Method for optimized design of headstock structure of vertical machining center

Abstract

The present invention relates to the technical field of machine tool structure optimized design. Disclosed is a method for the optimized design of a headstock structure of a vertical machining center. The method mainly comprises: parameterizing to establish a headstock three-dimensional model; establishing a finite element model of a headstock structure; according to limit working conditions in an actual machining process, performing static and dynamic characteristic analysis on the headstock structure; performing modal test testing on a headstock, carrying out comparative analysis with a simulation result, and correcting a finite element model of the headstock; using a sensitivity analysis method to select a key dimension of the headstock as an optimized design input parameter, while stress strain, low-order inherent frequency, and quality are target output parameters, and establishing a mathematical model for the optimized design of the headstock; and on the basis of a response surface model constructed by a second-order polynomial and a genetic algorithm, performing optimized design of the headstock until an optimal headstock structure which has higher performance and is lighter is obtained. According to the present invention, design efficiency is improved, prototype testing costs are reduced, and a better headstock structure is obtained.

Description

A method for optimizing the design of the spindle box structure of a vertical machining center

[0001] This application claims priority to the Chinese patent application filed with the Patent Office of China on August 17, 2023, with application number 202311039907.4 and invention name “A method for optimizing the design of the spindle box structure of a vertical machining center”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The invention belongs to the technical field of machine tool structure optimization design, and in particular relates to a method for optimizing the design of a spindle box structure of a vertical machining center. Background Art

[0003] With the continuous optimization and upgrading of the manufacturing industry, the CNC machine tool industry, such as machining centers, has developed rapidly in recent years, and new equipment such as various composite machine tools and multi-axis machine tools have continued to emerge. The current structural design of machining centers mainly relies on the experience of designers to design or improve existing products. Although design experience mostly comes from multiple tests and experiments, and has scientific basis and credibility under certain circumstances, it has certain limitations to a large extent. When designing new and high-end machine tools, the lack of reference basis and the complexity of the structure have led to an increasing R&D cycle and R&D costs. In order to meet the high-speed and high-precision requirements of machine tools, it is particularly important to analyze their static and dynamic characteristics and optimize their structures. Using the optimization analysis results as a basis for the design and development of machine tool structures has important engineering significance.

[0004] The spindle box is a component of a machining center. Its static and dynamic performance directly affects the stability, vibration resistance, and machining accuracy of the entire machining center. Therefore, static and dynamic analysis and structural optimization of the spindle box are of great significance to improving its performance and lightweight design.

[0005] Many scholars have conducted extensive research on the analysis and optimization of machining center components. However, most of the research content is mainly biased towards theoretical analysis and fails to be verified by combining it with experiments. At the same time, the complexity of the model and the influence of factors such as pre-processing and constraints make it impossible to judge the correctness of the analysis and optimization results.

[0006] In view of this, it is necessary to design a vertical machining center spindle box structure optimization design method to solve the above problems.

[0007] Summary of the Invention

[0008] The purpose of the present invention is to provide a method for optimizing the spindle box structure of a vertical machining center in order to address the current problems of lack of theoretical basis in the design and optimization of machining center structures and lack of experimental verification in analysis and optimization. The method uses a combination of theory and experiment to conduct analysis and verify the accuracy of the simulation. At the same time, the static and dynamic performance of the spindle box structure is improved through optimization, the design efficiency of designers is improved, the test cost of prototypes is reduced, and a reference and basis for the design of the spindle box structure is provided.

[0009] In response to the above-mentioned technical problems, the present invention provides a method for optimizing the design of a spindle box structure of a vertical machining center, comprising the following steps:

[0010] Step 1: Parameterize the 3D model of the spindle box. Preliminarily determine the optimized dimensions of the spindle box structure as input parameter variables and perform parameterization, and establish driving relationships for other dimensions other than the optimized dimensions.

[0011] Step 2: Establish a finite element model of the spindle box structure. Based on the finite element model of the spindle box, apply load conditions to the spindle box under the extreme working conditions in the actual processing process and perform static and dynamic characteristics analysis to find the weak links in the spindle box structure and extract results such as stress and strain, low-order natural frequency values ​​and corresponding vibration mode characteristics.

[0012] Step 3: Perform a modal test on the spindle box. Compare the modal test results with the simulation results in step 2. If the simulation results differ significantly from the test data, modify the finite element model established in step 2. Repeat steps 2 and 3 until an accurate finite element model is obtained.

[0013] Step 4: Use the sensitivity analysis method to screen the dimensions that need to be optimized in step 1, and select the key dimensions that have a greater impact on the performance of the spindle box as design variables. In step 2, analyze the stress, strain, natural frequency and mass of the spindle box as the objective function, and establish a mathematical model for the optimization design of the spindle box.

[0014] Step 5. Based on the mathematical model established in step 4, the optimal space filling (OSF) method is used for experimental design, and a response surface model is established using a second-order polynomial. Based on the response surface model, the mathematical model of the spindle box is solved and calculated using a multi-objective genetic algorithm. Among the calculation results of all test samples, a set of solutions with the best spindle box performance is selected as the optimization result.

[0015] Step 6: Reconstruct the spindle box model according to the optimal size parameters obtained in step 5, and perform simulation verification on the reconstructed model based on the constraints imposed in step 2.

[0016] Furthermore, the parameterization of the three-dimensional model of the spindle box in step 1 includes: preliminarily determining the dimensional parameters that need to be optimized, and establishing a connection between the parameterization and CAE software and inputting them into the design variables.

[0017] Furthermore, the step of establishing a driving relationship for dimensions other than the optimized dimensions in step one includes: when the dimensions initially determined in step one are continuously changed as design variables in the CAE software for calculation, the dimension driving relationship is established to ensure that the model will not be wrong when the model is reconstructed, thereby avoiding calculation termination during the CAE solution process.

[0018] Furthermore, the finite element model described in step 2 includes model simplification, assigning material properties, meshing, and adding constraints. The constraint addition method involves simulating a bolt connection using the BoltPretension type on the bolt holes on the spindle block surface, applying a certain preload, and applying a Cylindrical support constraint to the nut seat, with the axial direction set to free and the radial and tangential directions set to fixed constraints.

[0019] Furthermore, the load conditions for applying the load to the spindle box according to the extreme working conditions in the actual machining process in step 2 include:

[0020] The main cutting force is Radial force Feed force and its own gravity; among them, F C Main cutting force, F p is the radial force, F f is the feed force, C Fe is the first cutting coefficient, For the first cutting amount, is the first feed amount, is the first cutting speed, is the first correction coefficient, C Fp is the second cutting coefficient, For the second cutting amount, is the second feed amount, is the second cutting speed, is the second correction coefficient, C Ff is the third cutting coefficient, For the third knife amount, is the third feed amount, is the third cutting speed, is the third correction factor.

[0021] Furthermore, the modal test described in step three includes the following: During the modal test, the spindle box structural boundary conditions are consistent with the simulation conditions. The excitation method uses a hammer method with single-point input and multiple-point output. The excitation point is fixed at the spindle end, and the test is performed using a moving sensor. The hammer is excited three times at the fixed excitation point and the average is taken. To ensure measurement accuracy, the hammer excitation is always in the same direction and position, and the excitation force is consistent.

[0022] Furthermore, the mathematical model established in step 4 is as follows:

[0023] min F(x) or max F(x);

[0024] Where: F(x) is the optimization design target, h i and g i is the constraint condition, X is the design variable, m is the number of equality constraints, and n is the number of inequality constraints.

[0025] Furthermore, the experimental design using the OSF method described in step five includes: the OSF experimental design method can evenly distribute the designed sample points in the entire design interval, and the number of design points in the experimental design is ten times the number of design variables.

[0026] Furthermore, the response surface model established by the second-order polynomial in step 5 is as follows: x=[x1,x2,…,x i ,…,x k ] T , i=1,2,…k;

[0027] Where Y(x) is the target response value, β0, β i , β ii is the offset term coefficient, β ij is the interaction coefficient, k is the number of design variables, and ε is the statistical error.

[0028] The beneficial effects of the present invention are:

[0029] (1) The present invention constructs a simulation optimization model of the spindle box structure of a vertical machining center by combining simulation and experiment, and modifies the optimization model according to the performance indicators of the spindle box during actual working conditions to obtain an accurate optimization model.

[0030] (2) Through the sensitivity analysis method, the key dimensions that have a greater impact on the structural performance are selected as design variables, and a mathematical model of the spindle box structure of the vertical machining center is constructed. The response surface model constructed based on the second-order polynomial is optimized through the genetic algorithm, and finally the optimal spindle box structure is obtained, which improves the design efficiency and structural performance of the spindle box structure of the vertical machining center and saves design costs.

[0031] Figures in the specification

[0032] FIG1 is a step diagram of a method for optimizing the design of a spindle box structure according to the present invention;

[0033] Figure 2 is a schematic diagram of the spindle box's preliminary dimensions being parameterized as design variables in the CAE software;

[0034] Figure 3 is a schematic diagram of the response size of the primary size variables to the spindle box performance indicators. DETAILED DESCRIPTION

[0035] As shown in FIG1 , the present invention provides a method for optimizing the design of a spindle box structure of a vertical machining center, comprising the following steps:

[0036] Step 1: Preliminary selection of 41 dimensions of the spindle box structure as design variables for parameterization. At the same time, in order to prevent errors in model reconstruction during the calculation process and terminate the calculation, corresponding driving relationships are established for other related dimensions and associated with the CAE software. Figure 2 shows the design variables and initial dimensions that are automatically imported into the analysis software after parameterization.

[0037] Specifically, establishing driving relationships for dimensions other than optimized dimensions includes: when the dimensions initially determined in step 1 are used as design variables and continuously change in the CAE software for calculation, the dimension driving relationship is established to ensure that the model will not be wrong when the model is reconstructed, thereby avoiding calculation termination during the CAE solution process.

[0038] Step 2: Create a finite element model of the spindle box and delete the tiny features such as the spindle box bolt holes, chamfers, grooves, etc. The spindle box is an integral casting material of HT250, and the material properties are set to: elastic modulus 138GPa, density 7280kg / m 3 , Poisson's ratio 0.25. Meshing was performed using the solid87 element type. Based on the actual machining requirements and using empirical formulas, the machining center's main cutting force was calculated to be 1900N, the radial force 665N, and the feed force 630N. Constraints and load conditions were added according to the actual working conditions, and static and dynamic analysis of the spindle box was performed, resulting in a maximum stress of 4 MPa, a maximum deformation of 0.0068 mm, and a first-order natural frequency of 287 Hz.

[0039] Specifically, the finite element model described in step 2 includes: model simplification, assigning material properties, meshing, and adding constraints; the method of adding constraints is: using the Bolt Pretension type to simulate the bolt connection of the bolt hole on the spindle box slider surface, and applying a certain preload force, applying Cylindrical support cylindrical surface constraints to the nut seat, with the axial direction being free and the radial and tangential directions being set as fixed constraints.

[0040] The load conditions for applying the spindle box to the extreme working conditions in the actual machining process described in step 2 include:

[0041] The main cutting force is Radial force Feed force and its own gravity; among them, F C Main cutting force, F p is the radial force, F f For the feed force.

[0042] Step 3. Use the American Bestone dynamic signal analyzer to conduct a modal test on the spindle box. The hammer point is fixed at the front end of the spindle box, and the signals are output in the X, Y, and Z directions. After analysis by the analysis software, the natural frequency and vibration mode of the spindle box are obtained and compared with the simulation results. The results show that the simulation and experimental errors are both within 10%, thus verifying the correctness of the establishment of the finite element model of the spindle box and laying the foundation for improving the accuracy of the optimization design.

[0043] Specifically, the modal test contents include: during the modal test, the boundary conditions of the spindle box structure are consistent with the simulation conditions, the excitation method adopts the hammer method, single-point input and multiple-point output, the excitation point is the spindle end and is fixed, and the test is carried out by moving the sensor; the force hammer excites at the fixed excitation point three times and takes the average. In order to ensure the accuracy of the measurement, the force hammer excites in the same direction and the same position each time, and the excitation force remains consistent.

[0044] Step 4: Using parameters such as quality and static and dynamic analysis results as performance indicators, the dimensions initially selected in step 1 are screened by sensitivity analysis, and the key dimensions that have the greatest response to the spindle box performance indicators are selected as design variables for the optimization design. As shown in Figure 3, the response degree of each dimension variable to the performance indicator is shown. The results show that P1, P2, P3, P 12 、P 18 、P 30 、P 35 、P 37 、P 39 and P 41The key dimensions have a greater sensitivity to performance indicators, and the key dimensions are set as optimization design variables. The maximum first-order natural frequency of the spindle box is set as the target, the maximum deformation is less than 0.068mm, and the mass is less than the mass of the original model as constraints. The mathematical model of the spindle box optimization design is established as follows: MinF(x)=-f1(x); x=[P1,P2,P3,P 12 ,P 18 ,P 30 ,P 35 ,P 37 ,P 39 ,P 41 ] T ;

[0045] Where: f1(x), f2(x), f3(x) are the first-order natural frequency, mass, and total deformation respectively; P1, P2, P3, P 12 、P 18 、P 30 、P 35 、P 37 、P 39 and P 41 is the design variable.

[0046] Step 5: Using the key dimensions determined in step 4 as design variables and the performance indicators as the optimization objective function, 100 sets of sample points were designed using the OSF experimental design method. Based on the sample points, a response surface model was established using a second-order polynomial as follows:

[0047] Among them, p1, p2, p3, p 12 、p 18 、p 30 、p 37 、p 39 and p 41 They are P1, P2, P3, P 12 、P 18 、P 30 、P 35 、P 37 、P 39 and P 41 The specific value of , Y(x) is the target response value, k is the number of design variables, and ε is the statistical error.

[0048] Based on the response surface model, the genetic algorithm was used to solve the problem and obtain three groups of optimal solutions, as shown in the table below. The second group has the smallest deformation, the smallest mass, and a relatively high first-order natural frequency. Therefore, the second group was selected as the final optimization result: the first-order natural frequency is 293.73 Hz, the mass is 247.76 kg, and the deformation is 0.0064 mm.

[0049] Three sets of optimal solutions obtained after optimization design

[0050] Step 6: Round the design variables corresponding to the optimal solution and reconstruct the original headstock model to obtain the optimized latest model, as shown in the table above. Simulation verification based on the constraints and load conditions set in the static and dynamic analysis above shows that the headstock mass is reduced by 1%, the first-order natural frequency is increased by 2%, the maximum deformation is reduced by 4.4%, and the first-order natural frequency avoids the resonance band of the maximum excitation frequency. This improves the overall performance of the headstock and results in a structurally optimal headstock model, improving design efficiency and reducing the design error rate.

[0051] The above descriptions are only some of the specific implementation methods of the present invention. It should be pointed out that the protection scope of the present invention is not limited thereto. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can also make several improvements or equivalent replacements to the technical solutions of the present invention, all of which fall within the protection scope of the present invention.

Claims

1. A method for optimizing the design of a spindle box structure of a vertical machining center, characterized in that: include: Step 1: Parameterize the three-dimensional model of the spindle box, preliminarily determine the optimized size of the spindle box structure as the input parameter variable and parameterize it, and establish a driving relationship for other dimensions other than the optimized size; Step 2: Establish a finite element model of the spindle box structure. Based on the finite element model of the spindle box, apply load conditions to the spindle box under the extreme working conditions in the actual machining process and perform static and dynamic characteristic analysis to find out the weak links of the spindle box structure and extract stress and strain, low-order natural frequency values ​​and corresponding vibration mode characteristics; Step 3: Perform a modal test on the spindle box, and compare the modal test results with the simulation results of step 2. If the simulation results differ greatly from the test data, the finite element model established in step 2 is corrected, and steps 2 and 3 are repeated until an accurate finite element model is obtained. Step 4: Use the sensitivity analysis method to screen the dimensions that need to be optimized in step 1, select the key dimensions that have a greater impact on the performance of the spindle box as design variables, analyze the stress strain, natural frequency and mass of the spindle box in step 2 as the objective function, and establish a mathematical model for the optimization design of the spindle box; Step 5: Based on the mathematical model established in step 4, the OSF method is used for experimental design, and a response surface model is established using a second-order polynomial. Based on the response surface model, a multi-objective genetic algorithm is used to solve and calculate the mathematical model of the spindle box. Among the calculation results of all test samples, a set of solutions with the best performance of the spindle box is selected as the optimization result; Step six: reconstruct the spindle box model according to the optimal size parameters obtained in step five, and perform simulation verification on the reconstructed model based on the constraints imposed in step two.

2. The vertical machining center spindle box structure optimization design method according to claim 1 is characterized in that: The parameterization of the three-dimensional model of the spindle box in step 1 includes: preliminarily determining the dimensional parameters that need to be optimized, and establishing a connection between the parameterization and the CAE software and inputting them into the design variables.

3. The method for optimizing the design of the spindle box structure of a vertical machining center according to claim 1 is characterized in that: The step 1 of establishing a driving relationship for dimensions other than the optimized dimensions includes: when the dimensions initially determined in step 1 are continuously changed in the CAE software as design variables and participate in the calculation, the dimension driving relationship is established to ensure that the model will not go wrong when the model is reconstructed, thereby avoiding calculation termination during the CAE solution process.

4. The method for optimizing the structure of a spindle box of a vertical machining center according to claim 1 is characterized in that: The finite element model described in step 2 includes: model simplification, assigning material properties, meshing and adding constraints; the method of adding constraints is: using the Bolt Pretension type to simulate the bolt connection of the bolt holes on the slider surface of the spindle box, and applying a certain preload, applying Cylindrical support cylindrical surface constraints to the nut seat, the axial direction is free, and the radial and tangential directions are set as fixed constraints.

5. The method for optimizing the structure of a spindle box of a vertical machining center according to claim 1 is characterized in that: The load conditions for applying load to the spindle box under the extreme working conditions in the actual machining process described in step 2 include: The main cutting force is Radial force Feed force and its own gravity.

6. The method for optimizing the structure of a spindle box of a vertical machining center according to claim 1 is characterized in that: The modal test contents described in step three include: during the modal test, the boundary conditions of the spindle box structure are consistent with the simulation conditions, the excitation method adopts the hammer method, single-point input and multiple-point output, the excitation point is the spindle end and is fixed, and the test is carried out by moving the sensor; the force hammer excites at the fixed excitation point three times and takes the average. In order to ensure the accuracy of the measurement, the force hammer excites in the same direction and at the same position each time, and the excitation intensity remains consistent.

7. The method for optimizing the structure of a spindle box of a vertical machining center according to claim 1 is characterized in that: The mathematical model established in step 4 is as follows: min F(x) or max F(x); Where: F(x) is the optimization design target, h i and g i are constraints and X are design variables.

8. The method for optimizing the structure of a spindle box of a vertical machining center according to claim 1, wherein the step 5 of performing the experimental design using the OSF method comprises: The OSF experimental design method evenly distributes the designed sample points in the entire design interval, and the number of design points in the experimental design is ten times the number of design variables.

9. The method for optimizing the structure of a spindle box of a vertical machining center according to claim 1, characterized in that: The response surface model established by the second-order polynomial described in step 5 is as follows: x=[x1,x2,…,x i ,…,x k ] T , i=1,2,…k; In the formula, Y(x) is the target response value, β0, β i , β ii is the offset term coefficient, β ij is the interaction coefficient.

Images