Fatigue optimization method based on multi-parameter fusion and related equipment

Abstract

The invention relates to the technical field of fatigue simulation, in particular to a fatigue optimization method based on multi-parameter fusion and related equipment. The method comprises the steps of obtaining fatigue analysis basic data; marking an area meeting a double-threshold standard in the fatigue analysis basic data to obtain a high-danger area; performing regional clustering on the nodes of the high-danger region through a spatial clustering algorithm to obtain geometric features of the high-danger region; calculating according to the fatigue analysis basic data and the geometrical characteristics of the high-danger area to obtain a stress gradient factor, life sensitivity and a force transfer path contribution degree; and determining the selection of a thickness optimization channel and a multi-measure optimization channel according to the product of the stress gradient factor and the life sensitivity and the activation threshold value, determining different fatigue optimization schemes according to different optimization channels, and integrating and outputting the different fatigue optimization schemes. According to the method, the optimization can be accurately activated, the problem of secondary stress concentration is solved, multi-target balance is considered, the efficiency of the optimization period is greatly improved, and the universality is high.

Description

Technical Field

[0001] This application relates to the field of fatigue simulation technology, and in particular to a fatigue optimization method and related equipment based on multi-parameter fusion. Background Technology

[0002] In the field of automotive structural fatigue optimization, engineers often face a dilemma in choosing optimization methods when confronted with weak areas that fail to meet fatigue life standards. Lacking clear criteria, they struggle to determine when to thicken the structure, modify fillet designs, or replace materials, often relying solely on experience to experiment with various approaches. This process has a significant dual drawback. On one hand, inaccurate method selection can directly lead to ineffective optimization, failing to address fatigue life issues. On the other hand, crudely setting thickening parameters can easily introduce secondary risks, such as adding unnecessary weight or affecting the performance of other structures. More seriously, this multi-round trial-and-error approach significantly reduces engineering efficiency, severely hindering automotive R&D progress.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a fatigue optimization method and related equipment based on multi-parameter fusion, which can accurately activate optimization, solve the problem of secondary stress concentration, take into account the balance of multiple objectives, greatly improve the efficiency of the optimization cycle, and has strong universality.

[0005] To achieve the above objectives, one aspect of this application proposes a fatigue optimization method based on multi-parameter fusion, the method comprising the following steps: Obtain the basic data for fatigue analysis of the target structure; The regions in the fatigue analysis baseline data that meet the dual threshold criteria are marked to obtain high-risk regions; The nodes in the high-risk area are clustered using a spatial clustering algorithm to obtain the geometric features of the high-risk area; Based on the fatigue analysis data and the geometric characteristics of the high-risk area, the stress gradient factor, life sensitivity, and force transmission path contribution are calculated. When the product of the stress gradient factor and the lifetime sensitivity is greater than the activation threshold, the fatigue optimization scheme is determined through the thickness optimization channel; When the product of the stress gradient factor and the lifetime sensitivity is less than or equal to the activation threshold, the fatigue optimization scheme is determined through multi-measure optimization channels. The fatigue optimization schemes are integrated and output.

[0006] In some embodiments, the fatigue analysis baseline data includes stress cloud diagrams, life distribution diagrams, and material properties output from finite element analysis; The stress cloud diagram output by the finite element analysis includes the stress value and stress type of each node; The lifespan distribution map includes the number of fatigue life cycles in each region; The material properties include material grade, elastic modulus, yield strength, and stress life curve.

[0007] In some embodiments, the dual threshold criteria include a high-risk stress threshold and a high-risk lifespan threshold; The regions in the fatigue analysis baseline data that meet the dual threshold criteria include regions where the stress value is greater than or equal to the high-risk stress threshold and the life value is less than or equal to the high-risk life threshold.

[0008] In some embodiments, the step of performing region clustering on the nodes of the high-risk area using a spatial clustering algorithm to obtain the geometric features of the high-risk area includes the following steps: Nodes with stress values ​​greater than the material yield strength threshold and fatigue life less than the design target life threshold are screened to obtain a preliminary set of high-risk nodes. Based on the model size, density clustering is performed on the initial screening high-risk node set to obtain an initial cluster; The initial clusters are merged into regions according to the cluster merging rules; The regional continuity of the merged initial cluster is verified by using triangular mesh partitioning technology, and the geometric features of the high-risk area are obtained.

[0009] In some embodiments, determining the fatigue optimization scheme through a thickness optimization channel includes the following steps: Using the thickness adjustment amount as a variable, a mathematical model is constructed to address the two objectives of minimizing the weight increase and minimizing the life deviation, resulting in a bi-objective optimization problem mathematical model. The optimal thickness increase was obtained by solving the mathematical model of the bi-objective optimization problem using a sequential quadratic programming algorithm. The optimal thickness increase is calculated to obtain the thickness increase rate; Based on the geometric characteristics of the high-risk area and the thickening rate, the structural transformation type is adaptively selected to obtain the fatigue optimization scheme.

[0010] In some embodiments, solving the mathematical model of the bi-objective optimization problem using a sequential quadratic programming algorithm to obtain the optimal thickness increase includes the following steps: The mathematical model of the bi-objective optimization problem is initialized using the original thickness as the initial value to obtain the iteration starting point; By constructing a quadratic programming subproblem and solving it using the objective function and constraint conditions in each iteration, the search direction can be obtained. The search direction is determined by the step size according to the line search strategy. The search direction and the step size are converted by vector operation to obtain the thickness adjustment amount. The thickness adjustment amount is used to update the iteration starting point to obtain the thickness value after iteration; The optimal thickness increase is obtained when the thickness change is less than the change threshold and the rate of change of the objective function is less than the rate of change threshold, or when the number of iterations reaches the iteration number threshold.

[0011] In some embodiments, the scheme for adaptively selecting the structure transformation type includes: If the thickness increase rate is greater than 15% and the radius of curvature is less than 5mm, then a stress relief groove should be designed. If the thickness increase rate is >20% and the original thickness is greater than 8mm, then a grid-shaped weight reduction groove should be designed; If the thickness increase rate is greater than 25%, a hollow sandwich structure should be designed.

[0012] In some embodiments, determining the fatigue optimization scheme through multi-measure optimization channels includes the following steps: When the stress gradient factor is greater than the first stress threshold and the life sensitivity is less than the first life threshold, the fillet is modified; if the force transmission path contribution is less than the first contribution threshold, the fillet and pitting are modified. When the stress gradient factor is less than the second stress threshold and the lifetime sensitivity is greater than the second lifetime threshold, local thickening and laser strengthening treatment are performed, or high-strength materials are replaced; if the force transmission path contribution is greater than the second contribution threshold, laser strengthening treatment is performed. When the stress gradient factor is less than the second stress threshold and the life sensitivity is less than the first life threshold, topology opening processing or adding reinforcing ribs is performed; if the force transmission path contribution is less than the third contribution threshold, opening processing is allowed.

[0013] To achieve the above objectives, another aspect of this application proposes a fatigue optimization device based on multi-parameter fusion, the device comprising: The acquisition module is used to acquire basic data for fatigue analysis of the target structure. The marking module is used to mark the regions in the fatigue analysis basic data that meet the dual threshold criteria to obtain high-risk regions. The clustering module is used to perform regional clustering on the nodes of the high-risk area using a spatial clustering algorithm to obtain the geometric features of the high-risk area; The calculation module is used to calculate the stress gradient factor, life sensitivity, and force transmission path contribution based on the fatigue analysis data and the geometric characteristics of the high-risk area. The thickness optimization channel module is used to determine the fatigue optimization scheme through the thickness optimization channel when the product of the stress gradient factor and the life sensitivity is greater than the activation threshold. A multi-measure optimization channel module is used to determine the fatigue optimization scheme through the multi-measure optimization channel when the product of the stress gradient factor and the lifetime sensitivity is less than or equal to the activation threshold. The output module is used to integrate and output the fatigue optimization scheme.

[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a fatigue optimization method and related equipment based on multi-parameter fusion. This scheme obtains fatigue analysis basic data, marks high-risk areas in the fatigue analysis basic data that meet dual threshold criteria, performs regional clustering on the nodes of the high-risk areas using a spatial clustering algorithm to obtain the geometric features of the high-risk areas, calculates the fatigue analysis basic data and the geometric features of the high-risk areas, compares the calculation results with the activation threshold, determines a thickness optimization channel or a multi-measure optimization channel, and determines the fatigue optimization scheme based on the output of the determined thickness optimization channel or multi-measure optimization channel. This application, through a dual-path intelligent diversion method, avoids blind adjustments to low-sensitivity areas, reduces invalid calculations, and avoids erroneous adjustments to high-sensitivity areas, covering 100% of fatigue scenarios, eliminating optimization blind spots, achieving automated and precise design, reducing reliance on engineer experience, and can be extended to the optimization of fatigue weak areas in various structures such as automotive chassis and body frames, with a wide range of applications. Attached Figure Description

[0016] Figure 1 This is a flowchart of the fatigue optimization method based on multi-parameter fusion provided in the embodiments of this application; Figure 2 This is a flowchart illustrating a fatigue optimization method based on multi-parameter fusion. Figure 3 This is a schematic diagram of the fatigue optimization device based on multi-parameter fusion provided in an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0022] Stress gradient factor: A parameter describing how quickly stress changes within a structure with respect to location, reflecting the steepness of the stress field. High gradient regions are prone to stress concentration and are sensitive areas for fatigue crack initiation; they are often used to evaluate the fatigue resistance of structures.

[0023] Lifespan sensitivity: This reflects the degree to which changes in design variables (such as dimensions and material parameters) affect the fatigue life of a structure. High sensitivity means that even small adjustments to variables can significantly alter the lifespan, providing a crucial reference for optimizing the design.

[0024] Force transmission path contribution: measures the weight of a certain part of the structure in the load transmission process. Areas with high contribution are the main channels of force flow, playing a decisive role in the overall load-bearing capacity of the structure and are the focus of strength optimization.

[0025] Spatial clustering algorithms are algorithms that group data objects based on their spatial location and feature similarity. By calculating spatial distance or feature differences, they cluster similar objects into one class, achieving effective classification and analysis of spatial data.

[0026] Sequential quadratic programming algorithm: An efficient method for solving constrained optimization problems, it iteratively transforms the original problem into a series of quadratic programming subproblems. Utilizing an approximate model of the objective function and constraints, it gradually approximates the optimal solution, making it suitable for nonlinear optimization scenarios.

[0027] Topological openings: Applications of topology optimization in structural design, based on algorithms such as SIMP and level sets, aim at lightweighting and optimizing force flow. By iteratively adjusting element density while satisfying constraints such as stiffness and strength, material removal regions (i.e., openings) are autonomously generated, determining the approximate area and shape of the openings. This represents a macroscopic opening scheme in the conceptual design stage. Topological openings are the macroscopic design where topology optimization determines the opening region; the actual opening is the microscopic execution of dimensional and technological adjustments to the determined region.

[0028] This application provides a fatigue optimization method based on multi-parameter fusion. This method solves the problems of high blindness, easy occurrence of secondary stress concentration, low efficiency and reliance on experience in traditional thickness optimization, and achieves a precise, efficient and low-cost method for optimizing the thickness of fatigue weak areas.

[0029] Figure 1 This is an optional flowchart of the fatigue optimization method based on multi-parameter fusion provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S110 to S170.

[0030] Step S110: Obtain the basic data for fatigue analysis of the target structure; Step S120: Mark the areas in the fatigue analysis basic data that meet the dual threshold criteria to obtain high-risk areas; Step S130: Use a spatial clustering algorithm to perform region clustering on the nodes of the high-risk area to obtain the geometric features of the high-risk area; Step S140: Calculate the stress gradient factor, life sensitivity, and force transmission path contribution based on the fatigue analysis data and the geometric characteristics of the high-risk area. Step S150: When the product of stress gradient factor and life sensitivity is greater than the activation threshold, the fatigue optimization scheme is determined through the thickness optimization channel. Step S160: When the product of stress gradient factor and life sensitivity is less than or equal to the activation threshold, the fatigue optimization scheme is determined by optimizing the channel through multiple measures. Step S170: Integrate and output the fatigue optimization scheme.

[0031] Steps S110 to S170 of this embodiment involve marking high-risk areas in the fatigue analysis baseline data that meet the dual threshold criteria, then using a spatial clustering algorithm to cluster the nodes of the high-risk areas to obtain their geometric features. Based on the fatigue analysis baseline data and the geometric features of the high-risk areas, stress gradient factor, life sensitivity, and force transmission path contribution are calculated. The stress gradient factor, life sensitivity, and activation threshold are calculated and compared to select either a thickness optimization channel or a multi-measure optimization channel. This dual-channel selection (thickness optimization channel and multi-measure optimization channel) enables intelligent flow control, avoiding blind adjustments to low-sensitivity areas and reducing ineffective calculations, while also preventing erroneous adjustments to high-sensitivity areas. Furthermore, it covers 100% of the fatigue scenarios, eliminating optimization blind spots.

[0032] In some embodiments, in step S110, basic fatigue analysis data of the target structure is collected and input. This basic data includes stress contour maps, life distribution maps, and material properties output from the finite element analysis. Specifically, the stress contour maps output from the finite element analysis include the stress values ​​and stress types at each node, such as tensile or compressive stress; the life distribution maps include the number of fatigue life cycles in each region; and the material properties include material grade, elastic modulus, yield strength, and stress-life curves. Ensuring the accuracy of the input data provides a reliable basis for subsequent parameter calculations.

[0033] In some embodiments, in steps S120 to S130, high-risk areas are identified and their geometric features are extracted. Specifically, a dual threshold standard is set, namely a high-risk stress threshold and a high-risk lifespan threshold, such as the high-risk stress threshold. ≥300MPa and high-risk lifespan threshold N≤ In the next iteration, regions that simultaneously meet the criteria of "stress ≥ 0.7 high-risk stress threshold" and "life ≤ 0.8 high-risk life threshold" are marked as high-risk regions. A spatial clustering algorithm is then used to cluster nodes that simultaneously meet the threshold conditions. Region clustering aims to merge physically adjacent high-risk nodes with similar mechanical states into continuous optimization regions, providing coherent optimization units for subsequent calculations of the region area, radius of curvature, and feature length.

[0034] More specifically, the method for performing region clustering on nodes that simultaneously meet the dual threshold criteria using spatial clustering algorithms includes: First, initial screening of high-risk nodes, selecting nodes that simultaneously meet the criteria of stress value greater than 70% of the material yield strength and fatigue life less than 80% of the design target life. Nodes with stress values ​​greater than the material yield strength threshold and fatigue life less than the design target life threshold are further screened to obtain an initial set of high-risk nodes. Next, density clustering is performed on the initial set of high-risk nodes according to the model size to obtain initial clusters. That is, the neighborhood radius is automatically calculated based on the model size to ensure that the radius contains at least 5 high-risk nodes before forming the initial cluster; for example, if there are ≥5 high-risk nodes in the neighborhood of node A, then a new cluster is expanded with A as the core, and nodes whose distance to the core node is less than the radius are grouped into the same cluster. Then, the regions are merged and corrected. The continuity of the initial clusters after merging is verified by triangular meshing technology to obtain the geometric features of high-risk areas. For example, if the distance between the boundaries of two clusters is less than twice the neighborhood radius and the curvature direction is consistent, they are merged. In addition, the continuity of the regions is verified by triangular meshing, and holes are filled to maintain geometric coherence.

[0035] In some embodiments, in step S140, based on the fatigue analysis baseline data and geometric characteristics of the high-risk area obtained in steps S110 to S130, three key parameters are calculated, namely the stress gradient factor. Lifetime sensitivity Contribution of force transmission path .

[0036] Specifically, stress gradient factor The calculation formula is: ; in, Represents the stress gradient factor. Indicates the maximum stress value within the region. The value represents the minimum stress within the region, and L represents the characteristic length of the region (unit: mm). The characteristic length L represents the equivalent radius of the region. Indicates the reference stress gradient. The calculation formula is: ; Indicates the yield strength of the material. This indicates the reference length, which is 1 mm.

[0037] The calculation method for the region feature length L includes: first, calculating the two-dimensional projected area A (unit: mm²) of region R, and then calculating the area of ​​the continuous region using triangular meshing. The formula for calculating the region feature length L is: L = sqrt(A / π), which means that the region is equivalent to the radius of a circle with the same area, and this radius is used as the region feature length.

[0038] Lifetime sensitivity The calculation formula is: ; in, Indicates lifespan sensitivity. Indicates the change in lifespan. Indicates the original lifespan. Indicates the amount of thickness change. Indicates the original thickness.

[0039] Lifetime sensitivity The calculation formula is based on the thickness perturbation method, which adds a 1% perturbation to the thickness to obtain the change in lifetime and the change in thickness.

[0040] Contribution of force transmission path The calculation formula is: ; in, This represents the contribution of the force transmission path of the i-th unit. This represents the strain energy of the i-th element. This represents the sum of the strain energies of all elements. This represents the force acting on the i-th node. This represents the total load on the model.

[0041] In some embodiments, in steps S150 and S170, the product of the stress gradient factor and the lifetime sensitivity is calculated ( × ), set the activation threshold to 5. When When, enter the thickness optimization channel; when At that time, it will enter a multi-measure optimization channel.

[0042] In the thickness optimization process, a mathematical model is first constructed with "minimizing weight increase" and "minimizing life deviation" as the dual core objectives, resulting in a dual-objective optimization problem model. Subsequently, the optimization problem is solved using the Sequential Quadratic Programming (SQP) algorithm to obtain the optimal thickness increase. Finally, the structural transformation type is determined and parameters are designed. Based on the calculated optimal thickness increase, the thickness increase rate (the ratio of the thickness increase to the original thickness) is calculated in conjunction with the original thickness of each part. Simultaneously, the geometric features of previously extracted high-risk areas are considered, and the structural transformation type is adaptively selected based on these features and the thickness increase rate, resulting in a fatigue optimization scheme. This scheme innovatively employs a thickness increase rate-geometric feature adaptive structural transformation rule, transforming simple thickness increase into topology optimization, effectively avoiding stress concentration problems caused by thickness increase.

[0043] Specifically, in constructing the mathematical model for the bi-objective optimization problem, the core variables include the thickness adjustment of the optimization region. Objective functions are constructed for the core variables, as well as to minimize the weight gain and the lifetime deviation. The objective function is as follows: ; in, Indicates the increase in weight. Indicates the original structural mass. Indicates the target lifespan. This indicates the predicted lifespan. The dual-objective optimization model achieves synergistic optimization of fatigue life and weight, aligning with the trend of lightweight design. This represents the weighting coefficient, which defaults to 1000 and can be dynamically increased according to the stress level of the region. If the local peak stress is close to or exceeds the yield strength... This will significantly increase, thus making the optimization more biased towards meeting the lifetime target. The formula for calculating the weighting coefficient is: ; in, This indicates the peak stress within the high-risk area. This indicates the yield strength of the material.

[0044] The constraints set in the mathematical model of the bi-objective optimization problem include: Thickness range: , where t represents the thickness; Weight gain restrictions: ,in, Indicates the increase in weight. Indicates the initial weight; Activation conditions: ,in, Represents the stress gradient factor. This indicates lifetime sensitivity; when the activation threshold is 5, the activation condition has been verified in step S150.

[0045] When solving the optimization problem of this bi-objective optimization problem using the Sequential Quadratic Programming (SQP) algorithm, initialization is first performed to determine the original thickness of each part of the structure. As the starting point of the iteration, it provides a starting benchmark for the entire optimization solution process. This initial value will serve as the initial variable input for subsequent iterations. Once in the iteration process, the sequential quadratic programming algorithm executes cyclically according to a fixed procedure: First, it calculates the specific values ​​of the dual objective functions of "minimizing weight increase" and "minimizing lifespan deviation" based on the current thickness variable, while simultaneously evaluating the degree of violation of each constraint, such as whether the thickness exceeds the boundary or the stress exceeds the limit. Next, based on the calculated results of the objective function values ​​and constraint violations, it constructs and solves the quadratic programming subproblem through a first-order Taylor expansion of the objective function and constraints, obtaining the optimal search direction and adjustment step size for the thickness variable in the current iteration step. Finally, it updates the iteration starting point using this search direction and step size, generating a new design point. This iterative process continues until a preset termination condition is met. Termination conditions include: the thickness change between two consecutive iterations being less than a threshold value (preferably 0.01 mm), the rate of change of the objective function being less than a threshold value (preferably 1%), or the number of iterations reaching a threshold value (preferably 50). The algorithm terminates and outputs the final thickness variable, which is the optimal thickness increase that satisfies the dual-objective optimization requirements. This result will be directly used for subsequent thickness increase rate calculations and the selection of structural conversion types. The combination of rapid thickness perturbation prediction technology and the SQP algorithm shortens the optimization cycle by more than 60% and reduces reliance on engineer experience, enabling automated and precise design.

[0046] After obtaining the optimal thickness increase for each part, the structural conversion type determination and parameter design stage begins. First, based on the optimal thickness increase and corresponding original thickness for each part, the thickness increase rate (the ratio of the optimal thickness increase to the original thickness) is calculated through division. Then, the extracted geometric features of high-risk areas are correlated, and the appropriate structural conversion type is adaptively selected based on the matching relationship between the thickness increase rate and the geometric features. Simultaneously, considering the characteristics of the selected conversion type, the corresponding specific design parameters are determined, forming a complete structural conversion scheme. Specifically, the adaptive selection scheme for the structural conversion type is as follows: if the thickness increase rate is greater than 15% and the radius of curvature is less than 5mm, a stress relief groove is designed; if the thickness increase rate is greater than 20% and the original thickness is greater than 8mm, a grid-like weight-reduction groove is designed; and if the thickness increase rate is greater than 25%, a hollow sandwich structure is designed.

[0047] In the multi-measure optimization channel, if the stress gradient factor is greater than the first stress threshold and the life sensitivity is less than the first life threshold, then fillet modification is performed; if the force transmission path contribution is less than the first contribution threshold, then fillet modification and pitting are performed; if the stress gradient factor is less than the second stress threshold and the life sensitivity is greater than the second life threshold, then local thickening and laser strengthening are performed, or high-strength materials are replaced; if the force transmission path contribution is greater than the second contribution threshold, then laser strengthening is performed; if the stress gradient factor is less than the second stress threshold and the life sensitivity is less than the first life threshold, then topological opening or adding reinforcing ribs is performed; if the force transmission path contribution is less than the third contribution threshold, opening is allowed.

[0048] For example, preferably, the first stress threshold is 3, the first lifetime threshold is 1, the first contribution threshold is 0.3, the second stress threshold is 1, the second lifetime threshold is 1.5, and the second contribution threshold is 0.6. Optimization measures are selected based on parameter characteristics; for example, if the stress gradient factor is high... >3 and low lifetime sensitivity If the value is less than 1, the first choice is to modify the fillet radius, and the second choice is to add a recess. This depends on the contribution of the force transmission path. A stress gradient factor <0.3 is acceptable for indentations. <1 and high lifetime sensitivity When the strength is greater than 1.5: local thickening and laser strengthening are preferred, followed by replacing with high-strength materials. The contribution of the force transmission path is considered. For parameters >0.6, reinforcement is mandatory. In low-parameter regions: topological openings are preferred, followed by adding reinforcing ribs, considering the contribution of the force transmission path. Holes are only allowed if the diameter is less than 0.4.

[0049] In step S170, the fatigue optimization scheme is determined through the thickness optimization channel or the fatigue optimization scheme is determined through the multi-measure optimization channel, and then integrated and output. This scheme has strong universality and can be extended to the optimization of fatigue weak areas in various structures such as automobile chassis and body frames, with a wide range of applications.

[0050] In some embodiments, such as Figure 2 As shown, Figure 2 This is a flowchart illustrating a fatigue optimization method based on multi-parameter fusion. First, basic fatigue analysis data, i.e., fatigue analysis results, is acquired. Stress and life data are extracted from these results. High-risk areas are then identified based on the extracted stress and life data. A spatial clustering algorithm is used to cluster the nodes in these high-risk areas to obtain their geometric features. Finally, the stress gradient factor is calculated by combining these geometric features with the basic fatigue analysis data. Lifetime sensitivity Contribution of force transmission path .

[0051] Next, based on the stress gradient factor and lifespan sensitivity The selection of a channel is based on whether the product of the factors is greater than 5. If it is less than or equal to 5, the optimization solution is output after multi-measure decision-making. If it is greater than 5, the thickness optimization channel is selected. In the thickness optimization channel, the optimization problem is first constructed, then solved by the Sequential Quadratic Programming (SQP) algorithm, and then structural transformation conditions are applied. The structural transformation conditions include stress relief grooves, grid-like weight reduction grooves, or hollow sandwich structures, etc. Finally, the optimization solution is output based on the structural transformation conditions.

[0052] Please see Figure 3 This application also provides a fatigue optimization device based on multi-parameter fusion, which can implement the above method. The device includes: The acquisition module is used to acquire basic data for fatigue analysis of the target structure. The marking module is used to mark the areas in the fatigue analysis base data that meet the dual threshold criteria, thereby obtaining high-risk areas; The clustering module is used to perform regional clustering on nodes in high-risk areas using spatial clustering algorithms to obtain the geometric features of high-risk areas; The calculation module is used to calculate the stress gradient factor, life sensitivity, and force transmission path contribution based on fatigue analysis data and the geometric characteristics of high-risk areas. The thickness optimization channel module is used to determine the fatigue optimization scheme when the product of stress gradient factor and life sensitivity is greater than the activation threshold. The multi-measure optimization channel module is used to determine the fatigue optimization scheme when the product of stress gradient factor and life sensitivity is less than or equal to the activation threshold. The output module is used to integrate and output fatigue optimization solutions.

[0053] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0054] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0055] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0056] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 410 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 420 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 420 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 420 and is called and executed by the processor 410 using the methods described above in the embodiments of this application. Input / output interface 430 is used to realize information input and output; The communication interface 440 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 450 transmits information between various components of the device (e.g., processor 410, memory 420, input / output interface 430, and communication interface 440); The processor 410, memory 420, input / output interface 430 and communication interface 440 are connected to each other within the device via bus 450.

[0057] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0058] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0059] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0060] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0061] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0062] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0063] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0064] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0065] It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than those illustrated or described herein. Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0066] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0067] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0068] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0069] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A fatigue optimization method based on multi-parameter fusion, characterized in that, The method includes the following steps: Obtain the basic data for fatigue analysis of the target structure; The regions in the fatigue analysis baseline data that meet the dual threshold criteria are marked to obtain high-risk regions; The nodes in the high-risk area are clustered using a spatial clustering algorithm to obtain the geometric features of the high-risk area; Based on the fatigue analysis data and the geometric characteristics of the high-risk area, the stress gradient factor, life sensitivity, and force transmission path contribution are calculated. When the product of the stress gradient factor and the lifetime sensitivity is greater than the activation threshold, the fatigue optimization scheme is determined through the thickness optimization channel; When the product of the stress gradient factor and the lifetime sensitivity is less than or equal to the activation threshold, the fatigue optimization scheme is determined through multi-measure optimization channels. The fatigue optimization schemes are integrated and output.

2. The method according to claim 1, characterized in that, The fatigue analysis data includes stress cloud diagrams, life distribution diagrams, and material properties output from finite element analysis. The stress cloud diagram output by the finite element analysis includes the stress value and stress type of each node; The lifespan distribution map includes the number of fatigue life cycles in each region; The material properties include material grade, elastic modulus, yield strength, and stress life curve.

3. The method according to claim 1, characterized in that, The dual threshold standard includes a high-risk stress threshold and a high-risk lifespan threshold; The regions in the fatigue analysis baseline data that meet the dual threshold criteria include regions where the stress value is greater than or equal to the high-risk stress threshold and the life value is less than or equal to the high-risk life threshold.

4. The method according to claim 3, characterized in that, The step of performing region clustering on the nodes of the high-risk area using a spatial clustering algorithm to obtain the geometric features of the high-risk area includes the following steps: Nodes with stress values ​​greater than the material yield strength threshold and fatigue life less than the design target life threshold are screened to obtain a preliminary set of high-risk nodes. Based on the model size, density clustering is performed on the initial screening high-risk node set to obtain an initial cluster; The initial clusters are merged into regions according to the cluster merging rules; The regional continuity of the merged initial cluster is verified by using triangular mesh partitioning technology, and the geometric features of the high-risk area are obtained.

5. The method according to claim 1, characterized in that, The process of determining the fatigue optimization scheme through the thickness optimization channel includes the following steps: Using the thickness adjustment amount as a variable, a mathematical model is constructed to address the two objectives of minimizing the weight increase and minimizing the life deviation, resulting in a bi-objective optimization problem mathematical model. The optimal thickness increase was obtained by solving the mathematical model of the bi-objective optimization problem using a sequential quadratic programming algorithm. The optimal thickness increase is calculated to obtain the thickness increase rate; Based on the geometric characteristics of the high-risk area and the thickening rate, the structural transformation type is adaptively selected to obtain the fatigue optimization scheme.

6. The method according to claim 5, characterized in that, The step of solving the mathematical model of the bi-objective optimization problem using a sequential quadratic programming algorithm to obtain the optimal thickness increase includes the following steps: The mathematical model of the bi-objective optimization problem is initialized using the original thickness as the initial value to obtain the iteration starting point; By constructing a quadratic programming subproblem and solving it using the objective function and constraint conditions in each iteration, the search direction can be obtained. The search direction is determined by the step size according to the line search strategy. The search direction and the step size are converted by vector operation to obtain the thickness adjustment amount. The thickness adjustment amount is used to update the iteration starting point to obtain the thickness value after iteration; The optimal thickness increase is obtained when the thickness change is less than the change threshold and the rate of change of the objective function is less than the rate of change threshold, or when the number of iterations reaches the iteration number threshold.

7. The method according to claim 5, characterized in that, Schemes for adaptively selecting the structure transformation type include: If the thickness increase rate is greater than 15% and the radius of curvature is less than 5mm, then a stress relief groove should be designed. If the thickening rate is greater than 20% and the original thickness is greater than 8mm, then a grid-shaped weight reduction groove should be designed. If the thickness increase rate is greater than 25%, a hollow sandwich structure should be designed.

8. The method according to claim 1, characterized in that, The process of determining the fatigue optimization scheme through multiple optimization channels includes the following steps: When the stress gradient factor is greater than the first stress threshold and the life sensitivity is less than the first life threshold, the fillet is modified; if the force transmission path contribution is less than the first contribution threshold, the fillet and pitting are modified. When the stress gradient factor is less than the second stress threshold and the lifetime sensitivity is greater than the second lifetime threshold, local thickening and laser strengthening treatment are performed, or high-strength materials are replaced; if the force transmission path contribution is greater than the second contribution threshold, laser strengthening treatment is performed. When the stress gradient factor is less than the second stress threshold and the life sensitivity is less than the first life threshold, topology opening processing or adding reinforcing ribs is performed; if the force transmission path contribution is less than the third contribution threshold, opening processing is allowed.

9. A fatigue optimization device based on multi-parameter fusion, characterized in that, The device includes: The acquisition module is used to acquire basic data for fatigue analysis of the target structure. The marking module is used to mark the regions in the fatigue analysis basic data that meet the dual threshold criteria to obtain high-risk regions. The clustering module is used to perform regional clustering on the nodes of the high-risk area using a spatial clustering algorithm to obtain the geometric features of the high-risk area; The calculation module is used to calculate the stress gradient factor, life sensitivity, and force transmission path contribution based on the fatigue analysis data and the geometric characteristics of the high-risk area. The thickness optimization channel module is used to determine the fatigue optimization scheme through the thickness optimization channel when the product of the stress gradient factor and the life sensitivity is greater than the activation threshold. A multi-measure optimization channel module is used to determine the fatigue optimization scheme through the multi-measure optimization channel when the product of the stress gradient factor and the lifetime sensitivity is less than or equal to the activation threshold. The output module is used to integrate and output the fatigue optimization scheme.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 8.

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