Reliability improving method for wheel spoke and rim assembly
By systematically identifying control factors and noise factors, and combining finite element simulation and Taguchi method optimization, the multi-objective collaborative decision-making problem in improving the reliability of wheel spokes and rim assemblies was solved, achieving robustness and efficiency improvement in the design.
Patent Information
- Application Number
- CN202511499574.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-20
- Publication Date
- 2026-01-30
AI Technical Summary
Existing technologies for improving the reliability of wheel spokes and rim assemblies suffer from several problems: single optimization objective, neglect of parameter interaction, ineffective handling of noise factor effects, large-scale and inefficient simulation and experimentation, and lack of multi-objective collaborative decision-making. These issues result in insufficient prediction accuracy and poor robustness of design results under complex working conditions.
By collecting control factors and noise factors, performing single-factor analysis and initial screening, identifying main effects and interaction effects, using the Taguchi method to optimize simulation analysis, constructing an objective function to identify the optimal factor combination, and combining a finite element simulation model to simulate historical fault scenarios, the simulation scale is reduced and the optimization efficiency is improved.
It improves the reliability of wheel spokes and rim assemblies, enhances the design's adaptability and robustness to actual working conditions, and solves the problems of single optimization objectives, neglect of parameter interaction, and high computational resource consumption in traditional methods, thus having significant engineering application value.
Smart Images
Figure CN121435582A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wheel technology, and in particular to a method for improving the reliability of wheel spokes and rim assemblies. Background Technology
[0002] Currently, due to the harsh operating conditions of commercial vehicles and the widespread phenomenon of overloading, wheel spokes and rim assemblies are prone to failure modes such as fracture and deformation out of roundness, resulting in numerous market claims. Therefore, wheel reliability design requires a key focus. The technical challenge lies in implementing reliability-enhancing designs to achieve product lifespan targets while simultaneously considering cost and weight constraints and avoiding design waste.
[0003] Currently, existing technologies for improving the reliability of wheel spokes and rim assemblies mainly suffer from the following shortcomings:
[0004] The optimization objectives are singular and lack a systematic approach: Existing methods often focus on improving a single performance indicator, such as extending fatigue life or reducing weight, failing to systematically coordinate the balance between reliability, cost, and lightweighting. This often results in a "weakest link" effect in the design results, making it difficult to meet the comprehensive performance requirements of commercial vehicles. The interaction between parameters is ignored: Traditional design processes are usually based on single-factor analysis or empirical formulas, failing to fully consider the interaction between control factors (such as plate thickness, bolt preload, and materials). This leads to insufficient prediction accuracy under complex operating conditions, poor design robustness, and a tendency for early failure. The impact of noise factors is not effectively addressed: Existing methods do not effectively handle noise factors such as user overload, road surface excitation, and manufacturing tolerances. Insufficient or crude consideration of factors, failing to systematically incorporate noise factors into the design process, results in weak adaptability of products to fluctuations in operating conditions during actual use, and discrepancies between reliability assessments and real-world scenarios; large-scale and inefficient simulations and experiments: some existing technologies attempt to optimize parameters through extensive simulations or experiments, but have not established effective dimensionality reduction and screening mechanisms, resulting in a waste of computing resources and time costs, long design cycles, and difficulty in adapting to the needs of rapid iterative product development; lack of multi-objective collaborative decision-making mechanisms: when multiple conflicting objectives such as lifespan, cost, and weight are involved, existing methods lack scientific trade-offs and decision-making tools, often relying on human experience for compromises, leading to strong subjectivity in the optimization process and insufficient repeatability and engineering applicability of the results. Summary of the Invention
[0005] The purpose of this invention is to provide a method for improving the reliability of wheel spokes and rim assemblies, so as to solve at least one of the problems existing in the prior art.
[0006] This invention provides the following solution:
[0007] This invention provides a method for improving the reliability of a wheel spoke and rim assembly, comprising:
[0008] Collect control factors and noise factors of wheel spokes and rim assemblies;
[0009] Univariate analysis was used to initially screen control factors and noise factors to remove redundant factors;
[0010] Orthogonal simulation schemes were designed based on control factors to identify the main effects and interaction effects of control factors;
[0011] The Taguchi method was used to optimize the simulation analysis process of control factors and noise factors based on the main effects and interaction effects of control factors, so as to formulate a new orthogonal simulation scheme.
[0012] Construct the objective function and analyze the optimal factor set using orthogonal simulation scheme.
[0013] Further methods for screening control factors and noise factors include:
[0014] Select the variable ranges for control factors and noise factors;
[0015] A finite element simulation model was constructed based on the control factor and noise factor to obtain the single-factor response value;
[0016] The control factors and noise factors are initially screened based on the single-factor response values to remove redundant factors;
[0017] Plot a scatter plot of the control factors and the single-factor response values to identify the linear relationship of the control factors.
[0018] Furthermore, variable ranges are set for control factors and noise factors. Quantitative factors in control factors and noise factors are set to have three levels: upper limit, lower limit, and average value. Qualitative factors in control factors and noise factors are set to have a maximum of three levels.
[0019] A finite element model was established to simulate historical failure scenarios in order to analyze the single-factor response values under single-factor changes in control factors and noise factors.
[0020] Furthermore, control factors and noise factors are initially screened based on the single-factor response values. One-way ANOVA is used to determine the influence of noise factors and the significance of control factors. Noise factors with low influence and control factors with low significance are identified as redundant factors, and redundant factors in control factors and noise factors are removed.
[0021] Draw a scatter plot of the control factors and the single-factor response values, and observe whether there is a linear relationship.
[0022] Furthermore, simulation analysis methods for control factors include:
[0023] The number of factor levels is set based on the linear relationship of the control factors and the range of variables;
[0024] Design an orthogonal simulation scheme based on the number of factor levels of the control factors;
[0025] Simulation results of orthogonal simulation schemes based on finite element model analysis;
[0026] Identify the main effects and interaction effects of the control factors based on the simulation results.
[0027] Furthermore, the number of factor levels is set according to the linear relationship of the control factor and the range of variables. If the control factor has a non-linear relationship, the number of factor levels is set to 3, with the upper limit, lower limit and average value being 3 levels in total. If the control factor has a linear relationship, the number of factor levels is set to 2, with the upper limit and lower limit being 2 levels in total.
[0028] An orthogonal array is selected based on the number of factor levels of the control factors to obtain an orthogonal simulation scheme.
[0029] Furthermore, finite element analysis is used to calculate the simulation results of the orthogonal simulation schemes, and the simulation results are the lifetime / damage results of each orthogonal simulation scheme;
[0030] The main effects and second-order interaction effects are statistically analyzed based on the simulation results to identify the main effects and interaction effects of the control factors.
[0031] Furthermore, after excluding control factors with insignificant main effects in the orthogonal simulation scheme and deleting interaction terms in orthogonal simulation schemes with insignificant interaction effects, the Taguchi method was used to formulate a new orthogonal simulation scheme.
[0032] Furthermore, the objective function is constructed as follows:
[0033] min(f) = w1 × f1 + w2 × f2;
[0034] max(S / N)=w1×S / N1+w2×S / N2+w3×S / N3;
[0035] f3≥m;
[0036] Where f1 represents the cost target, f2 represents the weight target, f3 represents the lifespan target, min(f) represents the minimum cost weight, max(S / N) represents the maximum signal-to-noise ratio, w1 represents the cost weight coefficient, w2 represents the weight weight coefficient, w3 represents the lifespan weight coefficient, w1+w2+w3=1, m represents the minimum lifespan requirement, S / N1 represents the cost signal-to-noise ratio, S / N2 represents the weight signal-to-noise ratio, and S / N3 represents the lifespan signal-to-noise ratio.
[0037] Furthermore, based on the lifespan, cost, and weight of the orthogonal simulation scheme, the optimal combination of control factors is obtained by solving the objective function, and this combination is taken as the optimal factor set.
[0038] The above solution achieves the following beneficial technical effects:
[0039] This invention constructs a complete reliability improvement method for wheel spokes and rim assemblies through systematic parameter identification, single-factor initial screening, interaction analysis, Taguchi method optimization, and multi-objective decision-making. This method effectively identifies key parameters during the design phase, reduces simulation scale, and improves optimization efficiency, while simultaneously considering reliability, cost, and lightweight objectives. By introducing noise factor and signal-to-noise ratio analysis, the invention enhances the adaptability and robustness of the design to actual working conditions, solving problems such as single optimization objectives, neglect of parameter interactions, and high computational resource consumption inherent in traditional methods. It possesses significant engineering application value and promising prospects for widespread adoption. Attached Figure Description
[0040] Figure 1 This is a flowchart illustrating the method for improving the reliability of the wheel spokes and rim assembly according to the present invention.
[0041] Figure 2 This is a flowchart illustrating the screening method for control factors and noise factors of the present invention.
[0042] Figure 3 This is a flowchart illustrating the simulation analysis method for the control factors of this invention. Detailed Implementation
[0043] The following detailed description, in conjunction with the accompanying drawings and specific embodiments, further illustrates the method for improving the reliability of wheel spokes and rim assemblies disclosed in this invention. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated; they can be combined to achieve better technical effects. In the accompanying drawings of the following embodiments, the same reference numerals in each drawing represent the same features or components, which can be applied to different embodiments. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.
[0044] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings are merely for illustrative purposes and to aid those skilled in the art in understanding and reading the invention. They are not intended to limit the conditions under which the invention can be implemented. Any modifications to the structure, changes in proportions, or adjustments to size, provided they do not affect the effectiveness or purpose of the invention, should fall within the scope of the technical content disclosed in the invention. The scope of the preferred embodiments of the present invention includes other implementations, wherein functions may be performed not in the order stated or discussed, including substantially simultaneously or in reverse order, depending on the functions involved. This should be understood by those skilled in the art to which the embodiments of the present invention pertain.
[0045] Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification. In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.
[0046] In the description of the embodiments of this application, " / " means "or", and "and / or" is used to describe the relationship between related objects, indicating that there can be three relationships. For example, "A and / or B" means: A and B exist alone, B exists alone, and A and B exist simultaneously. In the description of the embodiments of this application, "multiple" refers to two or more embodiments.
[0047] Please see Figure 1 As shown, this is a method for improving the reliability of the wheel spokes and rim assembly in this embodiment, including:
[0048] Step S1: Collect control factors and noise factors of the wheel spokes and rim assembly. The control factors include material, plate thickness, gasket area, bolt preload and vent shape. The noise factors include user overload conditions, road surface excitation, dimensional dispersion and surface roughness. The control factors and noise factors are parameters identified by failure mode analysis to evaluate the reliability of the wheel spokes and rim assembly.
[0049] Specifically, in step S1 of this embodiment, a complete parameter system is constructed by systematically identifying control factors and noise factors, laying a scientific foundation for subsequent simulation and optimization. This comprehensively covers various factors affecting wheel reliability, avoids optimization deviations caused by parameter omissions, and thus improves the systematicness and completeness of the design. By introducing noise factors, the design's adaptability to uncertain factors in actual working conditions is enhanced, making the optimization results more practical in engineering.
[0050] Please continue reading. Figure 1As shown, the method for improving the reliability of the wheel spokes and rim assembly further includes:
[0051] Step S2: Use univariate analysis to initially screen control factors and noise factors to remove redundant factors.
[0052] Please see Figure 2 As shown, the screening method for control factors and noise factors includes:
[0053] Step S21: Select the variable ranges for the control factor and the noise factor.
[0054] Specifically, in step S21 of this embodiment, variable ranges are set for control factors and noise factors. The quantitative factors in control factors and noise factors are set to have three levels: upper limit, lower limit, and average value. The qualitative factors in control factors and noise factors are set to have a maximum of three levels.
[0055] Specifically, in step S21 of this embodiment, the quantitative factor is a quantitative variable among the control factor and noise factor, such as plate thickness, gasket area, bolt preload, and dimensional dispersion. The qualitative factor is a description of the target properties among the control factor and noise factor, such as material and vent shape. When setting the variable range for the qualitative factor, the user can set 2-3 levels according to the actual situation. For example, the material can be set to level 3, the vent shape can be set to level 2 (including circular and elliptical shapes), and the user can select the worst scenario for overload conditions and road excitation, and select level 2.
[0056] Specifically, in step S21 of this embodiment, by scientifically setting the variable range of each factor, a reasonable parameter space is established for subsequent analysis, so as to ensure that the parameter variation range covers the actual possibility in engineering, and avoids calculation redundancy due to excessive range, thus laying the foundation for efficient and accurate simulation analysis, while ensuring that the optimization results have practical engineering application value.
[0057] Please continue reading. Figure 2 As shown, the screening method for the control factor and noise factor further includes:
[0058] Step S22: Construct a finite element simulation model based on the control factor and noise factor to obtain single-factor response values. The single-factor response values include lifetime values and damage values, which are used to determine the impact of single-factor changes on lifetime or damage.
[0059] Specifically, in step S22 of this embodiment, a finite element model is established using HyperMesh software to simulate historical fault scenarios in order to analyze the single-factor response values under single-factor changes of control factors and noise factors.
[0060] Specifically, in step S22 of this embodiment, a high-precision finite element simulation model is established to accurately simulate the mechanical behavior of the wheel assembly under actual working conditions, so as to effectively reproduce historical failure modes, provide a reliable platform for parameter sensitivity analysis, ensure the accuracy of single-factor response value calculation, and provide reliable data support for subsequent optimization.
[0061] Please continue reading. Figure 2 As shown, the screening method for the control factor and noise factor further includes:
[0062] Step S23: Perform preliminary screening of control factors and noise factors based on single-factor response values to remove redundant factors.
[0063] Specifically, in step S23 of this embodiment, the control factors and noise factors are initially screened based on the single-factor response values. One-way ANOVA is used to determine the influence of the noise factors and the significance of the control factors. The noise factors with small influence and the control factors with small significance are regarded as redundant factors, and the redundant factors in the control factors and noise factors are removed.
[0064] Specifically, in step S23 of this embodiment, when judging the influence of the noise factor and the significance of the control factor, the significance level is set to 0.05, and the p-value of the one-way ANOVA is compared with the significance level to judge the influence of the noise factor and the significance of the control factor.
[0065] Specifically, in step S23 of this embodiment, statistical analysis methods are used to screen parameters for significance, effectively identifying factors that have a key impact on reliability, thereby significantly reducing the complexity of subsequent multi-factor analysis, improving optimization efficiency, and ensuring that important parameters are not overlooked, making the optimization process more focused on key influencing factors.
[0066] Please continue reading. Figure 2 As shown, the screening method for the control factor and noise factor further includes:
[0067] Step S24: Draw a scatter plot of the control factors and single-factor response values to identify the linear relationship of the control factors.
[0068] Specifically, in step S24 of this embodiment, a scatter plot of the control factor and the single-factor response value is drawn, and it is observed whether there is a linear relationship. If the scatter plot is a U-shaped curve, it is determined that the control factor has a non-linear relationship; if the scatter plot is a straight line, it is determined that the control factor has a linear relationship.
[0069] Specifically, in step S24 of this embodiment, the inherent relationship between parameters and response values is revealed through visualization analysis, so as to intuitively show the linear or nonlinear characteristics of the parameters, provide a direct basis for subsequent determination of the number of factor levels, and help engineers to deeply understand the impact of parameter changes on reliability.
[0070] Specifically, in step S2 of this embodiment, the dimensionality of the parameter space is effectively reduced by single-factor analysis, key factors that have a significant impact on reliability are screened out, and the amount of subsequent simulation calculations is significantly reduced. This improves optimization efficiency while ensuring analysis accuracy and avoids wasting resources on non-key parameters. At the same time, the variance test and scatter plot analysis initially reveal the intrinsic relationship between parameters and response, providing an important basis for subsequent multi-factor interaction analysis.
[0071] Please continue reading. Figure 1 As shown, the method for improving the reliability of the wheel spokes and rim assembly further includes:
[0072] Step S3: Design an orthogonal simulation scheme based on the control factors to identify the main effects and interaction effects of the control factors.
[0073] Please see Figure 3 As shown, this is a simulation analysis method for control factors, including:
[0074] Step S31: Set the number of factor levels based on the linear relationship of the control factors and the range of variables.
[0075] Specifically, in step S31 of this embodiment, the number of factor levels is set according to the linear relationship of the control factor and the range of variables. If the control factor is non-linear, the number of factor levels is set to 3, taking the upper limit, lower limit and average value as a total of 3 levels. If the control factor is linear, the number of factor levels is set to 2, taking the upper limit and lower limit as a total of 2 levels.
[0076] Specifically, in step S31 of this embodiment, the strategy of intelligently determining the number of levels based on parameter characteristics achieves the best balance between analysis accuracy and computational efficiency. Different processing methods are adopted for parameters with different characteristics, which not only ensures the analytical accuracy of nonlinear parameters, but also avoids over-analysis of linear parameters.
[0077] Please continue reading. Figure 3 As shown, the simulation analysis method for the control factor further includes:
[0078] Step S32: Design an orthogonal simulation scheme based on the number of factor levels of the control factors.
[0079] Specifically, in step S32 of this embodiment, an orthogonal table is selected based on the number of factor levels of the control factor to obtain an orthogonal simulation scheme.
[0080] Specifically, in step S32 of this embodiment, the user can select an appropriate orthogonal array based on the number of factor levels. For example, if the orthogonal array is L9, it can observe 4 control factors, and each control factor has 3 levels. The user can select based on the number of control factors and the number of factor levels.
[0081] Specifically, in step S32 of this embodiment, the orthogonal simulation scheme is scientifically arranged using the principle of orthogonal experimental design to obtain the richest parameter combination information under limited computing resources, so as to systematically examine the combined effects of multiple parameters and multiple levels and provide an optimized experimental scheme for a comprehensive analysis of the interaction between parameters.
[0082] Please continue reading. Figure 3 As shown, the simulation analysis method for the control factor further includes:
[0083] Step S33: Analyze the simulation results of the orthogonal simulation scheme based on the finite element model.
[0084] Specifically, in step S33 of this embodiment, finite element analysis is used to calculate the simulation results of the orthogonal simulation scheme, and the simulation results are the life / damage results of each orthogonal simulation scheme.
[0085] Specifically, in step S33 of this embodiment, the performance response data under various parameter combinations is obtained through the finite element calculation of the system, so as to accurately evaluate the life and damage characteristics of different design schemes, provide a complete and reliable data foundation for interaction effect analysis, and ensure that optimization decisions are based on accurate numerical simulation.
[0086] Please continue reading. Figure 3 As shown, the simulation analysis method for the control factor further includes:
[0087] Step S34: Identify the main effects and interaction effects of the control factors based on the simulation results.
[0088] Specifically, in step S34 of this embodiment, the main effects and second-order interaction effects are statistically analyzed based on the simulation results to identify the main effects and interaction effects of the control factors. Higher-order interaction effects are ignored in the analysis to simplify the analysis.
[0089] Specifically, in step S34 of this embodiment, statistical analysis methods are used to deeply analyze the main effects and interaction effects between parameters, so as to reveal the synergistic or antagonistic effects when multiple parameters change simultaneously, breaking through the limitations of traditional single-factor analysis and providing a scientific basis for formulating optimization strategies that comprehensively consider the coupling effects of parameters.
[0090] Specifically, in step S3 of this embodiment, the interaction between control factors is revealed through orthogonal experimental design and finite element simulation, breaking through the limitations of traditional single-factor optimization, so as to identify the synergistic or inhibitory effects between factors, thereby more accurately predicting the system behavior under complex parameter combinations. Through the analysis of main effects and second-order interaction effects, data support is provided for formulating efficient optimization strategies, significantly improving the scientificity and reliability of the design.
[0091] Please continue reading. Figure 1 As shown, the method for improving the reliability of the wheel spokes and rim assembly further includes:
[0092] Step S4: Based on the main effects and interaction effects of the control factors, the Taguchi method is used to optimize the simulation analysis process of the control factors and noise factors in order to formulate a new orthogonal simulation scheme.
[0093] Specifically, in step S4 of this embodiment, after excluding control factors with insignificant main effects in the orthogonal simulation scheme and deleting interaction terms in the orthogonal simulation scheme with insignificant interaction effects, the Taguchi method is used to re-formulate the orthogonal simulation scheme.
[0094] Specifically, in step S4 of this embodiment, the main effect refers to the influence of a single control factor. If the main effect is not significant, the control factor is meaningless. The interaction effect refers to the influence of the interaction term on the experimental results. An insignificant interaction effect means that the two factors do not affect each other, so there is no need to examine the interaction term. When using the Taguchi method to re-formulate the orthogonal simulation scheme, it is necessary to select an appropriate orthogonal array. After excluding the above insignificant control factors and interaction terms, Taguchi analysis can select a smaller orthogonal array to reduce the number of simulation schemes.
[0095] Specifically, in step S4 of this embodiment, the Taguchi method is introduced to further simplify the orthogonal simulation scheme, eliminate insignificant interaction terms, and greatly improve the efficiency of the optimization process. This effectively controls the simulation scale and reduces computational costs while ensuring the robustness of the results. Through signal-to-noise ratio analysis, the design's ability to resist noise factors is enhanced, making the final scheme more stable and reliable in practical applications.
[0096] Please continue reading. Figure 1 As shown, the method for improving the reliability of the wheel spokes and rim assembly further includes:
[0097] Step S5: Construct the objective function and analyze the optimal factor set using an orthogonal simulation scheme.
[0098] Specifically, in step S5 of this embodiment, the objective function is constructed as follows:
[0099] min(f) = w1 × f1 + w2 × f2;
[0100] max(S / N)=w1×S / N1+w2×S / N2+w3×S / N3;
[0101] f3≥m;
[0102] Where f1 represents the cost target, f2 represents the weight target, f3 represents the lifespan target, min(f) represents the minimum cost weight, max(S / N) represents the maximum signal-to-noise ratio, w1 represents the cost weight coefficient, w2 represents the weight weight coefficient, w3 represents the lifespan weight coefficient, w1+w2+w3=1, m represents the minimum lifespan requirement, S / N1 represents the cost signal-to-noise ratio, S / N2 represents the weight signal-to-noise ratio, and S / N3 represents the lifespan signal-to-noise ratio.
[0103] Specifically, in step S5 of this embodiment, based on the lifespan, cost, and weight of the orthogonal simulation scheme, the optimal combination of control factors is obtained by solving the objective function, and this combination is used as the optimal factor set.
[0104] Specifically, in step S5 of this embodiment, a multi-objective function that comprehensively considers lifespan, cost, and weight is established to achieve synergistic optimization among the three. Under the premise of meeting the minimum lifespan requirement, the weights of each objective can be flexibly adjusted to adapt to different engineering needs. By jointly solving the problem of maximizing the signal-to-noise ratio and minimizing cost and weight, the optimal parameter combination under complex constraints is obtained, providing a scientific decision-making basis for improving the overall performance of the wheel assembly.
[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method of improving reliability of a wheel spoke and rim assembly, characterized by, The reliability improvement method of the wheel spoke and rim assembly comprises the following steps: Collecting control factors and noise factors of the wheel spoke and rim assembly; Using single factor analysis to preliminarily screen the control factors and noise factors to remove redundant factors; Designing an orthogonal simulation scheme based on the control factors to identify the main effect and interaction effect of the control factors; Using the Taguchi method to optimize the simulation analysis process of the control factors and noise factors based on the main effect and interaction effect of the control factors to reformulate the orthogonal simulation scheme; Constructing an objective function and analyzing the optimal factor group in combination with the orthogonal simulation scheme.
2. The wheel web and rim assembly reliability enhancement method of claim 1, wherein The screening method of the control factors and noise factors comprises the following steps: Selecting the variable range of the control factors and noise factors; Constructing a finite element simulation model according to the control factors and noise factors to obtain single factor response values; Preliminarily screening the control factors and noise factors according to the single factor response values to remove redundant factors; Drawing a scatter plot of the control factors and single factor response values to identify the linear relationship of the control factors.
3. The wheel web and rim assembly reliability enhancement method of claim 2, wherein Setting the variable range of the control factors and noise factors, setting the quantitative factors in the control factors and noise factors to take the upper limit, lower limit and average value for a total of 3 levels, and setting the qualitative factors in the control factors and noise factors to take a maximum of 3 levels; Establishing a finite element model to simulate a historical failure scenario to analyze the single factor response values under the single factor changes of the control factors and noise factors.
4. The wheel web and rim assembly reliability enhancement method of claim 3, wherein Preliminarily screening the control factors and noise factors according to the single factor response values, using single factor variance analysis to judge the influence of the noise factors and the significance of the control factors, taking the noise factors with small influence and the control factors with small significance as redundant factors, and removing the redundant factors in the control factors and noise factors; Drawing a scatter plot of the control factors and single factor response values and observing whether there is a linear correlation.
5. The wheel web and rim assembly reliability enhancement method of claim 1 wherein, The simulation analysis method of the control factors comprises the following steps: Setting the factor level number according to the linear relationship and variable range of the control factors; Designing an orthogonal simulation scheme according to the factor level number of the control factors; Analyzing the simulation results of the orthogonal simulation scheme based on the finite element model; Identifying the main effect and interaction effect of the control factors according to the simulation results.
6. The wheel web and rim assembly reliability enhancement method of claim 5, wherein, Setting the factor level number according to the linear relationship and variable range of the control factors, if the control factors are in a nonlinear relationship, setting the factor level number to 3, taking the upper limit, lower limit and average value for a total of 3 levels, and if the control factors are in a linear relationship, setting the factor level number to 2, taking the upper limit and lower limit for a total of 2 levels; Selecting an orthogonal table according to the factor level number of the control factors to obtain the orthogonal simulation scheme.
7. The wheel web and rim assembly reliability enhancement method of claim 6, wherein, Using finite element analysis to calculate the simulation results of the orthogonal simulation scheme, wherein the simulation results are the life / damage results of each orthogonal simulation scheme; According to the simulation results, the main effect and second-order interaction effect are calculated to identify the main effect and interaction effect of the control factors.
8. The wheel web and rim assembly reliability enhancement method of claim 1, wherein, Excluding the control factors with non-significant main effect in the orthogonal simulation scheme, and deleting the interaction items in the orthogonal simulation scheme with non-significant interaction effect, the orthogonal simulation scheme is reformulated using the Taguchi method.
9. The wheel web and rim assembly reliability enhancement method of claim 1 wherein, The objective function is constructed as follows: min(f)=w1×f1+w2×f2; max(S / N)=w1×S / N1+w2×S / N2+w3×S / N3; f3≥m; Wherein, f1 represents the cost target, f2 represents the weight target, f3 represents the life target, min(f) represents the minimum cost weight, max(S / N) represents the maximum signal-to-noise ratio, w1 represents the cost weight coefficient, w2 represents the weight weight coefficient, w3 represents the life weight coefficient, w1+w2+w3=1, m represents the minimum life requirement value, S / N1 represents the signal-to-noise ratio of the cost, S / N2 represents the signal-to-noise ratio of the weight, S / N3 represents the signal-to-noise ratio of the life.
10. The wheel web and rim assembly reliability enhancement method of claim 9, wherein, According to the life, cost and weight of the orthogonal simulation scheme, the optimal combination of control factors is obtained by solving the objective function, and the optimal factor combination is obtained.