A design method, device, medium and equipment of a steel auxiliary frame

Abstract

The application provides a design method of a steel auxiliary frame, comprising the following steps: S1, constructing an initial structure model of the steel auxiliary frame; S2, determining a plurality of target key parameters that need to be optimized; taking minimization of the mass of the steel auxiliary frame as an objective function, optimizing the target key parameters, and obtaining a steel lightweight structure model; S3, performing performance simulation analysis on the steel lightweight structure model, and obtaining a performance simulation result; S4, if the performance simulation result does not meet the expected requirement, adjusting the section parameter, thickness parameter and / or lap parameter of each part, and cyclically executing steps S1 to S3 until the preset requirement is met, and outputting a target structure model; in this way, the stiffness, strength and modal of the steel auxiliary frame are guaranteed to meet the performance requirement from the source; taking minimization of the mass as an objective function, developing multi-objective optimization, and through performance simulation verification and closed-loop iterative adjustment, the auxiliary frame is balanced among performance, lightweight and cost.

Description

Technical Field

[0001] This invention relates to the field of subframe design technology that uses steel instead of aluminum, and particularly to a design method, apparatus, medium, and equipment for a steel subframe. Background Technology

[0002] As a key load-bearing component of a vehicle, the subframe directly affects the vehicle's safety, handling stability, and noise and vibration comfort. Currently, subframes are mainly made of aluminum alloy, which achieves weight reduction, but has significant drawbacks: aluminum alloy subframes mainly employ vacuum die casting, extruded aluminum profiles, and welding processes, resulting in large equipment investments, complex processes, and high processing costs; at the same time, the price of aluminum is typically 3 to 5 times that of steel, keeping material costs high and making it difficult to meet the cost reduction requirements for large-scale production of new energy vehicles.

[0003] To overcome the cost constraints of aluminum alloy subframes, the industry is gradually exploring technological pathways to replace aluminum with steel in subframe manufacturing. However, replacing aluminum with steel in subframe production is not a simple material substitution, but rather requires an optimal trade-off between performance, cost, and manufacturing processes.

[0004] However, existing technologies have not yet developed a scientific and systematic design method for replacing aluminum with steel in subframes. It is difficult to achieve the optimal balance between the steel-to-aluminum solution while ensuring the safety performance and operational stability (core performance) of the subframe, thus failing to fully leverage the cost and technological advantages of steel subframes.

[0005] Therefore, there is an urgent need for a design method for steel subframes to achieve a balance between core performance, lightweight design, and cost. Summary of the Invention

[0006] To address the problems existing in the prior art, embodiments of the present invention provide a design method, apparatus, medium, and equipment for a steel subframe, in order to solve or partially solve the technical problem in the prior art that it is impossible to achieve a balance between core performance (safety performance and operational stability performance), lightweighting, and cost when designing a vehicle subframe using steel instead of aluminum.

[0007] A first aspect of the present invention provides a design method for a steel subframe, the method comprising: S1, Construct an initial structural model of a steel subframe based on an aluminum alloy subframe. The initial structural model stores the cross-sectional parameters, thickness parameters, and overlap parameters of each component of the steel subframe. S2, perform sensitivity analysis on the initial structural model to obtain multiple key target parameters that need to be optimized; take minimizing the mass of the steel subframe as the objective function, and optimize the multiple key target parameters under multi-dimensional constraints to obtain a lightweight steel structural model; S3, Perform performance simulation analysis on the steel lightweight structure model to obtain the performance simulation results of the steel lightweight structure model; S4. If the performance simulation results do not meet the expected requirements, adjust the cross-sectional parameters, thickness parameters and / or overlap parameters of each component, and repeat steps S1 to S3 until the performance simulation analysis results meet the preset requirements, and output the target structural model of the steel subframe.

[0008] In the above scheme, the sensitivity analysis of the initial structural model to obtain the target key parameters that need to be optimized includes: Extract all optimizable parameters from the initial structural model; The sensitivity coefficient of each optimizable parameter for each key indicator is determined using a local sensitivity analysis algorithm. The key indicators include: total subframe mass, bending stiffness, torsional stiffness, first-order mode, maximum stress, and subframe manufacturing cost. If any sensitivity coefficient exceeds the preset sensitivity threshold, then the optimizable parameter is determined to be the target key parameter.

[0009] In the above scheme, the optimization of multiple key objective parameters, with the minimization of the steel subframe as the objective function and under multi-dimensional constraints, includes: The initial values ​​of the target key parameters are input into the initial structural model. Under the constraints of the multidimensional constraints, the objective function is solved using the objective optimization algorithm based on the initial structural model, and the target values ​​of the target key parameters are output.

[0010] In the above scheme, the multidimensional constraints include: cost constraints, process constraints, and stiffness constraints.

[0011] In the above scheme, the cost constraint is that the total cost of the steel subframe is less than the total cost of the aluminum alloy subframe; The process constraint is that the parts of the steel subframe need to be manufactured using a cold forming process. The stiffness constraint condition is that the bending section stiffness of the steel subframe is greater than or equal to the bending section stiffness of the aluminum alloy subframe.

[0012] In the above scheme, when the cross-section of the subframe component is circular, the stiffness constraint condition is: ;in, The The elastic modulus of steel, the The outer diameter of the circular cross-section of the steel is described as follows: For the thickness of the steel, the The elastic modulus of aluminum, the The outer diameter of the circular cross-section of the aluminum material is described as follows: This refers to the thickness of the aluminum material.

[0013] In the above scheme, when the cross-section of the subframe component is rectangular, the stiffness constraint condition is: ;in, The The elastic modulus of steel, the For the thickness of the steel, the The width of the rectangular cross-section of the steel is... The height of the rectangular section of the steel is... The elastic modulus of aluminum is described as follows. For the thickness of the aluminum material, the The width of the rectangular cross-section of the aluminum material is... The height of the rectangular section of the aluminum material.

[0014] A second aspect of the present invention provides a design apparatus for a steel subframe, the apparatus comprising: A construction unit is used to construct an initial structural model of a steel subframe based on an aluminum alloy subframe. The initial structural model stores the cross-sectional parameters, thickness parameters, and overlap parameters of each part of the steel subframe. An optimization unit is used to perform sensitivity analysis on the initial structural model to obtain multiple key target parameters that need to be optimized; taking the minimization of the mass of the steel subframe as the objective function, and under the constraints of multi-dimensional constraints, the multiple key target parameters are optimized to obtain a lightweight steel structural model. The analysis unit is used to perform performance simulation analysis on the steel lightweight structure model and obtain the performance simulation results of the steel lightweight structure model. The execution unit is used to adjust the cross-sectional parameters, thickness parameters and / or overlap parameters of each part if the performance simulation results do not meet the expected requirements, and to repeatedly execute steps S1 to S3 until the performance simulation analysis results meet the preset requirements, and output the target structural model of the steel subframe.

[0015] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any of the first aspects.

[0016] A fourth aspect of the present invention is a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the method described in any of the first aspects.

[0017] This invention provides a design method, apparatus, medium, and equipment for a steel subframe. The method includes: S1, constructing an initial structural model of a steel subframe based on an aluminum alloy subframe, wherein the initial structural model stores the cross-sectional parameters, thickness parameters, and overlap parameters of each component of the steel subframe; S2, performing sensitivity analysis on the initial structural model to obtain multiple key objective parameters that need to be optimized; optimizing the key objective parameters under multi-dimensional constraints, with minimizing the mass of the steel subframe as the objective function, to obtain a lightweight steel structural model; S3, performing performance simulation analysis on the lightweight steel structural model to obtain the performance simulation results; S4, if the performance simulation results do not meet the expected requirements, adjusting the cross-sectional parameters, thickness parameters, and / or overlap parameters of each component. The process involves iteratively executing steps S1 to S3 until the performance simulation analysis results meet the preset requirements, at which point the target structural model of the steel subframe is output. Thus, this invention constructs an initial structural model based on an aluminum alloy subframe, ensuring from the outset that the stiffness, strength, and modal characteristics of the steel subframe meet the requirements for safety performance and handling stability. Through sensitivity analysis, key optimization parameters are selected, with mass minimization as the objective function. Multi-objective optimization is then conducted under performance, cost, and process constraints to ensure optimal weight and controllable cost while maintaining core performance. Finally, through performance simulation verification and closed-loop iterative adjustments, the subframe achieves the optimal balance between core performance, lightweighting, and manufacturing cost, effectively solving the technical problem of traditional steel-for-aluminum designs struggling to balance performance, lightweighting, and cost. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic flowchart of a design method for a steel subframe according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a steel subframe design device according to an embodiment of the present invention is shown; Figure 3 A schematic diagram of the structure of an aluminum subframe according to an embodiment of the present invention is shown; Figure 4 A structural schematic diagram of a steel subframe according to an embodiment of the present invention is shown. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0020] This invention provides a design method for a steel subframe, such as... Figure 1 As shown, the method includes the following steps: S1. Construct an initial structural model of a steel subframe based on an aluminum alloy subframe. The initial structural model stores the cross-sectional parameters, thickness parameters, and overlap parameters of each part of the steel subframe.

[0021] The construction principle of the steel subframe is to ensure that the relative positions of the connecting hard points of the subframe are not changed compared with the positions of the connecting hard points of the original aluminum alloy subframe, and that there is no movement interference between the parts.

[0022] This invention can first use an aluminum alloy subframe with an integrated die-casting process and performance indicators that meet the vehicle design requirements as a reference subframe. Then, the reference subframe is disassembled into parts to obtain the cross-sectional shape, cross-sectional thickness and overlapping structure of each part, and extract the core parameters of the aluminum alloy subframe, such as hard point coordinates, installation interface size and load input boundary. Based on the mechanical properties and forming process characteristics of steel, the cross-sectional shape, thickness, lap structure, and core parameters of each component in the aluminum alloy subframe are used to determine the corresponding cross-sectional shape, thickness, lap structure, and core parameters of the steel subframe, thereby constructing an initial structural model of the steel subframe. This initial structural model contains the cross-sectional parameters, thickness parameters, and lap parameters of each component in the steel subframe.

[0023] The performance indicators include, but are not limited to: stiffness (bending stiffness, torsional stiffness), strength (static strength, fatigue strength), modal characteristics (first-order modal frequency, mode shape), and collision safety performance (energy absorption and anti-intrusion ability under frontal collision, side collision, and pole collision conditions).

[0024] S2, perform sensitivity analysis on the initial structural model to obtain multiple key target parameters that need to be optimized; take minimizing the mass of the steel subframe as the objective function, and optimize the key target parameters under multi-dimensional constraints to obtain a lightweight steel structural model.

[0025] To significantly reduce the parameter dimensionality of multi-objective optimization, avoid blind optimization, improve optimization efficiency and convergence stability, and ensure that subsequent multi-objective optimization is only carried out on key target parameters, this invention aims to achieve an optimal balance between lightweighting and manufacturing cost while ensuring that the core performance of the steel subframe, such as safety and handling stability, is not compromised. This embodiment of the invention also requires sensitivity analysis of the initial structural model. In one implementation, sensitivity analysis of the initial structural model is performed to obtain the key target parameters to be optimized, including: Extract all optimizable parameters from the initial structural model; The sensitivity coefficient of each optimizable parameter for each key indicator is determined using a local sensitivity analysis algorithm. The key indicators include: total subframe mass, bending stiffness, torsional stiffness, first-order mode, maximum stress, and subframe manufacturing cost. If any sensitivity coefficient exceeds the preset sensitivity threshold, then the parameter that can be optimized is determined as the target key parameter.

[0026] Specifically, optimizable parameters may include the cross-sectional dimensions, thickness gradient parameters, and lap parameters of each beam component (such as longitudinal beams, transverse beams, and reinforcing beams).

[0027] For example, the cross-sectional dimensions of each beam may include: Rectangular hollow beam: cross-sectional width, cross-sectional height, inner contour dimensions, etc.; Circular hollow beam: outer diameter, inner diameter, etc.; Open channel steel: flange width, web height, flange dimensions, etc. Variable cross-section beam: Gradually changing cross-sectional dimensions at different locations Localized reinforcement structure: height, width, spacing, etc. of the reinforcing ribs; For example, thickness gradient parameters can include: The thickness of longitudinal beams, crossbeams, the thickness of mounting points such as suspensions, and the thickness of non-load-bearing areas, etc.

[0028] For example, overlap parameters may include: The overlap length between longitudinal and transverse beams, weld location, weld length, number of weld points and bolts, etc.

[0029] For each currently optimizable parameter, while keeping other remaining optimizable parameters constant, the current optimizable parameter is slightly adjusted from its initial value, for example, by 1% to 5%. After adjustment, the changes in core indicators such as subframe total mass, bending stiffness, torsional stiffness, and first-order mode are calculated. Based on the ratio of the change in response indicators to the adjusted beam, the sensitivity coefficient of the current optimizable parameter to each core indicator is obtained.

[0030] Taking the sensitivity coefficient of longitudinal beam thickness to total mass as an example, the initial value of longitudinal beam thickness is X0mm, and the corresponding total mass of the subframe is Y0. Increase the thickness of the longitudinal beam by 1%, and the adjusted thickness of the longitudinal beam is X1mm. The corresponding total mass of the subframe is Y1. Then the sensitivity coefficient of the longitudinal beam thickness to the total mass S for: (1) Following the same method described above, the sensitivity coefficient of each optimizable parameter for each key indicator can be determined. For each optimizable parameter, if the absolute value of any sensitivity coefficient is greater than or equal to a preset threshold, the optimizable parameter is considered a target key parameter. The preset sensitivity threshold can be determined based on actual conditions, for example, it could be 0.5 or 0.7.

[0031] After identifying the multiple key parameters that need to be optimized, it is also necessary to construct objective functions and constraints for the initial structural model. Then, based on the initial structural model, guided by the objective functions and constrained by multi-dimensional constraints, the key parameters are optimized to obtain a lightweight steel structural model.

[0032] The objective function is to minimize the mass of the steel subframe, which can be expressed as: ;in, The total mass of the steel subframe, x For the set of key parameters of the target, , For the first n Key parameters of the target T This is the identifier for the transpose matrix.

[0033] The multidimensional constraints include: cost constraints, process constraints, and stiffness constraints. The cost constraint is that the total cost of the steel subframe is less than the total cost of the aluminum alloy subframe. The process constraint is that the steel subframe parts need to be manufactured using a cold forming process; The stiffness constraint condition is that the bending section stiffness of the steel subframe is greater than or equal to the bending section stiffness of the aluminum alloy subframe.

[0034] In one implementation, when the subframe component has a circular cross-section, the stiffness constraint condition is: (2) The elastic modulus of steel, The outer diameter of the circular cross-section of the steel is For steel thickness, Elastic modulus of aluminum The outer diameter of the circular cross-section of the aluminum material. This refers to the thickness of the aluminum material.

[0035] Formula (2) can also be simplified to: (3) In formula (3), Let be the moment of inertia of the circular cross-section of the steel. Let be the moment of inertia of the circular cross-section of the aluminum material.

[0036] In one implementation, when the subframe component has a rectangular cross-section, the stiffness constraint condition is: (4) In formula (4), The elastic modulus of steel, For steel thickness, The width of the rectangular section of the steel. The height of the rectangular section of the steel is The elastic modulus of aluminum. For aluminum material thickness, The width of the rectangular section of the aluminum material. The height of the rectangular section of the aluminum material.

[0037] Formula (4) can also be simplified to: (4) In formula (4), Let be the moment of inertia of the rectangular section of the steel. Let be the moment of inertia of the rectangular cross-section of the aluminum material.

[0038] Other constraints may include: thickness constraints, weld fatigue life cycle constraints, steel structure cross-sectional diameter constraints, steel structure strength safety factor constraints, bracket process constraints, frame longitudinal beam process constraints, frame cross beam process constraints, and first-order modal frequency constraints, etc.

[0039] For example, the thickness constraint is: The steel structure thickness must meet the specification of 3.5. ≥ ; The structural thickness of steel parts must meet the requirement of 1.6mm ≤ ≤3.0mm, for example Available thicknesses are 1.6mm, 1.8mm, 2.0mm, 2.2mm, 2.3mm, 2.5mm, 2.6mm, 2.8mm, and 3.0mm. The thickness of the steel brackets (mounting brackets and connecting brackets) must meet the requirement of 2.0mm ≤ ≤4.0mm.

[0040] The fatigue life cycle limit for welds is: ≥300,000 cycles for welds in steel structures; The constraint on the diameter of the steel structure section is: the diameter of the steel structure section shall not exceed the spatial installation dimension requirement of the part; The strength safety factor constraint for steel structures is: (The text abruptly ends here, so the translation stops as well.) 1.5; The process constraints for the bracket are as follows: the frame bracket portion can be made of hot-formed steel to withstand larger loads and reduce weight. The strength grades of the hot-formed steel include 1500MPa, 1800MPa, 2000MPa, and 2200MPa, etc.; the hot-formed steel can also include materials such as air-hardening steel.

[0041] The process constraints for the longitudinal beams and cross beams of the frame allow for the use of multiphase steel, balancing strength and toughness.

[0042] The first-order modal frequency constraint can be that the first-order modal frequency must be greater than 166Hz.

[0043] In addition, to improve the lightweighting of parts, a hollow design can be used. By optimizing topology to remove some material in specific areas, the weight of the parts can be reduced.

[0044] To improve the safety of the steel subframe, in one embodiment, it is necessary to meet the following requirements. , K For safety factors, when the material is hot-formed steel, the safety factor is 1.3 to 1.5; when the material is multiphase steel, the safety factor is 1.2 to 1.4; and when the material is low-alloy high-strength steel, the safety factor is 1.0 to 1.2.

[0045] In one embodiment, to improve the corrosion resistance of the parts, each part of the steel subframe can be made of hot-dip galvanized steel sheet. During the welding process, a welding process adapted to the hot-dip galvanized coating is adopted to avoid welding defects such as welding spatter and weld porosity, ensuring that the strength of the welded joint meets the load-bearing requirements of the subframe. At the same time, the hot-dip galvanized coating improves the corrosion resistance of the parts surface and extends the service life.

[0046] After stamping and welding, the steel subframe undergoes cathodic electrophoresis treatment. During the treatment process, the roughness of the electrophoretic paint film needs to be controlled to be less than 0.35μm, the paint film adhesion to be grade 0, and the electrophoretic stone impact resistance to be grade 2.5 or below. Under the national standard cyclic corrosion test conditions, the corrosion propagation width at the paint film scratch is less than 2mm to meet the corrosion protection requirements.

[0047] After the constraints are determined, the objective function is to minimize the mass of the steel subframe. Under the constraints of multi-dimensional conditions, the key parameters of the objective are optimized to obtain the lightweight steel structure model.

[0048] In one implementation, the objective function is to minimize the mass of the steel subframe. Under multi-dimensional constraints, the key parameters of the objective are optimized, including: The initial values ​​of the key target parameters are input into the initial structural model. Under the constraints of multidimensional constraints, the objective function is solved using the objective optimization algorithm based on the initial structural model, and the target values ​​of the key target parameters are output.

[0049] It should be noted that, in order to improve the accuracy of simulation experiments, the number of test sample points needs to be determined based on the number of target key parameters. Generally, the number of test sample points is 5 to 10 times the number of target key parameters. For example, assuming there are 9 target key parameters, then the number of test sample points can be 90, and each test sample point contains a set of values ​​for the target key parameters.

[0050] Since the sample points are discrete, directly using a non-dominated sorting genetic algorithm would significantly impact the solution efficiency. Therefore, this invention first calculates the first-order modal frequency response and total mass response corresponding to the experimental sample points, then uses a pre-trained interpolation-type approximation model, the Kriging model, to fit the response values ​​into a continuous response surface model, and finally solves for the optimal solution based on the response surface model and optimization algorithm.

[0051] The training process of the Kriging model is essentially a fitting process, which determines the model's hyperparameters (such as correlation function parameters and regression coefficients) by fitting (training) sample data. The Kriging model includes a global trend term and a local bias term; the global trend term uses a constant regression model to describe the global trend of the target key parameters and response values; the local bias term is a zero-mean Gaussian random process, which uses a Gaussian correlation function to characterize the spatial correlation between sample points and achieve local correction of the global trend.

[0052] The training steps can be as follows: A training dataset is constructed by taking multiple key target parameters of 100 sets of experimental sample points as inputs and outputting the corresponding first-order modal frequencies and total mass response values. The Kriging model is initialized, with a constant regression model used as the global trend term and a Gaussian correlation function used to describe the spatial correlation of local biases. The model hyperparameters (correlation parameters, regression coefficients, process variance) are solved by maximum likelihood estimation to complete the model fitting; Cross-validation is used to evaluate the model's prediction accuracy, ensuring R... 2Training is completed once the coefficient of determination is no less than 0.95 and the accuracy requirement is met. The coefficient of determination characterizes the prediction accuracy of the response surface model and reflects the degree of fit between the model's predicted values ​​and the actual sample response values. The closer the value is to 1, the higher the model's prediction accuracy and the better the fit.

[0053] The trained Kriging model can quickly map key target parameters to response values, replacing time-consuming finite element simulations and providing support for subsequent multi-objective optimization.

[0054] The objective optimization algorithm can be the Pareto optimality algorithm NSGA-II or NSGA-III; for example, when solving the objective function using the Pareto optimality algorithm, it is implemented as follows: Within the range of target key parameters, initial values ​​of the target key parameters are randomly generated to obtain an initial population; for example, if there are 100 experimental sample points, then the initial population has 100 individuals, and each individual represents a set of target parameter values; The response surface model is used to calculate the first-order modal frequency response value and total mass response value of each individual. Based on the first-order modal frequency response value and total mass response value, the initial population is non-dominated and sorted to obtain the non-dominated solution of the current generation. Crossover and mutation are performed on the current generation of non-dominated solutions to generate the next generation population. The next generation population is then sorted in a non-dominated manner to obtain the next generation of non-dominated solutions. Repeat the above steps until the termination condition is met, output the optimal solution set, and select the target value of the target key parameter from the optimal solution set.

[0055] The termination condition can be reaching a preset number of iterations or the population reaching convergence.

[0056] The target values ​​of the key parameters are selected from the optimal solution set, including: The individual with the lowest first-order modal frequency (166Hz) and the highest mass is selected from the optimal solution set as the target individual, and the values ​​of each key target parameter in the target individual are used as the target values ​​of the key target parameters.

[0057] The corresponding parameters in the initial structural model are updated based on the target values ​​of the target parameters, thereby obtaining the lightweight steel structural model.

[0058] The Pareto optimal solution algorithm used in this invention is a heuristic search algorithm rather than a machine learning model, and therefore does not require pre-training based on historical data. The algorithm directly searches the solution space for a set of Pareto optimal solutions that satisfy the constraints through steps such as initializing random solutions, iterative optimization, non-dominated sorting, and elite preservation, achieving the optimal balance between lightweighting, cost, and safety performance of the steel subframe.

[0059] This step involves screening key parameters through sensitivity analysis, optimizing them under multi-dimensional constraints with the goal of minimizing mass, and achieving the optimal balance between lightweighting, cost, and process while ensuring the core performance of the subframe, thereby improving design efficiency and the feasibility of the solution.

[0060] S3, Perform performance simulation analysis on the steel lightweight structure model to obtain the performance simulation results of the steel lightweight structure model.

[0061] After the lightweight steel structure model is determined, performance simulation analysis needs to be performed on the steel subframe to ensure its safety performance and obtain the simulation results. This simulation analysis primarily focuses on performance indicators such as stiffness, modal dynamics, strength, and fatigue life.

[0062] When performing performance simulation analysis, the lightweight steel structure model can be input into finite element simulation software, where stiffness, modal, strength, and fatigue life simulation analysis models can be established. The bending stiffness of the lightweight steel structure model can then be evaluated. Torsional stiffness First-order mode Maximum stress and fatigue life The core performance indicators are analyzed, and quantitative performance simulation results are output.

[0063] The finite element simulation software can be any of the following: Advanced Numerical Simulation System (ANSYS), Dassor Simonia's nonlinear finite element analysis software Abaqus, or the supercomputing simulation platform HyperWorks.

[0064] Specifically, a simulation model of the steel subframe under bending and torsional conditions can be established, the mounting points can be constrained and standard loads applied, and the bending stiffness can be calculated. Torsional stiffness ; It can perform free modal analysis, establish a finite element modal analysis model of a lightweight steel structure model, calculate and solve the modal frequencies and mode shapes of the subframe under unconstrained free boundary conditions, and then extract the first torsional mode and the first bending mode.

[0065] The maximum stress on the subframe can be calculated by applying loads based on typical vehicle driving conditions (braking, acceleration, steering).

[0066] The system can utilize the material's stress-life curve (SN curve) and the load spectrum of typical vehicle operating conditions to calculate the fatigue life of key components of the subframe using a fatigue simulation module. The material's SN curve, obtained from standard specimen fatigue tests, is a characteristic curve that characterizes the relationship between the stress amplitude S and the number of fatigue failure cycles N of the material used in the steel subframe (such as hot-dip coated high-strength steel) under alternating loads. It is the core input for evaluating the fatigue life of the structure.

[0067] S4. If the performance simulation results do not meet the expected requirements, adjust the cross-sectional parameters, thickness parameters and / or overlap parameters of each component, and repeat steps S1 to S3 until the performance simulation analysis results meet the preset requirements, and output the target structural model of the steel subframe.

[0068] After obtaining the performance simulation results, determine whether the performance simulation results meet the expected requirements. If so, output the target structural model of the steel subframe and enter the bench test stage based on the target structural model.

[0069] If not, adjust the cross-sectional parameters, thickness parameters and / or overlap parameters of each component, and repeat steps S1 to S3 until the performance simulation analysis results meet the preset requirements, and output the target structural model of the steel subframe.

[0070] During bench testing, various operating conditions such as bending, torsion, braking, and steering experienced in actual vehicle operation can be simulated. Key parameters of the steel subframe, including actual load-bearing stiffness, maximum stress value, natural frequency, and fatigue life, can be measured. These key parameters are then used to verify whether the steel subframe truly meets the requirements for vehicle safety performance, handling stability, and service life. Once these requirements are met, the final mass-produced steel structure can be determined.

[0071] This step uses the aluminum alloy subframe as the performance benchmark to complete the performance comparison and judgment of the steel structure, ensuring that the safety performance and handling stability of the subframe are not degraded. A closed-loop iterative optimization mechanism is established to address performance shortcomings, and structural parameters are precisely adjusted to maximize lightweighting while meeting performance requirements and avoiding over-design. Finally, bench testing is used to complete the physical performance verification, eliminate the deviation between simulation and actual working conditions, and ensure that the steel structure meets the requirements of the whole vehicle. This provides a full-process reliability guarantee for the engineering implementation of steel-for-aluminum subframes.

[0072] Based on the same inventive concept as in the foregoing embodiments, this embodiment also provides a design device for a steel subframe, such as... Figure 2 As shown, the device includes: Construction unit 21 is used to construct an initial structural model of a steel subframe based on an aluminum alloy subframe. The initial structural model stores the cross-sectional parameters, thickness parameters, and overlap parameters of each part of the steel subframe. The optimization unit 22 is used to perform sensitivity analysis on the initial structural model to obtain multiple target key parameters that need to be optimized; taking the minimization of the mass of the steel subframe as the objective function, the target key parameters are optimized under the constraints of multi-dimensional constraints to obtain a lightweight steel structural model. Analysis unit 23 is used to perform performance simulation analysis on the steel lightweight structure model and obtain the performance simulation results of the steel lightweight structure model; The execution unit 24 is used to adjust the cross-sectional parameters, thickness parameters and / or overlap parameters of each part if the performance simulation results do not meet the expected requirements, and to repeatedly execute steps S1 to S3 until the performance simulation analysis results meet the preset requirements, and output the target structural model of the steel subframe.

[0073] Since the apparatus described in the embodiments of this invention is used for implementing the design method of the steel subframe of the embodiments of this invention, those skilled in the art can understand the specific structure and variations of the apparatus based on the method described in the embodiments of this invention, and therefore will not be described in detail here. All apparatuses used in the methods of the embodiments of this invention fall within the scope of protection of this invention.

[0074] Based on the same inventive concept, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any step of the method described above.

[0075] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0076] In practical applications, when designing a car subframe using steel instead of aluminum based on the design method and apparatus provided in the above embodiments, the following is achieved: First, a car aluminum alloy rear subframe model was obtained. The load on the rear subframe under various working conditions was obtained through multibody dynamics analysis. The Y-direction stiffness value at the inner mounting point P101 of the left camber control arm was obtained as 83333 N / mm through static analysis. Under vertical strength conditions, the safety factor of the aluminum subframe is 1.7. The vertical strength conditions are simulated when the vehicle is fully loaded and passes through a bumpy road surface and is subjected to the maximum vertical impact load. Under extreme forward emergency braking conditions, the structural safety factor of the rear subframe is 3.1. Under extreme conditions of emergency braking while reversing, the structural safety factor of the rear subframe is 2.8. Under extreme conditions of high-speed cornering, the structural safety factor of the rear subframe is 2.1. Under extreme conditions of impact when crossing a bump, the structural safety factor of the rear subframe is 1.5. The first-order mode of the aluminum alloy rear subframe is the torsional mode, with a frequency of 166Hz.

[0077] Secondly, the aluminum alloy subframe was disassembled into components, and... Figure 3 The main structure of the aluminum subframe is divided into two crossbeams (A1, A3) and two longitudinal beams (A2, A4); the initial structural model of the steel subframe constructed based on the aluminum alloy subframe is shown below. Figure 4 The steel subframe consists of two crossbeams (S-1, S-3) and two longitudinal beams (S-2, S-4).

[0078] The cross-sectional shape, thickness gradient, and lap joint structure of each component are as follows: S-1: The central section of the front crossbeam of the aluminum alloy subframe is circular with a diameter of 83-88mm. Through stiffness formula calculation and spatial arrangement constraints, the steel material is replaced and a hydroforming process is adopted. The diameter of the circular tube is initially set to 80mm and the thickness to 2.0mm. Left and right longitudinal beams S-2 and S-4: The upper and lower plates of the U-shaped part are welded into a rectangular closed structure, and the front section is smoothly welded to S-1 in a Y-type lap joint. S-4: The front and rear main body are welded together using U-shaped cross-section stamped parts. The upper and lower sides of the part cross-section are at the same height as the left and right longitudinal beams. The cross beams are reinforced with corner brackets to increase the overall rigidity of the subframe. The steel control arm and H-arm mounting brackets are designed with reference to the mounting hard points of the aluminum subframe structure. They are welded to the cross and longitudinal beams using an integrated or split structure to form the initial structural model of the steel subframe.

[0079] Then, multi-objective optimization based on thickness and cross-sectional dimensions was carried out on the initial steel structure, with the key indicators being: first-order modal frequency and total mass; Constraints: First-order modal frequency ≥ 166Hz; Objective function: Minimize mass.

[0080] After sensitivity analysis, nine key target parameters were identified: front tube beam thickness, upper longitudinal beam plate thickness, lower longitudinal beam plate thickness, front rear crossbeam plate thickness, rear rear crossbeam plate thickness, front tube beam diameter, mid-section width of the longitudinal beam, overall height of the longitudinal beam, and height of the rear crossbeam. To improve the accuracy of the simulation experiment, the number of test sample points needs to be determined based on the number of key target parameters. Generally, the number of test sample points is 5 to 10 times the number of key target parameters, but it can also be set according to the actual situation without limitation.

[0081] Since the sample points are discrete, directly using a non-dominated sorting genetic algorithm would significantly impact computational efficiency. Therefore, this invention first calculates the first-order modal frequency response and total mass response corresponding to the experimental sample points, then uses the Kriging model to fit the response values ​​into a continuous response surface model, and finally solves for the optimal solution based on the response surface model and optimization algorithm.

[0082] In this embodiment, the number of experimental sample points is set to 100. Then, model analysis and calculation are performed on the 100 sample points according to the parameter values ​​to obtain 100 sets of first-order modal frequency response values ​​and total mass response values.

[0083] For example: Group 1: First-order mode frequency 168 Hz, mass 145 kg; Group 2: First-order modal frequency 172Hz, mass 148kg; ... Group 100: Mode 165Hz, Mass 139kg.

[0084] After the continuous response surface model is determined, the NSGA-II algorithm (non-dominated sorting genetic algorithm) is used to seek the optimal solution for multi-objective optimization, namely, the thickness of the front tube beam is 1.8mm, the thickness of the upper plate of the longitudinal beam is 1.8mm, the thickness of the lower plate of the longitudinal beam is 1.8mm, the thickness of the front plate of the rear crossbeam is 1.8mm, the thickness of the rear plate of the rear crossbeam is 1.8mm, the diameter of the front tube beam is reduced by 1.2mm, the width of the middle part of the longitudinal beam is increased by 11.7mm, the overall height of the longitudinal beam is reduced by 2.5mm, and the height of the rear crossbeam is increased by 18.01mm.

[0085] Round the above parameter values ​​and substitute them into the initial structural model to obtain the steel lightweight structural model.

[0086] A performance simulation analysis was conducted on a lightweight steel structure model to evaluate its bending stiffness. Torsional stiffness First-order mode Maximum stress and fatigue life The core performance indicators are analyzed, and quantitative performance simulation results are output.

[0087] The performance simulation results are as follows: The Y-axis stiffness value at mounting point P101 of the left camber control arm of the steel subframe is 76923 N / mm. The first-order mode of the steel subframe is torsional, with a first-order modal frequency of 169 Hz. Stiffness is checked using 800MPa pickled high-strength steel. The safety factor is 1.9 under vertical strength conditions, 3.1 under forward braking conditions, 2.9 under reverse braking conditions, 2.2 under turning conditions, 3.3 under turning braking conditions, and 1.4 under bump-crossing conditions. Under longitudinal and lateral bench fatigue conditions, the fatigue life of the subframe welds is 29,600 cycles.

[0088] The Y-direction stiffness value at the inner mounting point P101 of the left outward tilt control arm of the steel structure is 76923 < 83333 (aluminum structure), which does not meet the design requirements. The first-order mode of the steel subframe is the torsional mode, with a frequency of 169Hz > 166Hz (aluminum structure), which meets the requirements.

[0089] Under high-strength conditions, the design reference value for the safety factor should be ≥1.5, and the safety factor for the steel structure's crossing of the sill is 1.4 < 1.5 (for aluminum). This is because the plate thickness was optimized to 1.8mm during multi-objective optimization, resulting in excessive stress at the connection of the control arm mounting bracket. Therefore, it is necessary to optimize the bracket structure at this location or thicken the lower plate to improve strength performance.

[0090] In addition, under longitudinal and lateral bench fatigue conditions, the fatigue life of the subframe welds does not meet the design reference value of ≥300,000 cycles.

[0091] Therefore, it is necessary to iterate through steps S1 to S3, with the following parameters being changed: By modifying the overlap parameters of the H-arm, extending the end of the front mounting bracket of the H-arm to increase the overlap length with the front crossbeam; increasing the overlap length of the bracket of the toe angle control arm with the lower plate of the longitudinal beam, and increasing the thickness of the lower plate of the longitudinal beam from 1.8mm to 2.0mm; and increasing the thickness of the front plate of the rear crossbeam from 1.8mm to 2.6mm, the simulation results of optimized structural performance are as follows: The Y-axis stiffness value at mounting point P101 of the left camber control arm of the steel subframe is 90909. The first-order mode of the steel subframe is the torsional mode with a frequency of 171Hz. The safety factor is 2.8 under vertical strength conditions, 1.8 under forward braking conditions, 2.9 under reverse braking conditions, 2.0 under cornering conditions, 2.1 under cornering braking conditions, and 2.4 under bump crossing conditions. Under longitudinal and lateral bench fatigue conditions, the fatigue life of the subframe welds is 414,000 cycles and 318,000 cycles, respectively.

[0092] The Y-axis stiffness value at mounting point P101 of the left camber control arm of the steel subframe is 90909 > 83333 (aluminum structure), meeting the design requirements. The first-order mode of the steel subframe is torsional mode, with a frequency of 171Hz > 166Hz (aluminum structure), also meeting the requirements. The safety factor design reference value under strength conditions must be ≥1.5, and the fatigue life of the subframe welds under longitudinal and lateral bench fatigue conditions must meet 300,000 cycles. Therefore, the optimized structure performance indicators meet the standards, and the target structural model of the steel subframe can be output, allowing it to proceed to the bench testing stage.

[0093] Through one or more embodiments of the present invention, the present invention has the following beneficial effects or advantages: This invention provides a design method, apparatus, medium, and equipment for a steel subframe. The method includes: S1, constructing an initial structural model of a steel subframe based on an aluminum alloy subframe, wherein the initial structural model stores the cross-sectional parameters, thickness parameters, and overlap parameters of each component of the steel subframe; S2, performing sensitivity analysis on the initial structural model to obtain multiple key objective parameters that need to be optimized; optimizing the key objective parameters under multi-dimensional constraints, with minimizing the mass of the steel subframe as the objective function, to obtain a lightweight steel structural model; S3, performing performance simulation analysis on the lightweight steel structural model to obtain the performance simulation results; S4, if the performance simulation results do not meet the expected requirements, adjusting the cross-sectional parameters, thickness parameters, and / or overlap parameters of each component. The process involves iteratively executing steps S1 to S3 until the performance simulation analysis results meet the preset requirements, at which point the target structural model of the steel subframe is output. Thus, this invention constructs an initial structural model based on an aluminum alloy subframe, ensuring from the outset that the stiffness, strength, and modal characteristics of the steel subframe meet the requirements for safety performance and handling stability. Through sensitivity analysis, key optimization parameters are selected, with mass minimization as the objective function. Multi-objective optimization is then conducted under performance, cost, and process constraints to ensure optimal weight and controllable cost while maintaining core performance. Finally, through performance simulation verification and closed-loop iterative adjustments, the subframe achieves the optimal balance between core performance, lightweighting, and manufacturing cost, effectively solving the technical problem of traditional steel-for-aluminum designs struggling to balance performance, lightweighting, and cost.

[0094] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0095] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0096] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0097] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0098] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0099] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the gateway, proxy server, or system according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0100] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0101] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0102] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A design method for a steel subframe, characterized in that, The method includes: S1, Construct an initial structural model of a steel subframe based on an aluminum alloy subframe. The initial structural model stores the cross-sectional parameters, thickness parameters, and overlap parameters of each component of the steel subframe. S2, perform sensitivity analysis on the initial structural model to obtain multiple key target parameters that need to be optimized; take minimizing the mass of the steel subframe as the objective function, and optimize the multiple key target parameters under multi-dimensional constraints to obtain a lightweight steel structural model; S3, Perform performance simulation analysis on the steel lightweight structure model to obtain the performance simulation results of the steel lightweight structure model; S4. If the performance simulation results do not meet the expected requirements, adjust the cross-sectional parameters, thickness parameters and / or overlap parameters of each component, and repeat steps S1 to S3 until the performance simulation analysis results meet the preset requirements, and output the target structural model of the steel subframe.

2. The method as described in claim 1, characterized in that, The sensitivity analysis of the initial structural model yields the target key parameters that need to be optimized, including: Extract all optimizable parameters from the initial structural model; The sensitivity coefficient of each optimizable parameter for each key indicator is determined using a local sensitivity analysis algorithm. The key indicators include: total subframe mass, bending stiffness, torsional stiffness, first-order mode, maximum stress, and subframe manufacturing cost. If any sensitivity coefficient exceeds the preset sensitivity threshold, then the optimizable parameter is determined to be the target key parameter.

3. The method as described in claim 1, characterized in that, The objective function is to minimize the mass of the steel subframe. Under multi-dimensional constraints, the optimization of multiple key objective parameters includes: The initial values ​​of the target key parameters are input into the initial structural model. Under the constraints of the multidimensional constraints, the objective function is solved using the objective optimization algorithm based on the initial structural model, and the target values ​​of the target key parameters are output.

4. The method as described in claim 1, characterized in that, The multidimensional constraints include: cost constraints, process constraints, and stiffness constraints.

5. The method as described in claim 1, characterized in that, The cost constraint is that the total cost of the steel subframe is less than the total cost of the aluminum alloy subframe; The process constraint is that the parts of the steel subframe need to be manufactured using a cold forming process. The stiffness constraint condition is that the bending section stiffness of the steel subframe is greater than or equal to the bending section stiffness of the aluminum alloy subframe.

6. The method as described in claim 5, characterized in that, When the cross-section of the subframe components is circular, the stiffness constraint condition is: ;in, The The elastic modulus of steel, the The outer diameter of the circular cross-section of the steel is described as follows: For the thickness of the steel, the The elastic modulus of aluminum, the The outer diameter of the circular cross-section of the aluminum material is described as follows: This refers to the thickness of the aluminum material.

7. The method as described in claim 5, characterized in that, When the cross-section of the subframe components is rectangular, the stiffness constraint condition is: ;in, The The elastic modulus of steel, the For the thickness of the steel, the The width of the rectangular cross-section of the steel is... The height of the rectangular section of the steel is... The elastic modulus of aluminum is described as follows. For the thickness of the aluminum material, the The width of the rectangular cross-section of the aluminum material is... The height of the rectangular section of the aluminum material.

8. A design device for a steel subframe, characterized in that, The device includes: A construction unit is used to construct an initial structural model of a steel subframe based on an aluminum alloy subframe. The initial structural model stores the cross-sectional parameters, thickness parameters, and overlap parameters of each part of the steel subframe. An optimization unit is used to perform sensitivity analysis on the initial structural model to obtain multiple key target parameters that need to be optimized; taking the minimization of the mass of the steel subframe as the objective function, and under the constraints of multi-dimensional constraints, the multiple key target parameters are optimized to obtain a lightweight steel structural model. The analysis unit is used to perform performance simulation analysis on the steel lightweight structure model and obtain the performance simulation results of the steel lightweight structure model. The execution unit is used to adjust the cross-sectional parameters, thickness parameters and / or overlap parameters of each part if the performance simulation results do not meet the expected requirements, and to repeatedly execute steps S1 to S3 until the performance simulation analysis results meet the preset requirements, and output the target structural model of the steel subframe.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method according to any one of claims 1-7.

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