Paddy field land leveler structure inherent frequency optimization method based on size linkage and response surface method

Abstract

The invention discloses a method for optimizing inherent frequency of a paddy field grader structure based on size linkage and a response surface method. The method comprises the following steps: S01, establishing a simplified three-dimensional model of the paddy field grader structure; s02, carrying out parameterization setting on the model; s03, associating the SolidWorks with the Workbench, and establishing a size association and a size parameter set; s04, performing modal analysis to obtain total deformation of the land leveler structure and inherent frequencies of first six orders, and setting part of output results as output parameters to obtain a size parameter set; s05, analyzing the parameter sensitivity of the size parameter set, and screening input parameters with relatively high comprehensive sensitivity values; s06, optimizing a multi-target response surface to obtain an optimal design parameter; and S07, reconstructing a model according to the optimal parameters, performing modal analysis, and confirming an optimization effect. According to the method, response surface optimization can be accurately and efficiently carried out on the structure parameters to improve the inherent frequency and reduce the resonance risk, and the method belongs to the technical field of agricultural machinery structure optimization.

Description

Technical Field

[0001] The invention relates to agricultural machinery structure optimization technology, and in particular to a paddy field grader structure natural frequency optimization method based on size linkage and response surface method. Background Art

[0002] In modern agriculture, paddy field graders are an important agricultural machinery. Their main function is to improve soil structure through loosening and leveling operations, thereby improving the quality of paddy field cultivation and production efficiency. With the expansion of agricultural production scale and the improvement of mechanization efficiency requirements, higher requirements are put forward for the design of paddy field graders, especially in the optimization of their natural frequency, in order to reduce the risk of resonance and improve the stability and reliability of equipment operation. When the paddy field grader is in operation, it is mainly subject to excitation from the road surface and excitation from the engine. When the natural frequency of the grader is close to these excitation frequencies, resonance will occur. This resonance not only reduces the working efficiency, but also accelerates the wear and failure of the equipment and increases the maintenance cost. In addition, resonance may have a negative impact on the health of the operator and lead to unsatisfactory leveling effects, thereby affecting the yield of crops. Studies have shown that the excitation frequency range of the paddy field ground is usually between 0.869 and 4.34Hz, while the excitation frequency of the engine used by the paddy field grader is about 18Hz. This shows that the main excitation frequency of the external environment on the equipment is concentrated in the lower frequency band. Therefore, in order to avoid resonance, the low-order natural frequency should be increased as much as possible when designing and optimizing the structure of the paddy field grader.

[0003] However, in previous optimization methods, only the dimensional influence of a single component of the paddy field grader is usually focused on, while the complex linkage relationship between the dimensions in the entire assembly is ignored. Due to the lack of association between the characteristic parameters of the paddy field grader model, when adjusting the size of a certain feature, the parameters of the related features cannot be automatically updated synchronously, and multiple characteristic parameters need to be manually modified to respond to design changes. This not only greatly reduces the flexibility and efficiency of the natural frequency optimization of the paddy field grader structure, but also makes it difficult to achieve a globally optimal design solution for the paddy field grader structure. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the purpose of the present invention is to provide a method for optimizing the natural frequency of a paddy field grader structure based on size linkage and response surface method, which can accurately and efficiently perform response surface optimization on the structural parameters of the paddy field grader to improve its natural frequency and reduce the risk of resonance.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A method for optimizing the natural frequency of a paddy field grader structure based on size linkage and response surface method comprises the following steps:

[0007] S01: Establish a simplified 3D model of the initial paddy field grader structure;

[0008] S02: performing parameter setting on the three-dimensional model of the paddy field grader created in step S01;

[0009] S03: Associating SolidWorks with Workbench, launching Workbench directly from SolidWorks to import the three-dimensional model of the paddy field grader in step S02 into Ansys Workbench and perform parametric design, establish dimension association and establish dimension parameter set;

[0010] S04: Performing modal analysis on the simplified model of the paddy field grader imported into Ansys Workbench in step S03, obtaining the total deformation of the paddy field grader structure and the first six natural frequencies in the initial state, and setting part of the output results as output parameters to the size parameter set established in step S03;

[0011] S05: Associating the size parameter set in step S04 with the parameter correlation module in Ansys Workbench to perform parameter sensitivity analysis and calculate the comprehensive sensitivity, thereby screening out input parameters with higher comprehensive sensitivity values;

[0012] S06: Importing the parameterized paddy field grader structure model in step S04 into the response surface optimization module of Ansys Workbench to perform multi-objective response surface optimization and obtain optimal design parameters;

[0013] S07. Reconstruct the paddy field grader structure model according to the optimal parameters of the paddy field grader structure obtained in step S06 and perform modal analysis to confirm the first six natural frequencies and mass of the optimized paddy field grader structure, and compare and analyze the first six natural frequencies of the initial paddy field grader structure obtained in step S02 to confirm the optimization effect.

[0014] As a preferred embodiment, in step S04, the modal analysis specifically includes steps S41 to S45:

[0015] S41: Building a new modal analysis module, using parameter transfer to import the simplified model of the paddy field grader in step S3 into the modal analysis module;

[0016] S42: define material properties of paddy field grader structure model;

[0017] S43: Mesh division. The simplified model of the paddy field grader is meshed using tetrahedral elements. A mesh size of 15 mm is applied to the crossbeam and the left and right connecting rods, and a mesh size of 20 mm is applied to the rest of the parts.

[0018] S44: Add joints and constraints. According to the working principle of the paddy field grader, add corresponding joints and constraints to the model. Set each hinge connection of the paddy field grader as a revolving joint connection, set the connection between the elevation hydraulic rod and the elevation hydraulic cylinder as a moving joint connection, and set the connection between the horizontal hydraulic rod and the horizontal hydraulic cylinder as a moving joint connection. Apply cylindrical supports to the connection between the elevation hydraulic rod, the crossbeam, and the left and right connecting rods and the fuselage.

[0019] S45: performing a solution, solving the first six order modes to obtain their deformation cloud diagrams, thereby obtaining the first six order natural frequencies and corresponding vibration modes of the paddy field grader structure in the initial state;

[0020] S46: Output the results, output the calculated natural frequency and deformation cloud map, and set the first-order and second-order natural frequencies and structural mass as output parameters, and add them to the size parameter set established in step S03.

[0021] As a preferred embodiment, in step S06, the multi-objective response surface optimization includes the following steps:

[0022] S61, creating a response surface optimization module, using parameter transfer to import the simplified model of the paddy field grader in step S04 into the response surface optimization module;

[0023] S62, performing a Latin hypercube experimental design on the four input parameters with high comprehensive sensitivity values ​​screened out in step S05 in the response surface optimization module of Ansys Workbench;

[0024] S63, constructing a response surface model according to the test results obtained by performing the Latin hypercube test design in step S62;

[0025] S64, testing the response surface model constructed in step S63 to determine whether the response surface model meets the fit requirement;

[0026] S65. Construct a multi-objective response surface optimization design mathematical model according to the response surface model generated in step S63, and perform multi-objective response surface optimization on the paddy field grader structure to obtain optimal design parameters.

[0027] As a preferred embodiment, in step S01, when establishing a three-dimensional model of a paddy field grader in Solid Works, the assembly should be designed from top to bottom, so as to establish linkage features and realize the linkage of related dimensions between the parts of the assembly; first, use SolidWorks Treehouse to establish an assembly containing 9 parts, namely, a crossbeam, a left connecting rod, a right connecting rod, a mounting frame, a leveling shovel, an elevation hydraulic rod, an elevation hydraulic cylinder, a horizontal hydraulic rod, and a horizontal hydraulic cylinder; and arrange the loading order of the parts according to the actual installation order of the paddy field grader, and draw the parts in the assembly according to the assembly relationship.

[0028] As a preferred embodiment, in step S02, when setting parameters for the dimensions to be optimized in the model, two types of dimensions need to be set: one is the dimension parameter setting in the sketch. In the sketch editing mode, add "DS_" in front of the name of the corresponding dimension parameter; the other is the dimension parameter setting of the feature, such as the depth of the extruded feature. Since the dimension cannot be named in the form of a name when setting the dimension parameter of the feature, the parameter cannot be modified. Therefore, the parameter is defined in the form of a global variable to achieve the linkage between the modification of the parameter and the dimension. First, add a global variable to the assembly model named scraper. Enter from the menu bar: Tools->Equations, add a global variable. The name of the variable should be in English, and add a prefix "DS_" in front, such as "DS_Variable", and give the value of the variable. Then open the component where the feature is located, select the corresponding feature for editing, delete the dimension first when setting the dimension of the feature, then click the dimension input box with the mouse, and enter the expression = "DS_Variable@scraper.Assembly". The purpose of this expression is to reference the global variable "DS_Variable" in the assembly "scraper". In this way, it can be ensured that the size of the part feature can be automatically adjusted as the global variable "DS_Variable" in the assembly changes, achieving flexible linkage of dimensions.

[0029] As a preferred embodiment, in step S03, to start Workbench from SolidWorks, you need to install the ANSYS plug-in in SolidWorks first. In the main interface "Tools", click "ANSYS2022R1" → "Ansys Workbench" command to create a Geometry project in the Workbench interface. Double-click "Geometry" in the A2 column of Project A. At this time, DesignModeler will be loaded. In DM, update Attach1 to generate the model, thereby completing the import of the paddy field grader model. When performing parametric design on the paddy field grader model, find the variables defined in step S02 and check the small square in front. Set all variables involved in the optimization as input parameters and establish a size parameter set; in the generated size parameter set, set the parameter association according to the relationship between the parameters. If the relationship between the beam length l1 and the left / right connecting rod length l2 is l1=l2+30mm, then in the parameter l2 property interface, define the parameter value as the expression "l1-30".

[0030] As a preferred embodiment, in step S05, when performing correlation analysis on all input parameters in the size parameter set, only the first-order natural frequency and the second-order natural frequency are selected as key response variables for analysis, the correlation type is selected to use Spearman correlation, and the mean value accuracy is set to 0.01, and the standard deviation accuracy is set to 0.02; start the correlation analysis, and let the system calculate the influence of each input parameter on the first-order natural frequency and the second-order natural frequency of the selected output parameter; after the analysis is completed, check the generated sensitivity bar chart to screen out four input parameters with higher comprehensive sensitivity values.

[0031] As a preferred embodiment, in step S62, when performing the experimental design, only the input parameters with higher comprehensive sensitivity values ​​screened out in step S05 are checked, and the upper and lower limits are set for each input parameter, and then the "experimental type design" and "sample type" are set, and the method is selected as needed. Here, Latin hypercube sampling design and CCD sampling are selected, and the "experimental design" is updated to generate experimental design points; note that during the update of the "experimental design", do not close the paddy field grader model in SolidWorks associated with Ansys Workbench; during the update of the "experimental design", do not close the paddy field grader model in SolidWorks associated with Ansys Workbench; after updating the "experimental design", double-click to enter the "response surface" and set the type of response surface; here, select the standard response surface, and update to generate the corresponding response surface results; and import the "experimental design points" generated in step S62 into the software Design-Expert for multivariate regression fitting to obtain the prediction model of the first-order and second-order natural frequencies and structural quality of the paddy field grader.

[0032] As a preferred embodiment, in step S64, the absolute coefficient R is used 2 , Root mean square error E RMS , mean absolute error E RMA The accuracy of the fitting surface is evaluated. If the response surface model does not meet the requirements of goodness of fit, the response surface model is re-established until it meets the requirements of goodness of fit.

[0033] R 2 The calculation formula is:

[0034]

[0035] E RMS The calculation formula is:

[0036]

[0037] E RMA The calculation formula is:

[0038]

[0039] Among them, y i is the DOE test data value, is the predicted value of the response surface similarity model, is the mean value of DOE experimental data, where the DOE experimental data value is the sample data obtained after the DOE sampling test is carried out according to the predicted value of the response surface similarity model.

[0040] As a preferred embodiment, in step S65, the mathematical model for multi-objective response surface optimization design is:

[0041]

[0042] Among them, F(X) is the objective function finally output by the multi-objective response surface optimization calculation, max F(X) is the output value of the objective function when the objective function F(X) reaches the maximum value, that is, the 1st and 2nd order natural frequencies are the largest and the mass is the smallest; y1(X) is the 1st order natural frequency of the optimized structure, y2(X) is the 2nd order natural frequency of the optimized structure, and y3(X) is the mass of the optimized structure; γ1 is the weight coefficient of the 1st order natural frequency, γ2 is the weight coefficient of the 2nd order natural frequency, and γ3 is the weight coefficient of the structural mass; formula st is the constraint condition, 36Hz is twice the engine frequency of the paddy field grader; X is the decision vector, which represents the adjustable input parameter; x1, x2, x3, and x4 correspond to the design variables beam width w1, beam height h1, beam thickness d1, and beam length l1, respectively;

[0043] The first-order natural frequency model of the paddy field grader is:

[0044]

[0045] The second-order natural frequency model of the paddy field grader is:

[0046]

[0047] The mass model of the paddy field grader is:

[0048]

[0049] When multi-objective response surface optimization is performed on the structure of a paddy field grader, the optimization objectives are to maximize the first-order natural frequency and the first-order natural frequency of the paddy field grader structure and minimize the structural mass, and the first-order and second-order natural frequencies are greater than twice the paddy field ground excitation frequency and the engine frequency, and the range of design variables is used as a constraint; a multi-objective genetic algorithm (MOGA) is used for optimization, and the initial population size is set to 10,000, the number of new populations generated by sample iteration in each generation is 100, the maximum number of iterations is 20, the allowable deviation is 2%, and the maximum allowable Pareto percentage is 70%. The weights of the first-order natural frequency and the second-order natural frequency are set to higher importance, and the structural mass is set to the default importance. After setting the optimization properties, three candidate points that meet the conditions are generated by solving, and finally one is selected as the optimal design point by comprehensively considering the primary and secondary objectives.

[0050] The present invention has the following advantages:

[0051] (1) The present invention realizes the linkage of related dimensions of parts in an assembly, ensuring that the dimensions of parts can automatically coordinate and change when adjusted, thereby improving the flexibility and efficiency of the design.

[0052] (2) The present invention establishes real-time bidirectional association between SolidWorks and Ansys Workbench, achieving seamless connection from modeling to analysis and optimization, and uses parametric settings and global variables to manage dimensions, establishing a multi-objective response surface optimization of dimensional parameters based on real-time bidirectional association, so that the model can be easily adjusted during the optimization process, effectively shortening the reconstruction time of the optimized three-dimensional model and improving the efficiency of optimizing the natural frequency of the paddy field grader structure.

[0053] (3) The present invention adopts Latin hypercube experimental design and multivariate regression fitting method, combined with the accuracy evaluation of response surface model (such as R 2 , root mean square error), ensuring the high reliability and accuracy of the fitting model.

[0054] (4) The present invention uses a multi-objective genetic algorithm (MOGA) to seek the optimal solution, ensuring that the optimal parameter combination can be found in a complex design space. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The figure is a flow chart of the method of the present invention.

[0056] Figure 2 This is a schematic diagram of the overall structure of the paddy field grader used for the test.

[0057] Figure 3 Schematic diagram of the structural connection when adding a connection pair to a paddy field grader.

[0058] Figure 4This is the result diagram after meshing the simplified three-dimensional model.

[0059] Figure 5 This is the result diagram after sensitivity analysis of all input parameters.

[0060] Figure 6 Schematic diagram of structural parameters with higher comprehensive sensitivity values ​​screened out after parameter sensitivity analysis.

[0061] 1-elevation hydraulic rod, 2-elevation hydraulic cylinder, 3-crossbeam, 4-mounting frame, 5-horizontal hydraulic cylinder, 6-horizontal hydraulic rod, 7-leveling blade, 8-right connecting rod, 9-left connecting rod. DETAILED DESCRIPTION

[0062] The present invention will be further described in detail below in conjunction with specific implementation methods.

[0063] Figure 1 As shown, a method for optimizing the natural frequency of a paddy field grader structure based on size linkage and response surface method comprises the following steps:

[0064] S01: Establish a simplified 3D model of the initial paddy field grader structure;

[0065] S02: performing parameter setting on the three-dimensional model of the paddy field grader created in step S01;

[0066] S03: Associating SolidWorks with Workbench, launching Workbench directly from SolidWorks to import the three-dimensional model of the paddy field grader in step S02 into Ansys Workbench and perform parametric design, establish dimension association and establish dimension parameter set;

[0067] S04: Performing modal analysis on the simplified model of the paddy field grader imported into Ansys Workbench in step S03, obtaining the total deformation of the paddy field grader structure and the first six natural frequencies in the initial state, and setting part of the output results as output parameters to the size parameter set established in step S03;

[0068] S05: Associating the size parameter set in step S04 with the parameter correlation module in Ansys Workbench to perform parameter sensitivity analysis and calculate the comprehensive sensitivity, thereby screening out input parameters with higher comprehensive sensitivity values;

[0069] S06: Importing the parameterized paddy field grader structure model in step S04 into the response surface optimization module of Ansys Workbench to perform multi-objective response surface optimization and obtain optimal design parameters;

[0070] S07. Reconstruct the paddy field grader structure model according to the optimal parameters of the paddy field grader structure obtained in step S06 and perform modal analysis to confirm the first six natural frequencies and mass of the optimized paddy field grader structure, and compare and analyze the first six natural frequencies of the initial paddy field grader structure obtained in step S02 to confirm the optimization effect.

[0071] Specifically, in step S01, when establishing a three-dimensional model of a paddy field grader in Solid Works, the assembly should be designed from top to bottom, so as to establish linkage features and realize the linkage of related dimensions between the parts of the assembly; first, use SolidWorks Treehouse to establish an assembly containing 9 parts, namely, a crossbeam, a left connecting rod, a right connecting rod, a mounting frame, a leveling shovel, an elevation hydraulic rod, an elevation hydraulic cylinder, a horizontal hydraulic rod, and a horizontal hydraulic cylinder; and arrange the loading order of the parts according to the actual installation order of the paddy field grader, and draw the parts in the assembly according to the assembly relationship.

[0072] Specifically, in step S02, when setting parameters for the dimensions that need to be optimized in the model, two types of dimensions need to be set: one is the dimension parameter setting in the sketch. In the sketch editing mode, add "DS_" in front of the name of the corresponding dimension parameter; the other is the dimension parameter setting of the feature, such as the depth of the extruded feature. Since the dimension cannot be named in the form of a name when setting the dimension parameter of the feature, the parameter cannot be modified. Therefore, the parameter is defined in the form of a global variable to achieve the linkage between parameter modification and dimension. First, add a global variable to the assembly model with the document name scraper. Enter from the menu bar: Tools->Equations, add a global variable. The name of the variable should be in English, and add a prefix "DS_" in front, such as "DS_Variable", and give the value of the variable. Then open the component where the feature is located, select the corresponding feature for editing, delete the dimension when setting the dimension of the feature, then click the dimension input box with the mouse, and enter the expression = "DS_Variable@scraper.Assembly". The purpose of this expression is to reference the global variable "DS_Variable" in the assembly "scraper". In this way, it can be ensured that the size of the part feature can be automatically adjusted as the global variable "DS_Variable" in the assembly changes, achieving flexible linkage of dimensions.

[0073] Specifically, in step S03, to start Workbench from SolidWorks, you need to install the ANSYS plug-in in SolidWorks first. In the main interface "Tools", click "ANSYS2022R1" → "Ansys Workbench" command to create a Geometry project in the Workbench interface. Double-click "Geometry" in the A2 column of Project A. At this time, DesignModeler will be loaded. In DM, update Attach1 to generate the model, thereby completing the import of the paddy field grader model. When performing parametric design on the paddy field grader model, find the variables defined in step S02 and check the small square in front of them, set all the variables involved in the optimization as input parameters, and establish a size parameter set; in the generated size parameter set, set the parameter association according to the relationship between the parameters. For example, the relationship between the beam length l1 and the left / right connecting rod length l2 is l1=l2+30mm, then in the parameter l2 property interface, define the parameter value as the expression "l1-30".

[0074] Specifically, in step S04, the modal analysis specifically includes steps S41 to S45:

[0075] S41: Building a new modal analysis module, using parameter transfer to import the simplified model of the paddy field grader in step S3 into the modal analysis module;

[0076] S42: define material properties of paddy field grader structure model;

[0077] S43: Mesh division. The simplified model of the paddy field grader is meshed using tetrahedral elements. A mesh size of 15 mm is applied to the crossbeam and the left and right connecting rods, and a mesh size of 20 mm is applied to the rest of the parts.

[0078] S44: Add joints and constraints. According to the working principle of the paddy field grader, add corresponding joints and constraints to the model. Set each hinge connection of the paddy field grader as a revolving joint connection, set the connection between the elevation hydraulic rod and the elevation hydraulic cylinder as a moving joint connection, and set the connection between the horizontal hydraulic rod and the horizontal hydraulic cylinder as a moving joint connection. Apply cylindrical supports to the connection between the elevation hydraulic rod, the crossbeam, and the left and right connecting rods and the fuselage.

[0079] S45: performing a solution, solving the first six order modes to obtain their deformation cloud diagrams, thereby obtaining the first six order natural frequencies and corresponding vibration modes of the paddy field grader structure in the initial state;

[0080] S46: Output the results, output the calculated natural frequency and deformation cloud map, and set the first-order and second-order natural frequencies and structural mass as output parameters, and add them to the size parameter set established in step S03.

[0081] Specifically, in step S05, when performing correlation analysis on all input parameters in the size parameter set, only the first-order natural frequency and the second-order natural frequency are selected as key response variables for analysis, the correlation type is selected to use Spearman correlation, and the mean value accuracy is set to 0.01, and the standard deviation accuracy is set to 0.02; start the correlation analysis, and let the system calculate the impact of each input parameter on the first-order natural frequency and the second-order natural frequency of the selected output parameter; after the analysis is completed, check the generated sensitivity bar chart to screen out four input parameters with higher comprehensive sensitivity values.

[0082] Specifically, in step S06, the multi-objective response surface optimization includes the following steps:

[0083] S61, creating a response surface optimization module, using parameter transfer to import the simplified model of the paddy field grader in step S04 into the response surface optimization module;

[0084] S62, performing a Latin hypercube experimental design on the four input parameters with high comprehensive sensitivity values ​​screened out in step S05 in the response surface optimization module of Ansys Workbench;

[0085] S63, constructing a response surface model according to the test results obtained by performing the Latin hypercube test design in step S62;

[0086] S64, testing the response surface model constructed in step S63 to determine whether the response surface model meets the fit requirement;

[0087] S65. Construct a multi-objective response surface optimization design mathematical model according to the response surface model generated in step S63, and perform multi-objective response surface optimization on the paddy field grader structure to obtain optimal design parameters.

[0088] Specifically, in step S62, when performing the experimental design, only the input parameters with higher comprehensive sensitivity values ​​screened out in step S05 are checked, and the upper and lower limits are set for each input parameter, and then the "experimental type design" and "sample type" are set, and the method is selected as needed. Here, Latin hypercube sampling design and CCD sampling are selected, and the "experimental design" is updated to generate experimental design points; note that during the update of the "experimental design", do not close the paddy field grader model in SolidWorks associated with Ansys Workbench; during the update of the "experimental design", do not close the paddy field grader model in SolidWorks associated with Ansys Workbench; after updating the "experimental design", double-click to enter the "response surface" and set the type of response surface; here, select the standard response surface, and update to generate the corresponding response surface results; and import the "experimental design points" generated in step S62 into the software Design-Expert for multivariate regression fitting to obtain the prediction model of the 1st and 2nd order natural frequencies and structural quality of the paddy field grader.

[0089] Specifically, in step S64, the absolute coefficient R is used 2 , Root mean square error E RMS , mean absolute error E RMA The accuracy of the fitting surface is evaluated. If the response surface model does not meet the requirements of goodness of fit, the response surface model is re-established until it meets the requirements of goodness of fit.

[0090] R 2 The calculation formula is:

[0091]

[0092] E RMS The calculation formula is:

[0093]

[0094] E RMA The calculation formula is:

[0095]

[0096] Among them, y i is the DOE test data value, is the predicted value of the response surface similarity model, is the mean value of DOE experimental data, where the DOE experimental data value is the sample data obtained after the DOE sampling test is carried out according to the predicted value of the response surface similarity model.

[0097] Specifically, in step S65, the mathematical model for multi-objective response surface optimization design is:

[0098]

[0099] Among them, F(X) is the objective function finally output by the multi-objective response surface optimization calculation, max F(X) is the output value of the objective function when the objective function F(X) reaches the maximum value, that is, the 1st and 2nd order natural frequencies are the largest and the mass is the smallest; y1(X) is the 1st order natural frequency of the optimized structure, y2(X) is the 2nd order natural frequency of the optimized structure, and y3(X) is the mass of the optimized structure; γ1 is the weight coefficient of the 1st order natural frequency, γ2 is the weight coefficient of the 2nd order natural frequency, and γ3 is the weight coefficient of the structural mass; formula st is the constraint condition, 36Hz is twice the engine frequency of the paddy field grader; X is the decision vector, which represents the adjustable input parameter; x1, x2, x3, and x4 correspond to the design variables beam width w1, beam height h1, beam thickness d1, and beam length l1, respectively;

[0100] The first-order natural frequency model of the paddy field grader is:

[0101]

[0102] The second-order natural frequency model of the paddy field grader is:

[0103]

[0104] The mass model of the paddy field grader is:

[0105]

[0106] When multi-objective response surface optimization is performed on the structure of a paddy field grader, the optimization objectives are to maximize the first-order natural frequency and the first-order natural frequency of the paddy field grader structure and minimize the structural mass, and the first-order and second-order natural frequencies are greater than twice the paddy field ground excitation frequency and the engine frequency, and the range of design variables is used as a constraint; a multi-objective genetic algorithm (MOGA) is used for optimization, and the initial population size is set to 10,000, the number of new populations generated by sample iteration in each generation is 100, the maximum number of iterations is 20, the allowable deviation is 2%, and the maximum allowable Pareto percentage is 70%. The weights of the first-order natural frequency and the second-order natural frequency are set to higher importance, and the structural mass is set to the default importance. After setting the optimization properties, three candidate points that meet the conditions are generated by solving, and finally one is selected as the optimal design point by comprehensively considering the primary and secondary objectives.

[0107] This embodiment uses Figure 2The simplified three-dimensional model of the paddy field leveler shown in the figure is used as the initial model for subsequent optimization. The structure of the paddy field leveler consists of an elevation hydraulic rod 1, an elevation hydraulic cylinder 2, a crossbeam 3, a mounting frame 4, a horizontal hydraulic cylinder 5, a horizontal hydraulic rod 6, a leveling shovel 7, a right connecting rod 8, and a left connecting rod 9. The local features in the paddy field leveler that do not affect the simulation analysis effect are simplified. Among them, the right connecting rod and the left connecting rod are the same components, and are collectively referred to as connecting rods in the following text. Then, step S02 is performed to parameterize the simplified three-dimensional model according to Table 1. Specifically, the parameter type in the table is a sketch parameter. In the sketch editing mode, parameterization is achieved by adding "DS_" in front of the name of the corresponding dimension parameter; the parameter type in the table is a feature parameter. Parameterization is achieved by adding a global variable in the assembly model and referencing the variable when editing the feature size. Taking the parameterization setting of the beam length as an example, a global variable "DS_l1" is added to the assembly model with the document name scraper, and the value of the variable is given to be 820. Then open the component where the feature is located, select the corresponding feature for editing, and when setting the dimension of the feature, first delete the dimension, then click the dimension input box with the mouse, and enter the expression = "DS_l1@scraper.Assembly". According to the above parameter setting method, set the beam width w1, beam height h1, beam thickness d1, beam length l1, connecting rod width w2, connecting rod height h2, connecting rod thickness d2, and connecting rod length l2 respectively. The variable names are "DS_w1", "DS_h1", "DS_d1", "DS_l1", "DS_w2", "DS_h2", "DS_d2", and "DS_l2".

[0108] Table 1 Paddy field grader structural parameter settings

[0109] Design parameters Parameter Type Variable Name Initial Value Beam width / mm Sketch Parameters <![CDATA[DS_w1]]> 40 Beam height / mm Sketch Parameters <![CDATA[DS_h1]]> 60 Beam thickness / mm Sketch Parameters <![CDATA[DS_d1]]> 5 Beam length / mm Characteristic parameters <![CDATA[DS_l1]]> 820 Connecting rod width / mm Sketch Parameters <![CDATA[DS_w2]]> 40 Connecting rod height / mm Sketch Parameters <![CDATA[DS_h2]]> 60 Connecting rod thickness / mm Sketch Parameters <![CDATA[DS_d2]]> 5 Connecting rod length / mm Characteristic parameters <![CDATA[DS_l2]]> 790

[0110] When the simplified three-dimensional model of the paddy field grader imported into Ansys Workbench is parametrically designed in step S03, the variables defined in step S02 are found and the small square in front is checked, and all the variables involved in the optimization are set as input parameters to establish a size parameter set. In the generated parameter set, the connecting rod length l2 is associated with the beam length l1, and the parameter value is defined as the expression "l1-30" in the parameter l2 property interface, and the mounting frame width w3 is associated with the beam length w1, and the parameter value is defined as the expression "w1+10" in the parameter w2 property interface.

[0111] Then, step S04 is executed to perform modal analysis on the simplified 3D model of the paddy field grader imported into Ansys Workbench in S03, and the total deformation of the paddy field grader structure and the first six natural frequencies in the initial state are obtained, and some output results are set as output parameters. Specifically, the material properties are defined first. The material of the paddy field grader structure is Q235, and the density is 7.85g / cm 3 , Young's modulus is 200 GPa, and Poisson's ratio is 0.3. Then according to Figure 3 As shown in Table 2, a connection pair is set at each connection of the simplified model of the paddy field leveler, and a cylindrical support is applied at the connection between the elevation hydraulic rod, the crossbeam, and the left / right connecting rod and the fuselage. The simplified model of the paddy field leveler is meshed using tetrahedral elements, where a mesh size of 15 mm is applied to the crossbeam and the left / right connecting rod, and a mesh size of 20 mm is used for the rest of the parts. After meshing, Figure 3 Finally, the first six modes are solved, and the results are shown in Table 3.

[0112] Table 2 Structural connection relationship table of paddy field grader

[0113]

[0114]

[0115] Table 3 Modal analysis results of the initial paddy field grader structure

[0116] Degree Natural frequency / Hz Mode shape 1 4.00 The overall swing of the grader along the Y axis 2 7.35 Swing of the mounting frame around the X axis 3 9.54 Swing of the leveling shovel around the Y axis 4 15.23 The overall swing of the grader along the Z axis 5 18.16 Swing of the leveling blade and mounting frame along the Z axis 6 44.22 Swing of the leveling blade along the Y axis

[0117] After step S04, the modal analysis results are obtained, and the first-order and second-order natural frequencies and the structural mass are set as output parameters and added to the size parameter set, and are represented by symbols ω1, ω2 and m respectively.

[0118] After step S05, the sensitivity analysis result diagram of 7 input parameters is obtained, as shown in FIG. Figure 5 As shown, the comprehensive sensitivity values ​​of the seven input parameters are calculated, and the results are shown in Table 4. The four input parameters with the highest comprehensive sensitivity values ​​are selected, namely, beam width w1, beam height h1, beam thickness d1, and beam length l1. Figure 6 As indicated by the markings.

[0119] Table 4 Comprehensive sensitivity values ​​of various input parameters

[0120] Input Parameters <![CDATA[w1]]> <![CDATA[h1]]> <![CDATA[d1]]> <![CDATA[l1]]> <![CDATA[w2]]> <![CDATA[h2]]> <![CDATA[d2]]> Comprehensive sensitivity / % 22.52 15.81 21.11 35.39 1.95 1.84 1.38

[0121] In step S06, when multi-objective response surface optimization is performed on the paddy field grader structure, the constructed response surface model is tested. The results are shown in Table 5. The determination coefficient R of each output parameter is 2 Approaching 1, the root mean square error ERMS and the relative maximum absolute error E RMA All of them are close to 0, indicating that the accuracy of the response surface model is high and subsequent optimization can be performed. The optimization objectives are to maximize the first-order natural frequency and the second-order natural frequency of the paddy field grader structure and minimize the structural mass. The first-order and second-order natural frequencies are greater than twice the paddy field ground excitation frequency and the engine excitation frequency, and the range of design variables are set as constraints. The specific settings are shown in Tables 6 and 7. Finally, the multi-objective optimization genetic algorithm (MOGA) is used to optimize and solve the objective function, and the algorithm is configured as shown in Table 8. The initial population size is set to 10,000, the number of new populations generated by sample iteration in each generation is 100, the maximum number of iterations is 20, the allowable deviation is set to 2%, and the maximum allowable Pareto percentage is set to 70%.

[0122] Table 5 Response surface precision test

[0123] Error type <![CDATA[ω1]]> <![CDATA[ω2]]> m <![CDATA[R 2 ]]> 0.99961 0.99928 0.99941 <![CDATA[E RMS ]]> <![CDATA[3.98×10 -7 ]]> <![CDATA[8.27×10 -6 ]]> <![CDATA[7.14×10 -6 ]]> <![CDATA[E RMA ]]> <![CDATA[4.85×10 -6 ]]> <![CDATA[5.19×10 -5 ]]> <![CDATA[4.71×10 -5 ]]>

[0124] Table 6 Optimization objectives and constraint settings

[0125] Objective Function Optimization goals constraint Weight First order natural frequency maximize >36Hz Higher importance Second order natural frequency maximize >36Hz Higher importance quality minimize none Default Importance

[0126] Table 7 Design variable value range

[0127] symbol variable Initial value Lower limit Upper limit <![CDATA[w1]]> Beam width 40 20 60 <![CDATA[h1]]> Beam height 60 40 120 <![CDATA[d1]]> Beam thickness 5 2 8 <![CDATA[l1]]> Beam length 820 620 1020

[0128] Table 8 Multi-objective optimization algorithm configuration

[0129]

[0130]

[0131] After S06, we get Figure 6 The optimized values ​​of the structural parameters are rounded for easy processing, and the modal analysis is performed on the three-dimensional model of the optimized paddy field grader structure. The results are compared with the initial values. The specific comparison results are shown in Tables 8 and 9.

[0132] Table 9 Comparison of results before and after optimization

[0133] Parameter name symbol Before optimization After optimization contrast Beam Width <![CDATA[w1 / mm]]> 40 20.7 -48.25％ Liang Gao <![CDATA[h1 / mm]]> 60 41.4 -31％ Liang Hou <![CDATA[d1 / mm]]> 5 8 +60％ Liang Chang <![CDATA[l1 / mm]]> 820 621 -24.27％ 1st order natural frequency <![CDATA[ω1 / Hz]]> 4 40.523 +913.08％ 2nd order natural frequency <![CDATA[ω2 / Hz]]> 7.35 50.12 +581.9％ Grader quality m / Kg 119.58 114.26 -4.45％

[0134] Table 10 Comparison of modal analysis results before and after optimization

[0135] Degree Results before optimization / Hz Optimized results / Hz contrast 1 4.00 40.523 +913.08％ 2 7.35 50.12 +581.9％ 3 9.54 67.17 +604.09％ 4 15.23 96.18 +531.52％ 5 18.16 101.4 +458.37％ 6 44.22 161.55 +265.33％

[0136] According to the comparison table, the first six natural frequencies after optimization have been significantly improved. The improvement of the first six frequencies has expanded the dynamic stability range of the system, enabling it to operate more stably and withstand higher frequency dynamic loads. The first and second natural frequencies are far away from the external excitation frequency, which greatly reduces the risk of resonance and significantly improves the system's anti-vibration ability. The mass of the grader has also decreased slightly, achieving a balance between overall efficiency and cost.

[0137] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for optimizing the natural frequency of a paddy field grader structure based on size linkage and response surface method, characterized in that: The steps include: S01: Establish a simplified 3D model of the initial paddy field grader structure; S02: performing parameter setting on the three-dimensional model of the paddy field grader created in step S01; S03: Associating SolidWorks with Workbench, launching Workbench directly from SolidWorks to import the three-dimensional model of the paddy field grader in step S02 into Ansys Workbench and perform parametric design, establish dimension association and establish dimension parameter set; S04: Performing modal analysis on the simplified model of the paddy field grader imported into Ansys Workbench in step S03, obtaining the total deformation of the paddy field grader structure and the first six natural frequencies in the initial state, and setting part of the output results as output parameters to the size parameter set established in step S03; S05: Associating the size parameter set in step S04 with the parameter correlation module in Ansys Workbench to perform parameter sensitivity analysis and calculate the comprehensive sensitivity, thereby screening out input parameters with higher comprehensive sensitivity values; S06: Importing the parameterized paddy field grader structure model in step S04 into the response surface optimization module of Ansys Workbench to perform multi-objective response surface optimization and obtain optimal design parameters; S07. Reconstruct the paddy field grader structure model according to the optimal parameters of the paddy field grader structure obtained in step S06 and perform modal analysis to confirm the first six natural frequencies and mass of the optimized paddy field grader structure, and compare and analyze the first six natural frequencies of the initial paddy field grader structure obtained in step S02 to confirm the optimization effect.

2. A method for optimizing the structural natural frequency of a paddy field grader based on size linkage and response surface method according to claim 1, characterized in that: In step S04, the modal analysis specifically includes steps S41 to S45: S41: Building a new modal analysis module, using parameter transfer to import the simplified model of the paddy field grader in step S3 into the modal analysis module; S42: define material properties of paddy field grader structure model; S43: Mesh division. The simplified model of the paddy field grader is meshed using tetrahedral elements. A mesh size of 15 mm is applied to the crossbeam and the left and right connecting rods, and a mesh size of 20 mm is applied to the rest of the parts. S44: Add joints and constraints. According to the working principle of the paddy field grader, add corresponding joints and constraints to the model. Set each hinge connection of the paddy field grader as a revolving joint connection, set the connection between the elevation hydraulic rod and the elevation hydraulic cylinder as a moving joint connection, and set the connection between the horizontal hydraulic rod and the horizontal hydraulic cylinder as a moving joint connection. Apply cylindrical supports to the connection between the elevation hydraulic rod, the crossbeam, and the left and right connecting rods and the fuselage. S45: performing a solution, solving the first six order modes to obtain their deformation cloud diagrams, thereby obtaining the first six order natural frequencies and corresponding vibration modes of the paddy field grader structure in the initial state; S46: Output the results, output the calculated natural frequency and deformation cloud map, and set the first-order and second-order natural frequencies and structural mass as output parameters, and add them to the size parameter set established in step S03.

3. A method for optimizing the structural natural frequency of a paddy field grader based on size linkage and response surface method according to claim 1, characterized in that: In step S06, the multi-objective response surface optimization includes the following steps: S61, creating a response surface optimization module, using parameter transfer to import the simplified model of the paddy field grader in step S04 into the response surface optimization module; S62, performing a Latin hypercube experimental design on the four input parameters with high comprehensive sensitivity values ​​screened out in step S05 in the response surface optimization module of Ansys Workbench; S63, constructing a response surface model according to the test results obtained by performing the Latin hypercube test design in step S62; S64, testing the response surface model constructed in step S63 to determine whether the response surface model meets the fit requirement; S65. Construct a multi-objective response surface optimization design mathematical model according to the response surface model generated in step S63, and perform multi-objective response surface optimization on the paddy field grader structure to obtain optimal design parameters.

4. A method for optimizing the structural natural frequency of a paddy field grader based on size linkage and response surface method according to claim 1, characterized in that: In step S01, when establishing a three-dimensional model of a paddy field grader in Solid Works, the assembly should be designed from top to bottom, so as to establish linkage features and realize the linkage of related dimensions between the parts of the assembly; first, use SolidWorks Treehouse to establish an assembly containing 9 parts, including a crossbeam, a left connecting rod, a right connecting rod, a mounting frame, a leveling shovel, an elevation hydraulic rod, an elevation hydraulic cylinder, a horizontal hydraulic rod, and a horizontal hydraulic cylinder; and arrange the loading order of the parts according to the actual installation order of the paddy field grader, and draw the parts in the assembly according to the assembly relationship.

5. The method for optimizing the natural frequency of a paddy field grader structure based on size linkage and response surface method according to claim 1, characterized in that: In step S02, when setting parameters for the dimensions that need to be optimized in the model, two types of dimensions need to be set: one is the dimension parameter setting in the sketch. In the sketch editing mode, add "DS_" in front of the name of the corresponding dimension parameter; the other is the dimension parameter setting of the feature. The parameter is defined in the form of a global variable to achieve the linkage between parameter modification and dimension.

6. A method for optimizing the natural frequency of a paddy field grader structure based on size linkage and response surface method according to claim 1, characterized in that: In step S03, when performing parametric design on the paddy field grader model, the variables defined in step S02 are found and checked, all variables involved in the optimization are set as input parameters, and a size parameter set is established; in the generated size parameter set, parameter association settings are performed according to the relationship between the parameters.

7. A method for optimizing the structural natural frequency of a paddy field grader based on size linkage and response surface method according to claim 1, characterized in that: In step S05, when performing correlation analysis on all input parameters in the size parameter set, only the first-order natural frequency and the second-order natural frequency are selected as key response variables for analysis, the correlation type is selected to use Spearman correlation, and the mean value accuracy is set to 0.01, and the standard deviation accuracy is set to 0.02; start the correlation analysis to let the system calculate the impact of each input parameter on the first-order natural frequency and the second-order natural frequency of the selected output parameter; after the analysis is completed, check the generated sensitivity bar chart to screen out four input parameters with higher comprehensive sensitivity values.

8. A method for optimizing the structural natural frequency of a paddy field grader based on size linkage and response surface method according to claim 3, characterized in that: In step S62, when performing the experimental design, only the input parameters with higher comprehensive sensitivity values ​​screened out in step S05 are checked, and the upper and lower limits are set for each input parameter, and then the "experimental type design" and "sample type" are set, and the method is selected as needed. Here, Latin hypercube sampling design and CCD sampling are selected, and the "experimental design" is updated to generate the experimental design points; during the update of the "experimental design", do not close the paddy field grader model in SolidWorks associated with Ansys Workbench; after updating the "experimental design", double-click to enter the "response surface" and set the type of response surface; here, select the standard response surface, and update to generate the corresponding response surface results; The "experimental design points" generated in step S62 are imported into the software Design-Expert for multivariate regression fitting to obtain the prediction model of the first-order and second-order natural frequencies and structural quality of the paddy field grader.

9. A method for optimizing the structural natural frequency of a paddy field grader based on size linkage and response surface method according to claim 3, characterized in that: Step S64, using the absolute coefficient R 2 , Root mean square error E RMs , mean absolute error E RMA The accuracy of the fitting surface is evaluated. If the response surface model does not meet the requirements of goodness of fit, the response surface model is re-established until it meets the requirements of goodness of fit. R 2 The calculation formula is: E RMS The calculation formula is: E RMA The calculation formula is: Among them, y i is the DOE test data value, is the predicted value of the response surface similarity model, is the mean value of DOE experimental data, where the DOE experimental data value is the sample data obtained after the DOE sampling test is carried out according to the predicted value of the response surface similarity model.

10. The method for optimizing the structural natural frequency of a paddy field grader based on size linkage and response surface method according to claim 3, characterized in that: In step S65, the mathematical model for multi-objective response surface optimization design is: Among them, F(X) is the objective function finally output by the multi-objective response surface optimization calculation, max F(X) is the output value of the objective function when the objective function F(X) reaches the maximum value, that is, the 1st and 2nd order natural frequencies are the largest and the mass is the smallest; y1(X) is the 1st order natural frequency of the optimized structure, y2(X) is the 2nd order natural frequency of the optimized structure, and y3(X) is the mass of the optimized structure; γ1 is the weight coefficient of the 1st order natural frequency, γ2 is the weight coefficient of the 2nd order natural frequency, and γ3 is the weight coefficient of the structural mass; formula st is the constraint condition, 36Hz is twice the engine frequency of the paddy field grader; X is the decision vector, which represents the adjustable input parameter; x1, x2, x3, and x4 correspond to the design variables beam width w1, beam height h1, beam thickness d1, and beam length l1, respectively; The first-order natural frequency model of the paddy field grader is: The second-order natural frequency model of the paddy field grader is: The mass model of the paddy field grader is: When performing multi-objective response surface optimization on the paddy field grader structure, the optimization objectives are to maximize the first-order natural frequency and the second-order natural frequency of the paddy field grader structure and minimize the structural mass, and the first-order and second-order natural frequencies are greater than twice the paddy field ground excitation frequency and the engine frequency, and the range of design variables are used as constraints; a multi-objective genetic algorithm is used for optimization.

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