A material matching design method for machine tool structural parts used in forward design of machine tools

Abstract

The present invention discloses a material matching design method for machine tool structural parts for forward design of machine tools, comprising: step 1, obtaining material property parameters such as the damping loss factor and elastic modulus of the material through experiments; step 2, calculating the fixed joint surface parameters based on the Yoshimura Yoshitaka integration method, and calculating the sliding and rolling joint surface parameters based on the product manual; step 3, establishing a finite element model considering the joint surface parameters, and extracting the static and dynamic response values ​​of the key nodes of the machine tool through simulation results; step 4, determining the complexity of the model through DOE analysis and constructing a response surface model of response variables and independent variables; step 5, based on a multi-objective rime optimization algorithm, globally searching in the design space, optimizing the material combination mode of each structural part, so that the machine tool reaches the optimal balance point in multiple dynamic performance indicators on the basis of satisfying static stiffness; step 6, comparing and analyzing the static and dynamic performance before and after the material matching optimization through simulation results, and verifying the effect of the material matching.

Description

Technical Field

[0001] The present invention relates to the field of material matching design of numerically controlled machine tool structural parts, and in particular to a material matching design method for machine tool structural parts used in 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 tool. 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 the machine tool becomes 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 methods for matching machine tool materials to damping typically rely on empirical evidence or finite element simulation results. Not only do they lack experimental analysis of the materials, but the resulting models of dynamic response and the material properties of machine tool components are inaccurate and insufficiently represent the nonlinear relationship between dynamic response and damping. This results in a lack of effective global search and optimization within the design space, making it impossible to ensure the optimal material combination. Furthermore, models constructed using BP neural networks lack sufficient data for training, requiring the response surface methodology to more accurately construct the relationship between response and variables. Conventional optimization methods also have limitations in search efficiency and computational accuracy for complex, multi-objective design requirements. Summary of the Invention

[0004] The purpose of this invention is to overcome the inability of existing models to accurately describe nonlinear relationships, improve matching accuracy, and provide a material matching design solution that achieves optimal dynamic performance while meeting static performance requirements. This approach addresses the existing problems of complex machine tool material matching design, weak global search capabilities, and low optimization efficiency. By integrating experimental data, finite element analysis, response surface model fitting, and multi-objective optimization algorithms, this invention provides a more efficient and accurate approach to material damping optimization design.

[0005] The purpose 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, calculate the fixed joint surface stiffness and fixed joint surface damping parameters based on Yoshimura Yunxiao's integration method, and calculate the sliding joint surface stiffness, rolling joint surface stiffness, sliding joint surface damping parameters, and rolling joint surface damping parameters according to the product manual;

[0009] Step 3: Establish a finite element model that takes into account the parameters of each joint surface. Based on the optimal Latin hypercube sampling method, select the material combination of the structural components, obtain the experimental design points, and extract the static and dynamic response values ​​of the key points of the machine tool for different combinations through 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]. DOE analysis is then used to determine the complexity of the relationship between the response value and the variable, and different-order response surface models based on the design variables are established.

[0011] Step 5: Using static stiffness as a constraint 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 using the half-power bandwidth method, and calculate the elastic modulus using the dynamic method. The calculation formula is as follows:

[0016]

[0017] Among them, 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 resonant 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 measured 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 interfaces 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 component, the slider and the structural component, the bearing seat and the structural component, and the motor seat and the structural component, and calculate the normal surface pressure and stress action area of ​​the fixed joint surfaces;

[0021] Step 2.2: Calculate the fixed joint surface stiffness and fixed joint surface damping parameters based on Yoshimura Yunxiao's integration method. 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 are 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 are 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 races of the bearing according to the product manual. The stiffness of the guide rail-slider pair is the sliding joint stiffness, and the stiffness of the lead screw-screw nut pair and the inner and outer races of the bearing is the rolling joint stiffness.

[0025] Step 2.4: Place all structural components in the center, perform static analysis on the machine tool, extract the machine tool foot support reaction force, and calculate the foot interface parameters using the following formula;

[0026]

[0027] Among them, K v , K hThey represent the normal and tangential support stiffness respectively; F represents the foundation support 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. Equivalent the fixed interface parameters of the guide rail and structural component, the slider and structural component, the bearing seat and structural component, and the motor seat and structural component to the spring damping unit parameters as boundary conditions and input them into the finite element model. Equivalent the interface parameters of the sliding interface and the rolling interface to the spring damping unit parameters as boundary conditions and establish a finite element model that takes the interface parameters into account.

[0030] Step 3.2: For the design space of the four independent variables of the materials of the spindle box, column, bed, and slide, perform a combination matching design of the materials of the spindle box, column, bed, and slide 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 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 the four independent variables, and use the five discrete values ​​of [-1, 1] as the value range of the independent variables. 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 to 5 represent five materials HT300, HT350, QT500-7, QT600-3, QT800-2, corresponding to k i They are -1, -0.5, 0, 0.5, and 1 respectively;

[0036] Step 4.2: Determine the model complexity between each response value and the independent variable through DOE analysis, and establish a response surface model of the response value and the independent variable according to the different complexity. The nonlinearity of the three-dimensional 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-dimensional 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 linear term, quadratic term, cubic term and quartic term respectively, β 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: Use the obtained three-dimensional static stiffness of the machine tool as a constraint condition, and maximize the first-order natural frequency and minimize 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 number of material matching solutions. t represents a certain material combination, t represents the value sequence of the design variable, j represents the sequence number of the structural component, and rime particles x tj Indicates that the j-th structural component takes the t-th material ordinal number;

[0044] Step 5.3: Based on 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, which 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 algorithm's convergence speed and ability to escape local optimality, the position of the rime agent is updated through a hard rime puncture mechanism. This mechanism 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 forward greedy selection mechanism is used to compare the fitness values ​​of updated and unupdated rime agents, and the updated agent with better performance is used to replace the latter. 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 runnable 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 machine tool structural component material matching design method for machine tool forward design.

[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 machining accuracy of machine tools: By comprehensively utilizing experimental data, finite element analysis, response surface model fitting, and multi-objective optimization algorithms, the present invention effectively optimizes material matching during the machine tool design phase. While ensuring static performance, it can significantly improve the dynamic stiffness and vibration resistance of the machine tool, thereby improving machining accuracy and surface quality.

[0056] 2. Accurate Modeling of Nonlinear Relationships: This invention utilizes multi-order response surface modeling to avoid the overfitting and underfitting issues common in traditional methods, enabling a more accurate description of the nonlinear relationships between material properties and machine tool dynamic responses. Through normalization and DOE analysis, the response surface model accurately captures the complex relationships between design variables and responses.

[0057] 3. Global Search of the Optimal Design Space: Using the optimal Latin hypercube sampling method and the multi-objective rime optimization algorithm, we ensured a uniform distribution of experimental points within 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, the effectiveness of the material matching solution is ensured. If the design does not achieve the expected results, it can be quickly rolled back and the response surface model can be refitted, and repeated optimization can be performed 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 material combinations during the machine tool design stage, improve the efficiency and accuracy of machine tool material damping matching design, improve machine tool processing accuracy, reduce production and manufacturing 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 with reference to 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 intended 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. While ensuring the static stiffness, the three-way resonance amplitude is optimized as much as possible. The method of the present invention is as follows Figure 1 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, calculate the fixed joint surface stiffness and fixed joint surface damping parameters based on Yoshimura Yunxiao's integration method, and calculate the sliding joint surface stiffness, rolling joint surface stiffness, sliding joint surface damping parameters, and rolling joint surface damping parameters according to the product manual;

[0069] Step 3: Establish a finite element model that takes into account the interface parameters. Based on the optimal Latin hypercube sampling method, select the material combination of the structural components, obtain the experimental design points, and extract the static and dynamic response values ​​of the key points of the machine tool for different combinations through simulation results, including the three-dimensional static stiffness, first-order natural frequency, and 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]. DOE analysis is then used to determine the complexity of the relationship between the response and the variables, and different-order response surface models based on the design variables are established.

[0071] Step 5: Using static stiffness as a constraint 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 value range of the independent variable. 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 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 using the half-power bandwidth method, and calculate the elastic modulus using the dynamic method. The calculation formula is as follows:

[0076]

[0077] Among them, 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 resonant 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 measured results for each material as the material property parameter.

[0079] In step 2, from the perspective of dynamic modeling, in order to establish the 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 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 component, the slider and the structural component, the bearing seat and the structural component, and the motor seat and the structural component, and calculate the normal surface pressure and stress action area of ​​the fixed joint surfaces;

[0081] Step 2.2, calculate the fixed joint surface stiffness and damping parameters based on Yoshimura Yunxiao's 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 are 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 are 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 races of the bearing according to the product manual. The stiffness of the guide rail-slider pair is the sliding joint stiffness, and the stiffness of the lead screw-screw nut pair and the inner and outer races of the bearing is the rolling joint stiffness.

[0085] Step 2.4: Place all structural components in the center, perform static analysis on the machine tool, extract the reaction force of the machine tool foundation support, and calculate the foundation interface 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 foundation support reaction force.

[0088] In step 3, a finite element model is first established that takes into account the interface parameters. Then, spatial sampling is performed based on the optimal Latin hypercube sampling method. 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. Equivalent the fixed interface parameters of the guide rail and structural component, the slider and structural component, the bearing seat and structural component, and the motor seat and structural component to the spring damping unit parameters as boundary conditions and input them into the finite element model. Equivalent the interface parameters of the sliding interface and the rolling interface to the spring damping unit parameters as boundary conditions and establish a finite element model that takes the interface parameters into account.

[0090] Step 3.2: For the design space of the four independent variables of the materials of the spindle box, column, bed, and slide, perform a combination matching design of the materials of the spindle box, column, bed, and slide 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 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]. Use the four typical topological structures of the spindle box, column, bed, and slide as four independent variables, and use the five discrete values ​​of [-1, 1] as the value range of the independent variables. 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 to 5 represent five materials HT300, HT350, QT500-7, QT600-3, QT800-2, corresponding to k i They are -1, -0.5, 0, 0.5, and 1 respectively;

[0096] Step 4.2: Determine the model complexity between each response value and the independent variable through DOE analysis, and establish a response surface model of the response value and the independent variable according to the different complexity. The nonlinearity of the three-dimensional 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-dimensional 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 linear term, quadratic term, cubic term and quartic term respectively, β 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 and the minimum three-dimensional resonance amplitude. The flowchart of the multi-objective rime optimization algorithm is as follows: Figure 4 The specific steps are as follows:

[0100] Step 5.1: Use the obtained three-dimensional static stiffness of the machine tool as a constraint condition, and maximize the first-order natural frequency and minimize 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 number of material matching solutions. t represents a certain material combination, t represents the value sequence of the design variable, j represents the sequence number of the structural component, and rime particles xt j Indicates that the j-th structural component takes the t-th material ordinal number;

[0104] Step 5.3: Soft rime search strategy. Search is performed during the initial iteration. The update formula for 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, which 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 algorithm's convergence speed and ability to escape local optimality, the position of the rime agent is updated through a hard rime puncture mechanism. This mechanism 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 forward greedy selection mechanism is used to compare the fitness values ​​of updated and unupdated rime agents, and the updated agent with better performance is used to replace the latter. 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 further provide a specific implementation of an electronic device 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, wherein 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 steps of the machine tool structural component material matching design method for machine tool forward design 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. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements all steps of the material matching design method for machine tool structural parts for forward design of machine tools in the above-mentioned embodiment.

[0117] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the hardware + program embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0118] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain 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 flowcharts, more or fewer operation steps may be included based on conventional or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many steps and does not represent the only execution order. When an actual device or client product is executed, it can be executed in the order shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0120] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, 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 magnetic 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 that can direct a computer or other programmable data processing device to work 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 The 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 operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0123] The above-described embodiments merely express the implementation methods of the present invention, but should not 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 variations and improvements can be made without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A material matching design method for machine tool structural parts used in forward design of machine tools, 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, calculate the fixed joint surface stiffness and fixed joint surface damping parameters based on Yoshimura Yunxiao's integration method, and calculate the sliding joint surface stiffness, rolling joint surface stiffness, sliding joint surface damping parameters, and rolling joint surface damping parameters according to the product manual; Step 3: Establish a finite element model that takes into account the parameters of each joint surface. Based on the optimal Latin hypercube sampling method, select the material combination of the structural components, obtain the experimental design points, and extract the static and dynamic response values ​​of the key points of the machine tool for different combinations through 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]. DOE analysis is then used to determine the complexity of the relationship between the response value and the variable, and different-order response surface models based on the design variables are established. 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 the four independent variables, and use the five discrete values ​​of [-1, 1] as the value range of the independent variables. The mapping formula for each material is: ； （5） in, is the density of each material i, is the elastic modulus of each material i, is the Poisson's ratio of each material i, is the material loss factor of each material i, 、 、 、 、 is a constant, is the equivalent material property parameter of each material i, Respectively represent five materials HT300, HT350, QT500-7, QT600-3, QT800-2, corresponding They are -1, -0.5, 0, 0.5, and 1 respectively; Step 4.2: Determine the model complexity between each response value and the independent variable through DOE analysis. Develop response surface models for the response values ​​and independent variables based on the different levels of complexity. Considering the low nonlinearity of the three-dimensional static stiffness and the first-order natural frequency of the machine tool, a second-order response surface model based on the independent variable is constructed. Considering the high nonlinearity of the three-dimensional resonance amplitude of the spindle end, a fourth-order response surface model based on the independent variable is constructed. ； （6） ； （7） in, is the response variable; is a constant term, 、 、 、 are the coefficients of the linear term, quadratic term, cubic term, and quartic term, respectively. 、 、 、 is the interaction term coefficient, 、 is the independent variable; Step 5: Using static stiffness as a constraint 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 in 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 using the half-power bandwidth method, and calculate the elastic modulus using the dynamic method. The calculation formula is as follows: ； （1） ； （2） in, Indicates the half-power point frequency on the right side, Indicates the half-power point frequency on the left, represents the first-order resonant frequency, is the density of the material, l is the length of the cantilever beam plate; H is the thickness of the cantilever beam plate; is the first-order modal coefficient of the cantilever beam, ; Step 1.3: Repeat the test and take the average value of the measured results for each material as the material property parameter.

3. The material matching design method for machine tool structural parts used in machine tool forward design according to claim 1, characterized in that: The calculation of the stiffness and damping parameters of the fixed, sliding, and rolling interfaces 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 component, the slider and the structural component, the bearing seat and the structural component, and the motor seat and the structural component, and calculate the normal surface pressure and stress action area of ​​the fixed joint surfaces; Step 2.2: Calculate the fixed joint surface stiffness and fixed joint surface damping parameters based on Yoshimura Yunxiao's integration method. The calculation formula is as follows: ； （3） in, 、 They represent the equivalent spring stiffness in shear and vertical directions of the fixed joint surface respectively; 、 denote the equivalent viscous damping in the shear and vertical directions of the fixed joint surface respectively; 、 They represent the equivalent spring stiffness in shear and vertical directions per unit contact area of ​​the fixed joint surface respectively; 、 They represent the equivalent viscous damping in the shear and vertical directions per unit contact area of ​​the fixed joint surface. represents normal pressure; 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 races of the bearing according to the product manual. The stiffness of the guide rail-slider pair is the sliding joint stiffness, and the stiffness of the lead screw-screw nut pair and the inner and outer races of the bearing is the rolling joint stiffness. Step 2.4: Place all structural components in the center, perform static analysis on the machine tool, extract the machine tool foot support reaction force, and calculate the foot interface parameters using the following formula; ； （4） in, 、 denote the normal and tangential support stiffnesses respectively; Represents the foot support reaction force.

4. The material matching design method for machine tool structural parts used in 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. Equivalent the fixed interface parameters of the guide rail and structural component, the slider and structural component, the bearing seat and structural component, and the motor seat and structural component to the spring damping unit parameters as boundary conditions and input them into the finite element model. Equivalent the interface parameters of the sliding interface and the rolling interface to the spring damping unit parameters as boundary conditions and establish a finite element model that takes the interface parameters into account. Step 3.2: For the design space of the four independent variables of the materials of the spindle box, column, bed, and slide, perform a combination matching design of the materials of the spindle box, column, bed, and slide 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 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 in forward design of machine tools according to claim 1, characterized in that: Step 5 specifically includes: Step 5.1: Use the obtained three-dimensional static stiffness of the machine tool as a constraint condition, and maximize the first-order natural frequency and minimize 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. Composition, each rime agent Rime particles with random values ​​of four design variables As shown in formula (8), the rime population X is directly composed of rime particles It is expressed as shown in formula (9): ； ； (8) ； （9） Where N represents the total number of material matching solutions, Indicates a certain material combination, t indicates the value sequence of the design variable, j indicates the sequence of the structural component, rime particles Indicates that the j-th structural component takes the t-th material ordinal number; Step 5.3: Based on 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: ； （10） in, Indicates the Rime Agent No. The new position of the rime particles; is the position of the best rime agent in the current iteration, It is the control parameter that determines the direction of particle movement and takes a random value between the specific limits of [-1,1]. is a random number between 0 and 1, is a variable that changes with the number of iterations. , G represents the maximum number of iterations, g represents the current number of iterations, is an environmental factor that affects convergence as the iteration changes. , Set to 5, h is the adhesion, represents the condensation probability factor, , 、 are the upper and lower limits of the escape space respectively; Step 5.4: To improve the algorithm's convergence speed and ability to escape local optimality, the position of the rime agent is updated through a hard rime puncture mechanism. This mechanism satisfies the following formula: ； in, For the The normalized fitness value of the rime agent, 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 forward greedy selection mechanism is used to compare the fitness values ​​of updated and unupdated rime agents, and the updated agent with better performance is used to replace the latter. 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.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: 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 5 are implemented.

7. 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 according to any one of claims 1 to 5 are implemented.

Images