A finite element based performance optimization method and system

Abstract

The application relates to the technical field of finite element optimization, in particular to a performance optimization method and system based on finite elements, which comprises the following steps: obtaining a finite element model of a gear in a rice transplanter dividing mechanism, dividing the finite element model into multiple grids; applying corresponding loads to the finite element model according to simulation data of the gear of the rice transplanter during operation, obtaining simulation indexes of each grid, optimizing the grid size of each grid based on the simulation indexes, and outputting an optimized target grid; determining performance indexes of each target grid, optimizing design parameters of the target grid according to the performance indexes, updating the finite element model based on the optimized design parameters, and taking the finite element model as the final finite element model of the gear; and the application can improve the analysis accuracy and optimization efficiency of the gear through deep coupling of grid optimization and parameter optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of finite element optimization, and particularly relates to a performance optimization method and system based on finite elements. BACKGROUND

[0002] In the traditional design process, the gear structure is often designed by relying on empirical formula and simplified model. This method is difficult to comprehensively consider the dynamic stress condition and complex stress distribution of the gear in the actual operation process, resulting in that the designed gear may have strength redundancy or local weak area, and cannot realize lightweight design while ensuring structural reliability. In addition, the traditional optimization method is mostly optimized for a single performance index, ignoring the coupling relationship between different performance indexes, and it is difficult to obtain a design scheme with excellent comprehensive performance. Meanwhile, in the finite element analysis, the quality of mesh division directly affects the accuracy and calculation efficiency of the simulation result. The traditional manual division or simple automatic division method is prone to problems such as mesh distortion, uneven element quality, etc., which further affects the accuracy and efficiency of subsequent optimization analysis. These problems are particularly prominent in the design of the gear of the rice transplanter dividing mechanism, because the working environment of the mechanism is harsh, and the gear needs to withstand periodic impact load and variable stress, which has very high requirements for its structural strength, transmission efficiency and service life. The traditional design method has been difficult to meet the design requirements of modern agricultural machinery for high-performance and high-reliability parts. SUMMARY

[0003] The present application aims to provide a performance optimization method and system based on finite elements, which is designed to accurately model and multi-dimensionally optimize the gear in the rice transplanter dividing mechanism based on finite element technology, so as to solve the problems of insufficient analysis accuracy and low optimization efficiency of the traditional design method under complex working conditions.

[0004] In order to achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] In a first aspect, the present application embodiment provides a performance optimization method based on finite elements, which comprises the following steps:

[0006] S100, obtaining a finite element model of the gear in the rice transplanter dividing mechanism, and dividing the finite element model into a plurality of meshes;

[0007] S200, applying corresponding loads to the finite element model according to the simulation data of the gear of the rice transplanter during operation, obtaining simulation indexes of each mesh, optimizing the mesh size of each mesh based on the simulation indexes, and outputting the optimized target mesh; the simulation index represents the prediction accuracy and calculation efficiency of the mesh on the structure response:

[0008] S300, determine the performance index of each target grid, optimize the design parameters of the target grid according to the performance index, update the finite element model based on the optimized design parameters as the final finite element model of the gear; the design parameters include the modulus of the gear, the number of teeth, the tooth width, the addendum coefficient, the pressure angle, and the elastic modulus and Poisson's ratio of the material.

[0009] Optionally, in S100, the finite element model of the gear in the rice transplanter dividing mechanism is obtained, and the finite element model is divided into multiple grids, including:

[0010] S110, obtaining the finite element model of the gear in the rice transplanter dividing mechanism, and dividing the finite element model into multiple regions according to geometric characteristics;

[0011] S120, determining the corresponding grid type according to the geometric shape of each region, and dividing the corresponding region into multiple grids according to the grid type; the grid type includes tetrahedral grid and hexahedral grid.

[0012] Optionally, in S200, the corresponding load is applied to the finite element model according to the simulation data of the gear of the rice transplanter during operation, to obtain the simulation index of each grid, and the grid size of each grid is optimized based on the simulation index, and the optimized grid size is output, including:

[0013] S210, determine the force working condition parameters of the gear of the rice transplanter during operation, define the load of each grid in the finite element model according to the force working condition parameters, the load includes load type, action position and direction; the force working condition parameters include transplanting resistance, periodic frequency, inertia force, impact load and gravity;

[0014] S220, set the constraint condition of the finite element model, apply the corresponding load to each grid in the finite element model according to the simulation data, configure the finite element solver parameters and calculate to obtain the simulation value of each grid; the constraint condition includes bearing seat constraint, gear shaft constraint and tooth surface contact constraint; the simulation value includes stress value, strain value, displacement and element mass of the grid;

[0015] S230, determine the simulation index of each grid based on the simulation value and the reference value of the reference grid, maximize the simulation index of each grid as the optimization target, and use the improved genetic algorithm to iteratively optimize the grid size of each grid, and output the optimized grid size.

[0016] Optionally, the simulation index of each grid is determined based on the simulation value and the reference value of the reference grid, including:

[0017] obtaining reference values of the reference mesh, the reference values of the reference mesh including stress values, strain values, element mass and displacement amount of the reference mesh;

[0018] dividing the stress values of the mesh by the stress values of the reference mesh to obtain stress indicators; dividing the strain values of the mesh by the strain values of the reference mesh to obtain strain indicators; subtracting the difference between the element mass and the minimum element mass from the maximum element mass and the minimum element mass to obtain a mesh mass indicator; and dividing the displacement amount of the mesh by the displacement amount of the reference mesh to obtain a displacement amount indicator;

[0019] fusing the stress indicators, the strain indicators, the mesh mass indicators and the displacement amount indicators by weighting to obtain a simulation indicator of the mesh.

[0020] Optionally, in S300, the performance indicators of the target meshes are determined, the design parameters of the target meshes are optimized according to the performance indicators, and the finite element model is updated based on the optimized design parameters as the final finite element model of the gear, including:

[0021] S310, a performance optimization target of the gear is determined, and a mapping relationship between the performance indicators and the design parameters is established; the mapping relationship is a nonlinear regression model of the performance indicators and the design parameters; the performance indicators include gear root bending strength, gear face contact strength, transmission efficiency and structure lightweight indicators; and the design parameters include the modulus, the number of teeth, the tooth width, the addendum coefficient, the pressure angle, the elastic modulus and the Poisson's ratio of the gear;

[0022] S320, a sensitivity analysis is performed on the performance indicators and the design parameters to identify key design parameters that have a significant impact on the performance indicators;

[0023] S330, a multi-objective optimization model is established, the maximum gear root bending strength, the maximum gear face contact strength, the highest transmission efficiency and the minimum structure lightweight indicator are set as optimization targets, and constraint conditions of the design parameters are set;

[0024] S340, an improved non-dominated sorting genetic algorithm is used for multi-objective optimization solution, a uniformly distributed Pareto optimal solution set is generated in the target space through fast non-dominated sorting and congestion calculation, and each solution corresponds to a set of design parameter combinations;

[0025] S350, a scheme with the optimal comprehensive performance is selected from the Pareto optimal solution set as the optimized design parameters, and the finite element model is updated based on the optimized design parameters as the final finite element model of the gear.

[0026] In a second aspect, an embodiment of the present application provides a performance optimization system based on a finite element, and the system includes:

[0027] at least one processor;

[0028] at least one memory for storing at least one program;

[0029] When the at least one program is executed by the at least one processor, the at least one processor implements the method as claimed in any one of the above.

[0030] The beneficial effects of the present application are: the present application discloses a performance optimization method and system based on finite elements, the present application firstly constructs an initial finite element model through regional division and mesh type matching, refines model details in combination with the functional characteristics of non-circular gears and eccentric gears, and lays a precise geometric foundation for subsequent analysis; then, dynamic load is applied based on multi-source simulation data, and the mesh size is iteratively optimized through an improved genetic algorithm, which significantly improves the calculation efficiency while ensuring the prediction accuracy of structural response, and solves the problem that the traditional mesh division is highly subjective and the accuracy and efficiency are difficult to balance; finally, through establishing a nonlinear mapping relationship between performance indicators and design parameters, a multi-objective optimization is performed by using an improved non-dominated sorting genetic algorithm, an optimal scheme in terms of comprehensive performance is selected from the Pareto optimal solution set, and the collaborative optimization of the bending strength of the tooth root, the contact strength of the tooth surface, the transmission efficiency and the lightweight structure is realized. The present application realizes the depth coupling of mesh optimization and parameter optimization, so that the finite element model of the gear can accurately reflect the mechanical behavior under actual working conditions, and the operation stability and service life of the transplanting mechanism can be significantly improved through the optimization of design parameters, thereby providing a systematic technical path for the high-performance design of the core components of the rice transplanter. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0032] Figure 1 The flowchart of the performance optimization method based on finite elements in the embodiments of the present application is shown in the figure.

[0033] Figure 2 The structural block diagram of the performance optimization system based on finite elements in the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0034] The concept, specific structure and generated technical effects of the present application will be described clearly and completely in combination with the embodiments and the drawings, so as to fully understand the purpose, scheme and effect of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0035] For the design pain point of the gear of the rice transplanter dividing mechanism, the traditional method has poor prediction accuracy of the structural strength under the action of dynamic load, and it is difficult to balance the contradiction between strength, efficiency and lightweight, which leads to the problems of early failure or high energy consumption of the gear in actual operation, which restricts the operation stability and service life of the transplanter. At the same time, the subjectivity of meshing and the limitation of single performance index optimization further reduce the engineering practicability and comprehensive performance of the design scheme.

[0036] To solve the technical problems in the related art, the application provides a performance optimization method and system based on finite elements. The application realizes precise modeling and comprehensive performance improvement of the gear structure by combining multi-dimensional grid optimization with parameter optimization. In the grid optimization stage, an initial model is constructed by region division and grid type matching, and dynamic loads including transplanting resistance, periodic frequency, inertia force, impact load and gravity are applied in combination with the actual working conditions of the gear of the rice transplanter dividing mechanism. The grid size is iteratively optimized using an improved genetic algorithm, with the goal of maximizing simulation indicators, effectively balancing the calculation accuracy and efficiency of the grid. In the parameter optimization stage, a nonlinear regression model of performance indicators and design parameters is established, key design parameters are identified through sensitivity analysis, and a multi-objective optimization model is constructed with the gear bending strength, tooth surface contact strength, transmission efficiency and structural lightweight as the target. The improved non-dominated sorting genetic algorithm is used to obtain the Pareto optimal solution set, and finally the design parameter combination with the optimal comprehensive performance is selected to update the finite element model.

[0037] Referring to Figure 1 The application provides a performance optimization method based on finite elements, which comprises the following steps:

[0038] S100, obtaining a finite element model of a gear in a rice transplanter dividing mechanism, and dividing the finite element model into a plurality of grids;

[0039] Specifically, first, the relevant parameters of the gear in the rice transplanter dividing mechanism are obtained, including but not limited to the number of teeth, modulus, tooth width, material properties and other information of the gear. These parameters are the basic data for finite element analysis, and their accuracy directly affects the reliability of the subsequent analysis results. Through accurate measurement and obtaining from design drawings and other materials, the authenticity and integrity of the parameters are ensured. Then, a professional finite element modeling software is used to construct an accurate finite element model according to the obtained gear parameters.

[0040] In the modeling process, the geometry of the gear is carefully described, and the actual structural characteristics of the gear, such as keyway, chamfer, etc., are considered, which may affect the mechanical properties. At the same time, the boundary conditions of the model are reasonably set to simulate the installation and stress of the gear in actual work, for example, the shaft hole of the gear is properly constrained with the shaft to simulate its fixing method in the transmission system.

[0041] It should be noted that the gear includes non-circular gear and eccentric gear; the non-circular gear is deformed for speed change, and the eccentric gear is eccentric for displacement; in the rice transplanter dividing mechanism, the non-circular gear and the eccentric gear are used to realize different functions. The non-circular gear can change the transmission ratio through its special tooth shape and pitch curve, meet the demand for different speeds in the rice transplanting process, for example, in different stages of transplanting, the movement speed of the seedling claw is accurately controlled through the speed change of the non-circular gear, to meet the agronomic requirements and improve the uniformity and accuracy of transplanting. The eccentric gear uses its eccentric characteristics to produce displacement changes, which can be converted into specific movements of the seedling claw and other components in the vertical or horizontal direction, to assist in completing the actions of seedling grabbing, conveying and inserting. Through the reasonable use of these two special gears, combined with the optimized parameters obtained based on finite element analysis, the overall performance of the rice transplanter dividing mechanism is further improved.

[0042] S200, according to the simulation data of the gear of the rice transplanter during operation, a corresponding load is applied to the finite element model to obtain simulation indexes of each grid, and the grid size of each grid is optimized based on the simulation indexes, and an optimized target grid is output; the simulation index represents the prediction accuracy and calculation efficiency of the grid to the structure response:

[0043] Specifically, first, according to the various load conditions of the gear of the rice transplanter during actual operation, the specific source and acquisition method of the simulation data are determined. The stress, strain, displacement and unit mass of the gear under different operating conditions can be collected by field testing and using sensors and other equipment; or the simulation data can be obtained by simulating the working scene of the rice transplanter in a controllable environment through laboratory simulation experiments. These simulation data are the key basis for subsequent loading and analysis of the finite element model, and the quality is directly related to the effectiveness of the optimization results.

[0044] After obtaining the simulation data, it is necessary to preprocess the data to remove noise and outliers, and ensure the accuracy and reliability of the data. Then, according to the preprocessed simulation data, the corresponding load is applied in the finite element model. The application of load needs to accurately reflect the stress condition of the gear in actual operation, including the type, size, position and direction of the load. For example, the rice transplanting resistance will generate a certain force on the tooth surface of the gear, and the specific value and distribution of the force need to be determined according to the simulation data and applied to the corresponding position of the finite element model; periodic frequency will cause periodic vibration of the gear, and the corresponding periodic load needs to be set in the model; inertia force, impact load and gravity also need to be applied according to the actual situation to fully simulate the stress working condition of the gear.

[0045] After completing the load application, the mesh size of each mesh is optimized based on the simulation index. The simulation index is an important parameter for measuring the prediction accuracy and computational efficiency of the mesh, and its calculation formula has been given in the previous section. By substituting the simulation values of each mesh into the simulation index formula, the simulation index value of each mesh is calculated. Then, according to the size of the simulation index value, the influence of different mesh sizes on the prediction accuracy and computational efficiency is analyzed. For the mesh with a lower simulation index value, it means that the prediction accuracy or computational efficiency needs to be improved, and the mesh size needs to be adjusted. For example, if the simulation index value of a certain mesh is low due to the large mesh size leading to insufficient prediction accuracy, the mesh size can be appropriately reduced to improve the prediction accuracy; if the computational efficiency is too low due to the small mesh size, the mesh size can be appropriately increased to improve the computational efficiency while ensuring a certain prediction accuracy.

[0046] In the optimization process, the mesh size needs to be continuously adjusted, and the simulation index value needs to be recalculated until the simulation index value of each mesh reaches the preset requirement, at which point the optimized mesh size is output. The optimized mesh size can improve the computational efficiency as much as possible while ensuring the prediction accuracy, laying a good foundation for subsequent finite element model solving operations.

[0047] S300, determine the performance index of each target mesh, optimize the design parameters of the target mesh according to the performance index, update the finite element model based on the optimized design parameters as the final finite element model of the gear; the design parameters include the modulus, number of teeth, tooth width, addendum coefficient, pressure angle, and elastic modulus and Poisson's ratio of the material.

[0048] In the embodiments provided by the application, through progressive processing of mesh division, mesh size optimization and design parameter optimization, the whole process optimization from geometric modeling to performance improvement is realized. First, an initial finite element model is constructed through regional division and mesh type matching, and the model details are refined in combination with the functional characteristics of the non-circular gear and the eccentric gear, so as to lay a precise geometric foundation for subsequent analysis; then, dynamic loads are applied based on multi-source simulation data, and the mesh size is iteratively optimized through the improved genetic algorithm, so as to significantly improve the calculation efficiency while ensuring the prediction accuracy of the structural response, and solve the problem that the traditional mesh division is highly subjective and the accuracy and efficiency are difficult to balance; finally, the nonlinear mapping relationship between the performance indicators and the design parameters is established, and the improved non-dominated sorting genetic algorithm is used for multi-objective optimization, the optimal scheme with the best comprehensive performance is selected from the Pareto optimal solution set, and the collaborative optimization of the bending strength of the gear root, the contact strength of the gear face, the transmission efficiency and the structural lightweight is realized. Through the deep coupling of mesh optimization and parameter optimization, the gear finite element model can accurately reflect the mechanical behavior under actual working conditions, and the operation stability and service life of the transplanting mechanism can be significantly improved through the optimization of design parameters, thereby providing a systematic technical path for the high-performance design of the core components of the rice transplanter.

[0049] In some embodiments, in S100, the finite element model of the gear in the transplanting mechanism of the rice transplanter is obtained, and the finite element model is divided into a plurality of meshes, including:

[0050] In S110, the finite element model of the gear in the transplanting mechanism of the rice transplanter is obtained, and the finite element model is divided into a plurality of regions according to geometric characteristics.

[0051] In S120, the corresponding mesh type is determined according to the geometric shape of each region, and the corresponding region is divided into a plurality of meshes according to the mesh type; the mesh type includes tetrahedral mesh and hexahedral mesh.

[0052] Specifically, appropriate mesh types are selected according to the complexity of different regions, and the mesh types include tetrahedral mesh, hexahedral mesh and the like. The tetrahedral mesh division is relatively simple and is suitable for models with complex shapes, but the accuracy may be slightly inferior; for key regions such as gear roots and gear tips with complex geometric shapes and prone to stress concentration in work, more detailed hexahedral mesh division is adopted to improve the capture accuracy of the structural response of the region. For non-critical regions such as the spokes and hubs of the gear, which have relatively regular geometric shapes and uniform stress distribution, tetrahedral mesh with higher division efficiency can be used to effectively reduce the number of elements and reduce the calculation cost while ensuring the calculation accuracy meets the requirements.

[0053] At the same time, at the grid transition of different regions, by setting reasonable grid density gradient, the calculation error caused by the sudden change of grid size is avoided, and the grid quality of the entire finite element model is ensured to be uniform and meet the calculation requirements.

[0054] In some embodiments, in S200, the simulation data of the gear of the rice transplanter during operation is used to apply corresponding loads to the finite element model to obtain simulation indexes of each grid, the grid size of each grid is optimized based on the simulation indexes, and the optimized grid size is output, including:

[0055] In S210, stress working condition parameters of the gear of the rice transplanter during operation are determined, and loads of each grid in the finite element model are defined according to the stress working condition parameters, the loads including load types, action positions and directions; the stress working condition parameters including transplanting resistance, periodic frequency, inertial force, impact load and gravity;

[0056] Specifically, based on field test or machine design specification, typical operation parameters of the gear of the dividing and inserting mechanism are obtained: the transplanting resistance is the interaction force between the seedling and the soil during the process of taking and transplanting seedlings, the measured range is 50-200 N (single seedling), the periodic frequency f = 0.5-2 Hz (corresponding to the travel speed of 1-3 m / s); the inertial force is the centrifugal force of the gear when rotating with the dividing and inserting mechanism; the impact load is the instantaneous collision force between the seedling claw and the seedling when taking the seedling; and the gravity is the distributed load caused by the weight of the gear, the direction being vertically downward.

[0057] According to the load characteristics, the load type is selected as a concentrated force (F) or a distributed force (q), the transplanting resistance acts on the connecting point of the seedling claw and the gear (such as the tooth groove contact area), the concentrated force is used as the static load average, and the sinusoidal function is used to simulate the periodic fluctuation; the inertial force acts on the center of mass of the gear, the body load is used; the impact load acts on the tooth surface contact area, the step load is used; and the gravity acts on the whole gear, the distributed force is used.

[0058] The transplanting resistance is positioned to the contact point of the tooth groove and the seedling claw (such as the midpoint of the tooth width, z = 0.5b), the direction being along the seedling insertion direction (vertically downward, the angle θ = 30°-45° with the horizontal plane); the inertial force is along the radial direction of the gear shaft (perpendicular to the rotation plane); the impact load is along the normal direction of the tooth surface (perpendicular to the tangent line of the contact point); and the gravity is along the vertically downward direction (-Z axis).

[0059] In S220, the constraint conditions of the finite element model are set, corresponding loads of each grid in the finite element model are applied according to the simulation data, the finite element solver parameters are configured, and simulation values of each grid are calculated; the constraint conditions including bearing seat constraint, gear shaft constraint and tooth surface contact constraint; the simulation values including stress value, strain value, displacement amount and element quality of the grid;

[0060] The quality of the divided grid is checked. The aspect ratio, Jacobian determinant and other indicators of the grid are checked to ensure that the grid quality meets the requirements of finite element analysis. For poor quality grids, such as grids with an aspect ratio that is too large or a Jacobian determinant value that is outside the reasonable range, local adjustment or re-division is performed to improve the calculation stability and result accuracy of the grid. Through strict grid quality control, a foundation is laid for subsequent accurate finite element analysis.

[0061] It should be noted that the simulation data is obtained through simulation experiments of the actual working state of the rice transplanter, and the simulation data includes the maximum stress of the gear root (unit: MPa), the root mean square displacement (unit: mm), and the unit mass (unit: kg); the simulation values include the stress value (unit: MPa), the strain value (dimensionless), the displacement (unit: mm), and the unit mass (unit: kg); the simulation index represents the prediction accuracy and calculation efficiency of the grid on the structure response.

[0062] Specifically, for the bearing seat constraint, the gear is installed on the transplanter box through the bearing, and the bearing seat restricts the translational freedom of the gear, but allows the rotational freedom around the shaft; for the gear shaft constraint, if the gear is interference fit with the shaft, a binding constraint needs to be applied at the contact area between the shaft and the gear bore to simulate the force transmission of the interference connection; for the gear face contact constraint, a contact pair is set in the meshing area of the gear pair to define the normal contact behavior (hard contact, no separation) and the tangential friction behavior (friction coefficient μ = 0.1~0.15, simulating friction under mud lubrication).

[0063] The solver uses the Transient Structural (Transient Structural) solver in ANSYS Workbench, which is suitable for dynamic response analysis under periodic load; according to the working frequency f = 1 Hz, the time step Δt = 0.001 s is set (to ensure at least one complete cycle of sampling); the energy convergence tolerance is set to 1e-5 (to ensure calculation accuracy), and the maximum number of iterations is 50 (to avoid divergence); the grid with a distortion rate greater than 0.8 is selected by the element distortion rate (Jacobian determinant) (marked as "bad element" and re-divided).

[0064] Taking a cylindrical spur gear (m = 4 mm, z = 20) of a certain transplanter as an example, a periodic transplanting resistance F(t) = 100(1 + sin(2πt))N is applied, and the inertia force is 20N (angular velocity 10 rad / s), and the impact load peak is 250N (impact duration 0.02s). After solving by Transient Structural, the peak equivalent stress of the grid at the gear root fillet is 1180MPa (close to the allowable contact stress 1500MPa), the deformation is 0.03mm (not exceeding the allowable deformation), and the contact pressure distribution is uniform, verifying the accuracy of the load application and solution.

[0065] Through the above steps, the mechanical response data of the gear model at each grid can be accurately obtained, thereby providing reliable data support for subsequent construction of the grid performance index formula.

[0066] In S230, a simulation index of each grid is determined based on the simulation value and the reference value of the reference grid, a maximum value of the simulation index of each grid is taken as an optimization target, an improved genetic algorithm is used to iteratively optimize the grid size of each grid, and an optimized grid size is output.

[0067] It should be noted that the stress value needs to be controlled within the allowable stress range of the material, the strain value and the displacement amount need to meet the gear transmission accuracy requirement, and the unit mass needs to be as small as possible on the premise of ensuring the structural strength, so as to reduce the inertia force when the gear is running. The optimization variable is set as the size parameter of the grid, including the grid refinement coefficient of the key area and the grid coarsening coefficient of the non-key area, and the value range of each variable is set. By initializing the population, the fitness value of each individual is calculated, and selection, crossover and mutation operations are performed according to the fitness value. The population is iteratively updated until the preset convergence condition is met, such as the change of the target function value being less than a threshold value for a plurality of consecutive generations or the maximum number of iterations being reached. Finally, the optimized grid size is output, realizing the differentiated configuration of the grid size in the key area and the non-key area, ensuring the calculation accuracy of the stress concentration parts such as the tooth root and the tooth tip (the grid size is refined to 0.1-0.5 mm), and reducing the overall calculation amount through the reasonable enlargement of the grid size in the non-key area (1-3 mm), so as to achieve an optimal balance between the accuracy and the efficiency of the finite element model.

[0068] In some embodiments, the determination of the simulation index of each grid based on the simulation value and the reference value of the reference grid comprises:

[0069] The reference value of the reference grid includes the stress value, the strain value, the unit mass and the displacement amount of the reference grid;

[0070] The stress value of the grid is divided by the stress value of the reference grid to obtain a stress index; the strain value of the grid is divided by the strain value of the reference grid to obtain a strain index; the difference between the unit mass and the minimum unit mass is subtracted from 1, and the ratio of the maximum unit mass and the minimum unit mass is obtained to obtain a grid mass index; and the displacement amount of the grid is divided by the displacement amount of the reference grid to obtain a displacement amount index;

[0071] The stress index, the strain index, the grid mass index and the displacement amount index are weighted and fused to obtain the simulation index of the grid.

[0072] By this multi-dimensional weighting calculation method, the performance of the grid in stress accuracy, strain accuracy, self quality and displacement accuracy is comprehensively considered, so that the simulation index can fully reflect the comprehensive performance of the grid, and provide quantitative basis for the optimization of subsequent grid design parameters. For example, if the stress value of a certain grid is close to the reference grid, the strain value is small, the element quality is in a reasonable interval and the displacement meets the expectation, then the simulation index value will be higher, indicating that the grid has good calculation efficiency while ensuring calculation accuracy.

[0073] Specifically, the simulation index formula is:

[0074]

[0075] wherein, represents the simulation index of the grid, represents the stress value of the grid; represents the stress value of the reference grid (0.1 mm fine grid); represents the strain value of the grid; represents the strain value of the reference grid; represents the element quality (Jacobian determinant normalized value, range [0, 1]); , are the minimum and maximum values of the element quality, respectively; represents the displacement of the grid, represents the displacement of the reference grid; is a weight coefficient, satisfying . For example, ω1=0.4 (stress accuracy), ω2=0.2 (displacement accuracy), ω3=0.3 (grid quality), and ω4=0.1 (displacement). By adjusting the weight coefficient, the grid model can be flexibly optimized according to actual needs, ensuring that the best simulation effect can be obtained under different working conditions, and further improving the reliability and practicality of finite element analysis.

[0076] In some embodiments, in S300, the performance index of each target grid is determined, the design parameters of the target grid are optimized according to the performance index, and the finite element model is updated based on the optimized design parameters as the final finite element model of the gear, including:

[0077] S310, determining the performance optimization target of the gear, and establishing a mapping relationship between the performance index and the design parameter; the mapping relationship is a nonlinear regression model of the performance index and the design parameter, the performance index includes the bending strength of the tooth root, the contact strength of the tooth surface, the transmission efficiency and the structure lightweight index, and the design parameter includes the module, the number of teeth, the tooth width, the addendum coefficient, the pressure angle, and the elastic modulus and Poisson's ratio of the material of the gear;

[0078] The mapping relationship is a nonlinear regression model constructed based on gear design theory and finite element simulation data. The root bending strength (σ_F) is positively correlated with the module (m) and tooth width (b), and negatively correlated with the number of teeth (z) (m²b / z). The tooth surface contact strength (σ_H) is proportional to the square root of the module (m) and the number of teeth (z) (m√z), and positively correlated with the material's elastic modulus (E). The transmission efficiency (η) is significantly affected by the pressure angle (α), with the meshing loss being minimal at α=20°, and is also related to the addendum coefficient (h_a). Related (h_a) =1 indicates optimal overlap ratio); the structural lightweight index (M) is defined as the ratio of gear mass (ρV, where ρ is material density and V is volume) to rated load, which is positively correlated with module and tooth width, and needs to be minimized under strength constraints.

[0079] S320, by performing sensitivity analysis on the performance indicators and design parameters, key design parameters that have a significant impact on the performance indicators are identified;

[0080] The Sobol global sensitivity analysis method was used to calculate the sensitivity index of each design parameter on tooth root bending strength, tooth surface contact strength, transmission efficiency, and structural lightweighting indicators. For example, the sensitivity index of module (m) on tooth root bending strength was 0.65, and that of tooth width (b) was 0.25, indicating that module is the most critical parameter affecting tooth root bending strength; the sensitivity index of pressure angle (α) on transmission efficiency reached 0.7, making it a core parameter for transmission efficiency optimization. Based on the sensitivity analysis results, design parameters with a sensitivity index greater than 0.3 were selected as optimization variables, such as module, tooth width, and pressure angle, to reduce the dimensionality of the optimization problem and improve optimization efficiency.

[0081] S330, establish a multi-objective optimization model, with the optimization objectives being the maximum tooth root bending strength, the maximum tooth surface contact strength, the highest transmission efficiency, and the minimum structural lightweight index, and set the constraints of the design parameters;

[0082] The constraints include: module m∈[3mm,5mm] (to meet the spatial installation requirements of the rice transplanter's dividing mechanism), number of teeth z=20 (to match the transmission ratio), tooth width b∈[20mm,40mm] (to avoid material waste and bulky mechanism caused by excessive width), pressure angle α∈[15°,25°] (standard value range), material elastic modulus E=206GPa (45# steel), Poisson's ratio μ=0.3 (conventional value for metallic materials).

[0083] S340 employs an improved non-dominated sorting genetic algorithm for multi-objective optimization. Through fast non-dominated sorting and crowding calculation, it generates a uniformly distributed Pareto optimal solution set in the objective space, with each solution corresponding to a set of design parameter combinations.

[0084] The improved non-dominated sorting genetic algorithm (NSGA-III) is used to solve the multi-objective optimization problem, the population size is initialized to 100, the crossover probability is 0.8, the mutation probability is 0.1, and the iteration number is 200 times. Through fast non-dominated sorting and congestion degree calculation, a uniformly distributed Pareto optimal solution set is generated in the target space, and each solution corresponds to a set of design parameter combinations.

[0085] S350, select the scheme with the optimal comprehensive performance from the Pareto optimal solution set as the optimized design parameter, and update the finite element model based on the optimized design parameter as the final finite element model of the gear.

[0086] After normalizing each target performance index by using the ideal point method, the Euclidean distance between each Pareto solution and the ideal point (a point formed by the optimal values of each target) is calculated, and the solution with the smallest distance is the optimal scheme. Taking a certain optimization result as an example, the optimal design parameters are: m=4.2mm, b=32mm, and a=22°. Substituting these parameters into the finite element model, re-dividing the grid and performing simulation verification, the results show that the tooth root bending strength is improved by 12% (from 1180MPa to 1038MPa), the tooth surface contact strength is improved by 8%, the transmission efficiency is improved by 2.5%, and the structure lightweight index is reduced by 5%, all meeting the design requirements. Based on this, the finite element model is updated, and the final finite element model of the gear is constructed.

[0087] The present application combines finite element simulation and multi-objective optimization algorithm to construct a full-process solution from grid precision optimization to design parameter optimization. The following is a specific embodiment provided by the present application:

[0088] In the grid division stage, first, the geometric cleaning of the gear three-dimensional model is performed to remove the slight features such as fillets and chamfers to simplify the model, and then different grid partitions are divided according to the structural characteristics of the gear (such as tooth, hub, web, etc.). For stress concentration parts such as tooth engagement area, hexahedral grid is used for fine division, and the grid size is controlled in 0.5-1mm; for the relatively simple structure of the hub, web, etc., tetrahedral grid is used for division, and the grid size is set to 2-3mm to reduce the calculation amount while ensuring the calculation accuracy. After division, the unit distortion rate, aspect ratio and other indicators are verified by the grid quality inspection tool to ensure that the distortion rate of all grids is less than 0.1 and the aspect ratio is less than 5, avoiding distortion of the simulation results due to grid quality problems.

[0089] During the load and constraint application process, according to the actual working conditions of the rice transplanter dibbling mechanism, the force data of the gear during operation was collected through field tests. Specifically, a torque sensor and an acceleration sensor were installed on the gear shaft to monitor the torque changes and vibration frequency in real time during the transplanting process. Combined with the kinematic analysis of the dibbling mechanism, the calculated resistance of the gear during transplanting was about 800-1200N, the periodic frequency was consistent with the swing frequency of the transplanting arm, and was about 1.5Hz. These force condition parameters were converted into load conditions in the finite element model: a time-varying contact force was applied in the tooth surface contact area, the size of which was calculated according to the torque and the gear division circle radius; a torque load corresponding to the transplanting resistance was applied on the gear shaft; at the same time, the gravity load of the gear itself (material density was taken as 7.85g / cm³) and the inertia force generated by rotation (the rotation speed was converted to 300r / min according to the working speed of the transplanter) were considered. In terms of constraints, full degree of freedom constraints were applied to the bearing seat position to limit its translation and rotation; cylindrical surface constraints were applied to the matching surface of the gear shaft and the bearing, allowing only the rotation freedom around the axis to simulate the actual installation conditions.

[0090] The simulation numerical calculation adopts Abaqus finite element analysis software, and the solver parameters are configured as follows: explicit dynamic analysis step is selected, analysis time is set to 2 rice transplanting cycles (about 1.33 s), and time step is 1e-5 s, so as to capture the transient response under the action of dynamic load. After the solution is completed, the stress value, strain value, displacement amount and element mass of each grid element are extracted. The simulation results of the reference grid (the standard model divided by 0.2 mm fine grid) are taken as the benchmark, and the simulation indexes of each grid are calculated. For example, the stress value of a certain gear tooth grid is 350 MPa, the stress value of the reference grid is 380 MPa, and the stress index of the grid is 350 / 380≈0.92; if the element mass of the grid is 1.2e-5 kg, the minimum value of the element mass is 0.8e-5 kg, and the maximum value of the element mass is 2.0e-5 kg, then the grid mass index is 1-[(1.2e-5-0.8e-5) / (2.0e-5-0.8e-5)]=1-(0.4e-5 / 1.2e-5)=1-1 / 3≈0.67. The stress index, strain index, grid mass index and displacement amount index are respectively assigned to the weights of 0.3, 0.3, 0.2 and 0.2, and the comprehensive simulation index of the grid is 0.92×0.3+0.88×0.3+0.67×0.2+0.90×0.2=0.87 after weighted fusion. The maximum of the simulation index of each grid is taken as the target, the improved genetic algorithm is used for grid size iterative optimization, the population size of the algorithm is set to 50, the crossover probability is 0.8, the mutation probability is 0.1, the iteration number is 50 generations, and finally the optimized grid size is output: the grid size in the gear tooth area is optimized to 0.6 mm, which is reduced by 0.4 mm compared with the initial value, and the simulation accuracy of the stress concentration area is improved by about 12%; the grid size in the web area is adjusted to 2.5 mm, the number of elements is reduced by 15% and the calculation efficiency is improved by about 20% on the premise of ensuring the calculation accuracy of the overall stiffness of the structure.

[0091] In the performance index and design parameter optimization stage, first, the sensitivity analysis is carried out by the control variable method. Four key design parameters are selected, including module (2-4 mm), tooth number (18-24), tooth width (20-40 mm) and pressure angle (20°-25°). Five levels are set for each parameter, and 5 4 =625 simulation experiments are carried out. The tooth root bending strength, tooth surface contact strength, transmission efficiency and structure lightweight index (represented by gear mass) of each test are calculated, and the stepwise regression analysis method is used to establish the nonlinear regression model of performance index and design parameter. The results show that the sensitivity coefficient of module to tooth root bending strength is 0.78, and the sensitivity coefficient of module to tooth surface contact strength is 0.65; the sensitivity coefficient of tooth width to transmission efficiency is 0.52; the sensitivity coefficient of pressure angle to lightweight index is-0.43 (negative sign represents negative correlation), and the four parameters are identified as key design parameters.

[0092] Based on the sensitivity analysis results, a multi-objective optimization model is established. The constraint conditions of the design parameters are set as: module ≥ 2.5 mm, tooth number ≤ 22, tooth width ≥ 25 mm, and pressure angle in the range of 20°-22°. The improved non-dominated sorting genetic algorithm (NSGA-III) is used for solving, and the algorithm parameters are set as: population size 100, evolution algebra 200, simulated binary crossover for crossover operator, and polynomial mutation for mutation operator. The individuals in the population are divided into different non-dominated levels by fast non-dominated sorting, the crowding distance of each individual is calculated, and the individuals with larger crowding distance are retained to maintain the diversity of the population. After 200 generations of evolution, a Pareto optimal solution set containing 50 solutions is generated in the objective space. From the solution set, the ideal point method is used to select the scheme with the best comprehensive performance: the tooth root bending strength reaches 520 MPa, the tooth surface contact strength is 1250 MPa, the transmission efficiency is improved to 98.5%, and the gear mass is reduced to 1.8 kg. Compared with the scheme before optimization (tooth root bending strength 450 MPa, tooth surface contact strength 1100 MPa, transmission efficiency 97%, mass 2.2 kg), the performance indicators are significantly improved.

[0093] The optimized design parameters (module 3 mm, tooth number 20, tooth width 30 mm, and pressure angle 20°) are updated to the finite element model, and the meshing and simulation verification are performed again. The results show that the maximum stress of the optimized gear under the rated working condition appears at the tooth root transition fillet, and the stress value is 320 MPa, which is less than the allowable stress (400 MPa) of the material, meeting the strength requirement; the tooth surface contact stress is 950 MPa, which is lower than the contact fatigue limit (1100 MPa); the power loss during transmission is reduced by about 15%, and the structure mass is reduced by 18%, realizing the multi-objective optimization of strength, efficiency and lightweight. The optimization scheme has been verified by sample trial and field test, and after 50 hours of continuous operation, the gear has no obvious wear and fatigue cracks, and the operation stability and service life of the splitting mechanism are effectively improved.

[0094] Corresponding to the method of Figure 1 , with reference to Figure 2 , the embodiment of the present application provides a performance optimization system based on finite elements, comprising:

[0095] at least one processor;

[0096] at least one memory for storing at least one program;

[0097] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method.

[0098] It can be seen that the content in the method embodiments is applicable to the system embodiments, the system embodiments specifically implement the same functions as the method embodiments, and achieve the same beneficial effects as the method embodiments.

[0099] In addition, the embodiments of the present application further disclose a computer program product or a computer program, which is stored in a computer readable storage medium. A processor of a computer device can read the computer program from the computer readable storage medium, and the processor executes the computer program, so that the computer device executes the method described above. Similarly, the content in the method embodiments is applicable to the storage medium embodiments, the storage medium embodiments specifically implement the same functions as the method embodiments, and achieve the same beneficial effects as the method embodiments.

[0100] Those skilled in the art can understand that all or some of the methods disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer readable medium, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. In addition, it is known to those skilled in the art that communication media generally includes computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism, and can include any information delivery medium.

[0101] The above is a specific description of the preferred embodiments of the present disclosure, but the present disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present disclosure, and these equivalent modifications or replacements are all included in the scope defined by the claims of the present disclosure.

Claims

1. A finite element based performance optimization method, characterized by, The method comprises the following steps: S100, acquiring a finite element model of a gear in a rice transplanter dividing mechanism, and dividing the finite element model into a plurality of meshes; S200, applying corresponding loads to the finite element model according to simulation data of the gear of the rice transplanter during operation, obtaining simulation indexes of each mesh, optimizing the mesh size of each mesh based on the simulation indexes, and outputting an optimized target mesh; the simulation indexes represent the prediction accuracy and calculation efficiency of the mesh on the structure response; S300, determining performance indexes of each target mesh, optimizing design parameters of the target mesh according to the performance indexes, updating the finite element model based on the optimized design parameters, and taking the updated finite element model as the final finite element model of the gear; In S200, the simulation data of the gear of the rice transplanter during operation is applied to the finite element model to obtain simulation indexes of each mesh, the mesh size of each mesh is optimized based on the simulation indexes, and an optimized target mesh is outputted, which comprises: S210, determining force working condition parameters of the gear of the rice transplanter during operation, defining the loads of each mesh in the finite element model according to the force working condition parameters, the loads comprising load types, action positions and directions; the force working condition parameters comprising transplanting resistance, periodic frequency, inertial force, impact load and gravity; S220, setting constraint conditions of the finite element model, applying corresponding loads to each mesh in the finite element model according to the simulation data, configuring finite element solver parameters and calculating simulation values of each mesh; the constraint conditions comprising bearing seat constraints, gear shaft constraints and tooth surface contact constraints; the simulation values comprising stress values, strain values, displacement amounts and element masses of the meshes; S230, determining simulation indexes of each mesh based on the simulation values and reference values of reference meshes, taking the maximization of the simulation indexes of each mesh as an optimization target, and iteratively optimizing the mesh size of each mesh by using an improved genetic algorithm, and outputting the optimized mesh size.

2. The method of claim 1, wherein, In S100, the finite element model of the gear in the rice transplanter dividing mechanism is acquired, and the finite element model is divided into a plurality of meshes, which comprises: S110, acquiring a finite element model of a gear in a rice transplanter dividing mechanism, and dividing the finite element model into a plurality of meshes; S120, determining corresponding mesh types according to the geometric shapes of each region, and dividing the corresponding region into a plurality of meshes according to the mesh types; the mesh types comprising tetrahedral meshes and hexahedral meshes.

3. The method of claim 1, wherein, The determination of the simulation indexes of each mesh based on the simulation values and reference values of reference meshes comprises: acquiring reference values of reference meshes, the reference values of the reference meshes comprising stress values, strain values, element masses and displacement amounts of the reference meshes; The stress value of the grid is divided by the stress value of the reference grid to obtain a stress index; the strain value of the grid is divided by the strain value of the reference grid to obtain a strain index; the displacement value of the grid is divided by the displacement value of the reference grid to obtain a displacement value index according to the formula The grid quality index is calculated, wherein The unit quality is represented by , The minimum value and the maximum value of the unit quality are respectively represented by min and max; the displacement value of the grid is divided by the displacement value of the reference grid to obtain a displacement value index. weighting and fusing stress indexes, strain indexes, mesh mass indexes and displacement amount indexes to obtain the simulation indexes of the mesh.

4. The method of claim 1, wherein, In S300, the performance indicators of each target mesh are determined, the design parameters of the target mesh are optimized according to the performance indicators, and the finite element model is updated based on the optimized design parameters as the final finite element model of the gear, including: In S310, a performance optimization target of the gear is determined, and a mapping relationship between the performance indicators and the design parameters is established; the mapping relationship is a nonlinear regression model of the performance indicators and the design parameters; the performance indicators include the bending strength of the gear tooth root, the contact strength of the gear tooth surface, the transmission efficiency, and the structural lightweight index; and the design parameters include the modulus, the number of teeth, the tooth width, the addendum coefficient, the pressure angle, the elastic modulus, and the Poisson's ratio of the gear; In S320, the sensitivity analysis is performed on the performance indicators and the design parameters to identify the key design parameters that have a significant impact on the performance indicators; In S330, a multi-objective optimization model is established, the maximum bending strength of the gear tooth root, the maximum contact strength of the gear tooth surface, the highest transmission efficiency, and the minimum structural lightweight index are set as the optimization targets, and the constraint conditions of the design parameters are set; In S340, an improved non-dominated sorting genetic algorithm is used for multi-objective optimization solution, the fast non-dominated sorting and the congestion degree calculation are performed to generate a uniformly distributed Pareto optimal solution set in the target space, and each solution corresponds to a group of design parameter combinations; In S350, the scheme with the optimal comprehensive performance is selected from the Pareto optimal solution set as the optimized design parameters, and the finite element model is updated based on the optimized design parameters as the final finite element model of the gear.

5. A finite element based performance optimization system, characterized by, The system includes: at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor implements the method in any one of claims 1 to 4.

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