Design optimization method and system for control arm

Abstract

The invention discloses a design optimization method and system for a control arm, and particularly relates to the technical field of control arms. The method comprises the following steps: determining an optimization target and constraint conditions, identifying a high-stress region by utilizing finite element analysis, monitoring a dynamic load and a material performance degradation curve by combining a multi-axis sensor, and realizing quantitative evaluation of an optimization result and a real working condition deviation; when the deviation is large, real-time monitoring, feedback correction and dynamic design adjustment are further performed by optimizing a geometric structure and installing an intelligent sensor, so that the reliability and safety of an optimization result under complex loads and extreme working conditions are ensured, the durability and the fatigue resistance of the control arm are remarkably improved, the local failure risk is reduced, and the reliability of the control arm is improved. And finally, the overall safety and the performance stability of the vehicle are improved.

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] The design optimization of the control arm refers to the engineering process of improving the structure, material, geometry or manufacturing process of the control arm to improve its performance, reliability and manufacturing cost-effectiveness. In the automotive field, the control arm is an important part of the suspension system, which is responsible for connecting the wheel to the body, transmitting the force exerted on the wheel and ensuring driving stability. Therefore, design optimization usually focuses on goals such as weight reduction, strength enhancement, durability improvement and cost reduction to meet the performance requirements and market demand of the whole vehicle. For example, the optimized design of the control arm of a passenger car may reduce the weight of the control arm and improve fuel efficiency by using high-strength steel or aluminum alloy instead of traditional steel. In addition, by optimizing the geometry of the control arm through finite element analysis (FEA) and reducing stress concentration points, its fatigue resistance can be improved and its service life can be extended. Finally, by optimizing the manufacturing process, such as adopting new high-pressure casting or welding technologies, production costs can also be reduced while ensuring product quality consistency.

[0003] The prior art has the following deficiencies:

[0004] When optimizing the geometry of the control arm through finite element analysis (FEA), it is usually based on idealized boundary conditions and load assumptions. However, complex and unpredictable loads may occur in actual working conditions, such as dynamic loads such as emergency steering, high-speed driving through potholes, or lateral impacts. If the optimization design excessively pursues weight reduction or stress concentration reduction, it may cause certain areas of the control arm to be too thin or insufficiently rigid, resulting in local buckling or fatigue cracks. This local failure is usually manifested as a sudden fracture of the structure under extreme working conditions, which not only directly leads to loss of vehicle control, but may 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 solve 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: Clarify the goal of control arm design optimization, set the size limit, material selection, manufacturing process constraints and performance requirements of the control arm, 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, the dynamic load data of the vehicle under different working conditions is monitored over a long period of time to generate working condition distribution parameters; through laboratory accelerated aging tests, the performance degradation law of the control arm material under high humidity, corrosion, and extreme temperature conditions is obtained to generate 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 radius of the transition area fillet, 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 arm to monitor the load and stress distribution in real time. Compare the actual test results with the finite element analysis data to further modify the optimized design until the optimized results meet the performance and safety requirements.

[0013] Preferably, in S1, the objectives of the 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, and the method for obtaining the extreme load anomaly index is:

[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 represents 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 loads: set a time window T window , count the number of extreme loads N that occur in this 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: Among them, Δt i represents the duration of the ith extreme load event; the occurrence frequency and average duration are standardized: 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 standardized average duration, and the expression is: Among them, 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 test or field monitoring data. The initial performance is: P0 = P(t = 0); the degradation end point performance: Pend = P(tend); calculate the derivative of the curve, 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, and the expression is: 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 by 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 according to 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; a load-stress mapping relationship σFEA=f(LFEA) is generated by finite element analysis, where f is a mapping function of finite element analysis, and the corresponding FEA predicted stress σFEA(t) is obtained through the mapping function according to the actual monitoring load Lreal(t);

[0024] The calculation formula of the 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 algorithm 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 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] Demand Analysis and Design Input Module: Clarify the goal of control arm design optimization, set the size limit, material selection, manufacturing process constraints and performance requirements of the control arm, and define the actual use conditions of the control arm;

[0031] Modeling and finite element analysis module: 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;

[0032] Environmental impact assessment module: By installing multi-axis load sensors, the dynamic load data of the vehicle under different working conditions is monitored over a long period of time to generate working condition distribution parameters; through laboratory accelerated aging tests, the performance degradation law of the control arm material under high humidity, corrosion, and extreme temperature conditions is obtained 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 radius of the transition area fillet, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point;

[0035] Optimization feedback module: Install intelligent sensors on the control arm to monitor the load and stress distribution in real time, compare the actual test results with the finite element analysis data, and further modify the optimization design 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 clarifies 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 performed until the optimized design meets safety and performance requirements.

[0038] 2. The present invention significantly improves the reliability and durability of the control arm under complex loads and extreme working conditions, and effectively reduces the risk of local buckling and fatigue cracks. By introducing the extreme load anomaly index and material performance degradation index, the optimization results are scientifically quantified and dynamically corrected to ensure a high degree of matching between the design and the actual working conditions. This method not only improves the accuracy and efficiency of the design, but also reduces material waste and manufacturing costs, enhances the safety performance of the vehicle, and is suitable for the design optimization of various vehicle control arms. 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 drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0040] Figure 1 The present invention is a flow chart of the method.

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

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0044] S1: Clarify the goal of control arm design optimization, set the size limit, material selection, manufacturing process constraints and performance requirements of the control arm, 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, the dynamic load data of the vehicle under different working conditions is monitored over a long period of time to generate working condition distribution parameters; through laboratory accelerated aging tests, the performance degradation law of the control arm material under high humidity, corrosion, and extreme temperature conditions is obtained to generate 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 radius of the transition area fillet, 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 arm to monitor the load and stress distribution in real time. Compare the actual test results with the finite element analysis data to further modify the optimized design until the optimized results meet the performance and safety requirements.

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

[0051] By optimizing the geometry and selecting lightweight and high-strength materials, the total weight of the control arm can be reduced to improve the fuel economy of the vehicle or the endurance of electric vehicles, while 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 fillet corners, optimizing hole shape, rearranging ribs, etc., 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 process, the control arm is ensured to maintain strength, rigidity and durability during long-term use and to adapt to the requirements of high loads and complex environmental conditions.

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

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

[0057] The weak parts of structural parts (such as joints and reinforcement ribs) must meet manufacturing and strength requirements to prevent local deformation or welding difficulties caused by being too thin.

[0058] The control arms must accommodate the suspension system's range of motion and ensure 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 improve durability through surface treatment (such as electroplating, anodizing) to meet the needs of long-term exposure to humidity and high-salt environments. Comprehensively consider material costs, processing feasibility and manufacturing costs, and balance economic and performance requirements.

[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] According to the working conditions, necessary surface treatment processes (such as painting, electrophoresis, hot-dip galvanizing) are set to enhance the corrosion resistance and wear resistance.

[0063] Ensure the machining 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 over the vehicle's entire life cycle, which typically corresponds to millions of load cycles.

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

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

[0068] Loads when the vehicle is stationary: including the static vertical loads transmitted to the control arm by the vehicle body weight through the suspension system. Static boundary conditions: the fixing points and connection points of the control arm must meet the structural static equilibrium, and the load distribution is consistent with the theoretical assumptions. Dynamic loads caused by uneven roads: including transient loads transmitted to the control arm through the tires and suspension system during vehicle driving. Braking and acceleration conditions: during emergency braking, rapid acceleration and turning, the control arm needs to withstand large longitudinal and lateral loads. Impact loads: such as the impact force that the control arm may suffer during off-road driving, the frequency and amplitude of such extreme loads need to be specially considered. Connection point constraints: clarify the connection points between the control arm and other components of the suspension system (such as ball heads, shock absorber seats, body brackets) and their stress states. Kinematic constraints: define the geometric relationship and motion trajectory that the control arm needs to maintain during the movement of the suspension system. Temperature changes: the control arm needs to adapt to the performance stability from extremely low temperatures (such as -40°C) to high temperatures (such as 120°C). Corrosion and humidity: for use conditions in coastal or high humidity areas, the long-term effects of moisture and salt on material properties need to be fully considered. Impact vibration: The potential impact of vibration and transient impact generated during vehicle driving 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 the existing design or the expected structure and include key geometric features such as holes, ribs, and transition areas. Delete non-critical features (such as small fillets and decorative structures) to reduce the amount of calculation. Keep the geometric details of key parts, such as connection points, loading areas, and rib layout to ensure the accuracy of the analysis results.

[0070] Use finite element pre-processing software (such as ANSYS, Abaqus or HyperMesh) to mesh the geometric model. Select the appropriate unit type (such as shell elements for thin-walled structures and solid elements for overall structures). Make sure that the unit size of the mesh matches the geometric features, and use fine meshes in key areas (such as hole edges and stress concentration points). Check the quality parameters of the mesh (such as distortion rate, aspect ratio, smoothness) to ensure mesh accuracy and calculation convergence.

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

[0072] Define the properties of the control arm material 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). Add environmental influences when 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 loads, lateral loads, and impact loads, based on actual use conditions. Combined loads of braking, cornering, and acceleration can be superimposed when simulating multiple conditions. Boundary conditions: Apply fixed constraints to simulate the fixing points of the control arm to the body bracket, ball joint, and shock absorber. Define kinematic boundary conditions to ensure that the forces and motion paths of the control arm meet actual conditions.

[0074] Apply static loads to the control arm, calculate its stress distribution and deformation, 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 cloud diagram of the control arm, focusing on the area of ​​maximum principal stress (maximum tensile stress). Mark stress concentration points, which usually appear in areas with drastic geometric changes (such as sharp cross-sectional changes, small fillets, and hole edges). Analyze the deformation amount and direction to verify whether the stiffness of the control arm meets the design requirements. Combined with the analysis of geometric features, stress concentration may be caused by small fillets, structural discontinuities, or load concentration.

[0076] Export stress, deformation and fatigue life analysis reports, record the specific location, stress value and influencing factors of key high stress areas and stress concentration points. Propose optimization directions based on the analysis results, such as increasing the fillet radius, improving the hole edge shape or adjusting the rib layout.

[0077] S3: Use dynamic load sensors suitable for multi-axis measurement, which can simultaneously record vertical load, lateral load, longitudinal load and torque. Ensure that the sensors have high accuracy and real-time monitoring capabilities, and can withstand the shock and vibration loads during vehicle operation. Install the sensors at key connection points of the control arm (such as ball head connection points, body bracket connection points). Ensure that the sensors do not interfere with the normal movement of the control arm, and use appropriate protection devices to avoid the influence of mud, moisture or temperature changes.

[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 a variety of vehicle conditions, including high-speed driving, low-speed cornering, braking, acceleration, off-road, etc., to collect load data under different conditions. Pay special attention to load changes under extreme conditions (such as large-angle steering and 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 conditions.

[0079] According to the collected load data, the frequency distribution of the load amplitude is statistically analyzed to generate the operating condition distribution diagram (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 occurrence 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, and the load exceeding this value is regarded as an extreme load.

[0082] Extract load time series {L(t i )}, where t i represents 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. Calculate the frequency of extreme loads: Set a time window T window (e.g., 1 hour, 1 day), count the number of times the extreme load occurs in the time window N 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: Among them, Δt i represents the duration of the ith 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 standardized 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). Perform surface treatment on the samples to simulate the protection process such as anti-corrosion coating or anodizing under the actual working conditions of the control arm.

[0086] Setting up the 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 effect of moisture on material performance. Corrosion test: Use the salt spray test (ASTM B117 standard) to simulate salt corrosion in coastal environments. Extreme temperature: Set the temperature cycle range (such as -40°C to 120°C), cycle repeatedly, and evaluate the effect 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 effect under actual working conditions and obtain more realistic degradation laws.

[0087] Regularly test the key performance parameters of the samples, such as fatigue limit, tensile strength, yield strength, elastic modulus, etc.; record the trend of material performance changes over time or aging. Surface damage assessment: observe the degree of corrosion, crack initiation and propagation of the sample surface through a microscope; use X-ray diffraction (XRD) or scanning electron microscopy (SEM) to analyze microstructural changes.

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

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

[0090] The material performance degradation curve P(t) represents the change of material performance (such as strength, fatigue limit) over time t. The curve is generated by 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 a curve, first derivative Indicates the rate of degradation of performance over time. Second-order derivative Indicates the changing trend (acceleration) of the degradation rate.

[0093] Inflection point determination condition: The inflection point tinflection appears at the position where the second-order derivative is zero: Verify the inflection point location: Make sure 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 the set ratio R: P(tend) = P0·R; where R is usually 0.8, 0.5 or other specific values. Calculate the material performance degradation index MPDI, the expression is: 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 by 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 use 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 condition is compared with the deviation coefficient reference threshold pre-set according to 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, it means that the deviation 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, it means that the deviation between the optimization result and the actual usage condition 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 and 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 where the geometry changes suddenly (such as the section transition). Increase fillet radius: Replace the sharp edges of the transition area with smooth fillets. The optimization of the fillet radius R depends on the thickness s of the control arm. Generally, R / s ≥ 3 is recommended. Example optimization: Increasing the original fillet radius from 2mm to 5mm can significantly reduce the stress concentration factor.

[0102] Optimize the shape of hole edges: reduce the stress concentration at the hole edges and reduce the possibility of crack initiation. Specific measures: Optimize the edges of circular holes: add chamfers or fillet transitions to the edges of circular holes with obvious stress concentration. 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 circular holes with elliptical holes: In the force direction, using elliptical holes with the major axis along the load direction instead of circular holes can significantly reduce the stress concentration factor. Example: Replace a circular hole with a diameter of 10 mm with an elliptical hole with a major axis of 15 mm and a minor 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 rigidity of control arm and reduce uneven load distribution at stress concentration points. Place ribs in high stress areas to improve local rigidity. Use topology optimization technology to determine the optimal rib layout through algorithms to disperse stress to the maximum extent. Increase the thickness or width of the ribs to improve their load-bearing capacity; use variable cross-section rib design (such as a gradient shape from wide to narrow) to achieve smooth stress distribution. Ensure that there are no sharp corners or sudden changes in cross-section at the connection between the ribs and the control arm body, and use rounded corners to avoid adding new stress concentration points.

[0104] S6: Install smart sensors on the control arm to monitor the load and stress distribution in real time. Compare the actual test results with the finite element analysis data to further modify the optimized design until the optimized results meet the 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 device, and the time series data is recorded.

[0107] Using finite element analysis, a load-stress mapping relationship σFEA=f(LFEA) is generated.

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

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

[0110] According to the deviation location, size and distribution law, the influencing factors are classified and identified: High stress deviation: The stress deviation is significantly higher than the predicted value, which may be due to insufficient geometric design or working conditions beyond the design range. Local concentrated deviation: The deviation is concentrated in certain areas, which may be due to geometric mutation 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, expressed as: 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] Modify the boundary conditions, load distribution, and material parameters of the FEA model based on the monitored deviation. Use the modified FEA model for simulation to predict the new stress distribution and load response. Compare the modified FEA results with the sensor monitoring data and repeat the above steps until the deviation meets 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 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 can be 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 to optimize the geometry, material properties or boundary conditions by increasing the number of iterations.

[0117] In this embodiment, firstly, the design optimization goal is clarified, the size limit, material selection, manufacturing process constraints and performance requirements are set, and the actual use conditions are defined; based on finite element analysis (FEA), the initial geometric model of the control arm is established, the grid is divided, the material properties are set, the load and boundary conditions are applied, and the high stress area and stress concentration point are identified; the dynamic load is monitored for a long time by a multi-axis load sensor, the working condition distribution parameters are generated, and the material performance degradation curve is obtained through laboratory accelerated aging test; combined with the above parameters, the comprehensive analysis is performed to evaluate the degree of deviation between the optimization result and the actual use conditions. When the deviation is large, the geometric structure is optimized by increasing the radius of the transition area fillet, optimizing the edge shape of the hole, and adjusting the rib layout according to the position of the stress concentration point; finally, the intelligent sensor is installed on the control arm to monitor the load and stress distribution in real time, and the actual test results are compared with the FEA data, and the design is dynamically corrected until the optimization result meets the performance and safety requirements.

[0118] Example 2, please refer to Figure 2 As shown, a 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] Demand Analysis and Design Input Module: Clarify the goal of control arm design optimization, set the size limit, material selection, manufacturing process constraints and performance requirements of the control arm, and define the actual use conditions of the control arm;

[0120] Modeling and finite element analysis module: 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;

[0121] Environmental impact assessment module: By installing multi-axis load sensors, the dynamic load data of the vehicle under different working conditions is monitored over a long period of time to generate working condition distribution parameters; through laboratory accelerated aging tests, the performance degradation law of the control arm material under high humidity, corrosion, and extreme temperature conditions is obtained 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 radius of the transition area fillet, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point;

[0124] Optimization feedback module: Install intelligent sensors on the control arm to monitor the load and stress distribution in real time, compare the actual test results with the finite element analysis data, and further modify the optimization design until the optimization results meet the performance and safety requirements.

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

[0126] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0127] Those of ordinary skill 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 to be beyond the scope of this application.

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

Claims

1. A design optimization method for a control arm, characterized in that: The following steps are involved: S1: Clarify the goal of control arm design optimization, set the size limit, material selection, manufacturing process constraints and performance requirements of the control arm, 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, the dynamic load data of the vehicle under different working conditions is monitored over a long period of time to generate working condition distribution parameters; through laboratory accelerated aging tests, the performance degradation law of the control arm material under high humidity, corrosion, and extreme temperature conditions is obtained to generate 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 radius of the transition area fillet, 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 arm to monitor the load and stress distribution in real time. Compare the actual test results with the finite element analysis data to further modify the optimized design until the optimized results meet the 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 design optimization method of a control arm according to claim 1, characterized in that: 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 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 represents 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 loads: set a time window T window , count the number of extreme loads N that occur in this 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: Among them, Δt i represents the duration of the ith extreme load event; the occurrence frequency and average duration are standardized: 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 standardized average duration, and the expression is: Among them, α and β are weight coefficients, satisfying α+β=1.

4. The design optimization method of a control arm according to claim 3, characterized in that: 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: The material performance degradation curve P(t) represents the change of material performance over time t. The curve is generated by laboratory test or field monitoring data. The initial performance is: P0 = P(t = 0); the degradation end point performance: Pend = P(tend); calculate the derivative of the curve, 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, and the expression is: Where: ΔP inflection =P0-P (tinflection) represents the performance degradation at the inflection point.

5. The design optimization method of a control arm 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 by the normalized extreme load anomaly index and the material performance degradation index.

6. The design optimization method of a control arm according to claim 5, characterized in that: In S4, the deviation coefficient between the obtained optimization result and the actual usage condition is compared with a deviation coefficient reference threshold value pre-set according to 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 design optimization method of a control arm 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 stress σreal(t) and load Lreal(t) in real time; the monitoring value σreal(t) is obtained through a data acquisition device, and 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. According to the actual monitoring load Lreal(t), the corresponding FEA predicted stress σFEA(t) is obtained through the mapping function; The calculation formula of the 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 algorithm 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 Optimized deviation coefficient.

8. The design optimization method of a control arm 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 a 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; Demand Analysis and Design Input Module: Clarify the goal of control arm design optimization, set the size limit, material selection, manufacturing process constraints and performance requirements of the control arm, and define the actual use conditions of the control arm; Modeling and finite element analysis module: 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; Environmental impact assessment module: By installing multi-axis load sensors, the dynamic load data of the vehicle under different working conditions is monitored over a long period of time to generate working condition distribution parameters; through laboratory accelerated aging tests, the performance degradation law of the control arm material under high humidity, corrosion, and extreme temperature conditions is obtained 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 radius of the transition area fillet, optimizing the hole edge shape, and adjusting the rib layout according to the location of the stress concentration point; Optimization feedback module: Install intelligent sensors on the control arm to monitor the load and stress distribution in real time, compare the actual test results with the finite element analysis data, and further modify the optimization design until the optimization results meet the performance and safety requirements.

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