Machine tool structural part material matching design method for forward design of machine tool

Abstract

The invention discloses a machine tool structural part material matching design method for forward design of a machine tool. The method comprises the steps that 1, material attribute parameters such as damping loss factors and elasticity modulus of materials are obtained through experiments; 2, calculating a fixed joint surface parameter based on a Jiyun Xiaopian integral method, and calculating a sliding and rolling joint surface parameter based on a product manual; 3, establishing a finite element model considering parameters of the joint surface, and extracting static and dynamic response values of key nodes of the machine tool through a simulation result; 4, determining the complexity of the model through DOE analysis, and constructing a response surface model of the response variable and the independent variable; 5, global search is conducted in the design space based on a multi-target rime optimization algorithm, the material combination mode of all structural parts is optimized, and the machine tool reaches the optimal balance point on the multiple dynamic performance indexes on the basis that the static rigidity is met; and step 6, comparing and analyzing static and dynamic properties before and after material matching optimization through a simulation result, and verifying a material matching effect.

Description

Technical Field

[0001] The invention relates to the field of material matching design of numerical control machine tool structural parts, and in particular to a material matching design method of machine tool structural parts for forward design of machine tools. Background Art

[0002] Since dynamic characteristics are one of the main factors affecting the machining accuracy of machine tools, in order to improve the machining accuracy and surface quality of machine tools, it is necessary to improve the dynamic characteristics of machine tools as much as possible during the design stage and reduce the vibration amplitude of the tool end of the machine tools. Damping is a key factor affecting vibration. Under a certain topological configuration, how to select the material of machine tool structural parts to improve the dynamic stiffness and vibration resistance of machine tools has become a key issue in the design stage of CNC machine tools. Usually, matching design is carried out by adjusting the combination of materials with different damping properties.

[0003] Existing machine tool material damping matching methods generally rely on experience or finite element simulation results. Not only do they not conduct experimental analysis on materials, but the models of dynamic response and material properties of machine tool structural parts are not accurate enough to express the nonlinear relationship between dynamic response and damping, resulting in a lack of effective global search and optimization in the design space, and unable to ensure the best material combination. In addition, the model constructed based on BP neural network does not have enough data for training, and can only use response surface method to construct a more accurate relationship between response and variable. For complex multi-objective design requirements, conventional optimization methods also have certain limitations in search efficiency and calculation accuracy. Summary of the invention

[0004] The purpose of the present invention is to overcome the inability of existing models to describe more accurate nonlinear relationships, improve matching accuracy, and provide a material matching design solution that meets static performance and achieves optimal dynamic performance, so as to solve the problems of complex machine tool material matching design, weak global search capability, and low optimization efficiency in the prior art. The present invention comprehensively utilizes experimental data, finite element analysis, response surface model fitting, and multi-objective optimization algorithms to provide a more efficient and accurate material damping optimization design method.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] A material matching design method for machine tool structural parts used in forward design of machine tools, characterized by comprising the following steps:

[0007] Step 1: Obtain the material damping loss factor, elastic modulus, Poisson's ratio, and density of common materials for machine tool structural parts through experiments, and determine the property parameters of each material;

[0008] Step 2, under the allowable tightening torque of the bolts, for a machine tool with a given structural component combination, the fixed joint surface stiffness and the fixed joint surface damping parameter are calculated based on the Yoshimura Yoshitaka integral method, and the sliding joint surface stiffness, the rolling joint surface stiffness, the sliding joint surface damping parameter, and the rolling joint surface damping parameter are calculated according to the product manual;

[0009] Step 3: Establish a finite element model that takes into account the parameters of each joint surface, select the material combination of the structural parts based on the optimal Latin hypercube sampling method, obtain the experimental design points, and extract the static and dynamic response values ​​of the key points of the machine tool with different combinations through the simulation results, including the three-dimensional static stiffness of the machine tool, the first-order natural frequency, and the three-dimensional resonance amplitude of the spindle end;

[0010] Step 4: Based on the experimental results of different material properties, linear transformation and normalization are used to map the material property parameters to the design space of [-1,1], and the complexity of the relationship between the response value and the variable is determined through DOE analysis, and different order response surface models based on the design variables are established;

[0011] Step 5: Taking static stiffness as a constraint condition and dynamic performance as an objective function, a multi-objective rime optimization algorithm is used to perform a global search in the design space to achieve material matching;

[0012] Step 6. Compare and analyze the static and dynamic performance before and after the material matching optimization design through simulation results to verify the effectiveness of the material matching method. Under the condition of meeting the static performance, obtain the material matching design scheme that can achieve the optimal dynamic performance of the machine tool. Otherwise, re-fit the response surface model and repeat steps 3-6.

[0013] Furthermore, step 1 specifically includes:

[0014] Step 1.1: Process gray cast iron, ductile iron and structural steel according to national standards, including HT300, HT350, QT500-7, QT600-3, QT800-2 and Q235;

[0015] Step 1.2, calculate the density based on the Archimedean method, calculate the Poisson's ratio based on the tensile test method, measure the material frequency response curve in a constant temperature chamber based on the cantilever resonance method, calculate the material damping loss factor by the half-power bandwidth method, and calculate the elastic modulus using the dynamic method. The calculation formula is as follows:

[0016]

[0017] Where f2 represents the half-power point frequency on the right, f1 represents the half-power point frequency on the left, and f r1represents the first-order resonance frequency, ρ is the density of the material, l is the length of the cantilever beam plate; H is the thickness of the cantilever beam plate; C1 is the first-order modal coefficient of the cantilever beam, C1=0.55959;

[0018] Step 1.3: Repeat the test and take the average value of the test results for each material as the material property parameter.

[0019] Furthermore, the calculation of the stiffness and damping parameters of the fixed, sliding and rolling joint surfaces in step 2 specifically includes:

[0020] Step 2.1, determine the number of bolts and the bolt tightening torque between the fixed joint surfaces of the guide rail and the structural part, the slider and the structural part, the bearing seat and the structural part, and the motor seat and the structural part, and calculate the normal surface pressure and stress action area of ​​the fixed joint surface;

[0021] Step 2.2, based on Yoshimura Yunxiao integration method, calculate the fixed joint surface stiffness and fixed joint surface damping parameters, the calculation formula is as follows;

[0022]

[0023] Among them, K 1i , K 2i Respectively represent the equivalent spring stiffness in shear and vertical directions of the fixed joint surface; C 1i , C 2i They represent the equivalent viscous damping in the shear and vertical directions of the fixed joint surface respectively; k 1i , k 2i They represent the equivalent spring stiffness in shear and vertical directions per unit contact area of ​​the fixed joint surface; c 1i 、c 2i They represent the equivalent viscous damping P in shear and vertical directions per unit contact area of ​​the fixed joint surface. n Indicates normal pressure; A i Indicates the equivalent contact area within the stress action range;

[0024] Step 2.3, calculate the stiffness and damping parameters of the guide rail-slider pair, the lead screw-screw nut pair, and the inner and outer rings of the bearing according to the product manual; the stiffness of the guide rail-slider pair is the sliding joint surface stiffness, and the stiffness of the lead screw-screw nut pair and the inner and outer rings of the bearing is the rolling joint surface stiffness;

[0025] Step 2.4, place each structural component in the central position, perform static analysis on the machine tool, extract the reaction force of the machine tool foundation support, and calculate the foundation joint surface parameters using the following formula;

[0026]

[0027] Among them, K v , K hThey represent the normal and tangential support stiffness respectively; F represents the anchor reaction force.

[0028] Furthermore, in step 3, the static and dynamic response values ​​of the key nodes of the machine tool are obtained under different material combinations of the structural parts, specifically including:

[0029] Step 3.1, the fixed joint surface parameters of the guide rail and the structural part, the slider and the structural part, the bearing seat and the structural part, and the motor seat and the structural part are equivalent to the spring damping unit parameters as boundary conditions and input into the finite element model, and the joint surface parameters of the sliding joint surface and the rolling joint surface are equivalent to the spring damping unit parameters as boundary conditions, and the finite element model considering the joint surface parameters is established;

[0030] Step 3.2: For the design space of the four independent variables of the materials of the spindle box, column, bed and slide, a combination matching design of the materials of the spindle box, column, bed and slide is performed based on the optimal Latin hypercube sampling method to obtain the experimental design points;

[0031] Step 3.3: Perform batch simulation based on the parametric design of the workbench to extract the response values ​​of the key nodes of the machine tool, including: the three-dimensional static stiffness of the machine tool, the first-order natural frequency, and the three-dimensional resonance amplitude of the spindle end.

[0032] Furthermore, step 4 is as follows:

[0033] Step 4.1. Normalize the four material properties of cast iron material, namely, material damping loss factor, elastic modulus, Poisson's ratio and density. Use the constructor method to discretize the material properties of the five materials with a step size of 0.5 and map them to [-1,1]. Take the four typical topological structures of the spindle box, column, bed and slide as four independent variables, and use the 5 discrete values ​​of [-1,1] as the value range of the independent variable. The mapping formula for each material is:

[0034]

[0035] Among them, ρ i is the density of each material i, E i is the elastic modulus of each material i, ν i is the Poisson's ratio of each material i, η i is the material loss factor of each material i, a, b, c, d, e are constants, k i is the equivalent material property parameter of each material i, i=1~5 represents five materials HT300, HT350, QT500-7, QT600-3, QT800-2, corresponding to k i They are -1, -0.5, 0, 0.5, 1 respectively;

[0036] Step 4.2, determine the model complexity between each response value and the independent variable through DOE analysis, and establish the response surface model of the response value and the independent variable according to the different complexity. The nonlinearity of the three-way static stiffness and the first-order natural frequency of the machine tool is low, so a second-order response surface model based on the independent variable is constructed. The nonlinearity of the three-way resonance amplitude of the spindle end is high, so a fourth-order response surface model based on the independent variable is constructed.

[0037]

[0038] Among them, y is the response variable; β0 is the constant term, β m , β mm , β mmm , β mmmm are the coefficients of the linear term, quadratic term, cubic term, and quartic term, respectively, mf , β mmf , β mff , β mfmf is the interaction coefficient, x m 、x f is the independent variable.

[0039] Furthermore, step 5 specifically includes:

[0040] Step 5.1, taking the obtained three-dimensional static stiffness of the machine tool as a constraint condition, and taking the maximization of the first-order natural frequency and the minimization of the three-dimensional resonance amplitude of the spindle end as the objective function;

[0041] Step 5.2: First, initialize the rime population X, which consists of N rime agents P t Composition, each rime agent P t Rime particles x are randomly selected by four design variables tj As shown in formula (8), the rime population X is directly composed of rime particles x tj It is expressed as shown in formula (9):

[0042]

[0043] Where N represents the total number of material matching solutions, and P represents the total number of material matching solutions. t represents a certain material combination, t represents the value ordinal of the design variable, j represents the ordinal of the structural component, and rime particle x tj Indicates that the jth structural component takes the tth material ordinal number;

[0044] Step 5.3: According to the motion characteristics of rime condensing into soft rime, use the soft rime search strategy to search in the initial iteration process and update the position of rime particles. The update formula of rime particle position is as follows:

[0045]

[0046] in, represents the tth rime agent P t The new position of the jth rime particle; is the position of the best rime agent in the current iteration, r1 is the control parameter that determines the direction of particle movement, and takes a random value between the specific limits of [-1,1], r2 is a random number between 0 and 1, cosθ is a variable that changes with the number of iterations, θ = π(g / 10G), G represents the maximum number of iterations, g represents the current number of iterations, β is an environmental factor that affects convergence with iterations, β = 1-((wg) / G) / w, w is set to 5, h is the adhesion, E represents the condensation probability factor, are the upper and lower limits of the escape space respectively;

[0047] Step 5.4: To improve the convergence speed of the algorithm and the ability to escape from the local optimum, the position of the rime agent is updated through the hard rime puncture mechanism, which satisfies the following formula:

[0048]

[0049] Among them, F normr (P t ) is the normalized fitness value of the tth rime agent, r3 represents a random value between -1 and 1 that determines the exchange process;

[0050] Step 5.5, selection of positive greedy mechanism;

[0051] A hard rime puncture mechanism is used to promote information exchange between individuals. A positive greedy selection mechanism is used to compare the fitness values ​​of updated and unupdated rime agents, and the latter are replaced by updated agents with better performance. Then, it is checked whether the optimal solution is reached. If not, the soft rime search strategy is used again to update the population, and steps 5.2-5.5 are repeated. If the optimal solution is met, the multi-objective rime optimization algorithm is terminated.

[0052] Preferably, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a material matching design method for machine tool structural parts used for forward design of machine tools when executing the program.

[0053] Preferably, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the material matching design method for machine tool structural parts used for forward design of machine tools.

[0054] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0055] 1. Improve the dynamic performance and processing accuracy of machine tools: By comprehensively utilizing experimental data, finite element analysis, response surface model fitting and multi-objective optimization algorithm, the present invention effectively optimizes material matching in the machine tool design stage, and can significantly improve the dynamic stiffness and vibration resistance of the machine tool while ensuring static performance, thereby improving processing accuracy and surface quality.

[0056] 2. Accurate nonlinear relationship modeling: This invention avoids the common overfitting and underfitting problems in traditional methods through multi-order response surface model fitting, and can more accurately describe the nonlinear relationship between material properties and machine tool dynamic response. The response surface model can accurately capture the complex relationship between design variables and responses through normalization processing and DOE analysis.

[0057] 3. Global search of optimized design space: The optimal Latin hypercube sampling method and multi-objective rime optimization algorithm are used to ensure that the experimental points are evenly distributed in the design space, avoiding the high cost and time consumption of traditional full-factor experiments, while improving the efficiency and accuracy of the optimization search.

[0058] 4. Improve design efficiency and reduce manufacturing costs: This method reduces the number of repeated experimental operations, saves design and testing time, and through more accurate material combination schemes, helps to rationally plan the materials of machine tool structural parts, thereby reducing manufacturing costs and improving overall production efficiency.

[0059] 5. Effective optimization and verification mechanism: Through simulation and comparative analysis of static and dynamic performance before and after optimization design, the effectiveness of the material matching scheme is ensured. If the design does not achieve the expected goal, it can quickly retreat and refit the response surface model, and perform repeated optimization to ensure the optimality of the final solution.

[0060] In summary, the present invention provides an efficient and accurate material matching design method for machine tool structural parts, which can optimize the material combination in the machine tool design stage, improve the efficiency and accuracy of the machine tool material damping matching design, improve the machine tool processing accuracy, reduce production costs, and has broad application prospects and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flow chart of a material matching design method for machine tool structural parts used in forward design of machine tools;

[0062] Figure 2 This is the on-site installation diagram for the damping loss factor experiment of commonly used castings for machine tools;

[0063] Figure 3 Simplified model of machine tool and equivalent model diagram of joint surface;

[0064] Figure 4This is the flow chart of the multi-objective rime optimization algorithm. DETAILED DESCRIPTION

[0065] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific 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.

[0066] The present invention adopts different order response surface models to fit the relationship between the static and dynamic response values ​​of the machine tool and the material properties of the machine tool structural parts, and uses a multi-objective rime optimization algorithm to perform spatial search to obtain the optimal structural part material damping combination, so that the three-way resonance amplitude can be optimized as much as possible while ensuring the static stiffness. The method of the present invention is as follows Figure 1 As shown, the specific method is as follows:

[0067] Step 1: Obtain the material damping loss factor, elastic modulus, Poisson's ratio, density, etc. of common materials for machine tool structural parts through experiments, and determine the property parameters of each material;

[0068] Step 2, under the allowable tightening torque of the bolts, for a machine tool with a given structural component combination, the fixed joint surface stiffness and the fixed joint surface damping parameter are calculated based on the Yoshimura Yoshitaka integral method, and the sliding joint surface stiffness, the rolling joint surface stiffness, the sliding joint surface damping parameter, and the rolling joint surface damping parameter are calculated according to the product manual;

[0069] Step 3: Establish a finite element model that takes into account the parameters of the joint surface, select the material combination of the structural parts based on the optimal Latin hypercube sampling method, obtain the experimental design points, and extract the static and dynamic response values ​​of the key points of the machine tool with different combinations through the simulation results, including the three-dimensional static stiffness of the machine tool, the first-order natural frequency, and the three-dimensional resonance amplitude of the spindle end;

[0070] Step 4: Based on the experimental results of different material properties, linear transformation and normalization are used to map the material property parameters to the design space of [-1,1], and the complexity of the relationship between the response and the variable is determined through DOE analysis, and different order response surface models based on the design variables are established;

[0071] Step 5: Taking static stiffness as a constraint condition and dynamic performance as an objective function, a multi-objective rime optimization algorithm is used to perform a global search in the design space to achieve material matching;

[0072] Step 6. Compare and analyze the static and dynamic performance before and after the material matching optimization design through simulation results to verify the effectiveness of the material matching method. Under the condition of meeting the static performance, obtain the material matching design scheme that can achieve the optimal dynamic performance of the machine tool. Otherwise, re-fit the response surface model and repeat steps 3-6.

[0073] In step 1, obtaining the material properties of commonly used castings for machine tools is a prerequisite for determining the range of independent variables. In order to determine the damping loss factor of machine tool materials, the cantilever beam resonance method is used to perform experimental tests on the material damping loss factor. The experimental principle is as follows: Figure 2 As shown, the specific steps are as follows:

[0074] Step 1.1: Process gray cast iron, ductile iron and structural steel according to national standards, including HT300, HT350, QT500-7, QT600-3, QT800-2 and Q235;

[0075] Step 1.2, calculate the density based on the Archimedean method, calculate the Poisson's ratio based on the tensile test method, measure the material frequency response curve in a constant temperature chamber based on the cantilever resonance method, calculate the material damping loss factor by the half-power bandwidth method, and calculate the elastic modulus using the dynamic method. The calculation formula is as follows:

[0076]

[0077] Where f2 represents the half-power point frequency on the right, f1 represents the half-power point frequency on the left, and f r1 represents the first-order resonance frequency, ρ is the density of the material, l is the length of the cantilever beam plate; H is the thickness of the cantilever beam plate; C1 is the first-order modal coefficient of the cantilever beam, C1=0.55959;

[0078] Step 1.3: Repeat the test and take the average value of the test results for each material as the material property parameter.

[0079] In step 2, from the perspective of dynamic modeling, in order to establish a finite element model more accurately, the spring damping equivalent is performed on the bolt joint surface involved in the machine tool, including the joint surface between the component and the guide rail, the joint surface between the component and the bearing seat, etc. The equivalent model of the bolt joint surface of the machine tool is as follows: Figure 3 As shown, the specific steps are as follows:

[0080] Step 2.1, determine the number of bolts and the bolt tightening torque between the fixed joint surfaces of the guide rail and the structural part, the slider and the structural part, the bearing seat and the structural part, and the motor seat and the structural part, and calculate the normal surface pressure and stress action area of ​​the fixed joint surface;

[0081] Step 2.2, calculate the fixed joint surface stiffness and damping parameters based on Yoshimura Yunxiao integration method, the calculation formula is as follows;

[0082]

[0083] Among them, K 1i , K 2i Respectively represent the equivalent spring stiffness in shear and vertical directions of the fixed joint surface; C 1i , C2i They represent the equivalent viscous damping in the shear and vertical directions of the fixed joint surface respectively; k 1i , k 2i They represent the equivalent spring stiffness in shear and vertical directions per unit contact area of ​​the fixed joint surface; c 1i 、c 2i They represent the equivalent viscous damping P in shear and vertical directions per unit contact area of ​​the fixed joint surface. n Indicates normal pressure; A i Indicates the equivalent contact area within the stress action range;

[0084] Step 2.3, calculate the stiffness and damping parameters of the guide rail-slider pair, the lead screw-screw nut pair, and the inner and outer rings of the bearing according to the product manual; the stiffness of the guide rail-slider pair is the sliding joint surface stiffness, and the stiffness of the lead screw-screw nut pair and the inner and outer rings of the bearing is the rolling joint surface stiffness;

[0085] Step 2.4: Place each structural component in the central position, perform static analysis on the machine tool, extract the reaction force of the machine tool foundation support, and calculate the foundation joint surface parameters using the following formula.

[0086]

[0087] Among them, K v , K h They represent the normal and tangential support stiffness respectively; F represents the anchor reaction force.

[0088] In step 3, a finite element model considering the interface parameters is first established, then spatial sampling is performed based on the optimal Latin hypercube sampling method, and finally the response values ​​of the experimental design points are extracted based on the parametric simulation of the workbench. The specific steps are as follows:

[0089] Step 3.1, the fixed joint surface parameters of the guide rail and the structural part, the slider and the structural part, the bearing seat and the structural part, and the motor seat and the structural part are equivalent to the spring damping unit parameters as boundary conditions and input into the finite element model, and the joint surface parameters of the sliding joint surface and the rolling joint surface are equivalent to the spring damping unit parameters as boundary conditions, and the finite element model considering the joint surface parameters is established;

[0090] Step 3.2: For the design space of the four independent variables of the materials of the spindle box, column, bed and slide, a combination matching design of the materials of the spindle box, column, bed and slide is performed based on the optimal Latin hypercube sampling method to obtain the experimental design points;

[0091] Step 3.3: Perform batch simulation based on the parametric design of the workbench to extract the response values ​​of the key nodes of the machine tool, including: the three-dimensional static stiffness of the machine tool, the first-order natural frequency, and the three-dimensional resonance amplitude of the spindle end.

[0092] In step 4, the material property parameters are mapped to [-1, 1] through linear transformation and normalization, and the complexity of the model is analyzed through DOE analysis to establish a response surface model based on different orders of independent variables. The specific steps are as follows:

[0093] Step 4.1. Normalize the four material properties of cast iron (material damping loss factor, elastic modulus, Poisson's ratio and density). Use the constructor method to discretize the material properties of the five materials with a step size of 0.5 and map them to [-1,1]. Take the four typical topological structures of the spindle box, column, bed and slide as four independent variables, and use the 5 discrete values ​​of [-1,1] as the value range of the independent variable. The mapping formula for each material is:

[0094]

[0095] Among them, ρ i is the density of each material i, E i is the elastic modulus of each material i, ν i is the Poisson's ratio of each material i, η i is the material loss factor of each material i, a, b, c, d, e are constants, k i is the equivalent material property parameter of each material i, i=1~5 represents five materials HT300, HT350, QT500-7, QT600-3, QT800-2, corresponding to k i They are -1, -0.5, 0, 0.5, 1 respectively;

[0096] Step 4.2, determine the model complexity between each response value and the independent variable through DOE analysis, and establish the response surface model of the response value and the independent variable according to the different complexity. The nonlinearity of the three-way static stiffness and the first-order natural frequency of the machine tool is low, so a second-order response surface model based on the independent variable is constructed. The nonlinearity of the three-way resonance amplitude of the spindle end is high, so a fourth-order response surface model based on the independent variable is constructed.

[0097]

[0098] Among them, y is the response variable; β0 is the constant term, β m , β mm , β mmm , β mmmm are the coefficients of the linear term, quadratic term, cubic term, and quartic term, respectively, mf , β mmf , β mff , β mfmf is the interaction coefficient, x m 、x f is the independent variable.

[0099] In step 5, for the multi-objective optimization problem, first determine the constraint conditions as the three-dimensional static stiffness value is within the required range, the independent variable range is [-1,1], the objective function is the maximum first-order natural frequency, the minimum three-dimensional resonance amplitude, and the flowchart of the multi-objective rime optimization algorithm is as follows: Figure 4 As shown, the specific steps are as follows:

[0100] Step 5.1, taking the obtained three-dimensional static stiffness of the machine tool as a constraint condition, and taking the maximization of the first-order natural frequency and the minimization of the three-dimensional resonance amplitude of the spindle end as the objective function;

[0101] Step 5.2: First, initialize the rime population X, which consists of N rime agents P t Composition, each rime agent P t Rime particles x are randomly selected by four design variables tj As shown in formula (8), the rime population X is directly composed of rime particles x tj It is expressed as shown in formula (9):

[0102]

[0103] Where N represents the total number of material matching solutions, and P represents the total number of material matching solutions. t represents a certain material combination, t represents the value ordinal of the design variable, j represents the ordinal of the structural component, and rime particle xt j Indicates that the jth structural component takes the tth material ordinal number;

[0104] Step 5.3: Soft rime search strategy: Search in the initial iteration process. The update formula of rime particles is as follows:

[0105]

[0106] in, represents the tth rime agent P t The new position of the jth rime particle; is the position of the best rime agent in the current iteration, r1 is the control parameter that determines the direction of particle movement, and takes a random value between the specific limits of [-1,1], r2 is a random number between 0 and 1, cosθ is a variable that changes with the number of iterations, θ = π(g / 10G), G represents the maximum number of iterations, g represents the current number of iterations, β is an environmental factor that affects convergence with iterations, β = 1-((wg) / G) / w, w is set to 5, h is the adhesion, E represents the condensation probability factor, are the upper and lower limits of the escape space respectively;

[0107] Step 5.4: To improve the convergence speed of the algorithm and the ability to escape from the local optimum, the position of the rime agent is updated through the hard rime puncture mechanism, which satisfies the following formula:

[0108]

[0109] Among them, F normr (P t ) is the normalized fitness value of the tth rime agent, r3 represents a random value between -1 and 1 that determines the exchange process;

[0110] Step 5.5, selection of positive greedy mechanism;

[0111] A hard rime puncture mechanism is used to promote information exchange between individuals. A positive greedy selection mechanism is used to compare the fitness values ​​of updated and unupdated rime agents, and the latter are replaced by updated agents with better performance. Then, it is checked whether the optimal solution is reached. If not, the soft rime search strategy is used again to update the population, and steps 5.2-5.5 are repeated. If the optimal solution is met, the multi-objective rime optimization algorithm is terminated.

[0112] Preferably, the embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all steps in the material matching design method for machine tool structural parts for forward design of machine tools in the above embodiments, and the electronic device specifically includes the following contents:

[0113] Processor, memory, communications interface and bus;

[0114] Among them, the processor, memory, and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between related devices such as server-side devices, metering devices, and user-side devices.

[0115] The processor is used to call the computer program in the memory, and when the processor executes the computer program, all the steps in the material matching design method for machine tool structural parts used for forward design of machine tools in the above embodiment are implemented.

[0116] An embodiment of the present application also provides a computer-readable storage medium capable of implementing all steps of the material matching design method for machine tool structural parts for forward design of machine tools in the above-mentioned embodiment. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, all steps of the material matching design method for machine tool structural parts for forward design of machine tools in the above-mentioned embodiment are implemented.

[0117] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0118] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] Although the present application provides method operation steps such as embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0120] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0123] The above-described embodiments merely express the implementation methods of the present invention, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A material matching design method for machine tool structural parts used in machine tool forward design, characterized in that: The steps include: Step 1: Obtain the material damping loss factor, elastic modulus, Poisson's ratio, and density of common materials for machine tool structural parts through experiments, and determine the property parameters of each material; Step 2, under the allowable tightening torque of the bolts, for a machine tool with a given structural component combination, the fixed joint surface stiffness and the fixed joint surface damping parameter are calculated based on the Yoshimura Yoshitaka integral method, and the sliding joint surface stiffness, the rolling joint surface stiffness, the sliding joint surface damping parameter, and the rolling joint surface damping parameter are calculated according to the product manual; Step 3: Establish a finite element model that takes into account the parameters of each joint surface, select the material combination of the structural parts based on the optimal Latin hypercube sampling method, obtain the experimental design points, and extract the static and dynamic response values ​​of the key points of the machine tool with different combinations through the simulation results, including the three-dimensional static stiffness of the machine tool, the first-order natural frequency, and the three-dimensional resonance amplitude of the spindle end; Step 4: Based on the experimental results of different material properties, linear transformation and normalization are used to map the material property parameters to the design space of [-1,1], and the complexity of the relationship between the response value and the variable is determined through DOE analysis, and different order response surface models based on the design variables are established; Step 5: Taking static stiffness as a constraint condition and dynamic performance as an objective function, a multi-objective rime optimization algorithm is used to perform a global search in the design space to achieve material matching; Step 6. Compare and analyze the static and dynamic performance before and after the material matching optimization design through simulation results to verify the effectiveness of the material matching method. Under the condition of meeting the static performance, obtain the material matching design scheme that can achieve the optimal dynamic performance of the machine tool. Otherwise, re-fit the response surface model and repeat steps 3-6.

2. The material matching design method for machine tool structural parts used for forward design of machine tools according to claim 1, characterized in that: Step 1 specifically includes: Step 1.1: Process gray cast iron, ductile iron and structural steel according to national standards, including HT300, HT350, QT500-7, QT600-3, QT800-2 and Q235; Step 1.2, calculate the density based on the Archimedean method, calculate the Poisson's ratio based on the tensile test method, measure the material frequency response curve in a constant temperature chamber based on the cantilever resonance method, calculate the material damping loss factor by the half-power bandwidth method, and calculate the elastic modulus using the dynamic method. The calculation formula is as follows: Where f2 represents the half-power point frequency on the right, f1 represents the half-power point frequency on the left, and f r1 represents the first-order resonance frequency, ρ is the density of the material, l is the length of the cantilever beam plate; H is the thickness of the cantilever beam plate; C1 is the first-order modal coefficient of the cantilever beam, C1=0.55959; Step 1.3: Repeat the test and take the average value of the test results for each material as the material property parameter.

3. The material matching design method for machine tool structural parts used for forward design of machine tools according to claim 1, characterized in that: The calculation of the stiffness and damping parameters of the fixed, sliding and rolling joint surfaces in step 2 specifically includes: Step 2.1, determine the number of bolts and the bolt tightening torque between the fixed joint surfaces of the guide rail and the structural part, the slider and the structural part, the bearing seat and the structural part, and the motor seat and the structural part, and calculate the normal surface pressure and stress action area of ​​the fixed joint surface; Step 2.2, based on Yoshimura Yunxiao integration method, calculate the fixed joint surface stiffness and fixed joint surface damping parameters, the calculation formula is as follows; Among them, K 1i , K 2i Respectively represent the equivalent spring stiffness in shear and vertical directions of the fixed joint surface; C 1i , C 2i They represent the equivalent viscous damping in the shear and vertical directions of the fixed joint surface respectively; k 1i , k 2i They represent the equivalent spring stiffness in shear and vertical directions per unit contact area of ​​the fixed joint surface; c 1i 、c 2i They represent the equivalent viscous damping P in shear and vertical directions per unit contact area of ​​the fixed joint surface. n Indicates normal pressure; A i Indicates the equivalent contact area within the stress action range; Step 2.3, calculate the stiffness and damping parameters of the guide rail-slider pair, the lead screw-screw nut pair, and the inner and outer rings of the bearing according to the product manual; the stiffness of the guide rail-slider pair is the sliding joint surface stiffness, and the stiffness of the lead screw-screw nut pair and the inner and outer rings of the bearing is the rolling joint surface stiffness; Step 2.4, place each structural component in the central position, perform static analysis on the machine tool, extract the reaction force of the machine tool foundation support, and calculate the foundation joint surface parameters using the following formula; Among them, K v , K h They represent the normal and tangential support stiffness respectively; F represents the anchor reaction force.

4. The material matching design method for machine tool structural parts used for forward design of machine tools according to claim 1, characterized in that: In step 3, the static and dynamic response values ​​of the key nodes of the machine tool are obtained under different material combinations of the structural parts, including: Step 3.1, the fixed joint surface parameters of the guide rail and the structural part, the slider and the structural part, the bearing seat and the structural part, and the motor seat and the structural part are equivalent to the spring damping unit parameters as boundary conditions and input into the finite element model, and the joint surface parameters of the sliding joint surface and the rolling joint surface are equivalent to the spring damping unit parameters as boundary conditions, and the finite element model considering the joint surface parameters is established; Step 3.2: For the design space of the four independent variables of the materials of the spindle box, column, bed and slide, a combination matching design of the materials of the spindle box, column, bed and slide is performed based on the optimal Latin hypercube sampling method to obtain the experimental design points; Step 3.3: Perform batch simulation based on the parametric design of the workbench to extract the response values ​​of the key nodes of the machine tool, including: the three-dimensional static stiffness of the machine tool, the first-order natural frequency, and the three-dimensional resonance amplitude of the spindle end.

5. The material matching design method for machine tool structural parts used for forward design of machine tools according to claim 1, characterized in that: Step 4 is as follows: Step 4.

1. Normalize the four material properties of cast iron material, namely, material damping loss factor, elastic modulus, Poisson's ratio and density. Use the constructor method to discretize the material properties of the five materials with a step size of 0.5 and map them to [-1,1]. Take the four typical topological structures of the spindle box, column, bed and slide as four independent variables, and use the 5 discrete values ​​of [-1,1] as the value range of the independent variable. The mapping formula for each material is: Among them, ρ i is the density of each material i, E i is the elastic modulus of each material i, ν i is the Poisson's ratio of each material i, η i is the material loss factor of each material i, a, b, c, d, e are constants, k i is the equivalent material property parameter of each material i, i=1~5 represents five materials HT300, HT350, QT500-7, QT600-3, QT800-2, corresponding to k i They are -1, -0.5, 0, 0.5, 1 respectively; Step 4.2, determine the model complexity between each response value and the independent variable through DOE analysis, and establish the response surface model of the response value and the independent variable according to the different complexity. The nonlinearity of the three-way static stiffness and the first-order natural frequency of the machine tool is low, so a second-order response surface model based on the independent variable is constructed. The nonlinearity of the three-way resonance amplitude of the spindle end is high, so a fourth-order response surface model based on the independent variable is constructed. Among them, y is the response variable; β0 is the constant term, β m , β mm , β mmm , β mmmm are the coefficients of the linear term, quadratic term, cubic term, and quartic term, respectively, mf , β mmf , β mff , β mfmf is the interaction coefficient, x m 、x f is the independent variable.

6. The material matching design method for machine tool structural parts used for forward design of machine tools according to claim 1, characterized in that: Step 5 specifically includes: Step 5.1, taking the obtained three-dimensional static stiffness of the machine tool as a constraint condition, and taking the maximization of the first-order natural frequency and the minimization of the three-dimensional resonance amplitude of the spindle end as the objective function; Step 5.2: First, initialize the rime population X, which consists of N rime agents P t Composition, each rime agent P t Rime particles x are randomly selected by four design variables tj As shown in formula (8), the rime population X is directly composed of rime particles x tj It is expressed as shown in formula (9): Where N represents the total number of material matching solutions, and P represents the total number of material matching solutions. t represents a certain material combination, t represents the value ordinal of the design variable, j represents the ordinal of the structural component, and rime particle x tj Indicates that the jth structural component takes the tth material ordinal number; Step 5.3: According to the motion characteristics of rime condensing into soft rime, use the soft rime search strategy to search in the initial iteration process and update the position of rime particles. The update formula of rime particle position is as follows: in, represents the tth rime agent P t The new position of the jth rime particle; is the position of the best rime agent in the current iteration, r1 is the control parameter that determines the direction of particle movement, and takes a random value between the specific limits of [-1,1], r2 is a random number between 0 and 1, cosθ is a variable that changes with the number of iterations, θ = π(g / 10G), G represents the maximum number of iterations, g represents the current number of iterations, β is an environmental factor that affects convergence with iterations, β = 1-((wg)G)w, w is set to 5, h is the adhesion, E represents the condensation probability factor, are the upper and lower limits of the escape space respectively; Step 5.4: To improve the convergence speed of the algorithm and the ability to escape from the local optimum, the position of the rime agent is updated through the hard rime puncture mechanism, which satisfies the following formula: Among them, F normr (P t ) is the normalized fitness value of the tth rime agent, r3 represents a random value between -1 and 1 that determines the exchange process; Step 5.5, selection of positive greedy mechanism; A hard rime puncture mechanism is used to promote information exchange between individuals. A positive greedy selection mechanism is used to compare the fitness values ​​of updated and unupdated rime agents, and the latter are replaced by updated agents with better performance. Then, it is checked whether the optimal solution is reached. If not, the soft rime search strategy is used again to update the population, and steps 5.2-5.5 are repeated. If the optimal solution is met, the multi-objective rime optimization algorithm is terminated.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the machine tool structural component material matching design method for machine tool forward design according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the material matching design method for machine tool structural parts for forward design of machine tools as described in any one of claims 1 to 6 are implemented.

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