, the oil pad size and / or the multi-head pump selection parameters, and the output parameters include the single oil pad no-load W min , Maximum load W max , oil chamber pressure under no load and maximum load p 0 and p 1. Guide rail floating under no load and maximum load h 0 and h 1. Average static stiffness j e ; B40, in the control subject analysis unit, the response characteristics of the control system are analyzed, and a control system considering the position loop and the speed loop is established based on step B20; the input parameters include the position loop proportional gain, the speed loop proportional gain, the speed loop integral constant, the motor selection parameters, the transmission parameters and / or the controlled object parameters, and a closed-loop control transfer function is established for the input parameters, and then the aforementioned transfer function is analyzed and the ITAE index, ROUTH criterion, phase margin and amplitude margin are used as output parameters.

[0010] Furthermore, the sub-discipline analysis units in step B are as follows: The structural analysis unit is analyzed by the finite element method, importing the structural parameters and material parameters of the moving parts, and then performing meshing, applying constraints and loads to obtain the weight M of the moving parts, the maximum deformation of the model workbench under typical working conditions, and the maximum deformation of the model workbench under typical working conditions. s max , and finally perform finite element calculation; The transmission discipline analysis unit is based on the maximum torque T max , effective torque Trms , Maximum speed N M , Load inertia c The motion characteristics analysis is performed for the result, as shown in formula 1: , Where: T c is the cutting load torque of the machine tool, J l is the moment of inertia of the coupling, J r is the moment of inertia of the reducer, J s is the screw moment of inertia, J m is the motor moment of inertia, T sp The additional torque for the screw preload is T f Friction torque, T e is the equivalent inertia moment of the mechanism, The and that They are acceleration torque and acceleration time respectively. Tt and tb are uniform torque and uniform time respectively, Td and td They are deceleration torque and deceleration time respectively. P B is the screw lead, i is the reduction ratio of the reducer, Mt is the moving mass of the workbench, M h For additional workpiece mass, tc is the cycle time, V max The maximum rapid moving speed of the feed system; The fluid mechanics analysis unit is designed for the feed system with a hydrostatic guide rail. Due to the heavy-duty CNC machine tools’ working characteristics of heavy load and low speed, the static characteristics are mainly considered. W min , Maximum load W max Oil chamber pressure under p 0. p 1. Guide rail floating under no load and maximum load h 0. h 1 and average static stiffness j e Perform analytical calculations, as shown in Formula 2: , Where: A e is the effective bearing area, is the static pressure chamber structural coefficient, b , l They are the oil chamber size, B , L The oil pad size is respectively, m is the dynamic viscosity of the fluid, Q is the flow rate of a single head of a multi-head pump; The control discipline analysis unit is to establish a control model that takes into account the elasticity of the mechanical subsystem. Considering that the frequency bandwidth of the current loop is much larger than the bandwidth of the speed loop, the current loop is equivalent to 1, and a proportional-integral-differential controller and a PI-controlled speed loop and a P-controlled position loop are used to establish a control model, and a closed-loop control transfer function is obtained: , , Where: s is a complex variable, K m is the motor torque constant, K p is the position loop proportional gain, K vp is the speed loop proportional gain, K vi is the speed loop integral time constant, K S is the equivalent rigidity, i is the motion conversion coefficient, B is the system damping, Jl is the equivalent load inertia, J m Motor inertia; Based on the above closed-loop control transfer function, K p , K vp and T s Analyze the stability, phase margin, and amplitude margin of the control system; The stability is determined based on the ROUTH criterion to determine whether Formula 5 satisfies the following inequality. If not, it is unstable; if satisfied, it is stable: , The phase margin and amplitude margin are obtained by importing the transfer function into MATLAB and automatically calculating it through the margin() function; The ITAE index is shown in Formula 6, which is used to analyze the rapidity and stability of the control subject through the ITAE index: , Where: t is the time variable, t a is the response time, E rr (t) is the system error.

[0011] Furthermore, the critical speed included in the mechanical characteristics of the ball screw in step B20 is V c , the maximum allowable axial buckling load F c1 、 Maximum axial bending load F c2 Perform the calculation: , ， Where: l is the support bearing coefficient, L is the screw installation distance, r is the density of the screw, E is the Young's modulus of the screw, J are the parameters related to the screw moment of inertia, P B is the screw lead, d s is the screw diameter, s The allowable pulling pressure of the screw is A The cross-sectional area of ​​the screw is equal to , or1 is the support coefficient; The dynamic characteristics are mathematically modeled using the lumped mass method, and the dynamic equations of the feeding system are established according to the Lagrangian energy method and written in the form of a matrix: , in: ; ; , , Where: T is the matrix transpose, i M is the servo motor rotation angle, i S is the rotation angle of the ball screw at the worktable position, X S is the axial displacement of the ball screw at the nut position, X T is the displacement of the worktable, Q is the cutting force; J M is the servo motor moment of inertia, J S is the equivalent moment of inertia of the ball screw, M S , M T They are the equivalent mass of the ball screw and the moving mass of the worktable respectively; k rot is the torsional stiffness of the ball screw feed system, k ax is the axial stiffness of the ball screw feed system, K n Contact stiffness of the screw nut, α is the equivalent friction coefficient tangential to the direction of rotation of the screw shaft, i The axial displacement of the nut produced by each turn of the screw ; Solve the characteristic value of the dynamic equation of the feed system as shown in Equation 12 to obtain the natural frequency of the system; , Where: oh is the desired system natural frequency.

[0012] Furthermore, the step C is based on the analysis units of each discipline, determines the local design variables, design constraints, and design goals at the discipline level, and establishes a discipline optimization model including the aforementioned design variables, design constraints, and design goals: Structural disciplines include: Design variables DV :Workbench geometry x 1j ,in j is the number of size parameters; Design constraints st : Maximum deformation of the workbench s max Less than allowable deformation in ; Design goal: Minimize the weight of moving parts M ; Establish a discipline optimization model: , Transmission disciplines include: Design variables DV : Ball screw lead P B ,diameter d s ; Design constraints st : Load converted to the motor end, speed matching, maximum torque matching, rated torque matching, inertia matching; ball screw design maximum speed V s_max Should be less than the critical speed of the screw V c , designed maximum axial load F max Should be less than the maximum axial buckling load allowed by the screw F c1 and maximum axial bending load F c2 ; Design goal: Maximum first-order natural frequency of the transmission chain f ; , Where: N 0 is the maximum motor speed, V max is the maximum rapid moving speed of the workbench, P B is the screw lead, T max is the maximum torque required for work, T rms is the rated torque required for work, T motor_max is the maximum torque of the motor, T motor_rms is the rated torque of the motor, c _max is the maximum inertia ratio, c _min is the minimum inertia ratio, c is the inertia ratio; Fluid mechanics disciplines include: Design variables DV :Oil chamber size b , l , Oil pad size B , L ; Design constraints st : Maximum oil pad pressure p max Should be less than the maximum oil supply pressure of the selected multi-head pump p c , oil film thickness h 0. h 1. Should meet the design requirements; Design target: average oil film stiffness j e Maximum; Establish discipline optimization model: , Control disciplines include: Design variables DV : Input is the position loop proportional gain K p , speed loop proportional gain K vp , speed loop integral constant T S ; Design constraints st :To ensure the stability of the system, the control system should meet the Routh stability criterion and the gain margin should be greater than the design requirements A m_c , the phase margin is greater than the design requirement P m_c , overshoot should meet the design requirements M c ; Design goals: J _ITAE Minimum; Establish discipline optimization model: , Where: G 1 is the stability constraint based on ROUTH criterion, a 0. a 1. a 2. a 3. a 4. a 5 is the denominator coefficient of the transfer function in equations 3 and 4, G 2 is the gain margin and phase margin constraints, G 3 is the overshoot constraint, A mis the system amplitude margin, P m is the system phase margin, M p is the system overshoot.

[0013] Furthermore, the proxy model establishment process of the structural discipline analysis unit in step D is as follows: using the Latin hypercube sampling method to uniformly extract sample points from the structural discipline-related design parameter space, generating a sampling set of design parameters, substituting the sampling set into the finite element model, completing the finite element calculation of the structural discipline, and outputting the mass characteristics and maximum deformation as responses; then using the sampling results as input and the calculation results as output, fitting the proxy model using the following methods: constructing proxy models using the Kriging model, radial basis function model, and polynomial response surface model respectively, and using the complex correlation coefficient R 2 , global accuracy indicators root mean square error RMSE, maximum absolute error MAE to judge the fitting accuracy of the proxy model; R 2 The closer the value is to 1, the better the model fitting effect is, the smaller the RMSE value is, the closer the model prediction value is to the true value, and the smaller the MAE value is, the lower the worst prediction error of the model is. According to the above standards, the proxy model with the highest fitting accuracy is selected for output; R 2 The calculation formulas for , RMSE and MAE are: , Where: y i is the response value of the i-th sample state variable, is the approximate value of the state variable of the ith sample, is the mean of the ith sample response, n Indicates the number of samples.

[0014] Furthermore, the specific process of step E is as follows: E10. Given the expected value of system-level design variables Z ij , and the expected value Z ij Pass them to each discipline-level optimization model respectively; E20. Expected values ​​of design variables transferred from the system level to each discipline level Z ij After that, the subject analysis units connected to each subject-level optimization module are connected according to the design variables provided by the former. x i Conduct subject analysis to obtain design variables x i and expected values ​​of system-level design variables Z ij The difference between J i, where i represents the i-th subject in structure, control, fluid mechanics, and transmission, and j represents the j-th design variable in the subject, i=1,2,3, j=1,2,3; E30. Each discipline uses the discipline-level optimization model established in step C to perform discipline-level optimization of the corresponding discipline, minimizing the J i , calculate and obtain the optimal value of each subject-level design variable Y ij ,i=1,2,3,j=1,2,3; E40. Optimize the optimal values ​​of each discipline-level design variable Y ij Return to the system level, the system level according to the optimal value of each subject level design variable Y ij , construct the inter-disciplinary consistency constraint G for system-level optimization; E50: Using the discipline-level optimization model established in step E30, the quality of workbench movement is obtained under the condition of satisfying the consistency constraints between disciplines. M , first-order natural frequency f 、ITAE index J ITAE , average stiffness of oil film j e The optimal solution of the system-level design variable is obtained after system-level optimization. Z ij ' , the optimal value Z ij ' As a new system-level design vector expectation value Z ij Delivered to each subject level; E60. Repeat steps E10 to E50 until the system-level optimization target converges, and obtain the final optimization result and output it.

[0015] Furthermore, the structural discipline-level optimization model under the collaborative optimization framework in the E30 step is: , Where: J 1 is the difference between the expected values ​​of discipline design variables and system-level design variables in structural disciplines.

[0016] The transmission discipline-level optimization model under the collaborative optimization framework in the E30 step is: , Where: J 2 is the difference between the expected values ​​of discipline design variables and system-level design variables in the transmission discipline.

[0017] The fluid mechanics discipline-level optimization model under the collaborative optimization framework in the E30 step is: , Where: J 3 is the difference between the expected values ​​of the subject design variables and the system-level design variables in fluid mechanics.

[0018] The control discipline-level optimization model under the collaborative optimization framework in step E30 is: , Where: J 4 is the difference between the expected values ​​of the subject design variables and the system-level design variables in the control discipline.

[0019] The system-level optimization model under the collaborative optimization framework in the E30 step and the inter-disciplinary consistency constraint G of the E40 system-level optimization are: , Where: J 1 ',J 2 ',J 3 ',J 4 ' Indicates the consistency constraint function between disciplines at the system level; ' The quantity represents the optimal value transmitted from each subject level to the system level. Sum the consistency constraint functions between disciplines, t is the relaxation factor, usually 10 -3 Up to 10 -5 .

[0020] The storage medium of the present invention is implemented as follows: a computer program is stored thereon, and the computer program can be executed by one or more processors to implement the aforementioned CNC machine tool feed system optimization design method based on the collaborative optimization strategy.

[0021] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention establishes a system-level optimization model for the multi-unit design of the CNC machine tool feed system, as well as subject-level optimization models for the structural unit, transmission unit, fluid mechanics unit, and control unit, and analyzes the design variables and design constraints of each unit, thereby forming a systematic and integrated design method, which can obtain the optimal comprehensive performance of the CNC machine tool feed system.

[0022] 2. Aiming at the integrity of the design domain of the design object of the CNC machine tool feed system, the present invention provides an analysis model that takes into account the structure, control, transmission and fluid mechanics of the CNC machine tool feed system, and improves the overall design model.

[0023] 3. Regarding the design process of the CNC machine tool feed system, the present invention provides a collaborative optimization process that coordinates and unifies the analysis models of the structure, control, transmission and fluid mechanics disciplines. In addition, each discipline can be analyzed and optimized in parallel, thereby improving the design efficiency.

[0024] 4. Aiming at the complexity of the design of CNC machine tool feed system, the present invention provides a subject analysis method based on agent model. By constructing the agent model, the calculation time can be shortened to improve the design efficiency.

[0025] In summary, the present invention has the characteristics of simple operation, high design efficiency, and significantly improved reliability and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of the present invention; Figure 2 Establishing a proxy model flow chart of a structural discipline analysis unit for an embodiment; Figure 3 A flowchart of collaborative optimization steps in an embodiment; Figure 4 The second-order RSM proxy model of the embodiment predicts the maximum deformation of the workbench Figure 5 This is a diagram showing the effect of the second-order RSM agent model on the workbench movement quality prediction of the embodiment; Figure 6 This is a schematic diagram of the Isight collaborative optimization framework of the embodiment; Figure 7 It is a comparison diagram of optimization iterations for each optimization target of the embodiment; Figure 8 It is a Pareto solution diagram of an embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] like Figure 1 to 6 As shown, the CNC machine tool feed system optimization design method based on the collaborative optimization strategy of the present invention includes equipment selection, construction of sub-discipline analysis units, establishment of sub-discipline-level optimization models, construction of structural discipline agent models, and collaborative optimization steps. The specific process is as follows: A. Equipment selection: Determine the design indicators of the CNC machine tool feed system and complete the pre-selection of major equipment; B. Constructing sub-discipline analysis units: Based on the collaborative optimization method, sub-discipline analysis units in the MDO framework module of the CNC machine tool feed system are established, and the sub-discipline analysis units include a structure discipline analysis unit, a transmission discipline analysis unit, a fluid mechanics discipline analysis unit, and a control discipline analysis unit; C. Establishment of sub-discipline optimization model: Combine the sub-discipline analysis units constructed in step B to create optimization models for each discipline; D. Constructing a proxy model for structural disciplines: Establishing a proxy model for the structural discipline analysis unit. Establishing the proxy model includes experimental design, DOE sampling calculation, finite element calculation, judging model accuracy, and outputting the proxy model with the highest fitting accuracy. E. Collaborative optimization: Determine the mutual influence and transmission relationship between the analysis units of each discipline, combine the optimization models of each discipline constructed in step C and the agent model constructed in step D, create a system-level optimization model and a discipline optimization model under the collaborative optimization framework, and then carry out collaborative cycle optimization of the discipline-level optimization model and the system-level optimization model until the system-level optimization target converges, and obtain the final optimization result and output it.

[0029] In step A, the design indicators of the CNC machine tool feed system include maximum rapid moving speed, acceleration, maximum load, table size and / or moving stroke, and the main equipment pre-selection includes motors and multi-head pumps.

[0030] The specific process of step B is as follows: B10. In the structural analysis unit, the moving parts of the feed system are analyzed using the finite element method; the input parameters include the structural dimensions, the maximum load (design requirements) and / or the structural material properties, and the output parameters include the mass of the moving parts and the maximum deformation; B20. In the transmission discipline analysis unit, the transmission chain motion characteristics, ball screw mechanical characteristics, and dynamic characteristics of the feed system are analyzed; wherein the input parameters include the ball screw lead, diameter, moving part mass, selected motor related parameters (including rated speed, peak torque, rated torque, rotor inertia), maximum fast moving speed and / or acceleration (design requirements); the transmission chain motion characteristics analysis uses the maximum torque, effective torque, maximum speed and / or load inertia as output parameters; the ball screw mechanical characteristics use the critical speed and maximum axial load as output parameters; the dynamic characteristics are modeled using the concentrated mass method with the transmission chain low-order response characteristics as output parameters; B30. In the fluid mechanics analysis unit, the working characteristics of the hydrostatic guide rail are analyzed; the input parameters include the oil chamber size, oil pad size and / or multi-head pump selection parameters (including the number of oil outlets, flow rate per outlet, and outlet pressure); the output parameters include a single oil pad no-load W min , Maximum load W max , oil chamber pressure under no load and maximum load p 0 and p 1. Guide rail floating under no load and maximum load h 0 and h 1. Average static stiffness j e ; B40, in the control subject analysis unit, the response characteristics of the control system are analyzed, and a control system considering the position loop and the speed loop is established based on step B20; the input parameters include the position loop proportional gain, the speed loop proportional gain, the speed loop integral constant, the motor selection parameters including the torque constant, the motor rotor moment of inertia), the transmission parameters (including the transmission chain stiffness, the ball screw lead) and / or the controlled object parameters, a closed-loop control transfer function is established for the input parameters, and then the above transfer function is analyzed and the ITAE index (Integral of Time and Absolute Error), ROUTH criterion, phase margin and amplitude margin are used as output parameters.

[0031] The specific sub-discipline analysis units in step B are as follows: The structural analysis unit is analyzed by the finite element method, importing the structural parameters and material parameters of the moving parts, and then performing meshing, applying constraints and loads to obtain the weight M of the moving parts, the maximum deformation of the model workbench under typical working conditions, and the maximum deformation of the model workbench under typical working conditions. s max , and finally perform finite element calculation; The transmission discipline analysis unit is based on the maximum torque T max , effective torque Trms , Maximum speed N M , Load inertia c The motion characteristics analysis is performed for the result, as shown in formula 1: , Where: T c is the cutting load torque of the machine tool, J l is the moment of inertia of the coupling, J r is the moment of inertia of the reducer, Js is the moment of inertia of the screw, J m is the motor moment of inertia, T sp The additional torque for the screw preload is T f Friction torque, T e is the equivalent inertia moment of the mechanism, The and that They are acceleration torque, acceleration time, Tt and tb are uniform torque and uniform time respectively, Td and td They are deceleration torque and deceleration time respectively. P B is the screw lead, i is the reduction ratio of the reducer, M t is the moving mass of the workbench, M h For additional workpiece mass, tc is the cycle time, V max The maximum rapid moving speed of the feed system; The fluid mechanics analysis unit is designed for the feed system with a hydrostatic guide rail. Due to the heavy-duty CNC machine tool's heavy-duty, low-speed working characteristics, the static characteristics are mainly considered. W min , Maximum load W max Oil chamber pressure under p 0. p 1. Guide rail floating under no load and maximum load h 0. h 1 and average static stiffness j e Perform analytical calculations, as shown in Formula 2: , Where: A e is the effective bearing area, is the static pressure chamber structural coefficient, b , l They are the oil chamber size, B , L The oil pad size is respectively, m is the dynamic viscosity of the fluid, Q is the flow rate of a single head of a multi-head pump; The control discipline analysis unit is to establish a control model that takes into account the elasticity of the mechanical subsystem. Considering that the frequency bandwidth of the current loop is much larger than the bandwidth of the speed loop, the current loop is equivalent to 1, and a proportion-integration-differentiation controller (PID) and a PI-controlled speed loop and a P-controlled position loop are used to establish a control model, and a closed-loop control transfer function is obtained: , , Where: s is a complex variable, K m is the motor torque constant, K p is the position loop proportional gain, K vp is the speed loop proportional gain, K vi is the speed loop integral time constant, K S is the equivalent rigidity, i is the motion conversion coefficient, B is the system damping, J l is the equivalent load inertia, J m Motor inertia; Based on the above closed-loop control transfer function, K p , K vp and T s Analyze the stability, phase margin, and amplitude margin of the control system; The stability is determined based on the ROUTH criterion to determine whether Formula 5 satisfies the following inequality. If not, it is unstable; if satisfied, it is stable: , The phase margin and amplitude margin are obtained by importing the transfer function into MATLAB and automatically calculating it through the margin() function; The ITAE index is shown in Formula 6, which is used to analyze the rapidity and stability of the control subject through the ITAE index: , Where: t is the time variable, t a is the response time, E rr (t) is the system error.

[0032] The critical speed included in the mechanical characteristics of the ball screw in step B20 V c , the maximum allowable axial buckling load F c1 、 Maximum axial bending load F c2 Perform the calculation: , ， Where: l is the support bearing coefficient, L is the screw installation distance, r is the density of the screw, E is the Young's modulus of the screw, J are the parameters related to the screw moment of inertia, P B is the screw lead, d s is the screw diameter, s The allowable pulling pressure of the screw is A The cross-sectional area of ​​the screw is equal to , or 1 is the support coefficient; The dynamic characteristics are mathematically modeled using the lumped mass method, and the dynamic equations of the feeding system are established according to the Lagrangian energy method and written in the form of a matrix: , in: ; ; , , Where: T is the matrix transpose, i M is the servo motor rotation angle, i S is the rotation angle of the ball screw at the worktable position, X S is the axial displacement of the ball screw at the nut position, X T is the displacement of the worktable, Q is the cutting force; J M is the servo motor moment of inertia, J S is the equivalent moment of inertia of the ball screw, M S , M TThey are the equivalent mass of the ball screw and the moving mass of the worktable respectively; k rot is the torsional stiffness of the ball screw feed system, k ax is the axial stiffness of the ball screw feed system, K n Contact stiffness of the screw nut, α is the equivalent friction coefficient tangent to the rotation direction of the screw shaft, i The axial displacement of the nut produced by each turn of the screw ; Solve the characteristic value of the dynamic equation of the feed system as shown in Equation 12 to obtain the natural frequency of the system; , Where: oh is the desired system natural frequency.

[0033] The above step C is based on the analysis units of each discipline, determines the local design variables, design constraints, and design goals at the discipline level, and establishes a discipline optimization model including the above-mentioned design variables, design constraints, and design goals: Structural disciplines include: Design variables DV :Workbench geometry x 1j ,in j is the number of size parameters; Design constraints st : Maximum deformation of the workbench s max Less than allowable deformation in ; Design goal: Minimize the weight of moving parts M ; Establish a discipline optimization model: , Transmission disciplines include: Design variables DV : Ball screw lead P B ,diameter d s ; Design constraints st : Load converted to the motor end, speed matching, maximum torque matching, rated torque matching, inertia matching; ball screw design maximum speed V s_max Should be less than the critical speed of the screw V c , designed maximum axial load F max Should be less than the maximum axial buckling load allowed by the screw Fc1 and maximum axial bending load F c2 ; Design goal: Maximum first-order natural frequency of the transmission chain f ; , Where: N 0 is the maximum speed of the motor, V max is the maximum rapid moving speed of the workbench, P B is the screw lead, T max is the maximum torque required for work, T rms is the rated torque required for work, T motor_max is the maximum torque of the motor, T motor_rms is the rated torque of the motor, c _max is the maximum inertia ratio, c _min is the minimum inertia ratio, c is the inertia ratio; Fluid mechanics disciplines include: Design variables DV :Oil chamber size b , l , Oil pad size B , L ; Design constraints st : Maximum oil pad pressure p max Should be less than the maximum oil supply pressure of the selected multi-head pump p c , oil film thickness h 0. h 1. Should meet the design requirements; Design target: average oil film stiffness j e Maximum; Establish discipline optimization model: , Control disciplines include: Design variables DV : Input is the position loop proportional gain K p , speed loop proportional gain K vp , speed loop integral constant T S ; Design constraints st:To ensure the stability of the system, the control system should meet the Routh stability criterion and the gain margin should be greater than the design requirements A m_c , the phase margin is greater than the design requirement P m_c , overshoot should meet the design requirements M c ; Design goals: J _ITAE Minimum; Establish discipline optimization model: , Where: G 1 is the stability constraint based on ROUTH criterion, a 0. a 1. a 2. a 3. a 4. a 5 is the denominator coefficient of the transfer function in equations 3 and 4, G 2 is the gain margin and phase margin constraints, G 3 is the overshoot constraint, A m is the system amplitude margin, P m is the system phase margin, M p is the system overshoot.

[0034] The establishment process of the proxy model of the structural discipline analysis unit in step D is as follows: using the Latin hypercube sampling method to uniformly extract sample points from the structural discipline-related design parameter space, generating a sampling set of design parameters, substituting the sampling set into the finite element model, completing the finite element calculation of the structural discipline, and outputting the mass characteristics and maximum deformation as responses; then using the sampling results as input and the calculation results as output, fitting the proxy model using the following methods: constructing the proxy model using the Kriging model, radial basis function model, and polynomial response surface model respectively, and using the complex correlation coefficient R 2 , global accuracy indicators root mean square error RMSE, maximum absolute error MAE to judge the fitting accuracy of the proxy model; R 2 The closer the value is to 1, the better the model fitting effect is, the smaller the RMSE value is, the closer the model prediction value is to the true value, and the smaller the MAE value is, the lower the worst prediction error of the model is. According to the above standards, the proxy model with the highest fitting accuracy is selected for output; R 2 The calculation formulas for , RMSE and MAE are: , Where: y i is the response value of the i-th sample state variable, is the approximate value of the state variable of the ith sample, is the mean of the ith sample response, n Indicates the number of samples.

[0035] The specific process of step E is as follows: E10. Given the expected value of system-level design variables Z ij , and the expected value Z ij Pass them to each discipline-level optimization model respectively; E20. Expected values ​​of design variables transferred from the system level to each discipline level Z ij After that, the subject analysis units connected to each subject-level optimization module are connected according to the design variables provided by the former. x i Conduct subject analysis to obtain design variables x i and expected values ​​of system-level design variables Z ij The difference between J i , where i represents the i-th subject in structure, control, fluid mechanics, and transmission, and j represents the j-th design variable in the subject, i=1,2,3, j=1,2,3; E30. Each discipline uses the discipline-level optimization model established in step C to perform discipline-level optimization of the corresponding discipline, minimizing the J i , calculate and obtain the optimal value of each subject-level design variable Y ij ,i=1,2,3,j=1,2,3; E40. Optimize the optimal values ​​of each discipline-level design variable Y ij Return to the system level, the system level according to the optimal value of each subject level design variable Y ij , construct the inter-disciplinary consistency constraint G for system-level optimization; E50: Using the discipline-level optimization model established in step E30, the quality of workbench movement is obtained under the condition of satisfying the consistency constraints between disciplines. M , first-order natural frequency f 、ITAE index J ITAE , average stiffness of oil film j e The optimal solution of the system-level design variable is obtained after system-level optimization. Z ij ', the optimal value Z ij ' As a new system-level design vector expectation value Z ij Delivered to each subject level; E60. Repeat steps E10 to E50 until the system-level optimization target converges, and obtain the final optimization result and output it.

[0036] The structural discipline-level optimization model under the collaborative optimization framework in the E30 step is: , Where: J 1 is the difference between the expected values ​​of discipline design variables and system-level design variables in structural disciplines.

[0037] The transmission discipline-level optimization model under the collaborative optimization framework in the E30 step is: , Where: J 2 is the difference between the expected values ​​of discipline design variables and system-level design variables in the transmission discipline.

[0038] The fluid mechanics discipline-level optimization model under the collaborative optimization framework in the E30 step is: , Where: J 3 is the difference between the expected values ​​of the subject design variables and the system-level design variables in fluid mechanics.

[0039] The control discipline-level optimization model under the collaborative optimization framework in step E30 is: , Where: J 4 is the difference between the expected values ​​of the subject design variables and the system-level design variables in the control discipline.

[0040] The system-level optimization model under the collaborative optimization framework in the E30 step and the inter-disciplinary consistency constraint G of the E40 system-level optimization are: , Where: J 1 ',J 2 ',J 3 ',J 4 ' Indicates the consistency constraint function between disciplines at the system level; ' The quantity represents the optimal value transmitted from each subject level to the system level. Sum the consistency constraint functions between disciplines, t is the relaxation factor, usually 10 -3 Up to 10 -5 .

[0041] The storage medium of the present invention stores a computer program thereon, and the computer program can be executed by one or more processors to implement the aforementioned CNC machine tool feed system optimization design method based on the collaborative optimization strategy. Example

[0042] like Figure 1 to 8 As shown in the figure, the feed system of a heavy-duty gantry machining center is optimized and designed, and the X-axis feed system is optimized and designed. The main drive of the X-axis feed system of the target heavy-duty CNC machine tool adopts ball screw and servo motor, equipped with a full closed-loop control system, the guide rail type is open hydrostatic guide rail, and the guide rail structure is one V and one flat. Based on the MDO idea, the design optimization problem is divided into four sub-problems: structure discipline, transmission discipline, fluid mechanics discipline and control discipline. The optimization design process is as follows: S100. Determine the design indicators of the CNC machine tool feed system (including maximum rapid traverse speed, acceleration, maximum load, table size and / or travel stroke) and complete the selection of major equipment. Complete the pre-selection of major equipment as shown in Table 1.

[0043] Table 1 Main design parameters of the X-axis feed system

[0044] S200: Based on the collaborative optimization method, sub-discipline analysis units in the MDO framework module of the CNC machine tool feed system are established, and the sub-discipline analysis units include a structural discipline analysis unit, a transmission discipline analysis unit, a fluid mechanics discipline analysis unit, and a control discipline analysis unit; the structural discipline analysis unit performs analysis based on ANASYS, and the transmission discipline analysis unit, the fluid mechanics discipline analysis unit, and the control discipline analysis unit perform analysis based on MATLAB respectively.

[0045] S300: Combine the sub-discipline analysis units constructed in step S200 to determine the mutual influence and transmission relationship between the discipline analysis units, determine the local design variables, design constraints, and design goals at the discipline level, and establish the optimization models at each discipline level in the MDO framework module of the CNC machine tool feed system. The local design variables and ranges at each discipline level are shown in Table 2.

[0046] Table 2 Subject-level local design variables and their ranges

[0047] Based on actual design requirements, sub-discipline level optimization model is established: (1) In order to ensure the static rigidity of the workbench, under typical working conditions, the maximum deformation of the workbench should be less than the design requirement of 0.03mm. The lightweight design should be used as the principle to minimize the mass of the moving parts of the workbench. M As the goal, a structural discipline optimization model is established: , Where: x 11 is the thickness of the workbench structure plate, x 12 is the width of the longitudinal reinforcement of the workbench structure, x 13 is the width of the cross bar of the workbench structure, x 14 is the width of the longitudinal reinforcement of the workbench structure; s max is the maximum deformation of the workbench, in is the allowable deformation of the workbench; (2) When the system is working, the speed matching, maximum torque matching, rated torque matching, and inertia matching between the motor and the load end must be met. The design maximum speed of the ball screw should be less than the critical speed of the screw, and the design maximum axial load should be less than the maximum buckling load allowed by the screw. F c1 ,Maximum bending load F c2 , with high vibration resistance as the design criterion, maximizing the first-order natural frequency of the feed system f as the goal.

[0048] , Where: P B is the ball screw lead, d s is the ball screw diameter; V s_max Design the maximum speed for the ball screw. V c is the critical speed of the screw; F max Design the maximum axial load for the screw. F c1 is the maximum axial buckling load allowed for the screw, F c2 The maximum axial bending load allowed for the screw; N 0 is the maximum speed of the motor, V max is the maximum rapid moving speed of the workbench, P B is the screw lead, T max is the maximum torque required for work,T rms is the rated torque required for work, T motor_max is the maximum torque of the motor, T motor_rms is the rated torque of the motor, c _max is the maximum inertia ratio, c _min is the minimum inertia ratio, c is the inertia ratio.

[0049] (3) In the design of hydrostatic guide rails, the maximum pressure of the oil pad should be less than the maximum oil supply pressure of the selected multi-head pump. Considering the pipeline pressure loss, the maximum pressure is 1Mpa, and the oil film thickness is 0.03-0.06mm according to the recommended value for medium and heavy machine tools. The design criterion is high load-bearing rigidity to maximize the average rigidity of the hydrostatic guide rail oil film. j e as the goal.

[0050] , Where: b , l is the oil chamber size, B , L is the oil pad size; p max is the maximum pressure of the oil pad, p c The maximum oil supply pressure of the selected multi-head pump; h 0. h 1 is the oil film thickness, j e is the average stiffness of the oil film.

[0051] (4) To ensure the stability of the system, the coefficients of the system characteristic polynomial should meet the Routh stability criterion. To ensure the robustness of the system, the gain margin and phase margin should be greater than 5db and 40°. To ensure the stability of the system operation, the system overshoot should be less than 5%. With high accuracy and high response as the design criteria, the performance index ITAE, which is the absolute value of the error multiplied by the time integral, is minimized: .

[0052] Where: G1 is the stability constraint based on ROUTH criterion, a 0. a 1. a 2. a 3. a 4. a 5 is the denominator coefficient of the transfer function in equations 3 and 4, G 2 is the gain margin and phase margin constraints, G 3 is the overshoot constraint, Am is the system amplitude margin, P m is the system phase margin, M p is the system overshoot.

[0053] S400: Establishing a proxy model for the structural discipline analysis unit, which includes the steps of experimental design, DOE sampling calculation, finite element calculation, judging model accuracy, and outputting the proxy model with the highest fitting accuracy.

[0054] The experimental design and finite element calculation steps are as follows: according to the initially determined design indicators, the initial values ​​and upper and lower limits of the design variables are established as shown in Table 2; the orthogonal experimental design method is adopted, and the maximum deformation of the workbench and the weight of the workbench in the structural subsystem are sampled 50 times using the uniform Latin hypercube method. The numerical results of the experimental design are shown in Table 3.

[0055] Table 3 Experimental design values

[0056] The steps of judging the model accuracy and outputting the proxy model with the highest fitting accuracy are as follows: for the 50 groups of sample points of the above structural units, respectively, the proxy models are constructed using the Kriging model, the Radial-Basis Function model (RBF), and the Response Surface Methodology (RSM), and the complex correlation coefficient R is used to select the best proxy model. 2 The accuracy of the surrogate model is judged by the root mean square error (RMSE) and the maximum absolute error (MAE). 2 , RMSE, and MAE expressions are: , Where: y i is the response value of the i-th sample state variable, is the approximate value of the state variable of the ith sample, is the mean of the ith sample response, n Indicates the number of samples.

[0057] The accuracy of the proxy model for the maximum deformation of the workbench is shown in Table 4.

[0058] Table 4. Accuracy of proxy model for maximum deformation of workbench

[0059] From Table 4, we can see that s max The RMSE and MAE of the second-order RSM surrogate model are the smallest. 2 is closest to 1, so the second-order RSM proxy model has the best prediction ability for the two response values. Figure 4 The prediction effect of the second-order RSM agent model on the maximum deformation of the workbench is demonstrated.

[0060] Similarly, refer to the above process to establish the workbench mobile quality agent model. Figure 5 The prediction effect and model accuracy of the workbench movement quality proxy model are demonstrated.

[0061] S500: Determine the mutual influence and transmission relationship between the analysis units of each discipline, combine the optimization models of each discipline constructed in step S300 and the agent model constructed in step S400, create a system-level optimization model and a discipline optimization model under the collaborative optimization framework, and then carry out collaborative cycle optimization of the discipline-level optimization model and the system-level optimization model until the system-level optimization target converges, and obtain the final optimization result and output it.

[0062] The structural discipline-level optimization model under the collaborative optimization framework is: , Where: J 1 is the difference between the expected values ​​of discipline design variables and system-level design variables in structural disciplines.

[0063] The transmission discipline-level optimization model under the collaborative optimization framework is: , Where: J 2 is the difference between the expected values ​​of discipline design variables and system-level design variables in the transmission discipline.

[0064] The fluid mechanics discipline-level optimization model under the collaborative optimization framework is: , Where: J 3 is the difference between the expected values ​​of the subject design variables and the system-level design variables in fluid mechanics.

[0065] The control discipline-level optimization model under the collaborative optimization framework is: , Where: J 4 is the difference between the expected values ​​of the subject design variables and the system-level design variables in the control discipline.

[0066] The system-level optimization model under the collaborative optimization framework and the inter-disciplinary consistency constraint G of system-level optimization are: , Where: J 1 ',J 2 ',J 3 ',J 4 ' Indicates the consistency constraint function between disciplines at the system level; ' The quantity represents the optimal value transmitted from each subject level to the system level. Sum the consistency constraint functions between disciplines, t is the relaxation factor, usually 10 -3 Up to 10 -5 .

[0067] Using the existing technology of Isight to realize the collaborative optimization framework Figure 6 shown.

[0068] Result output: Four target optimization iterations are compared. Figure 7 As shown; using NSGA-II to solve the optimal Pareto solution set of the four objectives after optimization is as follows Figure 8 shown.

[0069] Select a satisfactory optimal solution from the Pareto frontier. Extract the final optimization solution and adjust the optimization solution considering manufacturability. Table 5 also lists the comparison of the initial solution and the optimized design solution.

[0070] Table 5 Comparison between the optimized solution and the initial solution

[0071] It can be seen from Table 5 that compared with the initial solution, the final optimized solution reduces the moving mass of the workbench from 2904kg to 2636kg, achieving a weight reduction of 9.22%. On the basis of ensuring the static rigidity of the workbench, the lightweight design of the structure is achieved. In addition, the JITAE index is reduced by 68.44%, and the first-order natural frequency is f and the average stiffness of the oil film j e The improvements were 1.4% and 13.5% respectively, achieving the optimization of the overall system performance.

[0072] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An optimization design method for a CNC machine tool feed system based on a collaborative optimization strategy, characterized in that: Including equipment selection, construction of sub-discipline analysis units, establishment of sub-discipline-level optimization models, construction of structural discipline agent models, and collaborative optimization steps. The specific process is as follows: A. Equipment selection: Determine the design indicators of the CNC machine tool feed system and complete the pre-selection of major equipment; B. Constructing sub-discipline analysis units: Based on the collaborative optimization method, sub-discipline analysis units in the MDO framework module of the CNC machine tool feed system are established, and the sub-discipline analysis units include a structure discipline analysis unit, a transmission discipline analysis unit, a fluid mechanics discipline analysis unit, and a control discipline analysis unit; C. Establishment of sub-discipline optimization model: Combine the sub-discipline analysis units constructed in step B to create optimization models for each discipline; D. Constructing a proxy model for structural disciplines: Establishing a proxy model for the structural discipline analysis unit. Establishing the proxy model includes experimental design, DOE sampling calculation, finite element calculation, judging model accuracy, and outputting the proxy model with the highest fitting accuracy. E. Collaborative optimization: Determine the mutual influence and transmission relationship between the analysis units of each discipline, combine the optimization models of each discipline constructed in step C and the agent model constructed in step D, create a system-level optimization model and a discipline optimization model under the collaborative optimization framework, and then carry out collaborative cycle optimization of the discipline-level optimization model and the system-level optimization model until the system-level optimization target converges, and obtain the final optimization result and output it.

2. The CNC machine tool feed system optimization design method based on collaborative optimization strategy according to claim 1 is characterized in that: In step A, the design indicators of the CNC machine tool feed system include maximum rapid moving speed, acceleration, maximum load, table size and / or moving stroke, and the main equipment pre-selection includes motors and multi-head pumps.

3. The CNC machine tool feed system optimization design method based on collaborative optimization strategy according to claim 1 is characterized in that: The specific process of step B is as follows: B10, in the structural subject analysis unit, the moving parts of the feed system are analyzed using the finite element method; wherein the input parameters include the structural size, the maximum load and / or the structural material properties, and the output parameters include the mass of the moving parts and the maximum deformation; B20, in the transmission discipline analysis unit, the transmission chain motion characteristics, ball screw mechanical characteristics, and dynamic characteristics of the feed system are analyzed; wherein the input parameters include the ball screw lead, diameter, moving part mass, selected motor related parameters, maximum fast moving speed and / or acceleration; the transmission chain motion characteristics analysis uses the maximum torque, effective torque, maximum speed and / or load inertia as output parameters; the ball screw mechanical characteristics use the critical speed and maximum axial load as output parameters; the dynamic characteristics are modeled using the concentrated mass method with the transmission chain low-order response characteristics as output parameters; B30. In the fluid mechanics analysis unit, the working characteristics of the hydrostatic guide rail are analyzed; the input parameters include the oil chamber size, the oil pad size and / or the multi-head pump selection parameters, and the output parameters include the single oil pad no-load W min , Maximum load W max , oil chamber pressure under no load and maximum load p 0 and p 1 , guide rail floating under no load and maximum load h 0 and h 1 , average static stiffness j e ; B40, in the control subject analysis unit, the response characteristics of the control system are analyzed, and a control system considering the position loop and the speed loop is established based on step B20; the input parameters include the position loop proportional gain, the speed loop proportional gain, the speed loop integral constant, the motor selection parameters, the transmission parameters and / or the controlled object parameters, and a closed-loop control transfer function is established for the input parameters, and then the aforementioned transfer function is analyzed and the ITAE index, ROUTH criterion, phase margin and amplitude margin are used as output parameters.

4. The CNC machine tool feed system optimization design method based on collaborative optimization strategy according to claim 3 is characterized by: The specific sub-discipline analysis units in step B are as follows: The structural analysis unit is analyzed by the finite element method, importing the structural parameters and material parameters of the moving parts, and then performing meshing, applying constraints and loads to obtain the weight M of the moving parts, the maximum deformation of the model workbench under typical working conditions, and the maximum deformation of the model workbench under typical working conditions. σ max , and finally perform finite element calculation; The transmission discipline analysis unit is based on the maximum torque T max , effective torque Trms , Maximum speed N M , Load inertia γ The motion characteristics analysis is performed for the result, as shown in formula 1: ， Where: T c is the cutting load torque of the machine tool, J l is the coupling moment of inertia, J r is the moment of inertia of the reducer, J s is the moment of inertia of the screw, J m is the motor moment of inertia, T sp The additional torque for the screw preload is T f Friction torque, T e is the equivalent inertia moment of the mechanism, Ta and ta They are acceleration torque, acceleration time, Tt and tb are uniform torque and uniform time respectively, Td and td They are deceleration torque and deceleration time respectively. P B is the screw lead, i is the reduction ratio of the reducer, M t is the moving mass of the workbench, M h For additional workpiece mass, tc is the cycle time, V max The maximum rapid moving speed of the feed system; The fluid mechanics analysis unit is designed for the feed system with a hydrostatic guide rail. Due to the heavy-duty CNC machine tool's heavy-duty, low-speed working characteristics, the static characteristics are mainly considered. W min , Maximum load W max Oil chamber pressure under p 0 , p 1 , guide rail floating under no load and maximum load h 0 , h 1 and average static stiffness j e Perform analytical calculations, as shown in Formula 2: ， Where: A e is the effective bearing area, is the static pressure chamber structural coefficient b , l They are the oil chamber size, B , L The oil pad size is respectively, μ is the dynamic viscosity of the fluid, Q is the flow rate of a single head of a multi-head pump; The control discipline analysis unit is to establish a control model that takes into account the elasticity of the mechanical subsystem. Considering that the frequency bandwidth of the current loop is much larger than the bandwidth of the speed loop, the current loop is equivalent to 1, and a proportional-integral-differential controller and a PI-controlled speed loop and a P-controlled position loop are used to establish a control model, and a closed-loop control transfer function is obtained: ， ， Where: s is a complex variable, K m is the motor torque constant, K p is the position loop proportional gain, K vp is the speed loop proportional gain, K vi is the speed loop integral time constant, K S is the equivalent rigidity, i is the motion conversion coefficient, B is the system damping, J l is the equivalent load inertia, J m Motor inertia; Based on the above closed-loop control transfer function, K p , K vp and T s Analyze the stability, phase margin, and amplitude margin of the control system; The stability is determined based on the ROUTH criterion to determine whether Formula 5 satisfies the following inequality. If not, it is unstable; if satisfied, it is stable: ， The phase margin and amplitude margin are obtained by importing the transfer function into MATLAB and automatically calculating it through the margin() function; The ITAE index is shown in Formula 6, which is used to analyze the rapidity and stability of the control subject through the ITAE index: ， Where: t is the time variable, t a is the response time, E rr (t) is the system error.

5. The CNC machine tool feed system optimization design method based on collaborative optimization strategy according to claim 4 is characterized in that: The critical speed included in the mechanical characteristics of the ball screw in step B20 V c , the maximum allowable axial buckling load F c1 、 Maximum axial bending load F c2 Perform the calculation: ， ， Where: λ is the support bearing coefficient, L is the screw installation distance, ρ is the density of the screw, E is the Young's modulus of the screw, J are the parameters related to the screw moment of inertia, P B is the screw lead, d s is the screw diameter, σ The allowable pulling pressure of the screw is A The cross-sectional area of ​​the screw is equal to , η 1 is the support coefficient; The dynamic characteristics are mathematically modeled using the lumped mass method, and the dynamic equations of the feeding system are established according to the Lagrangian energy method and written in the form of a matrix: ， in: ; ; ， ， Where: T is the matrix transpose, θ M is the servo motor rotation angle, θ S is the rotation angle of the ball screw at the worktable position, X S is the axial displacement of the ball screw at the nut position, X T is the displacement of the worktable, Q is the cutting force; J M is the servo motor moment of inertia, J S is the equivalent moment of inertia of the ball screw, M S , M T They are the equivalent mass of the ball screw and the moving mass of the worktable respectively; k rot is the torsional stiffness of the ball screw feed system, k ax is the axial stiffness of the ball screw feed system, K n Contact stiffness of the screw nut, α is the equivalent friction coefficient tangential to the direction of rotation of the screw shaft, i The axial displacement of the nut produced by each turn of the screw ; Solve the characteristic value of the dynamic equation of the feed system as shown in Equation 12 to obtain the natural frequency of the system; ， Where: ω is the desired system natural frequency.

6. The CNC machine tool feed system optimization design method based on collaborative optimization strategy according to claim 1 is characterized by: The above step C is based on the analysis units of each discipline, determines the local design variables, design constraints, and design goals at the discipline level, and establishes a discipline optimization model including the above-mentioned design variables, design constraints, and design goals: Structural disciplines include: Design variables DV :Workbench geometry x 1j ,in j is the number of size parameters; Design constraints st : Maximum deformation of the workbench σ max Less than allowable deformation σ' ; Design goal: Minimize the weight of moving parts M ; Establish a discipline optimization model: ， Transmission disciplines include: Design variables DV : Ball screw lead P B ,diameter d s ; Design constraints st : Load converted to the motor end, speed matching, maximum torque matching, rated torque matching, inertia matching; ball screw design maximum speed V s_max Should be less than the critical speed of the screw V c , designed maximum axial load F max Should be less than the maximum axial buckling load allowed by the screw F c1 and maximum axial bending load F c2 ; Design goal: Maximum first-order natural frequency of the transmission chain f ; ， Where: N 0 is the maximum speed of the motor, V max is the maximum rapid moving speed of the workbench, P B is the screw lead, T max is the maximum torque required for work, T rms is the rated torque required for work, T motor_max is the maximum torque of the motor, T motor_rms is the rated torque of the motor, γ _max is the maximum inertia ratio, γ _min is the minimum inertia ratio, γ is the inertia ratio; Fluid mechanics disciplines include: Design variables DV :Oil chamber size b , l , Oil pad size B , L ; Design constraints st : Maximum oil pad pressure p max Should be less than the maximum oil supply pressure of the selected multi-head pump p c , oil film thickness h 0 , h 1 Should meet the design requirements; Design target: average oil film stiffness j e Maximum; Establish discipline optimization model: ， Control disciplines include: Design variables DV : Input is the position loop proportional gain K p , speed loop proportional gain K vp , speed loop integral constant T S ; Design constraints st :To ensure the stability of the system, the control system should meet the Routh stability criterion and the gain margin should be greater than the design requirements A m_c , the phase margin is greater than the design requirement P m_c , overshoot should meet the design requirements M c ; Design goals: J _ITAE Minimum; Establish discipline optimization model: ， Where: G 1 is the stability constraint based on ROUTH criterion, a0, a1, a2, a3, a4, a5 are the denominator coefficients of the transfer function in equations 3 and 4, G 2 is the gain margin and phase margin constraints, G 3 is the overshoot constraint, A m is the system amplitude margin, P m is the system phase margin, M p is the system overshoot.

7. The CNC machine tool feed system optimization design method based on collaborative optimization strategy according to claim 1 is characterized by: The establishment process of the proxy model of the structural discipline analysis unit in step D is as follows: using the Latin hypercube sampling method to uniformly extract sample points from the structural discipline-related design parameter space, generating a sampling set of design parameters, substituting the sampling set into the finite element model, completing the finite element calculation of the structural discipline, and outputting the mass characteristics and maximum deformation as responses; then using the sampling results as input and the calculation results as output, fitting the proxy model using the following methods: constructing the proxy model using the Kriging model, radial basis function model, and polynomial response surface model respectively, and using the complex correlation coefficient R 2 , global accuracy indicators root mean square error RMSE, maximum absolute error MAE to judge the fitting accuracy of the proxy model; R 2 The closer the value is to 1, the better the model fitting effect is, the smaller the RMSE value is, the closer the model prediction value is to the true value, and the smaller the MAE value is, the lower the worst prediction error of the model is. According to the above standards, the proxy model with the highest fitting accuracy is selected for output; R 2 The calculation formulas for , RMSE and MAE are: ， Where: y i is the response value of the i-th sample state variable, is the approximate value of the state variable of the ith sample, is the mean of the ith sample response, n Indicates the number of samples.

8. The method for optimizing the feeding system of a CNC machine tool based on a collaborative optimization strategy according to any one of claims 1 to 7, characterized in that: The specific process of step E is as follows: E10. Given the expected value of system-level design variables Z ij , and the expected value Z ij Pass them to each discipline-level optimization model respectively; E20. Expected values ​​of design variables transferred from the system level to each discipline level Z ij After that, the subject analysis units connected to each subject-level optimization module are connected according to the design variables provided by the former. x i Conduct subject analysis to obtain design variables x i and expected values ​​of system-level design variables Z ij The difference between J i , where i represents the i-th subject in structure, control, fluid mechanics, and transmission, and j represents the j-th design variable in the subject, i=1,2,3, j=1,2,3; E30. Each discipline uses the discipline-level optimization model established in step C to perform discipline-level optimization of the corresponding discipline, minimizing the J i , calculate and obtain the optimal value of each subject-level design variable Y ij ,i=1,2,3,j=1,2,3; E40. Optimize the optimal values ​​of each discipline-level design variable Y ij Return to the system level, the system level according to the optimal value of each subject level design variable Y ij , construct the inter-disciplinary consistency constraint G for system-level optimization; E50: Using the discipline-level optimization model established in step E30, the quality of workbench movement is obtained under the condition of satisfying the consistency constraints between disciplines. M , first-order natural frequency f 、ITAE index J ITAE , average stiffness of oil film j e The optimal solution of the system-level design variable is obtained after system-level optimization. Z ij ' , the optimal value Z ij ' As a new system-level design vector expectation value Z ij Delivered to each subject level; E60. Repeat steps E10 to E50 until the system-level optimization target converges, and obtain the final optimization result and output it.

9. The CNC machine tool feed system optimization design method based on collaborative optimization strategy according to claim 8 is characterized in that: The structural discipline-level optimization model under the collaborative optimization framework in the E30 step is: ， Where: J 1 is the difference between the expected values ​​of the discipline design variables and the system-level design variables in the structural discipline; The transmission discipline-level optimization model under the collaborative optimization framework in the E30 step is: ， Where: J 2 is the difference between the expected values ​​of the subject design variables and the system-level design variables in the transmission discipline; The fluid mechanics discipline-level optimization model under the collaborative optimization framework in the E30 step is: ， Where: J 3 is the difference between the expected values ​​of the subject design variables and the system-level design variables in fluid mechanics; The control discipline-level optimization model under the collaborative optimization framework in step E30 is: ， Where: J 4 is the difference between the expected values ​​of the subject design variables and the system-level design variables in the control discipline; The system-level optimization model under the collaborative optimization framework in the E30 step and the inter-disciplinary consistency constraint G of the E40 system-level optimization are: ， Where: J 1 ',J 2 ',J 3 ',J 4 ' Indicates the consistency constraint function between disciplines at the system level; ' The quantity represents the optimal value transmitted from each subject level to the system level. Sum the consistency constraint functions between disciplines, τ is the relaxation factor, usually 10 -3 Up to 10 -5 .

10. A storage medium having a computer program stored thereon, characterized in that: The computer program can be executed by one or more processors to implement the CNC machine tool feed system optimization design method based on collaborative optimization strategy as described in any one of claims 1 to 9.

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