Multi-target simulation optimization method for motorized spindle

Abstract

The invention relates to a multi-target simulation optimization method of an electric spindle. The method comprises the following steps: establishing a finite element model of the electric spindle; selecting a plurality of optimization targets and a plurality of factors related to the optimization targets; creating an orthogonal test scheme table by adopting an orthogonal test method; according to the orthogonal test scheme table, performing a simulation test by using a finite element model to obtain a plurality of groups of test data of each optimization target; analyzing all test data by using a range method to obtain the influence degree of each factor on each optimization target; and analyzing all test data by using an entropy weight distribution method to obtain the weight of each optimization target. Based on the design concept of multi-objective optimization, the optimal solution based on optimization objectives and factors can be found, various requirements of the motorized spindle can be met, and the optimal balance can be obtained among the objectives; by carrying out multi-physics field coupling simulation analysis on the motorized spindle, test data can be quickly obtained, and the analysis efficiency of the relation between the optimization target and the factor can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of electric spindle structure optimization, and in particular to a multi-objective simulation optimization method for an electric spindle. Background Art

[0002] When a high-power electric spindle is in operation, parameters such as temperature and axial deformation can affect its operational stability. Minimizing these parameters within the allowable range can improve the operating stability and durability of the electric spindle. Furthermore, when optimizing the electric spindle structure, the influence of multiple design factors must also be considered.

[0003] Multi-objective optimization design theory is a key theory in modern engineering and management, designed to solve problems with multiple objectives or requirements. In practical applications, it is often necessary to consider the balance and trade-offs between multiple objectives, which may be mutually constrained or conflicting. Multi-objective optimization design theory provides a systematic set of methods and tools to help decision-makers make scientific and rational decisions in complex situations, thereby achieving the optimal overall results. Its characteristic is that it requires optimizing multiple objective functions simultaneously, and these objective functions are often not simultaneously optimal. When solving multi-objective optimization problems, it is necessary to consider the trade-offs and balances between the various objectives, as well as the possible mutual constraints between them.

[0004] According to the multi-objective optimization design theory, the relevant parameters of the electric spindle are studied and analyzed through physical field coupling simulation to obtain an optimization scheme for the electric spindle, so as to improve the working stability and durability of the electric spindle. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a multi-objective simulation optimization method for an electric spindle in view of the above shortcomings.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A multi-objective simulation optimization method for an electric spindle comprises the following steps:

[0008] Establish the finite element model of the electric spindle;

[0009] Selecting several optimization objectives and several factors related to the optimization objectives;

[0010] Use orthogonal test method to create orthogonal test plan table;

[0011] According to the orthogonal test plan table, the finite element model is used to conduct simulation tests to obtain several groups of test data for each optimization target;

[0012] Use the range method to analyze all test data and obtain the influence of each factor on each optimization target;

[0013] Use the entropy weight distribution method to analyze all test data and obtain the weight of each optimization objective;

[0014] The optimal solution is obtained based on all experimental data and the weight of each optimization objective.

[0015] Furthermore, the optimization objectives include bearing temperature, motor temperature and axial deformation;

[0016] The design variables include the number of small water channels, the number of water areas, the inlet water temperature and the inlet water velocity of the cooling water jacket.

[0017] Furthermore, the steps of creating an orthogonal experiment plan table using the orthogonal experiment method include:

[0018] Determine the number of levels for each factor based on its range of variation;

[0019] According to the number of levels of each factor, an orthogonal experimental plan table is established.

[0020] Furthermore, the steps of using the range method to obtain the influence of each factor on each optimization target include:

[0021] Calculate the range of each factor for each optimization target. The calculation formula is:

[0022]

[0023] Where R jb is the extreme value of the jth factor for the bth optimization objective, K ijb is the sum of the experimental data of the bth optimization target when the jth factor is at level i, and S is the number of occurrences of the jth factor and level i;

[0024] The larger the range value, the greater the influence of the factor on the optimization target.

[0025] Furthermore, if at least one factor has a different number of levels from the other factors, after calculating the range values ​​of each factor, the following steps are also included:

[0026] The coefficient is used to convert the extreme value, and the calculation formula is:

[0027]

[0028] Where R jb ′ is the reduced range value of the jth factor for the bth optimization target, R j is the range value of the j-th factor for the b-th optimization target, d is the conversion number, and m is the number of factors corresponding to each level.

[0029] Furthermore, the steps of obtaining the weights of the optimization objectives by using the entropy weight distribution method include:

[0030] Use range standardization to standardize the test data of each optimization target. The calculation formula is:

[0031]

[0032] Where, X ′ ab is the standardized value of the experimental data of the bth optimization target of the ath group experiment, X ab is the experimental data of the bth optimization target of the ath group test, min(X b ) and max(X b ) are the minimum and maximum values ​​of the experimental data of the bth optimization objective respectively;

[0033] The normalized test data is processed positively, and the calculation formula is:

[0034]

[0035] Where Pab is the proportion of the experimental data of the bth optimization target in the ath group of experiments in the standardized value, and n is the number of experimental groups;

[0036] Calculate the entropy value of each optimization objective using the following formula:

[0037]

[0038] Where H b is the entropy value of the bth optimization target, and m is the number of optimization targets;

[0039] Calculate the weight of each optimization objective using the following formula:

[0040]

[0041] Where w b is the weight of the b-th optimization objective.

[0042] Furthermore, before using the finite element model to conduct simulation tests and obtain test data, the simulation of the finite element model is verified, including the following steps:

[0043] Build an experimental platform;

[0044] Run the experimental platform and detect relevant parameters of several nodes to obtain measured data of several nodes;

[0045] Use the finite element model to perform simulation analysis and obtain simulation data of several corresponding nodes;

[0046] Compare the simulation data and measured data of each node and determine whether the difference is less than the preset threshold. If so, the simulation test of the finite element model passes the verification; if not, the simulation test verification of the finite element model fails, and the relevant parameters of the finite element model and simulation test need to be reset.

[0047] Furthermore, the step of obtaining the optimal solution based on the test data and the weight of each optimization objective includes:

[0048] According to the weight of each optimization goal, calculate the comprehensive score of all optimization goals in each set of data;

[0049] If the comprehensive score of the corresponding group data is lower, the corresponding factor will have less influence on all optimization objectives at the corresponding level. The optimal solution is to select a group of factors and the corresponding number of levels with the lowest comprehensive score.

[0050] After adopting the above technical solution, the present invention has the following advantages compared with the prior art:

[0051] The present invention is based on the design concept of multi-objective optimization and can find the optimal solution based on the optimization objectives and factors, which can not only meet the various requirements of the electric spindle, but also achieve the best balance between various objectives;

[0052] By performing multi-physics field coupling simulation analysis on the electric spindle to quickly obtain test data, the efficiency of analyzing the relationship between optimization goals and factors can be improved.

[0053] The present invention is described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the cross section of the three-dimensional model of the electric spindle;

[0055] Figure 2 Diagrams of the shapes of small waterways and water areas in the cooling system, where: (a) is a schematic diagram of the cooling system when the number of small waterways is 60 and the number of water areas is 12; (b) is a schematic diagram of the cooling system when the number of small waterways is 24 and the number of water areas is 12; (c) is a schematic diagram of the cooling system when the number of small waterways is 60 and the number of water areas is 18; (d) is a schematic diagram of the cooling system when the number of small waterways is 60 and the number of water areas is 24;

[0056] Figure 3 is the steady-state temperature field cloud diagram of the electric spindle;

[0057] Figure 4 This is the steady-state axial deformation diagram of the electric spindle shaft;

[0058] Figure 5 This is the influence diagram of various factors on bearing temperature;

[0059] Figure 6 This is a diagram showing the influence of various factors on the motor temperature;

[0060] Figure 7 This is the influence diagram of various factors on the axial deformation;

[0061] Figure 8 This is the influence diagram of each factor on the comprehensive score value;

[0062] Figure 9 This is the steady-state temperature field cloud diagram of the optimized electric spindle;

[0063] Figure 10 This is the steady-state axial deformation diagram of the optimized electric spindle shaft.

[0064] In the accompanying drawings, the components represented by the reference numerals are as follows:

[0065] 1. Main shaft; 2. Lower bearing seat; 3. Bearing 7022 module; 4. Machine body; 5. Spindle box; 6. Stator; 7. Rotor; 8. Air block; 9. Bearing 7016 module; 10. Top cover; 11. Winding copper wire; 12. Cooling system; 12.1. Small waterway; 12.2. Water area. DETAILED DESCRIPTION

[0066] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0067] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", "clockwise" and "counterclockwise" and the like to indicate directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0068] Example 1:

[0069] In this embodiment, a multi-objective optimization method for an electric spindle is demonstrated, taking the cooling system parameters of the electric spindle as factors and the bearing temperature, motor temperature, and axial deformation of the electric spindle as optimization targets. The method includes the following steps:

[0070] S1. Establish the finite element model of the electric spindle

[0071] like Figure 1 and Figure 2As shown in the figure, a three-dimensional model of the electric spindle is established. After simplifying the three-dimensional model according to the actual situation, the finite element software is used to divide the three-dimensional model into a tetrahedral mesh model, and the mesh of the cooling system is refined to obtain the finite element mesh model of the electric spindle (i.e., the electric spindle finite element model);

[0072] Then, based on the created finite element model of the electric spindle, the following configuration is performed:

[0073] Create a fluid simulation analysis: Determine the material parameters of each component of the electric spindle, set the heat source parameters of the bearings and motor and the heat transfer coefficient at each position, set the water inlet speed, temperature, and pressure in the cooling system, calculate the thermal simulation results of the electric spindle, and obtain the temperature of each key node.

[0074] Create a steady-state structural simulation analysis: Apply load constraints to the electric spindle, import the electric spindle thermal simulation results, calculate the electric spindle deformation structure, and obtain the axial deformation of the shaft.

[0075] The specific simulation results are as follows: Figure 3 and Figure 4 As shown in the figure, through finite element simulation, the temperature of bearing 7022-1 is 36.42℃, the temperature of bearing 7022-4 is 35.63℃, the temperature of bearing 7016-1 is 36.48℃, the temperature of stator copper wire is 33.84℃, the outlet water temperature of cooling system is 24.68℃, and the axial deformation is 62.80um.

[0076] Specifically, bearing 7022-1 represents the first bearing outer ring in bearing 7022 module 3, bearing 7022-4 represents the fourth bearing outer ring in bearing 7022 module 3, and bearing 7016-1 represents the first bearing outer ring in bearing 7016 module 9.

[0077] S2. Build an electric spindle experimental platform to verify the simulation results

[0078] Build an experimental platform, use temperature sensors to measure the temperature of the bearing outer ring and the winding copper wire of the motor rotor, and use displacement sensors to measure the axial deformation of the shaft;

[0079] And judge whether the difference between the temperature and deformation of each node in the simulation results and the actual experimental results is less than the preset threshold. If so, the verification passes; if not, the verification fails;

[0080] The comparison between the experimental results and the simulation results is shown in Table 1:

[0081] Table 1. Comparison of simulation data and experimental data

[0082] Experimental data Simulation data Difference Difference rate Bearing 7022-1 temperature (℃) 36.5 36.42 0.08 -0.22％ Bearing 7022-4 temperature (℃) 34.9 35.63 -0.73 2.09％ Bearing 7016-1 temperature (℃) 35.0 36.48 -1.48 4.23％ Motor-stator temperature (℃) 31.3 33.84 -2.54 8.12％ Experimental outlet water temperature (℃) 23.1 24.68 -1.58 6.84％ Spindle axial thermal deformation (um) 66.6 62.80 3.80 -5.71％

[0083] It can be seen from the above table that the data trends and values ​​of the simulation results are basically consistent with those of the experimental results and are within a reasonable range, which proves the accuracy of the simulation analysis results.

[0084] S3. Simulation optimization of the electric spindle cooling system

[0085] S31. Determine design variables

[0086] Aiming at the parameter optimization problem of the electric spindle cooling system, the influence of four factors, namely the number of small water channels, the number of water areas, the water inlet speed and the water inlet temperature of the cooling water jacket, on the cooling effect of the electric spindle is studied when the parameters such as the ambient temperature, the heating power of the electric spindle and the heat exchange coefficient of each position remain unchanged. The small water channels and water areas are evenly distributed in the cooling water jacket. The four factors (i.e. the number of small water channels, the number of water areas, the water inlet speed and the water inlet temperature of the cooling water jacket) are set as A, B, C and D respectively. The shapes of the small water channels and water areas are as follows: Figure 2 shown.

[0087] S32. Determine optimization goals

[0088] The temperature of each key point is an extremely important consideration in the process of optimizing the structure of a high-power electric spindle. Excessive temperature will lead to a decline in the performance of the bearings and motors, affecting the working stability and service life of the electric spindle. In addition, in the process of optimizing the structure of a high-power electric spindle, the axial deformation of the electric spindle shaft is also a factor that cannot be ignored. Axial deformation will cause poor contact of the electric spindle bearings, thereby affecting the operating accuracy and stability of the electric spindle. Therefore, three objectives are set for optimization, namely: bearing temperature, motor temperature and axial deformation. Among them, the bearing temperature takes the highest temperature of the three bearings, and the motor temperature takes the highest temperature of the motor. The three optimization objectives are set as: P1, P2 and P3 respectively.

[0089] S33. Create an orthogonal test plan table

[0090] The number of levels for each factor is determined based on the actual situation. Factor A is set to three levels, namely A1, A2, and A3; Factor B is set to four levels, namely B1, B2, B3, and B4; Factor C is set to four levels, namely C1, C2, C3, and C4; Factor D is set to four levels, namely D1, D2, D3, and D4. The experimental factor level table is shown in Table 2:

[0091] Table 2. Experimental factor level table

[0092]

[0093] In order to explore the mutual influence between various factors evenly and to maximize the independence between the test points, L12(3 1 ×4 3) Experimental design scheme, the orthogonal experimental scheme table is shown in Table 3:

[0094] Table 3. Orthogonal test plan

[0095]

[0096] Based on the orthogonal experimental plan, while keeping all other boundary conditions constant, we investigated the effects of four factors—the number of water areas, the number of small water channels, the water inlet temperature, and the water inlet velocity—on the bearing temperature, motor temperature, and shaft axial deformation of the electric spindle. In each set of experiments, the maximum temperature of bearings 7022 and 7016 was used as the bearing temperature, the maximum temperature of the motor stator winding was used as the motor temperature, and the axial deformation of the machining end was used as the shaft axial deformation. By taking the maximum value in each set of experiments, we minimized calculation errors. The results of the orthogonal experiments are shown in Table 4.

[0097] Table 4. Orthogonal test results

[0098] Test number Bearing temperature (℃) Stator temperature (℃) Axial deformation (um) 1 51.97 31.46 58.48 2 53.19 33.67 85.08 3 54.54 34.15 93.13 4 55.76 35.71 61.87 5 53.17 32.56 84.61 6 54.34 34.02 91.42 7 56.22 36.99 102.85 8 51.75 30.67 76.00 9 54.61 34.73 93.31 10 56.25 34.38 102.70 11 51.60 30.43 74.91 12 52.92 32.07 82.40

[0099] S34. Evaluation method of the impact of various factors on each optimization goal

[0100] Use the range analysis method to evaluate the impact of each factor on a single optimization target and calculate the range value of each factor. The larger the range value, the greater the impact of the factor on the target value. Therefore, find the optimal level combination of each factor to achieve the optimal target value.

[0101] The calculation principle is:

[0102]

[0103] Where R jb is the extreme value of the jth factor for the bth optimization objective, K ijb is the sum of the experimental data of the bth optimization target when the jth factor is at level i, and S is the number of occurrences of the jth factor and level i;

[0104] When the number of factor levels is not exactly the same, it is unreliable to directly compare the range values, because when two factors have the same impact on the indicator, the range value of the factor with more levels will be larger. Therefore, it is necessary to use coefficients to convert the range. The conversion coefficient table is shown in Table 5, and the conversion formula is: Where R ′ is the converted range value, R is the range value, d is the converted number, and m is the number of indicators corresponding to each level.

[0105] Table 5. Conversion coefficient table

[0106] Number of levels Conversion number 2 0.71 3 0.52 4 0.45 5 0.4 6 0.37

[0107] S35. Evaluation method of comprehensive impact of various factors on multiple optimization objectives

[0108] In multi-objective optimization, the entropy weighting method can be used to assign weights to various indicators. It calculates the information entropy of each indicator to measure the information content of each indicator and determine its weight. Information entropy is used here to measure the uncertainty or diversity of the data for an indicator. A higher entropy value indicates more dispersed and uncertain information for the indicator, and therefore a smaller weight should be assigned. Conversely, a lower entropy value indicates more concentrated and certain data for the indicator, and therefore a larger weight should be assigned. The entropy weighting method is calculated as follows:

[0109] The first step is to make the target value dimensionless. Using range normalization to normalize the experimental value of the optimization target can eliminate the influence of different scales. The range normalization formula is:

[0110]

[0111] Where, X ′ ab is the standardized value of the experimental data of the bth optimization target of the ath group experiment, X ab is the experimental data of the bth optimization target of the ath group test, min(X b ) and max(X b ) are the minimum and maximum values ​​of the experimental data of the bth optimization objective respectively;

[0112] The second step is positive processing. Since dimensionless processing may produce negative values ​​or relatively small values, it is necessary to perform data shifting. The data shift formula is:

[0113]

[0114] Where Pab is the proportion of the experimental data of the bth optimization target in the ath group of experiments in the standardized value, and n is the number of experimental groups;

[0115] The third step is to calculate the entropy value. The entropy value calculation formula is:

[0116]

[0117] Where H b is the entropy value of the bth optimization target, and m is the number of optimization targets;

[0118] The fourth step is to calculate the weight. Assign weights to the entropy values ​​of different target values ​​to reflect the impact of the target on the experiment. The calculation formula is:

[0119]

[0120] Where wb is the weight of the b-th optimization objective.

[0121] S36. Target optimization results and analysis

[0122] (1) Analysis of bearing temperature test results

[0123] According to Table 6, the influence of various factors on bearing temperature is in the following order: C>D>B>A, that is, water inlet temperature>water inlet speed>number of small waterways>number of water areas. Figure 5 The impact of various factors on bearing temperature at different levels can be analyzed. The number of water areas remains essentially unchanged, the number of small waterways initially increases and then decreases, the inlet water temperature continuously increases, and the inlet water rate initially decreases and then increases. The optimal bearing temperature combination is determined to be A3B1C1D3, which means 24 water areas, 24 small waterways, an inlet water temperature of 16°C, and an inlet water rate of 18 L / min.

[0124] Table 6. Bearing temperature intuitive analysis table

[0125]

[0126]

[0127] (2) Analysis of motor temperature test results

[0128] According to Table 7, the influence of various factors on motor temperature is in the following order: C>A>D>B, that is, water inlet temperature>water area number>water inlet speed>small waterway number. Figure 6 The impact of different levels of various factors on motor temperature can be analyzed. The number of water areas shows a decreasing trend, the number of small waterways first increases and then decreases, the inlet water temperature shows an increasing trend, and the inlet water rate first decreases and then increases. The optimal motor temperature combination can be determined to be A3B4C1D3, which means the number of water areas is 24, the number of small waterways is 60, the inlet water temperature is 16°C, and the inlet water rate is 18L / min.

[0129] Table 7. Stator temperature intuitive analysis table

[0130]

[0131] (3) Analysis of axial deformation test results

[0132] According to Table 8, the influence of each factor on the axial deformation is in the following order: A>C>D>B, that is, the number of water areas>water inlet temperature>water inlet speed>the number of small waterways. Figure 7The impact of different levels of various factors on axial deformation can be analyzed. The number of water areas first increases and then remains constant, the number of small waterways and the inlet temperature first increase and then decrease, and the inlet rate shows a continuously decreasing trend. The optimal mix ratio for axial deformation can be determined to be A1 B4 C1 D4, i.e., 12 water areas, 60 small waterways, 16°C inlet temperature, and 20 L / min inlet rate.

[0133] Table 8. Visual analysis of the axial deformation of the shaft

[0134]

[0135] (4) Analysis of multi-objective comprehensive test results

[0136] Combined with the calculation formula in step S35, the teammate entropy value and weight ratio of each optimization target are obtained, as shown in Table 9.

[0137] Table 9. Calculation results of entropy value and entropy weight for each target value

[0138] Target value Entropy Entropy weight Bearing temperature 1.978 0.317 stator temperature 2.024 0.332 Axial deformation 2.085 0.351

[0139] Combining Table 4 and Table 9, weighted calculation is performed on the optimization objectives to obtain the comprehensive score of the target value, as shown in Table 10.

[0140] Table 10. Comprehensive score table

[0141] Test number Bearing temperature (℃) Stator temperature (℃) Axial deformation (um) Comprehensive score 1 51.97 31.46 58.48 47.45 2 53.19 33.67 85.08 57.90 3 54.54 34.15 93.13 61.32 4 55.76 35.71 61.87 51.25 5 53.17 32.56 84.61 57.36 6 54.34 34.02 91.42 60.61 7 56.22 36.99 102.85 66.20 8 51.75 30.67 76.00 53.26 9 54.61 34.73 93.31 61.59 10 56.25 34.38 102.70 65.29 11 51.60 30.43 74.91 52.75 12 52.92 32.07 82.40 56.35

[0142] By weighting each parameter, we can get the average value of each factor on the comprehensive score, and obtain the intuitive analysis table 11. According to Table 11, the influence of each factor on the comprehensive score is in the following order: A>C>B>D, that is, the number of water areas>water inlet temperature>water inlet speed>the number of small waterways. Figure 8 The impact of different levels of each factor on the comprehensive score can be analyzed. The number of water bodies first increases and then decreases, the number of small waterways first increases and then decreases, the inlet temperature first increases and then remains constant, and the inlet rate shows a continuously decreasing trend. The optimal combination for the comprehensive score (i.e., the optimal solution) can be determined to be A1B4C1D4, which means the number of water bodies is 12, the number of small waterways is 60, the inlet temperature is 16°C, and the inlet rate is 20L / min.

[0143] Table 11. Comprehensive score value intuitive analysis table

[0144]

[0145]

[0146] S4: Input the optimal solution into the finite element model of the electric spindle, conduct simulation tests, generate simulation results, and verify the optimization results.

[0147] The optimal solution obtained from the orthogonal experiment was used to simulate the temperature field and thermal deformation, that is, the number of cooling water jacket water areas is 2, the number of cooling water jacket small water channels is 10, the cooling water temperature is 16℃, the cooling water flow rate is 20L / min, the ambient temperature is 21℃, and the electric spindle speed is 7000r / min.

[0148] like Figure 9 and Figure 10 As shown, the maximum temperature of the electric spindle is 51.60℃, which is 4.4℃ lower than the existing cooling system parameters; the maximum temperature of the motor end is 33.42℃, which is 5.71℃ lower than the existing cooling system parameters; the maximum temperature of the flow field end is 21.05℃, the outlet temperature is 18.83℃, and the inlet and outlet temperature difference is 2.83℃, which is 0.15℃ higher than the existing cooling system parameters; the maximum temperature of the bearing 7022 end is 51.60℃, which is 4.44℃ lower than the existing cooling system parameters; the maximum temperature of the bearing 7016 end is 34.92℃, which is 5.21℃ lower than the existing cooling system parameters; the deformation of the axial processing end (right end) is 33.35um, which is 29.51um lower than the existing cooling system parameters.

[0149] The comparison before and after optimization is shown in Table 12:

[0150] Table 12. Comparison of key point data changes before and after optimization

[0151] Key Points Before optimization After optimization difference Maximum temperature of electric spindle 56.04℃ 51.60℃ 4.44℃ Maximum temperature of motor end 39.13℃ 33.42℃ 5.71℃ Temperature difference between flow field inlet and outlet 2.68℃ 2.83℃ -0.15℃ Maximum temperature of bearing 7022 end 56.04℃ 51.6℃ 4.44℃ Maximum temperature of bearing 7016 end 40.13℃ 34.92℃ 5.21℃ Axial deformation of the machining end 62.86um 33.35um 29.51um

[0152] The foregoing is an example of the best mode of carrying out the present invention. Any portion not described in detail herein is common knowledge within the skill of one of ordinary skill in the art. The scope of protection of the present invention is determined by the claims. Any equivalent transformation based on the technical teachings of the present invention is also within the scope of protection of the present invention.

Claims

1. A multi-objective simulation optimization method for an electric spindle, characterized in that: The following steps are involved: Establish the finite element model of the electric spindle; Selecting several optimization objectives and several factors related to the optimization objectives; Use orthogonal test method to create orthogonal test plan table; According to the orthogonal test plan table, the finite element model is used to conduct simulation tests to obtain several groups of test data for each optimization target; Use the range method to analyze all test data and obtain the influence of each factor on each optimization target; Use the entropy weight distribution method to analyze all test data and obtain the weight of each optimization objective; The optimal solution is obtained based on all experimental data and the weight of each optimization objective.

2. The multi-objective simulation optimization method of the electric spindle according to claim 1, characterized in that: The optimization targets include bearing temperature, motor temperature and axial deformation; The design variables include the number of small water channels, the number of water areas, the inlet water temperature and the inlet water velocity of the cooling water jacket.

3. The multi-objective simulation optimization method of the electric spindle according to claim 1, characterized in that: The steps to create an orthogonal experiment plan table using the orthogonal experiment method include: Determine the number of levels for each factor based on its range of variation; According to the number of levels of each factor, an orthogonal experimental plan table is established.

4. The multi-objective simulation optimization method of the electric spindle according to claim 1, characterized in that: The steps of using the range method to obtain the influence of each factor on each optimization target include: Calculate the range of each factor for each optimization target. The calculation formula is: Where R jb is the extreme value of the jth factor for the bth optimization objective, K ijb is the sum of the experimental data of the bth optimization target when the jth factor is at level i, and S is the number of occurrences of the jth factor and level i; The larger the range value, the greater the influence of the factor on the optimization target.

5. The multi-objective simulation optimization method of the electric spindle according to claim 4, characterized in that: If at least one factor has a different number of levels from the other factors, after calculating the range values ​​of each factor, the following steps are also included: The coefficient is used to convert the extreme value, and the calculation formula is: Where R jb ′ is the reduced range value of the jth factor for the bth optimization target, R j is the range value of the j-th factor for the b-th optimization target, d is the conversion number, and m is the number of factors corresponding to each level.

6. The multi-objective simulation optimization method of the electric spindle according to claim 1, characterized in that: The steps of obtaining the weights of each optimization objective using the entropy weight distribution method include: Use range standardization to standardize the test data of each optimization target. The calculation formula is: Where, X ′ ab is the standardized value of the experimental data of the bth optimization target of the ath group experiment, X ab is the experimental data of the bth optimization target of the ath group test, min(X b ) and max(X b ) are the minimum and maximum values ​​of the experimental data of the bth optimization objective respectively; The normalized test data is processed positively, and the calculation formula is: Where Pab is the proportion of the experimental data of the bth optimization target in the ath group of experiments in the standardized value, and n is the number of experimental groups; Calculate the entropy value of each optimization objective using the following formula: Where H b is the entropy value of the bth optimization target, and m is the number of optimization targets; Calculate the weight of each optimization objective using the following formula: Where w b is the weight of the b-th optimization objective.

7. The multi-objective simulation optimization method of the electric spindle according to claim 1, characterized in that: Before using the finite element model to conduct simulation tests and obtain test data, the simulation of the finite element model must be verified, including the following steps: Build an experimental platform; Run the experimental platform and detect relevant parameters of several nodes to obtain measured data of several nodes; Use the finite element model to perform simulation analysis and obtain simulation data of several corresponding nodes; Compare the simulation data and measured data of each node and determine whether the difference is less than the preset threshold. If so, the simulation test of the finite element model passes the verification; if not, the simulation test verification of the finite element model fails, and the relevant parameters of the finite element model and simulation test need to be reset.

8. The multi-objective simulation optimization method of the electric spindle according to claim 1, characterized in that: The step of obtaining the optimal solution based on the test data and the weight of each optimization objective includes: Calculate the comprehensive score of the optimization objective in each set of test data based on the weight of each optimization objective and all test data; If the comprehensive score of a set of test data is lower, the corresponding factor will have less influence on all optimization objectives at the corresponding level. The optimal solution is the set of factors with the lowest comprehensive score and the corresponding number of levels.

Images