A control arm design optimization method and system

Abstract

The present invention discloses a control arm design optimization method and system, specifically relating to the technical field of control arms; by clarifying optimization goals and constraints, using finite element analysis to identify high-stress areas, and combining multi-axis sensors to monitor dynamic loads and material performance degradation curves, a quantitative evaluation of the deviation between the optimization result and the actual working condition is achieved; when the deviation is large, real-time monitoring and feedback correction are further performed by optimizing the geometric structure and installing intelligent sensors, and the design is dynamically adjusted to ensure the reliability and safety of the optimization result under complex loads and extreme working conditions, significantly improving the durability and fatigue resistance of the control arm, reducing the risk of local failure, and ultimately improving the overall safety and performance stability of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of control arms, and in particular to a design optimization method and system for a control arm. Background Art

[0002] Control arm design optimization refers to the engineering process of improving a control arm's structure, materials, geometry, or manufacturing process to enhance its performance, reliability, and cost-effectiveness. In the automotive industry, control arms are essential components of the suspension system, connecting the wheels to the vehicle body, transmitting wheel forces, and ensuring driving stability. Therefore, design optimization typically focuses on objectives such as weight reduction, strength enhancement, improved durability, and cost reduction to meet vehicle performance requirements and market demands. For example, optimizing the control arm design of a passenger car might replace traditional steel with high-strength steel or aluminum alloy, thereby reducing weight and improving fuel efficiency. Furthermore, optimizing the control arm's geometry through finite element analysis (FEA) to reduce stress concentration points can improve fatigue resistance and extend its service life. Finally, optimizing the manufacturing process, such as adopting new high-pressure casting or welding technologies, can reduce production costs while ensuring consistent product quality.

[0003] The existing technology has the following shortcomings:

[0004] Finite element analysis (FEA) optimization of the control arm's geometry is typically based on idealized boundary conditions and load assumptions. However, actual operating conditions can present complex and unpredictable loads, such as dynamic loads from sudden steering, high-speed pothole driving, or lateral impacts. If the optimized design excessively pursues weight reduction or stress concentration reduction, certain areas of the control arm may become too thin or lack rigidity, leading to localized buckling or fatigue cracking. This localized failure typically manifests as sudden fracture of the structure under extreme operating conditions, which not only directly leads to vehicle loss of control but can also endanger the safety of occupants and other road users, with extremely serious consequences. Summary of the Invention

[0005] The purpose of the present invention is to provide a control arm design optimization method and system to address the deficiencies in the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a design optimization method for a control arm, comprising the following steps:

[0007] S1: Define the control arm design optimization goals, set the control arm size limits, material selection, manufacturing process constraints and performance requirements, and define the actual use conditions of the control arm;

[0008] S2: Establish the initial geometric model of the control arm, divide the mesh based on the finite element analysis method, set material properties, apply loads and define boundary conditions, perform finite element analysis on the initial geometric model of the control arm, and identify high stress areas and stress concentration points;

[0009] S3: By installing multi-axis load sensors, dynamic load data of the vehicle under different operating conditions is monitored over a long period of time to generate operating condition distribution parameters. Accelerated aging tests are performed in the laboratory to determine the performance degradation patterns of the control arm material under high humidity, corrosion, and extreme temperature conditions, generating material performance degradation curves.

[0010] S4: Comprehensively analyze the generated operating condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and the actual use conditions;

[0011] S5: When the deviation is large, the control arm geometry is optimized by increasing the corner radius of the transition area, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point;

[0012] S6: Install smart sensors on the control arms to monitor load and stress distribution in real time. Compare the actual test results with the finite element analysis data to further refine the design until the optimized results meet performance and safety requirements.

[0013] Preferably, in S1, the goals of control arm design optimization are clearly defined, including reducing weight, reducing stress concentration, improving fatigue resistance and extending service life; and the actual operating conditions of the control arm are defined, including static loads, dynamic loads, boundary conditions and environmental impact parameters.

[0014] Preferably, in S3, the extreme load anomaly index is generated after analyzing the occurrence frequency and duration of the extreme load. The extreme load anomaly index is obtained by:

[0015] According to the design specifications and material properties of the control arm, a load threshold Lthreshold is set. The load exceeding this value is regarded as an extreme load. The load time series {L(t i )}, where t i Indicates the i-th time point, marking all the conditions that satisfy L(t i )>Lthreshold, record the occurrence time of these extreme loads and the corresponding load values, and calculate the frequency of extreme load occurrence: set a time window T window , count the number of extreme loads N that occur in the time window extreme ; Calculate the frequency of occurrence of extreme loads f extreme , the expression is: For each extreme load event, record its start time tstart and end time tend, calculate the duration Δt = tend - tstart; calculate the average duration of all extreme load events The expression is: Where, Δt i represents the duration of the i-th extreme load event; the frequency and average duration are normalized: Among them, f ref and Δt ref is the reference value, f norm is the normalized frequency of occurrence of extreme loads, Δt norm The extreme load anomaly index ELAI is calculated as the normalized average duration, and the expression is: Wherein, α and β are weight coefficients, satisfying α+β=1.

[0016] Preferably, in S3, the material performance degradation index is generated after analyzing the inflection point and the end point in the extracted degradation curve. The material performance degradation index is obtained by:

[0017] The material performance degradation curve P(t) represents the change of material performance over time t. The curve is generated by laboratory testing or field monitoring data. The initial performance is: P0 = P(t = 0); the performance at the end of degradation is: Pend = P(tend); the derivative of the curve is calculated, the first-order derivative Indicates the rate of performance degradation over time, the second-order derivative Indicates the changing trend of degradation rate;

[0018] The inflection point tinflection occurs where the second-order derivative is zero: Make sure that the inflection point corresponds to the acceleration phase of the degradation curve, that is: It decreases and becomes stable near tinflection;

[0019] When the material performance drops to the set ratio R: P(tend) = P0·R; where R is 0.8 or 0.5, the material performance degradation index MPDI is calculated as follows: Where: ΔP inflection =P0-P(tinflection) represents the performance degradation at the inflection point.

[0020] Preferably, in S4, a comprehensive analysis is performed on the generated operating condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and the actual use conditions;

[0021] The extreme load anomaly index and the material performance degradation index are normalized, and the deviation coefficient between the optimization result and the actual use condition is calculated based on the normalized extreme load anomaly index and the material performance degradation index.

[0022] Preferably, in S4, the deviation degree coefficient between the obtained optimization result and the actual usage condition is compared with a deviation degree coefficient reference threshold value pre-set based on historical data. If the deviation degree coefficient between the optimization result and the actual usage condition is greater than or equal to the pre-set deviation degree coefficient reference threshold value, it means that the deviation degree between the optimization result and the actual usage condition is large; if the deviation degree coefficient between the optimization result and the actual usage condition is less than the pre-set deviation degree coefficient reference threshold value, it means that the deviation degree between the optimization result and the actual usage condition is small.

[0023] Preferably, in S6, a multi-axis strain gauge or MEMS sensor is installed at a key position of the control arm to monitor the stress σreal(t) and the load Lreal(t) in real time; the monitoring value σreal(t) is obtained through a data acquisition device, and the time series data is recorded; finite element analysis is used to generate a load-stress mapping relationship σFEA=f(LFEA), where f is a mapping function of the finite element analysis, and the corresponding FEA predicted stress σFEA(t) is obtained through the mapping function according to the actual monitored load Lreal(t);

[0024] The calculation formula of real-time deviation Δσ(t) is: Δσ(t) = σreal(t) - σFEA(t); where: Δσ(t) is the difference between the real-time monitored stress and the FEA predicted stress; σreal(t) is the actual stress monitored by the sensor; σFEA(t) is the stress predicted by the FEA model; the cumulative deviation Δσ avg The calculation formula is: Wherein, T is the monitoring time period;

[0025] Based on the topology optimization method, the deviation is reduced by adjusting the material distribution or geometry of key parts: minΦ=∫ Ω Δσ 2 (x)dΩ; where: Φ is the objective function, which represents the integral of the square of the deviation; Δσ 2 (x) is the square of the real-time deviation value; Ω is the design area;

[0026] Use numerical optimization algorithms to solve the geometric correction parameters; if the deviation is caused by material performance degradation, update the FEA input parameters according to the degradation law, the expression is: E new =E initial (1-k t); where: E new is the corrected elastic modulus of the material, k is the degradation rate constant, t is the service time, E initial is the elastic modulus of the material before correction;

[0027] Evaluate the correction effect by calculating the deviation coefficient between the optimized design and the actual monitoring conditions: Deviation coefficient: Δσ avg,new is the corrected mean deviation; Δσ avg,initial is the average deviation before correction; D optimized The optimized deviation coefficient.

[0028] Preferably, the obtained deviation coefficient is compared with the deviation threshold. If the deviation coefficient is less than or equal to the deviation threshold, it means that the correction effect is good and the optimized design meets the safety and performance requirements. At this time, the iterative optimization is terminated and the final correction parameters are recorded as the design results. If the deviation coefficient is greater than the deviation threshold, it means that the correction effect is poor and the design needs to be further adjusted by increasing the number of iterations to optimize the geometric shape, material properties or boundary conditions.

[0029] The present invention also provides a control arm design optimization system, including a demand analysis and design input module, a modeling and finite element analysis module, an environmental impact assessment module, a deviation assessment module, a geometric structure optimization module, and an optimization feedback module;

[0030] Requirements Analysis and Design Input Module: This module clarifies the control arm design optimization goals, sets the control arm's size limits, material selection, manufacturing process constraints, and performance requirements, and defines the actual operating conditions of the control arm.

[0031] Modeling and Finite Element Analysis Module: This module establishes the initial geometric model of the control arm, divides the mesh based on the finite element analysis method, sets material properties, applies loads, and defines boundary conditions. It also performs finite element analysis on the initial geometric model of the control arm to identify high stress areas and stress concentration points.

[0032] Environmental Impact Assessment Module: By installing multi-axis load sensors, the dynamic load data of the vehicle under different operating conditions is monitored over a long period of time to generate operating condition distribution parameters. Through laboratory accelerated aging testing, the performance degradation patterns of the control arm material under high humidity, corrosion, and extreme temperature conditions are determined to generate material performance degradation curves.

[0033] Deviation evaluation module: conducts comprehensive analysis on the generated operating condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and the actual use conditions;

[0034] Geometry Optimization Module: When the deviation is large, the control arm geometry is optimized by increasing the corner radius of the transition area, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point;

[0035] Optimization feedback module: Intelligent sensors are installed on the control arm to monitor load and stress distribution in real time. Based on the actual test results and comparison with the finite element analysis data, the design is further modified and optimized until the optimization results meet the performance and safety requirements.

[0036] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0037] 1. The present invention overcomes the problem of large deviation from actual working conditions caused by idealized assumptions in traditional optimization design by combining finite element analysis and actual working condition monitoring. The present invention clearly defines the optimization goals and actual working conditions, uses finite element analysis to identify high stress areas, and installs multi-axis sensors to monitor dynamic loads and stress distribution in real time, while combining laboratory accelerated aging tests to generate material performance degradation curves. Comprehensively analyze the working condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and actual use conditions. When the deviation is large, it is corrected by optimizing the geometric structure (such as increasing the fillet radius, optimizing the hole edge shape, and adjusting the rib layout) and material distribution, and real-time monitoring and dynamic iterative optimization are carried out until the optimized design meets safety and performance requirements.

[0038] 2. This invention significantly improves the reliability and durability of control arms under complex loads and extreme operating conditions, effectively reducing the risk of local buckling and fatigue cracking. By introducing an extreme load anomaly index and a material degradation index, the optimization results are scientifically quantified and dynamically corrected, ensuring a high degree of compatibility between the design and actual operating conditions. This method not only improves design accuracy and efficiency, but also reduces material waste and manufacturing costs, enhancing vehicle safety performance. It is suitable for the design optimization of control arms on various vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0040] Figure 1 Flow chart of the method of the present invention.

[0041] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] Example 1, please refer to Figure 1 As shown, the design optimization method of a control arm described in this embodiment includes the following steps:

[0044] S1: Define the control arm design optimization goals, set the control arm size limits, material selection, manufacturing process constraints and performance requirements, and define the actual use conditions of the control arm;

[0045] S2: Establish the initial geometric model of the control arm, divide the mesh based on the finite element analysis method, set material properties, apply loads and define boundary conditions, perform finite element analysis on the initial geometric model of the control arm, and identify high stress areas and stress concentration points;

[0046] S3: By installing multi-axis load sensors, dynamic load data of the vehicle under different operating conditions is monitored over a long period of time to generate operating condition distribution parameters. Accelerated aging tests are performed in the laboratory to determine the performance degradation patterns of the control arm material under high humidity, corrosion, and extreme temperature conditions, generating material performance degradation curves.

[0047] S4: Comprehensively analyze the generated operating condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and the actual use conditions;

[0048] S5: When the deviation is large, the control arm geometry is optimized by increasing the corner radius of the transition area, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point;

[0049] S6: Install smart sensors on the control arms to monitor load and stress distribution in real time. Compare the actual test results with the finite element analysis data to further refine the design until the optimized results meet performance and safety requirements.

[0050] In S1, the control arm design optimization objectives include the following specific contents:

[0051] By optimizing the geometry and selecting lightweight, high-strength materials, the total weight of the control arm can be reduced to improve vehicle fuel economy or electric vehicle endurance, while also reducing the unsprung mass of the suspension system and improving vehicle handling performance and comfort.

[0052] By improving the structural design of the control arm, such as adding fillets, optimizing hole shapes, and rearranging ribs, local stress concentration areas can be reduced to reduce the risk of cracks and fatigue failure.

[0053] Combined with fatigue analysis and optimized design, it ensures that the control arm can maintain structural integrity under long-term cyclic loads and extend its service life, especially in harsh operating environments (such as off-road or high-dynamic conditions).

[0054] By comprehensively optimizing geometric design, material selection, and manufacturing processes, the control arm is ensured to maintain its strength, rigidity, and durability during long-term use, adapting to the requirements of high loads and complex environmental conditions.

[0055] Set dimensional limits, material selection, manufacturing constraints, and performance requirements for the control arm, including:

[0056] The geometric design of the control arm must strictly follow the layout requirements of the vehicle suspension system, including the spatial relationship with other components (such as shock absorbers, steering knuckles, and ball joints) to avoid interference.

[0057] The weak parts of structural parts (such as joints and reinforcements) must meet manufacturing and strength requirements to prevent local deformation or welding difficulties caused by excessive thinness.

[0058] The control arms must accommodate the suspension system's range of motion and maintain structural integrity under various operating conditions, such as compression and rebound.

[0059] Choose high-strength, low-density materials (such as aluminum alloys, carbon fiber reinforced composites or high-strength steel) that take into account strength, stiffness and fatigue resistance.

[0060] Prioritize the use of corrosion-resistant materials or enhance durability through surface treatments (such as electroplating and anodizing) to accommodate long-term exposure to humidity and high-salt environments. Balance economic efficiency with performance requirements by comprehensively considering material cost, processing feasibility, and manufacturing costs.

[0061] The design needs to be compatible with common manufacturing processes (such as stamping, casting, forging or welding) to ensure the feasibility and consistency of the production process.

[0062] Set up necessary surface treatment processes (such as painting, electrophoresis, hot-dip galvanizing) according to working conditions to enhance corrosion resistance and wear resistance.

[0063] Ensure the processing accuracy of key connection parts (such as ball head holes and mounting holes) to avoid installation difficulties or performance degradation due to dimensional deviations.

[0064] Ensure that the control arm does not yield or fail within the designed load range and maintains the necessary stiffness to meet the suspension system's requirements for vehicle handling stability.

[0065] The design should meet fatigue strength requirements throughout the vehicle's life cycle, typically corresponding to millions of load cycles.

[0066] Optimizing structural shape and material distribution to reduce noise, vibration and harshness (NVH) performance of the control arm under vibration and impact loads.

[0067] Define the actual operating conditions of the control arm, including:

[0068] Static vehicle loads: These include the static vertical loads transmitted to the control arms by the vehicle's weight through the suspension system. Static boundary conditions: The control arms' mounting points and connection points must maintain structural static equilibrium, with load distribution consistent with theoretical assumptions. Dynamic loads caused by road irregularities: These include transient loads transmitted to the control arms by the tires and suspension system during vehicle operation. Braking and acceleration conditions: The control arms must withstand significant longitudinal and lateral loads during emergency braking, rapid acceleration, and cornering. Impact loads: The control arms may be subject to impact forces during off-road driving, and the frequency and amplitude of these extreme loads must be specifically considered. Connection point constraints: These define the connection points between the control arms and other suspension system components (such as ball joints, shock absorber mounts, and body brackets), as well as the load states they are subjected to. Kinematic constraints: These define the geometric relationships and motion trajectories that the control arms must maintain during suspension system movement. Temperature variations: The control arms must maintain performance stability in environments ranging from extremely low temperatures (e.g., -40°C) to high temperatures (e.g., 120°C). Corrosion and humidity: For use in coastal or high-humidity areas, the long-term effects of moisture and salt on material properties must be fully considered. Impact vibration: The potential impact of vibration and transient impact generated during vehicle operation on the performance of the control arm.

[0069] S2: Use computer-aided design (CAD) software (such as SolidWorks, CATIA, or NX) to create an initial geometric model of the control arm. The model should be based on an existing design or the intended structure and include key geometric features such as hole locations, ribs, and transition areas. Remove non-critical features (such as small fillets and decorative structures) to reduce computational complexity. Retain geometric details in critical areas, such as connection points, loading zones, and rib layout, to ensure accurate analysis results.

[0070] Mesh the geometry using finite element preprocessing software (such as ANSYS, Abaqus, or HyperMesh). Select the appropriate element type (e.g., shell elements for thin-walled structures, solid elements for monolithic structures). Ensure that the element size of the mesh matches the geometric features, and use a fine mesh in critical areas (e.g., hole edges, stress concentration points). Check mesh quality parameters (e.g., distortion, aspect ratio, smoothness) to ensure mesh accuracy and computational convergence.

[0071] Larger cells are used in non-critical areas to reduce computational cost while maintaining higher resolution in critical areas.

[0072] Define the control arm material's properties, such as elastic modulus, Poisson's ratio, yield strength, tensile strength, and fatigue limit. For complex cases, use nonlinear material models (such as plasticity or viscoelasticity). Incorporate environmental influences where necessary, such as the degradation of material properties at high temperatures or the estimation of corrosion effects.

[0073] Apply static and dynamic loads, including vertical, lateral, and impact loads, based on actual operating conditions. Simulate multiple operating conditions, combining braking, cornering, and acceleration loads. Boundary conditions: Apply fixed constraints to simulate the attachment points of the control arm to the body mount, ball joint, and shock absorber. Define kinematic boundary conditions to ensure the control arm's forces and motion paths match actual operating conditions.

[0074] Apply static loads to the control arm, calculate its stress distribution and deformation, and identify high stress areas and potential stress concentration points (such as hole edges or sharp corners). Based on the load cycle history, evaluate the fatigue life of the control arm and identify locations prone to fatigue cracks.

[0075] Generate a stress distribution contour map of the control arm, focusing on areas of maximum principal stress (maximum tensile stress). Mark stress concentration points, which typically occur in areas of dramatic geometric change (such as sharp cross-sectional changes, narrow fillets, and hole edges). Analyze the amount and direction of deformation to verify that the control arm's stiffness meets design requirements. Combined with geometric feature analysis, stress concentrations can be caused by narrow fillets, structural discontinuities, or concentrated loads.

[0076] Export stress, deformation, and fatigue life analysis reports, documenting the specific locations, stress values, and influencing factors of critical high-stress areas and stress concentration points. Based on these analysis results, propose optimization options, such as increasing corner radius, improving hole edge shape, or adjusting rib layout.

[0077] S3: Select a dynamic load cell suitable for multi-axis measurement that can simultaneously record vertical, lateral, and longitudinal loads, as well as torque. Ensure the sensor offers high accuracy and real-time monitoring capabilities, and is capable of withstanding the shock and vibration loads experienced during vehicle operation. Install the sensor at key connection points on the control arm (e.g., ball joint connection, body bracket connection). Ensure the sensor does not interfere with the normal movement of the control arm, and use appropriate protective devices to prevent the effects of mud, moisture, or temperature fluctuations.

[0078] Connect the data acquisition equipment and synchronize the acquisition system with the load sensor. Set the sampling frequency (such as 1kHz or higher) to ensure that dynamic load changes can be accurately captured. Operate under various vehicle operating conditions, including high-speed driving, low-speed cornering, braking, acceleration, off-roading, etc., and collect load data under different operating conditions. Pay special attention to load changes under extreme conditions (such as large-angle steering and impact on potholes). Store the load data in a database and perform statistical analysis on the data collected over a long period of time to identify the load distribution characteristics under different operating conditions.

[0079] Based on the collected load data, the frequency distribution of the load amplitude is statistically analyzed to generate an operational load spectrum (OLS). Key parameters such as maximum load, minimum load, mean load, and cyclic load amplitude are extracted.

[0080] The extreme load anomaly index is generated by analyzing the frequency and duration of extreme loads. The extreme load anomaly index is obtained as follows:

[0081] According to the design specifications and material properties of the control arm, a load threshold Lthreshold is set. Loads exceeding this value are considered extreme loads.

[0082] Extract the load time series {L(t i )}, where t i Indicates the i-th time point, marking all the conditions that satisfy L(t i )>Lthreshold, record the occurrence time of these extreme loads and the corresponding load values. Calculate the frequency of extreme loads: set a time window T window (For example, 1 hour, 1 day), count the number of extreme loads N that occur within the time window extreme Calculate the frequency of occurrence of extreme loads f extreme , the expression is: Among them, f extreme The unit is number / time. For each extreme load event, record its start time tstart and end time tend, and calculate the duration Δt=tend-tstart.

[0083] Find the average duration of all extreme load events The expression is: Where, Δt i represents the duration of the i-th extreme load event.

[0084] Normalize the frequency and average duration to eliminate dimension effects: Among them, fref and Δt ref is a reference value, which can be determined based on historical data or design specifications. norm is the normalized frequency of occurrence of extreme loads, Δt norm The extreme load anomaly index ELAI is calculated as the normalized average duration, and the expression is: Among them, α and β are weight coefficients, reflecting the relative importance of occurrence frequency and duration to the anomaly index, satisfying α + β = 1.

[0085] Obtain material samples (such as aluminum alloy, steel, or composite materials) from actual control arm manufacturing and prepare standardized specimens (such as rectangular specimens or smooth round bar specimens). Surface treatment is performed on these specimens, using protective processes such as anti-corrosion coating or anodizing to simulate the actual operating conditions of a control arm.

[0086] Setting up an accelerated aging test environment includes: Environmental condition simulation: High humidity: Place the sample in a constant humidity environment (such as 95% RH) to evaluate the impact of moisture on material properties. Corrosion testing: Use the salt spray test (ASTM B117 standard) to simulate salt corrosion in coastal environments. Extreme temperature: Set a temperature cycle range (such as -40°C to 120°C) and repeatedly cycle to evaluate the impact of thermal expansion and contraction on the material. Loading conditions: Apply cyclic loads to the sample during the aging process to simulate the dynamic stress effects under actual working conditions and obtain more realistic degradation patterns.

[0087] Regularly test samples for key performance parameters such as fatigue limit, tensile strength, yield strength, and elastic modulus; record trends in material properties over time or aging. Surface damage assessment: Observe the extent of surface corrosion, crack initiation, and propagation using a microscope; analyze microstructural changes using X-ray diffraction (XRD) or scanning electron microscopy (SEM).

[0088] Fitting experimental data into degradation curves. Commonly used formulas include exponential decay models or power law models to describe how material properties change over time or aging. Example formula: P(t) = P0·e -k·t Where P(t) is the material performance value after time t, P0 is the initial performance, and k is the degradation rate constant. The inflection point (the stage where performance significantly decreases) and the end point (the moment of failure) in the degradation curve are extracted for life prediction.

[0089] The material performance degradation index is generated by analyzing the inflection points and termination points in the extracted degradation curve. The material performance degradation index is obtained as follows:

[0090] The material degradation curve P(t) shows how material properties (such as strength and fatigue limit) change over time t. The curve is generated from laboratory testing or field monitoring data. The initial performance is: P0 = P(t = 0).

[0091] Degradation end point performance: Pend = P(tend), usually defined as the performance dropping to a certain proportion of the initial value (such as 80% or 50%).

[0092] Calculate the derivative of the curve, first-order derivative Indicates the rate of performance degradation over time. Indicates the changing trend (acceleration) of the degradation rate.

[0093] Inflection point judgment condition: The inflection point tinflection appears at the position where the second-order derivative is zero: Verify the inflection point location: Ensure that the inflection point corresponds to the acceleration phase of the degradation curve, that is: Near the tinflection, it decreases and then becomes stable.

[0094] When the material performance drops to a set ratio R: P(tend) = P0·R; where R is usually 0.8, 0.5 or other specific values. The material performance degradation index MPDI is calculated as: Where: ΔP inflection =P0-P(tinflection) represents the performance degradation at the inflection point.

[0095] S4: Comprehensively analyze the generated operating condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and the actual usage conditions.

[0096] The extreme load anomaly index and the material performance degradation index are normalized, and the deviation coefficient between the optimization result and the actual use condition is calculated based on the normalized extreme load anomaly index and the material performance degradation index.

[0097] For example, the present invention can use the following formula to calculate the deviation coefficient between the optimization result and the actual usage condition, and the calculation expression is: Where L is the deviation coefficient between the optimization result and the actual use condition, ELAI is the extreme load anomaly index, MPDI is the material performance degradation index, w1 and w2 are the proportional coefficients of the extreme load anomaly index and the material performance degradation index, and w2>w1>0.

[0098] The deviation coefficient between the obtained optimization result and the actual usage conditions is compared with the deviation coefficient reference threshold pre-set based on historical data. If the deviation coefficient between the optimization result and the actual usage conditions is greater than or equal to the pre-set deviation coefficient reference threshold, it means that the deviation between the optimization result and the actual usage conditions is large; if the deviation coefficient between the optimization result and the actual usage conditions is less than the pre-set deviation coefficient reference threshold, it means that the deviation between the optimization result and the actual usage conditions is small.

[0099] S5: When the deviation is large, the control arm geometry is optimized by increasing the corner radius of the transition area, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point.

[0100] Increase the fillet radius of the transition area: Reduce stress concentration caused by geometric mutations. Improve the fatigue performance of the structure under dynamic loads.

[0101] Identify stress concentration areas: Use finite element analysis (FEA) to identify high-stress areas in the control arm at geometrically abrupt changes, such as cross-section transitions. Increase fillet radius: Replace sharp edges in transition areas with smooth, rounded corners. Optimizing the fillet radius R depends on the thickness s of the control arm; a value of R / s ≥ 3 is generally recommended. Example optimization: Increasing the original fillet radius from 2 mm to 5 mm significantly reduces the stress concentration factor.

[0102] Optimize the shape of the hole edge: reduce the stress concentration at the hole edge and reduce the possibility of crack initiation. Specific measures: Optimize the edge of the circular hole: add chamfers or fillet transitions to the edges of the circular holes where stress concentration is obvious. Increase the fillet radius r of the hole edge. It is generally recommended that r ≥ 0.1, where d is the diameter of the hole. Replace the circular hole with an elliptical hole: In the direction of force, using an elliptical hole with the long axis along the load direction instead of a circular hole can significantly reduce the stress concentration factor. Example: Replace a circular hole with a diameter of 10 mm with an elliptical hole with a long axis of 15 mm and a short axis of 10 mm. Optimize the hole spacing: Increase the spacing l between adjacent holes to avoid stress superposition between holes. It is generally recommended that l ≥ 3d.

[0103] Adjust rib layout: Improve local stiffness in the control arm and reduce uneven load distribution at stress concentration points. Place ribs in high-stress areas to increase local stiffness. Use topology optimization techniques to algorithmically determine the optimal rib placement for maximum stress dispersion. Increase rib thickness or width to improve load-bearing capacity; use variable-section rib designs (e.g., a gradient from wide to narrow) for smooth stress distribution. Ensure there are no sharp corners or sudden changes in cross-section at the junction of the rib and the control arm body, using rounded corners to avoid adding stress concentration points.

[0104] S6: Install smart sensors on the control arms to monitor load and stress distribution in real time. Compare the actual test results with the finite element analysis data to further refine the design until the optimized results meet performance and safety requirements.

[0105] Multi-axis strain gauges or MEMS sensors are installed at key locations of the control arm (such as high stress areas and connection points) to monitor the stress σreal(t) and load Lreal(t) in real time.

[0106] The monitoring value σreal(t) is obtained through the data acquisition equipment, and the time series data is recorded.

[0107] Finite element analysis is used to generate a load-stress mapping relationship σFEA=f(LFEA).

[0108] According to the actual monitoring load Lreal(t), the corresponding FEA predicted stress σFEA(t) is obtained through the mapping function.

[0109] The formula for calculating the real-time deviation Δσ(t) is: Δσ(t) = σreal(t) - σFEA(t); where Δσ(t) is the difference (deviation) between the real-time monitored stress and the FEA predicted stress; σreal(t) is the actual stress monitored by the sensor; and σFEA(t) is the stress predicted by the FEA model. avg The calculation formula is: Wherein, T is the monitoring time period;

[0110] Based on the deviation location, size, and distribution, we can categorize and identify influencing factors: High stress deviation: Stress deviation is significantly higher than the predicted value, which may be due to insufficient geometric design or operating conditions exceeding the design range. Localized concentrated deviation: Deviation is concentrated in certain areas, which may be due to sudden geometric changes or abnormal material properties.

[0111] Based on the topology optimization method, the deviation is reduced by adjusting the material distribution or geometry of key parts: minΦ = ∫ Ω Δσ 2 (x)dΩ; where: Φ is the objective function, which represents the integral of the square of the deviation; Δσ 2 (x) is the square of the real-time deviation value; Ω is the design area.

[0112] Use numerical optimization algorithms (such as gradient descent method and genetic algorithm) to solve the geometric correction parameters.

[0113] If the deviation is caused by material performance degradation, the FEA input parameters can be updated according to the degradation law, and the expression is: E new =E initial (1-k t); where: E newis the corrected elastic modulus of the material, k is the degradation rate constant, t is the service time, E initial is the elastic modulus of the material before correction.

[0114] Based on the monitored deviations, the FEA model's boundary conditions, load distribution, and material parameters are modified. Simulations are performed using the modified FEA model to predict the new stress distribution and load response. The modified FEA results are compared with the sensor monitoring data, and the above steps are repeated until the deviations meet the design requirements.

[0115] Evaluate the correction effect by calculating the deviation coefficient between the optimized design and the actual monitoring conditions: Deviation coefficient: Δσ avg,new is the corrected mean deviation; Δσ avg,initial is the average deviation before correction; D optimized The optimized deviation coefficient.

[0116] Compare the obtained deviation coefficient with the deviation threshold. If the deviation coefficient is less than or equal to the deviation threshold, it means that the correction effect is good and the optimized design meets the safety and performance requirements. At this time, the iterative optimization can be terminated and the final correction parameters are recorded as the design results. If the deviation coefficient is greater than the deviation threshold, it means that the correction effect is poor and the deviation between the optimized design and the actual use conditions is still beyond the tolerance range. The design needs to be further adjusted by increasing the number of iterations to optimize the geometric shape, material properties or boundary conditions.

[0117] In this embodiment, the design optimization objectives are first clarified, with dimensional limits, material selection, manufacturing process constraints, and performance requirements set to define actual operating conditions. Finite element analysis (FEA) is then used to establish an initial geometric model of the control arm, meshing the model, setting material properties, applying loads and boundary conditions, and identifying high-stress areas and stress concentration points. Dynamic loads are monitored over a long period of time using multi-axis load sensors to generate operating condition distribution parameters, and material performance degradation curves are obtained through laboratory accelerated aging testing. Combined with these parameters, a comprehensive analysis is conducted to assess the degree of deviation between the optimized results and actual operating conditions. If the deviation is significant, geometric structure optimization is performed based on the location of the stress concentration points by increasing the corner radius of the transition area, optimizing the hole edge shape, and adjusting the rib layout. Finally, intelligent sensors are installed on the control arm to monitor the load and stress distribution in real time. The actual test results are compared with the FEA data, and the design is dynamically modified until the optimized results meet performance and safety requirements.

[0118] Example 2, please refer to Figure 2 As shown, the control arm design optimization system described in this embodiment includes a demand analysis and design input module, a modeling and finite element analysis module, an environmental impact assessment module, a deviation assessment module, a geometric structure optimization module, and an optimization feedback module;

[0119] Requirements Analysis and Design Input Module: This module clarifies the control arm design optimization goals, sets the control arm's size limits, material selection, manufacturing process constraints, and performance requirements, and defines the actual operating conditions of the control arm.

[0120] Modeling and Finite Element Analysis Module: This module establishes the initial geometric model of the control arm, divides the mesh based on the finite element analysis method, sets material properties, applies loads, and defines boundary conditions. It also performs finite element analysis on the initial geometric model of the control arm to identify high stress areas and stress concentration points.

[0121] Environmental Impact Assessment Module: By installing multi-axis load sensors, the dynamic load data of the vehicle under different operating conditions is monitored over a long period of time to generate operating condition distribution parameters. Through laboratory accelerated aging testing, the performance degradation patterns of the control arm material under high humidity, corrosion, and extreme temperature conditions are determined to generate material performance degradation curves.

[0122] Deviation evaluation module: conducts comprehensive analysis on the generated operating condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and the actual use conditions;

[0123] Geometry Optimization Module: When the deviation is large, the control arm geometry is optimized by increasing the corner radius of the transition area, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point;

[0124] Optimization feedback module: Intelligent sensors are installed on the control arm to monitor load and stress distribution in real time. Based on the actual test results and comparison with the finite element analysis data, the design is further modified and optimized until the optimization results meet the performance and safety requirements.

[0125] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0126] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0127] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0128] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A design optimization method for a control arm, characterized by: The following steps are involved: S1: Define the control arm design optimization goals, set the control arm size limits, material selection, manufacturing process constraints and performance requirements, and define the actual use conditions of the control arm; S2: Establish the initial geometric model of the control arm, divide the mesh based on the finite element analysis method, set material properties, apply loads and define boundary conditions, perform finite element analysis on the initial geometric model of the control arm, and identify high stress areas and stress concentration points; S3: By installing multi-axis load sensors, dynamic load data of the vehicle under different operating conditions is monitored over a long period of time to generate operating condition distribution parameters. Accelerated aging tests are performed in the laboratory to determine the performance degradation patterns of the control arm material under high humidity, corrosion, and extreme temperature conditions, generating material performance degradation curves. S4: Comprehensively analyze the generated operating condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and the actual use conditions; S5: When the deviation is large, the control arm geometry is optimized by increasing the corner radius of the transition area, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point; S6: Install smart sensors on the control arms to monitor load and stress distribution in real time. Compare the actual test results with the finite element analysis data to further refine the design until the optimized results meet performance and safety requirements.

2. The control arm design optimization method according to claim 1, characterized in that: In S1, the goals of control arm design optimization are clearly defined, including reducing weight, reducing stress concentration, improving fatigue resistance and extending service life; the actual operating conditions of the control arm are defined, including static loads, dynamic loads, boundary conditions and environmental impact parameters.

3. The control arm design optimization method according to claim 1, characterized in that: In S3, the extreme load anomaly index is generated after analyzing the occurrence frequency and duration of extreme loads. The extreme load anomaly index is obtained as follows: According to the design specifications and material properties of the control arm, a load threshold Lthreshold is set. The load exceeding this value is regarded as an extreme load. The load time series {L(t i )}, where t i Indicates the i-th time point, marking all the points that satisfy L(t i )>Lthreshold, record the occurrence time of these extreme loads and the corresponding load values, and calculate the frequency of extreme load occurrence: set a time window T window , count the number of extreme loads N that occur in the time window extreme ; Calculate the frequency of occurrence of extreme loads f extreme , the expression is: For each extreme load event, record its start time tstart and end time tend, and calculate the duration Δt = tend - tstart; Find the average duration of all extreme load events The expression is: Where, Δt i represents the duration of the i-th extreme load event; the frequency and average duration are normalized: Among them, f ref and Δt ref is the reference value, f norm is the normalized frequency of occurrence of extreme loads, Δt norm The extreme load anomaly index ELAI is calculated as the normalized average duration, and the expression is: Wherein, α and β are weight coefficients, satisfying α+β=1.

4. The control arm design optimization method according to claim 3, characterized in that: In S3, the inflection point and the end point of the extracted degradation curve are analyzed to generate a material performance degradation index. The material performance degradation index is obtained as follows: The material performance degradation curve P(t) represents the change of material performance over time t. The curve is generated by laboratory testing or field monitoring data. The initial performance is: P0 = P(t = 0); the performance at the end of degradation is: Pend = P(tend); the derivative of the curve is calculated, the first-order derivative Indicates the rate of performance degradation over time, the second-order derivative Indicates the changing trend of degradation rate; The inflection point tinflection occurs where the second-order derivative is zero: Make sure that the inflection point corresponds to the acceleration phase of the degradation curve, that is: It decreases and becomes stable near tinflection; When the material performance drops to the set ratio R: P(tend) = P0·R; where R is 0.8 or 0.5, the material performance degradation index MPDI is calculated as follows: Where: ΔP inflection =P0-P(tinflection) represents the performance degradation at the inflection point.

5. The control arm design optimization method according to claim 4, characterized in that: In S4, the generated operating condition distribution parameters and material performance degradation curves are comprehensively analyzed to evaluate the degree of deviation between the optimization results and the actual use conditions; The extreme load anomaly index and the material performance degradation index are normalized, and the deviation coefficient between the optimization result and the actual use condition is calculated based on the normalized extreme load anomaly index and the material performance degradation index.

6. The control arm design optimization method according to claim 5, characterized in that: In S4, the obtained deviation coefficient between the optimization result and the actual usage condition is compared with the deviation coefficient reference threshold value pre-set based on historical data. If the deviation coefficient between the optimization result and the actual usage condition is greater than or equal to the pre-set deviation coefficient reference threshold value, it means that the deviation degree between the optimization result and the actual usage condition is large; if the deviation coefficient between the optimization result and the actual usage condition is less than the pre-set deviation coefficient reference threshold value, it means that the deviation degree between the optimization result and the actual usage condition is small.

7. The control arm design optimization method according to claim 1, characterized in that: In S6, multi-axis strain gauges or MEMS sensors are installed at key positions of the control arm to monitor the stress σreal(t) and load Lreal(t) in real time. The monitoring value σreal(t) is obtained through data acquisition equipment and the time series data is recorded. Finite element analysis is used to generate a load-stress mapping relationship σFEA=f(LFEA), where f is the mapping function of the finite element analysis. Based on the actual monitored load Lreal(t), the corresponding FEA predicted stress σFEA(t) is obtained through the mapping function. The calculation formula of real-time deviation Δσ(t) is: Δσ(t) = σreal(t) - σFEA(t); where: Δσ(t) is the difference between the real-time monitored stress and the FEA predicted stress; σreal(t) is the actual stress monitored by the sensor; σFEA(t) is the stress predicted by the FEA model; the cumulative deviation Δσ avg The calculation formula is: Wherein, T is the monitoring time period; Based on the topology optimization method, the deviation is reduced by adjusting the material distribution or geometry of key parts: minΦ=∫ Ω Δσ 2 (x)dΩ; where: Φ is the objective function, which represents the integral of the square of the deviation; Δσ 2 (x) is the square of the real-time deviation value; Ω is the design area; Use numerical optimization algorithms to solve the geometric correction parameters; if the deviation is caused by material performance degradation, update the FEA input parameters according to the degradation law, the expression is: E new =E initial (1-k t); where: E new is the corrected elastic modulus of the material, k is the degradation rate constant, t is the service time, E initial is the elastic modulus of the material before correction; Evaluate the correction effect by calculating the deviation coefficient between the optimized design and the actual monitoring conditions: Deviation coefficient: Δσ avg,new is the corrected mean deviation; Δσ avg,initial is the average deviation before correction; D optimized The optimized deviation coefficient.

8. The control arm design optimization method according to claim 7, characterized in that: Compare the obtained deviation coefficient with the deviation threshold. If the deviation coefficient is less than or equal to the deviation threshold, it means that the correction effect is good and the optimized design meets the safety and performance requirements. At this time, the iterative optimization is terminated and the final correction parameters are recorded as the design results. If the deviation coefficient is greater than the deviation threshold, it means that the correction effect is poor and the design needs to be further adjusted by increasing the number of iterations to optimize the geometry, material properties or boundary conditions.

9. A control arm design optimization system, used to implement the control arm design optimization method according to any one of claims 1 to 8, characterized in that: It includes demand analysis and design input module, modeling and finite element analysis module, environmental impact assessment module, deviation assessment module, geometric structure optimization module and optimization feedback module; Requirements Analysis and Design Input Module: This module clarifies the control arm design optimization goals, sets the control arm's size limits, material selection, manufacturing process constraints, and performance requirements, and defines the actual operating conditions of the control arm. Modeling and Finite Element Analysis Module: This module establishes the initial geometric model of the control arm, divides the mesh based on the finite element analysis method, sets material properties, applies loads, and defines boundary conditions. It also performs finite element analysis on the initial geometric model of the control arm to identify high stress areas and stress concentration points. Environmental Impact Assessment Module: By installing multi-axis load sensors, the dynamic load data of the vehicle under different operating conditions is monitored over a long period of time to generate operating condition distribution parameters. Through laboratory accelerated aging testing, the performance degradation patterns of the control arm material under high humidity, corrosion, and extreme temperature conditions are determined to generate material performance degradation curves. Deviation evaluation module: conducts comprehensive analysis on the generated operating condition distribution parameters and material performance degradation curves to evaluate the degree of deviation between the optimization results and the actual use conditions; Geometry Optimization Module: When the deviation is large, the control arm geometry is optimized by increasing the corner radius of the transition area, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point; Optimization feedback module: Intelligent sensors are installed on the control arm to monitor load and stress distribution in real time. Based on the actual test results and comparison with the finite element analysis data, the design is further modified and optimized until the optimization results meet the performance and safety requirements.

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