A method for lightweighting axles of trailers

Abstract

The invention discloses a method for lightweighting a trailer axle, comprising the following steps: S1, parametric modeling of the trailer axle, measuring the inner cavity size of the axle entity, and modeling using three-dimensional modeling software; S2, establishing a CAE finite element model, importing the established parametric three-dimensional model into the software to construct an optimized finite element model of the axle; S3, establishing a multi-objective optimization model, and optimizing the axle by adopting a numerical optimization method; S4, constructing a response surface; S5, solving a multi-objective genetic algorithm to obtain the optimal solution for lightweighting the trailer axle. The method of the invention achieves lightweighting of the trailer axle through simulation analysis and experimental analysis without changing the original axle appearance size and the outer matching surface.

Description

Technical Field

[0001] The invention belongs to the technical field of lightweight design, and in particular relates to a method for lightweighting a trailer axle. Background Art

[0002] As a key component of a semi-trailer, the strength and life of the axle have a great impact on the reliability, safety and performance of the entire vehicle. Therefore, fatigue strength analysis and optimal design of the axle are of great significance to reducing costs, reducing weight and improving economic benefits.

[0003] Some people used finite element simulation to perform performance verification and structural improvement of trailer axles under single load conditions, fatigue load conditions, and 6 times load conditions, improving the problem of stress concentration. The relevant mechanical properties and SN curves of the alloy structural steel 20Mn2 of the trailer axle material were obtained by testing. In the finite element analysis of the 6 times load condition, the elastic-plastic problem of the trailer axle was solved using a bilinear material model. Others adjusted the furnace load according to the load capacity of the quenching water tank, selected the quenching cooling medium and its stirring method according to the material characteristics of 20Mn2, determined the optimal quenching temperature of the trailer axle material through experiments, and solved the problem of unqualified hardness and metallographic structure of the end of the axle after heat treatment. Relevant workers analyzed the current status of lightweight axles of heavy-duty commercial vehicles in my country. Axle companies pay more and more attention to the lightweighting of products, and the ability and level of lightweight axle design are constantly improving. It is pointed out that the measures for lightweighting heavy-duty commercial axles in my country mainly include structural optimization design, material optimization design, and the use of advanced manufacturing processes. Later, someone used Power-Scan-Pro 2.0M 3D scanner to conduct 3D mapping of trailer axles, used UG to establish a 3D geometric model of trailer axles, used ABAQUS to conduct finite element analysis of the performance of metal trailer axles, and conducted bench tests on metal trailer axles to verify the results of finite element analysis. Finally, a solution for carbon fiber composite trailer axles was proposed. The new material trailer axles meet the performance requirements and are 23.2% lighter than metal trailer axles. However, traditional trailer axle design or other parts design mainly focuses on the overall performance of the trailer axle. Only when local performance does not meet the requirements will local performance optimization be performed, such as local heat treatment to improve local strength. Such a design leads to uneven distribution of axle performance and excess performance of some local structures, which does not meet the principle of equal strength in mechanical design. Summary of the invention

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to propose a method for lightweighting a trailer axle. Without changing the original axle appearance size and the outer matching surface, the lightweighting of the trailer axle is achieved through simulation analysis and experimental analysis.

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

[0006] A method for lightweighting a trailer axle comprises the following steps:

[0007] S1. Parametric modeling of trailer axle: measure the inner dimensions of the axle entity and model it using 3D modeling software;

[0008] S2. Establish a CAE finite element model, and import the established parameterized three-dimensional model into the software to construct an axle optimization finite element model;

[0009] S3. Establish a multi-objective optimization model and use numerical optimization method to optimize the design of the axle;

[0010] S4, response surface construction;

[0011] S5. Multi-objective genetic algorithm is used to solve the optimal solution for trailer axle lightweighting and verify it.

[0012] Furthermore, step S1 is specifically as follows:

[0013] The dimensions of the axle's inner cavity were measured using a thickness gauge, and multiple dimensions of the inner cavity structure were selected as optimized dimensional parameters. A parametric three-dimensional model of the axle was established using SolidWorks and DesignModler, in which the parameter selection included the entire axle's inner cavity.

[0014] Furthermore, step S2 is specifically as follows:

[0015] Import the built parametric 3D model into ANSYS Workbench to build the axle optimization finite element model, determine the constraint load of the finite element model, and use the rolling bearing as the beam support, which is symmetrically distributed on the left and right.

[0016] The minimum load and maximum load are set according to the expected goal of lightweighting, and the setting is based on the factory-specified maximum shaft load; the load action position is the leaf spring seat position, which is symmetrical on both sides; the load amount is the maximum load during the vertical bending stiffness and strength test, and the test load of the vertical bending fatigue life is set as a pulsating cycle load, and the corresponding cycle load range and loading frequency are set;

[0017] Since the yield limit of the material at the axle bearing is different from that of the main body material due to heat treatment, the bearing area is divided in the finite element environment to examine the strength of the bearing area separately and optimize the constraints independently;

[0018] The model is discretized using solid units, and the mesh unit size of the axle square tube and a part of the transition part and the unit size of the other part and the circular tube part are set. After the setting, a total of nodes and solid units will be formed. After the mesh quality check, if the constructed model meets the requirements of the calculation and analysis, the target model is established. If the constructed model does not meet the requirements of the calculation and analysis, it is necessary to reasonably adjust the mesh unit size of the axle square tube and a part of the transition part and the unit size of the other part and the circular tube part to meet the calculation requirements.

[0019] Furthermore, step S3 is specifically as follows:

[0020] Use numerical optimization method to optimize the design of axles, determine which factors will affect the optimization results and product performance, and the relationship between these factors;

[0021] The design process requires the design variables of the axle, that is, a series of optimization target points selected in the axle;

[0022] The overall mass of the axle, the maximum deformation of the axle, the maximum stress of the axle body, the maximum stress of the two bearings of the axle, the fatigue life of the axle, the yield limit of the two bearings of the axle after quenching and tempering, the safety factor, and the maximum deformation of the axle per meter of wheelbase are considered and constrained. The above parameters are important factors in the design optimization process. The mathematical model of the specific constraint relationship is described as follows:

[0023] X(x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ,x 7 ,x 8 ) T ; min m(X);

[0024] Where X is the design variable; m(X) is the axle mass; g 1 (X) is the maximum deformation of the axle; g 2 (X) is the maximum stress of the axle body; g 3 (X) is the maximum stress at the two bearing positions of the axle, g 4 (X) is the fatigue life of the axle; σ 0.2-2 is the yield limit of the two bearings of the axle after tempering and quenching; s is the safety factor, which is determined according to national standards and actual application requirements; a is the maximum deformation of the axle allowed per meter of wheelbase;.

[0025] Furthermore, step S4 is specifically as follows:

[0026] Multi-objective optimization design is a numerical analysis method that uses sampling within a given interval of design variables to conduct experimental design and solve the corresponding response value of each design point. On this basis, a fitting model is established and the fitting model is used to predict the response of unknown points or the information of known points is used to obtain the response of unknown points through interpolation calculation.

[0027] The Latin hypercube sampling design method is used to obtain the design points of the above-mentioned target points to be optimized after multiple simulations. In the process, the parallel solution method is used to accelerate the solution process.

[0028] Based on the above design points, the response surface was constructed using the Kriging method;

[0029] Obtain the stiffness response surface, strength response surface, fatigue life response surface and response surface of the trailer axle. By analyzing the results of the determination coefficient and sensitivity, obtain the relationship between the target points to be optimized, and analyze which points have a greater impact on stiffness, which points have a greater impact on strength, which points have a greater impact on fatigue life, and which points have a greater impact on the quality of the trailer axle.

[0030] Furthermore, in step S5, the multi-objective genetic algorithm solves the following problem:

[0031] The multi-objective genetic algorithm needs to design a suitable population size, crossover probability and mutation probability; after a corresponding number of iterative calculations, the Pareto optimal solution set is obtained;

[0032] There are multiple sets of optimal solutions. According to the actual processing conditions and processing technology of the material, the point closest to the optimization target is selected as the optimization result;

[0033] After obtaining the final result, its feasibility needs to be studied and analyzed, that is, by conducting bench tests on the mass, maximum deformation and fatigue life of the trailer axle or comparing them with finite element calculations. The verified reliable results are the final optimized values, and the optimization is completed.

[0034] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0035] 1. The present invention reduces the weight of the trailer axle without changing the external dimensions of the trailer axle and the mating surface of the exterior, with a weight reduction ratio of more than 10%. Although the weight of the axle is reduced, the stiffness, strength and fatigue life of the axle are still superior to those of the axle before lightweighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of the method of the present invention;

[0037] Figure 2 It is a schematic diagram of the trailer axle structure;

[0038] Figure 3 It is the parameter diagram of the inner cavity size of the trailer axle;

[0039] Figure 4 is the constraint load diagram of the trailer axle finite element model;

[0040] Figure 5 This is a schematic diagram of the measurement point arrangement for the trailer axle vertical bending stiffness test;

[0041] Figure 6 It is a schematic diagram of the measurement point layout for the vertical bending strength test of trailer axles. DETAILED DESCRIPTION

[0042] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0043] Example

[0044] like Figure 1 As shown, the present invention provides a method for lightweighting a trailer axle, comprising the following steps:

[0045] S1. Parametric modeling of trailer axle: measure the inner dimensions of the axle entity and model it using 3D modeling software;

[0046] In this embodiment, specifically:

[0047] The physical inner cavity dimensions of the axle were measured using a thickness gauge, and a total of 8 dimensions that control the inner cavity structure were selected as optimized dimensional parameters. The selection of these 8 parameters mainly includes the entire axle inner cavity. In the transition stage, several more points need to be selected to ensure their reliability. A parametric 3D model of the axle was established using SolidWorks and DesignModler.

[0048] like Figure 2 As shown, it is a schematic diagram of the trailer axle structure; Figure 3 The figure shows the parameter diagram of the inner cavity size of the trailer axle.

[0049] S2. Establish a CAE finite element model, and import the established parameterized three-dimensional model into the software to construct an axle optimization finite element model;

[0050] In this embodiment, specifically:

[0051] Import the built parametric 3D model into ANSYS Workbench to build the axle optimization finite element model, determine the constraint load of the finite element model, the beam support is a rolling support, symmetrically distributed on the left and right, the minimum load and maximum load are set to 0.25 times and 2.5 times the factory maximum axle load, that is, the minimum load is 3.25t, the maximum load is 32.5t, and the load action position is the leaf spring seat position, symmetrical on the left and right. The load amount is the maximum load during the vertical bending stiffness and strength test, and the test load of the vertical bending fatigue life is set to a pulsating cycle load, ranging from 3.25t to 32.5t, and the loading frequency is 60 times / min.

[0052] like Figure 4 As shown, it is the constraint load diagram of the trailer axle finite element model.

[0053] Since the yield limit of the material at the axle bearing is different from that of the main body material due to heat treatment, the bearing area is divided out in the finite element environment, and the strength of the bearing area is examined separately and optimized independently.

[0054] The model is discretized using solid elements. The mesh element size of the axle square tube and a part of the transition part is set to 8 mm, and the element size of the other transition part and the circular tube part is set to 4 mm. It is divided into 210,402 nodes and 109,599 solid elements in total.

[0055] In this embodiment, the final results show that the maximum deformation of the axle occurs in the axial center section, with a maximum deformation of 2.25 mm. Under the rated load condition, the maximum stress of the axle body occurs at the leaf spring seat, i.e., the loading point, with a maximum stress of 137.55 MPa. The maximum stress at the two bearing positions is 115.78 MPa. The fatigue life of the axle is greater than 1.2 million times. The results of the finite element analysis of various performance indicators of the axle are basically consistent with the results of the original axle bench test, indicating that the constructed finite element model of the axle is reliable.

[0056] S3. Establish a multi-objective optimization model and adopt a numerical optimization method to optimize the design of the axle; in this embodiment, specifically:

[0057] Use numerical optimization method to optimize the design of axles, determine which factors will affect the optimization results and product performance, and the relationship between these factors;

[0058] The design process requires the design variables of the axle, that is, a series of optimization target points selected in the axle;

[0059] The overall mass of the axle, the maximum deformation of the axle, the maximum stress of the axle body, the maximum stress of the two bearings of the axle, the fatigue life of the axle, the yield limit of the two bearings of the axle after quenching and tempering, the safety factor, and the maximum deformation of the axle per meter of wheelbase are considered and constrained. The above parameters are important factors in the design optimization process. Among them, the optimization mathematical model of the axle can be described as

[0060] X(x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ,x 7 ,x 8 ) T ; min m(X);

[0061] Where X is the design variable; m(X) is the axle mass (kg); g 1 (X) is the maximum deformation of the axle (2.76mm); g 2 (X) is the maximum stress of the axle body (137.55MPa); g 3 (X) is the maximum stress at the two bearings of the axle (156.80MPa), g 4 (X) is the fatigue life of the axle (8×10 5 times); 0.2-2 is the yield limit of the two bearings of the axle after tempering and quenching, which is taken as 940.8MPa; s is the safety factor, s=6; a is the maximum deformation of the axle allowed per meter of wheelbase, a=1.5mm.

[0062] In this embodiment, the above constraint factors are taken as an example, but are not limited to the above factors.

[0063] S4, response surface construction; in this embodiment, specifically:

[0064] Multi-objective optimization design is a numerical analysis method that uses sampling within a given interval of design variables to perform experimental design and solve the corresponding response value of each design point. On this basis, a fitting model is established and the fitting model is used to predict the response of unknown points or the information of known points is used to obtain the response of unknown points through interpolation calculation.

[0065] In this embodiment, the Latin hypercube sampling design method is adopted. After 135 simulations, a total of 45 design points are obtained. During the process, a parallel solution method is used to accelerate the solution process. Based on the above design points, the Kriging method is used to construct the response surface.

[0066] Obtain the stiffness response surface, strength response surface, fatigue life response surface and response surface of the trailer axle. By analyzing the results of the determination coefficient and sensitivity, obtain the relationship between the target points to be optimized, and analyze which points have a greater impact on stiffness, which points have a greater impact on strength, which points have a greater impact on fatigue life, and which points have a greater impact on the quality of the trailer axle.

[0067] like Figure 5 As shown in the figure, it is a schematic diagram of the layout of the test points for the vertical bending stiffness test of the trailer axle; Figure 6 The figure shows the schematic diagram of the measurement point arrangement for the vertical bending strength test of the trailer axle.

[0068] S5. Multi-objective genetic algorithm is used to solve the optimal solution for trailer axle lightweighting; specifically:

[0069] The multi-objective genetic algorithm needs to design a suitable population size, crossover probability and mutation probability; after a corresponding number of iterative calculations, the Pareto optimal solution set is obtained;

[0070] There are multiple sets of optimal solutions. According to the actual processing conditions and processing technology of the material, the point closest to the optimization target is selected as the optimization result;

[0071] After obtaining the final result, its feasibility needs to be studied and analyzed, that is, by conducting bench tests on the mass, maximum deformation and fatigue life of the trailer axle or comparing them with finite element calculations. The verified reliable results are the final optimized values, and the optimization is completed.

[0072] In this embodiment, a multi-objective genetic algorithm (MOGA) is used, the population size is set to 80, the crossover probability is set to 0.9, and the mutation probability is set to 0.02. After 36 iterations, three groups of Pareto axle lightweight optimal solutions are obtained. The optimized sizes of 8 parameters are obtained from the Pareto optimal solution.

[0073] The Pareto optimal solution provides designers with a variety of possible options. After comprehensive consideration of factors such as process, the optimized values ​​of the selected eight parameters are finally determined, and the final optimization result of the axle and its performance response are obtained. Ultimately, the above method is used to achieve multi-objective optimization of the trailer axle without changing the appearance size and surface matching surface of the trailer axle, and the mass is reduced by more than 10%.

[0074] It should also be noted that in this specification, terms such as "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0075] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for lightweighting a trailer axle, characterized in that: The following steps are involved: S1. Parametric modeling of trailer axle: measure the inner dimensions of the axle entity and model it using 3D modeling software; S2, establish a CAE finite element model, import the established parameterized three-dimensional model into the software to construct an axle optimization finite element model; step S2 is specifically as follows: Import the built parametric 3D model into ANSYS Workbench to build the axle optimization finite element model, determine the constraint load of the finite element model, and use the rolling bearing as the beam support, which is symmetrically distributed on the left and right. The minimum load and maximum load are set according to the expected goal of lightweighting, and the setting is based on the factory-specified maximum shaft load; the load action position is the leaf spring seat position, which is symmetrical on both sides; the load amount is the maximum load during the vertical bending stiffness and strength test, and the test load of the vertical bending fatigue life is set as a pulsating cycle load, and the corresponding cycle load range and loading frequency are set; Since the yield limit of the material at the axle bearing is different from that of the main body material due to heat treatment, the bearing area is divided in the finite element environment to examine the strength of the bearing area separately and optimize the constraints independently; The model is discretized using solid units, and the mesh unit size of the axle square tube and a part of the transition part and the unit size of the other part and the circular tube part are set. After setting, a total of nodes and solid units will be formed. After the mesh quality check, if the constructed model meets the requirements of the calculation and analysis, the target model is established. If the constructed model does not meet the requirements of the calculation and analysis, it is necessary to reasonably adjust the mesh unit size of the axle square tube and a part of the transition part and the unit size of the other part and the circular tube part to meet the calculation requirements; S3, establish a multi-objective optimization model, and adopt a numerical optimization method to optimize the design of the axle; step S3 is specifically as follows: Use numerical optimization method to optimize the design of axles, determine which factors will affect the optimization results and product performance, and the relationship between these factors; The design process requires the design variables of the axle, that is, a series of optimization target points selected in the axle; The overall mass of the axle, the maximum deformation of the axle, the maximum stress of the axle body, the maximum stress of the two bearings of the axle, the fatigue life of the axle, the yield limit of the two bearings of the axle after quenching and tempering, the safety factor, and the maximum deformation of the axle per meter of wheelbase are considered and constrained. The above parameters are important factors in the design optimization process. The mathematical model of the specific constraint relationship is described as follows: X(x1,x2,x3,x4,x5,x6,x7,x8) T ; minm(X); Among them, X is the design variable; m(X) is the mass of the axle; g1(X) is the maximum deformation of the axle; g2(X) is the maximum stress of the axle body; g3(X) is the maximum stress of the two bearings of the axle, and g4(X) is the fatigue life of the axle; σ 0.2-2 is the yield limit of the two bearings of the axle after quenching and tempering; s is the safety factor, which is determined according to national standards and actual application requirements; a is the maximum deformation per meter of wheelbase allowed by the axle; S4, response surface construction; S5. Multi-objective genetic algorithm is used to solve the optimal solution for trailer axle lightweighting and verify it.

2. A method for lightweighting a trailer axle according to claim 1, characterized in that: Step S1 is specifically as follows: The dimensions of the axle's inner cavity were measured using a thickness gauge, and multiple dimensions of the inner cavity structure were selected as optimized dimensional parameters. A parametric three-dimensional model of the axle was established using SolidWorks and DesignModler, in which the parameter selection included the entire axle's inner cavity.

3. The method for lightweighting a trailer axle according to claim 1, characterized in that: Step S4 is specifically as follows: Multi-objective optimization design is a numerical analysis method that uses sampling within a given interval of design variables to conduct experimental design and solve the corresponding response value of each design point. On this basis, a fitting model is established and the fitting model is used to predict the response of unknown points or the information of known points is used to obtain the response of unknown points through interpolation calculation. The Latin hypercube sampling design method is used to obtain the design points of the above-mentioned optimization target points after multiple simulations. The parallel solution method is used to accelerate the solution process. Based on the above design points, the response surface was constructed using the Kriging method; Obtain the stiffness response surface, strength response surface, fatigue life response surface and response surface of the trailer axle, and analyze the results of the determination coefficient and sensitivity to obtain the relationship between the optimization target points and points, and analyze which points have a greater impact on stiffness, which points have a greater impact on strength, which points have a greater impact on fatigue life, and which points have a greater impact on the quality of the trailer axle.

4. The method for lightweighting a trailer axle according to claim 1, characterized in that: In step S5, the multi-objective genetic algorithm solves the following problem: The multi-objective genetic algorithm needs to design a suitable population size, crossover probability and mutation probability; after a corresponding number of iterative calculations, the Pareto optimal solution set is obtained; There are multiple sets of optimal solutions. According to the actual processing conditions and processing technology of the material, the point closest to the optimization target is selected as the optimization result; After obtaining the final result, its feasibility needs to be studied and analyzed, that is, by conducting bench tests on the mass, maximum deformation and fatigue life of the trailer axle or comparing them with finite element calculations. The verified reliable results are the final optimized values, and the optimization is completed.

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