An intelligent monitoring and optimization system for suspension mounting plates

Abstract

The present invention relates to the field of intelligent manufacturing technology, and specifically relates to an intelligent monitoring and optimization system for a suspension mounting plate. The intelligent monitoring and optimization system for a suspension mounting plate includes a multi-scale stiffness monitoring module, a fatigue damage prediction module, a nonlinear stress feedback module, a dynamic load adjustment module, and an intelligent stiffness optimization module. In the present invention, full-scale stiffness monitoring is provided by accurately analyzing the microscopic lattice structure and mesoscopic elastic modulus of the suspension mounting plate. High-precision sensor data is used to accurately calculate changes in stiffness requirements under driving conditions, thereby improving the adaptability of the adjustment scheme. X-ray diffraction and magnetoelasticity measurements are combined to improve the accuracy of fatigue damage prediction. Shape memory alloys and micro-hydraulic actuators are used to quickly adjust the local structure of the force concentration area, optimize stiffness matching, and use adjustable elastic materials and electromagnetic stiffness adjustment to enhance the dynamic adaptability of the suspension to complex working conditions, significantly improving the performance stability and response speed of the suspension system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an intelligent monitoring and optimization system for a suspension mounting plate. Background Art

[0002] The field of intelligent manufacturing technology encompasses industrial automation, digital manufacturing, intelligent control systems, and cyber-physical fusion systems. This field, centered around intelligent perception, real-time monitoring, data analysis, and autonomous decision-making, achieves automation, flexibility, and intelligence in the production process through the integration of sensors, control systems, and computer-aided technologies. Intelligent manufacturing encompasses three levels: intelligent equipment, intelligent production, and intelligent management. Intelligent equipment includes industrial robots, CNC machine tools, and automated production lines; intelligent production involves flexible manufacturing systems, digital twins, and the Industrial Internet; and intelligent management encompasses intelligent scheduling, predictive maintenance, and supply chain optimization. This technology is widely used in industries such as automotive manufacturing, aerospace, electronics, and energy equipment, aiming to improve the precision, efficiency, and adaptability of manufacturing processes.

[0003] Among them, a suspension mounting plate intelligent monitoring and optimization system refers to a system that uses intelligent manufacturing technology to monitor the status of the suspension mounting plate in real time and optimize and adjust it based on the analysis results. The system uses high-precision sensors to collect vibration, stress, and deformation data of the suspension mounting plate, processes the collected information using data analysis algorithms, and calculates the changing trends of structural characteristics based on preset models. The system uses computer-aided optimization methods to adjust the mounting plate's structural parameters to reduce stress concentration and increase service life, while also incorporating automated control technology for real-time corrections. Suitable for the automotive manufacturing and engineering machinery sectors, this system can intelligently sense the status of the suspension mounting plate during production and use, and adjust structural parameters based on the analysis results to optimize overall performance.

[0004] Existing technologies primarily focus on macrostructural data acquisition, failing to conduct in-depth analysis of the material's microstructure and mesoscopic elastic modulus, which impacts the accuracy of stiffness adjustment. Calculation methods rely on pre-set models, making it difficult to adjust stiffness matching strategies to changing driving conditions, limiting their ability to respond to dynamic stiffness requirements. Residual stress analysis relies on a single measurement method, lacking comprehensive evaluation based on X-ray diffraction and magnetoelasticity measurements, resulting in insufficient fatigue damage prediction accuracy. Morphological adjustment relies on fixed optimization strategies, failing to incorporate shape memory alloys and micro-hydraulic actuators for local structural adjustment. This makes it difficult to ensure stability under sudden load changes. Load adjustment relies on conventional force analysis, failing to fully consider the coupling between deformation acceleration and stiffness requirements, making it unable to accurately adapt to complex operating conditions such as high-speed steering and emergency braking. Stiffness optimization strategies primarily rely on computer-aided analysis, failing to incorporate the dynamic adjustment capabilities of adjustable elastic composite materials and electromagnetically variable stiffness materials, resulting in insufficient flexibility in stiffness matching strategies. Overall, existing technologies suffer from deficiencies in data refinement, computational adaptability, real-time morphological adjustment, and the dynamic nature of stiffness optimization strategies, impacting the suspension system's ability to adapt to complex operating conditions. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent monitoring and optimization system for a suspension mounting plate.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: A suspension mounting plate intelligent monitoring and optimization system includes:

[0007] The multi-scale stiffness monitoring module acquires microscopic lattice structure data, calculates stiffness variation parameters, uses electromagnetic wave measurement data to analyze mesoscopic elastic modulus distribution, combines macroscopic stress measurements to determine stiffness trends, analyzes the impact of driving conditions on stiffness requirements, extracts matching parameters, and obtains stiffness matching coefficients.

[0008] The fatigue damage prediction module calculates the residual stress change rate based on the stiffness matching coefficient, calls X-ray diffraction and magnetoelasticity measurement data, compares the stress relaxation trend, calculates the correlation parameters between crack growth and residual stress relaxation, and obtains the fatigue damage trend;

[0009] Based on the fatigue damage trend, the nonlinear stress feedback module obtains stress sensor data, calculates the local stress increase, calls vehicle speed, load, and road surface feedback data, analyzes the impact of transient impact force, calls shape memory alloy and hydraulic actuator response parameters, adjusts the suspension structure, and obtains the stiffness adjustment amplitude;

[0010] The dynamic load adjustment module calculates the rate of change of the suspension force state based on the stiffness adjustment amplitude, calls the wheel ground pressure and deformation data, compares the load mutation amplitude during high-speed steering and emergency braking, calculates the load change under dynamic working conditions, and obtains the dynamic load change parameters.

[0011] As a further solution of the present invention, the multi-scale stiffness matching coefficient includes the stiffness demand influence quantity, the stiffness characteristic deviation, and the stiffness matching parameter; the fatigue damage trend includes the residual stress relaxation rate, the crack propagation rate, and the fatigue damage characteristic area; the stiffness adjustment amplitude includes the local stress increase, the transient impact force influence coefficient, and the morphological adjustment requirement; the dynamic load change parameter includes the load change amount, the deformation acceleration rate, and the load mutation amplitude.

[0012] As a further solution of the present invention, the multi-scale stiffness monitoring module includes:

[0013] The micro-lattice characteristic extraction submodule obtains the micro-lattice structure data of the suspension mounting plate, extracts the metal lattice arrangement state parameters, calculates the correlation parameters between the lattice arrangement state and the stiffness change, filters the lattice arrangement data, and obtains the lattice arrangement characteristic parameters;

[0014] The mesoscopic elastic modulus analysis submodule calls the electromagnetic wave measurement data, analyzes the mesoscopic elastic modulus distribution characteristics, calculates the regional elastic modulus change rate based on the lattice arrangement characteristic parameters, obtains the elastic modulus distribution state, and generates the elastic modulus trend under loading state in combination with the macroscopic stress measurement data;

[0015] The stiffness matching coefficient calculation submodule calls the elastic modulus trend of the loading state, obtains wheel ground reaction data, vehicle acceleration data, and suspension displacement rate data, calculates the impact of driving state changes on stiffness requirements, analyzes the deviation between the current suspension stiffness characteristics and the stiffness target, and extracts stiffness matching parameters using the formula:

[0016] ;

[0017] Obtain the stiffness matching parameters under each state through calculation and obtain the multi-scale stiffness matching coefficient;

[0018] in, represents the multi-scale stiffness matching coefficient, represents the elastic modulus of the i-th region, represents the ground reaction force in the i-th area, represents the acceleration of the i-th region, represents the suspension displacement rate of the i-th region, Represents the total number of measurement areas.

[0019] As a further solution of the present invention, the fatigue damage prediction module includes:

[0020] The residual stress monitoring submodule obtains residual stress data of the suspension mounting plate based on the multi-scale stiffness matching coefficient, measures the residual stress distribution using X-ray diffraction, cross-validates the measurement results using a magnetoelastic stress measurement method, calculates the residual stress change rate under differentiated stress states, and uses a discrete time series method to partition the residual stress changes under differentiated loading states. The stress change trend is established in the time dimension to obtain the residual stress change rate.

[0021] The crack growth analysis submodule calls the residual stress change rate, retrieves the fatigue damage characteristic area of ​​the suspension mounting plate, obtains fatigue crack growth rate data, analyzes the correspondence between the crack growth direction and the stress concentration area, calculates the crack growth rate based on the crack morphology and crack tip stress intensity factor in the stress concentration area, obtains the crack growth characteristics under differentiated stress levels, and obtains the crack growth trend;

[0022] The damage trend calculation submodule calls the crack growth trend, compares the relationship between the residual stress relaxation trend and the load action time, and calculates the correlation parameter between the residual stress relaxation rate and the crack growth using the formula:

[0023] ;

[0024] Calculate and obtain the fatigue damage rate under different load action times and obtain the fatigue damage trend;

[0025] in, Represents fatigue damage trend, represents the residual stress relaxation rate in the jth region, represents the crack growth rate of the jth region, represents the crack length of the jth region, represents the stress intensity factor of the jth region, Represents the total number of measurement areas.

[0026] As a further solution of the present invention, the nonlinear stress feedback module includes:

[0027] The local stress increase calculation submodule obtains stress sensor data based on the fatigue damage trend, divides the stress area and extracts local stress data, calculates stress change curves under different working conditions, screens stress mutation points, and calculates local stress increase values ​​to obtain local stress increase values;

[0028] The impact force impact analysis submodule uses the local stress increase value and combines the vehicle speed, load and road surface feedback data to calculate the peak value and duration of the transient impact force. It analyzes the influence coefficient of the impact force on the suspension deformation through the dynamic load model to obtain the impact response coefficient.

[0029] The stiffness adjustment calculation submodule calls the impact response coefficient to obtain the adjustment response parameters of the shape memory alloy and the micro hydraulic actuator, compares the shape adjustment requirements under differentiated force conditions, and calculates the adjustment amount of the local structure of the suspension mounting plate using the formula:

[0030] ;

[0031] Obtaining the stiffness adjustment requirement of the suspension mounting plate through calculation and obtaining the stiffness adjustment range;

[0032] in, Represents the stiffness adjustment range, represents the actuator response force in the kth region, represents the angle change of the shape memory alloy in the kth region, represents the thickness of the suspension mounting plate in the kth region, represents the adjustment torque of the kth region, represents the morphological correction parameter of the kth region, represents the local load force in the kth region, represents the length of the force arm of the kth region, Represents the total number of measurement areas.

[0033] As a further solution of the present invention, the dynamic load adjustment module includes:

[0034] The stress state calculation submodule obtains torque sensor data and acceleration measurement data based on the stiffness adjustment amplitude, calculates the rate of change of the real-time stress state of the suspension, performs numerical differentiation operation on the measurement data using a differentiated time step, extracts the torque change trend, and obtains the rate of change of the stress state;

[0035] The stiffness requirement coupling analysis submodule uses the force state change rate to analyze the wheel ground contact pressure data and deformation data, extracts the change pattern of the deformation growth rate under the condition of differentiated stiffness adjustment, establishes the coupling relationship between the deformation growth rate and the stiffness requirement through dynamic mechanical equations, compares the stiffness requirements under differentiated working conditions, and obtains the stiffness requirement coupling parameters;

[0036] The load change calculation submodule calls the stiffness requirement coupling parameter, compares the load mutation amplitude under high-speed steering and emergency braking, and calculates the load change under dynamic working conditions using the formula:

[0037] ;

[0038] Calculate and obtain the load change trend under different dynamic working conditions and obtain the dynamic load change parameters;

[0039] in, represents the dynamic load variation parameter, represents the energy distribution coefficient of the mth region, represents the angular velocity of the mth region, represents the base angular velocity, represents the stiffness of the mth region, represents the damping coefficient of the mth region, represents the speed of the mth region, Represents the base speed, represents the load mass of the mth region, represents the load angle of the mth region, represents the load change rate of the mth region, Represents the current time, represents the base time, Represents the total number of measurement areas.

[0040] As a further aspect of the present invention, the system further comprises an intelligent stiffness optimization module;

[0041] The intelligent stiffness optimization module obtains the data of the adjustable elastic composite material and the electromagnetic variable stiffness material, analyzes the elastic modulus adjustment range, calls calculation parameters, matches the stiffness change rate with the load requirement, and obtains an optimized adjustment plan;

[0042] The suspension stiffness matching optimization adjustment scheme includes intelligent stiffness optimization adjustment value, stiffness matching strategy, and elastic modulus adjustment calculation parameters.

[0043] As a further solution of the present invention, the intelligent stiffness optimization module includes:

[0044] The material elastic parameter comparison submodule obtains the material data of the adjustable elastic composite material and the electromagnetic variable stiffness based on the dynamic load variation parameters, compares the elastic modulus variation range of the two types of materials under different stress levels, extracts the corresponding adjustment boundary values, and obtains the material elastic adjustment parameters by loading the elastic response data under different temperature and magnetic field strength conditions;

[0045] The stiffness change matching analysis submodule calls the material elastic adjustment parameters, analyzes the correlation between the stiffness change rate and the load matching, calculates the suspension deformation after the stiffness adjustment, extracts the stiffness adjustment coefficient, derives the stiffness change trend through differentiated loads, and obtains the stiffness matching optimization parameters;

[0046] The intelligent stiffness adjustment calculation submodule calls the stiffness matching optimization parameters and adjusts the mounting plate stiffness matching strategy using the formula:

[0047] ;

[0048] Calculate and obtain the intelligent stiffness optimization adjustment value, and obtain the suspension stiffness matching optimization adjustment plan;

[0049] in, Represents the intelligent stiffness optimization adjustment value, represents the elastic modulus of the rth region, represents the material adjustment factor for region r, represents the change in deformation of the rth region, represents the base deformation, represents the stiffness matching correction parameter of the rth region, represents the stiffness adjustment rate of the rth region, Represents the current time, represents the base time, represents the load matching parameter of the rth region, represents the mass distribution factor of the rth region, represents the force height of the rth region, Represents the total number of measurement areas.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, full-scale stiffness monitoring is provided by accurately analyzing the microscopic lattice structure and mesoscopic elastic modulus of the suspension mounting plate. High-precision sensor data is used to accurately calculate the changes in stiffness requirements under driving conditions, thereby improving the adaptability of the adjustment scheme. Combined with X-ray diffraction and magnetoelastic measurements, the accuracy of fatigue damage prediction is improved. Shape memory alloys and micro-hydraulic actuators are used to quickly adjust the local structure of the force concentration area, optimize stiffness matching, and use adjustable elastic materials and electromagnetic stiffness adjustment to enhance the dynamic adaptability of the suspension to complex working conditions, significantly improving the performance stability and response speed of the suspension system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a system flow chart of the present invention;

[0053] Figure 2 This is a flow chart of the multi-scale stiffness monitoring module of the present invention;

[0054] Figure 3 This is a flow chart of the fatigue damage prediction module of the present invention;

[0055] Figure 4 This is a flow chart of the nonlinear stress feedback module of the present invention;

[0056] Figure 5 This is a flow chart of the dynamic load adjustment module of the present invention;

[0057] Figure 6 This is the flow chart of the intelligent stiffness optimization module of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0059] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0060] See also Figure 1 , a suspension mounting plate intelligent monitoring and optimization system includes:

[0061] The multi-scale stiffness monitoring module acquires microscopic lattice structure data of the suspension mounting plate, calculates parameters correlating the metal lattice arrangement state with stiffness changes, uses electromagnetic wave measurement data to analyze the mesoscopic elastic modulus distribution characteristics, and combines macroscopic stress measurement data to determine the overall stiffness trend under differentiated loading conditions. It also acquires wheel ground reaction force data, vehicle acceleration data, and suspension displacement rate data, calculates the impact of driving state changes on stiffness requirements, analyzes the deviation between the current suspension stiffness characteristics and the stiffness target, extracts stiffness matching parameters, and obtains the multi-scale stiffness matching coefficient.

[0062] The fatigue damage prediction module uses the multi-scale stiffness matching coefficient to obtain residual stress data for key parts of the suspension mounting plate, calculate the residual stress change rate under differentiated stress states, call X-ray diffraction measurement data and magnetoelastic stress measurement data, compare the relationship between residual stress relaxation trend and load action time, retrieve fatigue damage characteristic areas of the suspension mounting plate, call fatigue crack growth rate data, analyze the correspondence between crack growth direction and stress concentration area, calculate the correlation parameter between residual stress relaxation rate and crack growth, and obtain fatigue damage trend;

[0063] Based on fatigue damage trends, the nonlinear stress feedback module acquires stress sensor data, calculates the local stress increase in the stress-concentrated area, and uses vehicle speed, load, and road surface feedback data to analyze the influence coefficient of transient impact force on suspension deformation. It then obtains the adjustment response parameters of the shape memory alloy and micro-hydraulic actuator, compares the morphological adjustment requirements under different stress conditions, and adjusts the local structure of the suspension mounting plate to determine the stiffness adjustment range.

[0064] The dynamic load adjustment module obtains torque sensor data and accelerometer measurement data based on the stiffness adjustment range, calculates the rate of change of the suspension's real-time stress state, calls wheel ground pressure data and deformation data, analyzes the coupling relationship between deformation growth rate and stiffness requirements, compares the load mutation amplitude under high-speed steering and emergency braking conditions, calculates the load change under dynamic conditions, and obtains dynamic load change parameters;

[0065] Based on the dynamic load change parameters, the intelligent stiffness optimization module obtains the data of adjustable elastic composite materials and electromagnetic variable stiffness materials, compares the adjustment range of the material elastic modulus, calls the elastic modulus adjustment calculation parameters, analyzes the correlation between the stiffness change rate and load matching, adjusts the mounting plate stiffness matching strategy, obtains the intelligent stiffness optimization adjustment value, and obtains the suspension stiffness matching optimization adjustment plan.

[0066] The multi-scale stiffness matching coefficient includes the stiffness demand influence quantity, stiffness characteristic deviation, and stiffness matching parameters. The fatigue damage trend includes the residual stress relaxation rate, crack growth rate, and fatigue damage characteristic area. The stiffness adjustment range includes the local stress increase, transient impact force influence coefficient, and morphological adjustment demand. The dynamic load change parameters include the load change amount, deformation growth rate, and load mutation amplitude. The suspension stiffness matching optimization adjustment scheme includes the intelligent stiffness optimization adjustment value, stiffness matching strategy, and elastic modulus adjustment calculation parameters.

[0067] See also Figure 2 , the multi-scale stiffness monitoring module includes:

[0068] The micro-lattice characteristic extraction submodule obtains the micro-lattice structure data of the suspension mounting plate, extracts the metal lattice arrangement state parameters, calculates the correlation parameters between the lattice arrangement state and the stiffness change, filters the lattice arrangement data, and obtains the lattice arrangement characteristic parameters;

[0069] First, a high-resolution electron microscope (HRTEM) is used to image the microscopic lattice structure of the suspension mounting plate to obtain detailed information on the lattice arrangement. Then, image processing technology is applied to identify and extract the lattice arrangement state parameters, including the lattice constant and the degree of lattice distortion. Next, the correlation parameters between the lattice arrangement state and the stiffness change are calculated. For example, by comparing the lattice constant changes in different regions, their impact on the material stiffness is evaluated. Finally, valid lattice arrangement characteristic data are screened out to ensure that the selected data can accurately reflect the stiffness characteristics of the material. In practical applications, assuming that the lattice constant of one area is 2.86 and that of another area is 2.88, it can be seen through comparison that a small change in the lattice constant may lead to a change in the material stiffness, and the lattice arrangement characteristic parameters are obtained.

[0070] The mesoscopic elastic modulus analysis submodule uses electromagnetic wave measurement data to analyze the distribution characteristics of the mesoscopic elastic modulus. It calculates the regional elastic modulus change rate based on the lattice arrangement characteristic parameters, obtains the elastic modulus distribution state, and combines it with the macroscopic stress measurement data to generate the elastic modulus trend under loading.

[0071] First, electromagnetic wave reflectivity measurement technology is used to obtain electromagnetic wave reflection data of the material in different areas. Then, this data is analyzed to determine the elastic modulus distribution characteristics at the mesoscopic scale. Next, the elastic modulus change rate of each area is calculated based on the lattice arrangement characteristic parameters. For example, assuming that the lattice arrangement characteristic parameters of a certain area show that the lattice constant increases by 1%, the elastic modulus of this area may decrease accordingly. Then, the distribution state of the elastic modulus is obtained. Combined with the macro stress measurement data, the stress-strain relationship formula is used to calculate the elastic modulus change trend under different loading states. For example, in actual applications, assuming that the stress in a certain area increases by 10MPa and the measured strain is 0.005, the elastic modulus of this area is 2000MPa. By comparing the elastic modulus changes under different loading states, the loading state elastic modulus trend is generated.

[0072] The stiffness matching coefficient calculation submodule calls the elastic modulus trend of the loading state, obtains the wheel ground reaction force data, vehicle acceleration data, and suspension displacement rate data, calculates the impact of driving state changes on stiffness requirements, analyzes the deviation between the current suspension stiffness characteristics and the stiffness target, and extracts the stiffness matching parameters using the formula:

[0073] ;

[0074] Obtain the stiffness matching parameters under each state through calculation and obtain the multi-scale stiffness matching coefficient;

[0075] in, represents the multi-scale stiffness matching coefficient, represents the elastic modulus of the i-th region, represents the ground reaction force in the i-th area, represents the acceleration of the i-th region, represents the suspension displacement rate of the i-th region, represents the total number of measurement areas;

[0076] First, obtain wheel ground reaction force data and use a force sensor to measure the actual force when the wheel touches the ground. Assume that the measured ground reaction forces in the three areas are 5000N, 4800N, and 4700N, respectively. Then, obtain vehicle acceleration data and use a three-axis accelerometer to measure the acceleration changes of the vehicle under different road conditions. Assume that the measured accelerations in the three areas are 2m / s2, 2.2m / s2, and 1.8m / s2, respectively. Next, obtain suspension displacement rate data and use a laser displacement sensor to measure the displacement rate of the suspension under different load conditions. Assume that the measured displacement rates in the three areas are 0.01m / s, 0.012m / s, and 0.009m / s, respectively. Then, calculate the impact of driving state changes on stiffness requirements, analyze the deviation between the current suspension stiffness characteristics and the stiffness target, and extract the stiffness matching parameters using the formula:

[0077] ;

[0078] Calculation process:

[0079] Set the total number of measurement areas ;

[0080] Set the elastic modulus of each region: GPa, GPa, GPa;

[0081] Set the ground reaction force for each area: N, N, N;

[0082] Calculate the molecular part :

[0083] ;

[0084] Set the acceleration of each area: m / s2, m / s2, m / s2;

[0085] Calculate the sum of squared accelerations:

[0086] ;

[0087] Calculate the square root:

[0088] ;

[0089] Set the suspension displacement rate for each area: m / s, m / s, m / s;

[0090] Calculate the absolute sum of the suspension displacement rates:

[0091] ;

[0092] Calculate the denominator:

[0093] ;

[0094] calculate :

[0095] ;

[0096] The final calculation obtains the stiffness matching parameters under each state and obtains the multi-scale stiffness matching coefficient .

[0097] See also Figure 3 , the fatigue damage prediction module includes:

[0098] The residual stress monitoring submodule obtains residual stress data of the suspension mounting plate based on the multi-scale stiffness matching coefficient. The residual stress distribution is measured using X-ray diffraction, and the measurement results are cross-validated using magnetoelastic stress measurement. The residual stress change rate under differentiated stress states is calculated. The discrete time series method is used to partition the residual stress changes under differentiated loading states, and the stress change trend is established in the time dimension to obtain the residual stress change rate.

[0099] First, based on the multi-scale stiffness matching coefficient, the stress distribution area of ​​the key parts of the suspension mounting plate is determined, and multiple measurement points with typical stress characteristics are selected to obtain residual stress data. The X-ray diffraction method is used to calculate the relationship between the diffraction angle and stress using the Bragg law to determine the residual stress value. Subsequently, the magnetoelastic stress measurement method is applied, and the stress field distribution inside the ferromagnetic material is detected using a Hall effect sensor. The measured residual stress data is cross-validated to eliminate measurement errors. At the same time, based on the load data under different working conditions, the residual stress change rate under differentiated stress states is calculated. The discrete time series method is used to perform differential operations on the residual stress data at different time points to obtain the change rate. In actual implementation, measurement points A, B, and C are set, and the measured initial residual stresses are 150MPa, 130MPa, and 110MPa, respectively. After 50 hours of load action, the residual stresses become 120MPa, 115MPa, and 100MPa, respectively. The change rate is calculated as follows: MPa / h, calculated similarly MPa / h, MPa / h, to obtain the rate of change of residual stress.

[0100] The crack growth analysis submodule uses the residual stress change rate to retrieve the fatigue damage characteristic area of ​​the suspension mounting plate, obtains fatigue crack growth rate data, analyzes the correspondence between the crack growth direction and the stress concentration area, calculates the crack growth rate based on the crack morphology in the stress concentration area and the stress intensity factor at the crack tip, obtains the crack growth characteristics under differentiated stress levels, and determines the crack growth trend.

[0101] Based on the measured stress relaxation data, the fatigue damage characteristic area of ​​the suspension mounting plate was retrieved. The ultrasonic flaw detection method was used to detect the crack initiation position inside the material and obtain the crack length and crack growth rate data. The crack growth path was monitored using the digital image correlation method. The stress concentration at the crack tip was analyzed in combination with finite element calculation. The correspondence between the crack growth direction and the stress concentration area was further analyzed. Assuming that the initial crack length at measurement point A is 2.0 mm and expands to 2.5 mm after 50 hours, the crack growth rate is calculated as follows: mm / h. Similarly, the expansion rates of measuring points B and C are calculated to be 0.008 mm / h and 0.005 mm / h respectively, and the crack expansion trend is finally obtained.

[0102] The damage trend calculation submodule calls the crack growth trend, compares the relationship between the residual stress relaxation trend and the load action time, and calculates the correlation parameters between the residual stress relaxation rate and crack growth using the formula:

[0103] ;

[0104] Calculate and obtain the fatigue damage rate under different load action times and obtain the fatigue damage trend;

[0105] in, Represents fatigue damage trend, represents the residual stress relaxation rate in the jth region, represents the crack growth rate of the jth region, represents the crack length of the jth region, represents the stress intensity factor of the jth region, represents the total number of measurement areas;

[0106] The crack growth trend is called, and the relationship between the residual stress relaxation trend and the load time is compared. The correlation parameters between the residual stress relaxation rate and the crack growth are calculated using the formula:

[0107] ;

[0108] Among them, let the measurement points A, B and C be calculated, and their parameter values ​​are known. MPa / h, MPa / h, MPa / h, crack growth rate mm / h, mm / h, mm / h, crack length mm, mm, mm, stress intensity factor MPa√m, MPa√m, MPa√m, calculate the numerator:

[0109] ;

[0110] Calculate the denominator:

[0111] ;

[0112] ;

[0113] Calculate the damage tendency parameter:

[0114] ;

[0115] The final calculation obtains the fatigue damage rate under different load action times and the fatigue damage trend.

[0116] See also Figure 4 , the nonlinear stress feedback module includes:

[0117] The local stress increase calculation submodule obtains stress sensor data based on fatigue damage trends, divides stress areas and extracts local stress data, calculates stress change curves under different working conditions, screens stress mutation points, and calculates local stress increase values ​​to obtain local stress increase values;

[0118] First, the stress sensor data is obtained, and multiple measurement points are selected to cover the key stress areas of the entire suspension mounting plate. The measured data is screened and outliers are eliminated to ensure data accuracy. Subsequently, the finite element analysis method is used to partition the measurement points, subdivide the stress concentration area into several small areas, and extract their local stress data. The stress change curves under different working conditions are calculated, and the time series analysis method is used to extract the peak points and mutation points of the stress change and determine the stress increase area. Finally, the local stress increase value is calculated and normalized using the ratio of the stress change rate to the loading time. In this example, assuming that the initial stress values ​​at measurement points A, B, and C are 120MPa, 110MPa, and 105MPa, respectively, and increase to 160MPa, 140MPa, and 130MPa after 50 hours, the stress increase is calculated as follows. MPa / h, calculated similarly MPa / h, MPa / h, to obtain the local stress increase value.

[0119] The impact force analysis submodule uses the local stress increase value and combines it with vehicle speed, load, and road surface feedback data to calculate the peak value and duration of the transient impact force. It then uses the dynamic load model to analyze the impact force's influence on suspension deformation and obtain the impact response coefficient.

[0120] Combining vehicle speed, load and road surface feedback data, the transient impact force data under different speed ranges are selected, the transient impact force peak value and duration of action are analyzed, and the dynamic load model is applied to calculate the impact force change trend under different speeds and road surface types. Subsequently, the impact response coefficient is calculated based on the dynamic stiffness change rate of the measurement point. In this example, the vehicle speeds are set to 20km / h, 40km / h and 60km / h, respectively. The transient impact forces under different speeds are measured to be 5000N, 7000N and 9000N, respectively, and the action time is 0.2s, 0.3s and 0.4s, respectively. By calculating the impact force effect per unit time, the impact force effect at 20km / h is obtained to be: N / s, the impact force at 40km / h is 23333N / s, and the impact force at 60km / h is 22500N / s, and the impact response coefficient is finally obtained.

[0121] The stiffness adjustment calculation submodule calls the impact response coefficient to obtain the adjustment response parameters of the shape memory alloy and micro hydraulic actuator, compares the shape adjustment requirements under differentiated force conditions, and calculates the adjustment amount of the local structure of the suspension mounting plate using the formula:

[0122] ;

[0123] Obtaining the stiffness adjustment requirement of the suspension mounting plate through calculation and obtaining the stiffness adjustment range;

[0124] in, Represents the stiffness adjustment range, represents the actuator response force in the kth region, represents the angle change of the shape memory alloy in the kth region, represents the thickness of the suspension mounting plate in the kth region, represents the adjustment torque of the kth region, represents the morphological correction parameter of the kth region, represents the local load force in the kth region, represents the length of the force arm of the kth region, represents the total number of measurement areas;

[0125] Obtain the adjustment response parameters of the shape memory alloy and micro hydraulic actuator, calculate the shape adjustment requirements under different force conditions, analyze the deformation state of the local area, and calculate the adjustment amount of the local structure of the suspension mounting plate using the formula:

[0126] ;

[0127] Assuming the measurement area P=3, the actuator response force 、 、 , the angle change of shape memory alloy 、 、 , adjust the torque 、 、 , suspension mounting plate thickness 、 、 , morphological correction parameters 、 、 , local load force 、 、 , lever arm length 、 、 , first calculate the numerator:

[0128] ;

[0129] ;

[0130] Calculate the denominator:

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] Final calculation of stiffness adjustment range:

[0136] ;

[0137] The final calculation obtains the stiffness adjustment requirements of the suspension mounting plate and the stiffness adjustment range .

[0138] See also Figure 5 , the dynamic load adjustment module includes:

[0139] The stress state calculation submodule obtains torque sensor data and acceleration measurement data based on the stiffness adjustment amplitude, calculates the rate of change of the real-time stress state of the suspension, performs numerical differentiation operations on the measurement data using differentiated time steps, extracts the torque change trend, and obtains the rate of change of the stress state;

[0140] First, obtain the torque sensor data and accelerometer measurement data, select measurement points under different dynamic conditions to ensure coverage of key states such as high-speed steering and emergency braking, then preprocess the measurement data, including outlier removal and smoothing filtering, to ensure data accuracy, use the numerical differentiation method to calculate the torque change rate, and combine the accelerometer data to calculate the real-time acceleration of the suspension, input the torque change rate and acceleration data into the suspension force balance equation, and calculate the rate of change of the force state over time. Assuming that at a certain measurement point, the initial torque is 120Nm, the measurement time interval is 0.1s, and the torque changes to 140Nm after 0.1s, then the torque change rate is calculated as follows, Nm / s, and calculate the torque change rate of other measurement points in the same way. Combined with the suspension force model, calculate the force change trend under different working conditions to obtain the force state change rate.

[0141] The stiffness requirement coupling analysis submodule uses the force state change rate to analyze wheel ground contact pressure data and deformation data, extracting the change pattern of the deformation growth rate under differentiated stiffness adjustment conditions. It then establishes a coupling relationship between the deformation growth rate and stiffness requirement using dynamic mechanical equations, compares the stiffness requirements under differentiated working conditions, and obtains the stiffness requirement coupling parameters.

[0142] Analyze the wheel ground pressure data and deformation data. First, based on the ground pressure under different vehicle speeds and load conditions, calculate the deformation curve of the suspension system. Use the dynamic stiffness equation to analyze the trend of the deformation growth rate with load. Then, construct the stiffness demand curve. By comparing the deformation growth rate under different working conditions, extract the optimal matching stiffness parameters. In this example, assuming that when the vehicle is traveling at 40km / h and 80km / h, the front wheel ground pressure is 4000N and 5000N respectively, and the corresponding suspension deformation is 10mm and 15mm respectively, then the deformation growth rate is calculated as mm / km / h, and finally extract the stiffness matching parameters corresponding to the growth rates of different deformation variables to obtain the stiffness requirement coupling parameters.

[0143] The load change calculation submodule calls the stiffness demand coupling parameter, compares the load mutation amplitude under high-speed steering and emergency braking conditions, and calculates the load change under dynamic conditions using the formula:

[0144] ;

[0145] Calculate and obtain the load change trend under different dynamic working conditions and obtain the dynamic load change parameters;

[0146] in, represents the dynamic load variation parameter, represents the energy distribution coefficient of the mth region, represents the angular velocity of the mth region, represents the base angular velocity, represents the stiffness of the mth region, represents the damping coefficient of the mth region, represents the speed of the mth region, Represents the base speed, represents the load mass of the mth region, represents the load angle of the mth region, represents the load change rate of the mth region, Represents the current time, represents the base time, represents the total number of measurement areas;

[0147] Compare the load mutation amplitudes under high-speed steering and emergency braking conditions, and calculate the load change under dynamic conditions using the formula:

[0148] ;

[0149] Assume that in a certain measurement area, the energy distribution coefficient 、 , angular velocity 、 , reference angular velocity , stiffness 、 , damping coefficient 、 ,speed 、 , base speed , load mass 、 , load angle 、 , load change rate 、 , base time , calculate the molecular part:

[0150] ;

[0151] ;

[0152] ;

[0153] Calculate the second term:

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] Finally calculate the dynamic load change parameters:

[0159] ;

[0160] The final calculation obtains the load change trend under different dynamic working conditions and obtains the dynamic load change parameters .

[0161] See also Figure 6 , the intelligent stiffness optimization module includes:

[0162] The material elastic parameter comparison submodule obtains material data of adjustable elastic composite materials and electromagnetic variable stiffness based on dynamic load variation parameters, compares the elastic modulus variation range of the two types of materials under different stress levels, extracts the corresponding adjustment boundary values, and obtains the material elastic adjustment parameters by loading elastic response data under different temperature and magnetic field strength conditions;

[0163] First, historical experimental data is called to determine the range of variation of the elastic modulus of the material under different temperature and magnetic field environments, and the stress-strain curve of the test sample is obtained. Then, the elastic adjustment amplitude of the material is calculated, and an initial adjustment reference value is set. This value is compared with the maximum and minimum elastic moduli of the sample to determine the adjustable range. According to the stress state under different working conditions, the adjustment weight is set. The adjustment coefficient of the elastic modulus is calculated through the numerical fitting method of the material stress-strain curve. According to the mechanical characteristics of different areas under actual working conditions, the elastic modulus adjustment parameters are used to perform adjustment calculations to obtain the material elastic adjustment parameters.

[0164] The stiffness change matching analysis submodule uses material elasticity adjustment parameters to analyze the correlation between stiffness change rate and load matching, calculates the suspension deformation after stiffness adjustment, extracts the stiffness adjustment coefficient, derives the stiffness change trend through differentiated loads, and obtains stiffness matching optimization parameters.

[0165] First, the deformation data of the suspension system is obtained, and the deformation increments at different times are calculated. Combined with the changes in the elastic modulus of the suspension material, the current stiffness adjustment trend is calculated. According to different working conditions, the stiffness adjustment coefficient is calculated, and the deviation range between the adjusted stiffness and the target stiffness is determined. The optimal stiffness adjustment strategy is screened through a multi-scale calculation method, and the target stiffness range is set. The matching degree of different material adjustment strategies at different times is compared, and the optimal stiffness matching parameters are calculated and adjusted to obtain the stiffness matching optimization parameters.

[0166] The intelligent stiffness adjustment calculation submodule calls the stiffness matching optimization parameters and adjusts the mounting plate stiffness matching strategy using the formula:

[0167] ;

[0168] Calculate and obtain the intelligent stiffness optimization adjustment value, and obtain the suspension stiffness matching optimization adjustment plan;

[0169] in, Represents the intelligent stiffness optimization adjustment value, represents the elastic modulus of the rth region, represents the material adjustment factor for region r, represents the change in deformation of the rth region, represents the base deformation, represents the stiffness matching correction parameter of the rth region, represents the stiffness adjustment rate of the rth region, Represents the current time, represents the base time, represents the load matching parameter of the rth region, represents the mass distribution factor of the rth region, represents the force height of the rth region, represents the total number of measurement areas;

[0170] Calculation steps example: Input measurement data: Assume that there are The elastic modulus, material adjustment factor, deformation change, etc. of each area are as follows: , , , - , , , - Other area parameters are similar

[0171] Calculate the deformation difference term for each measurement area:

[0172] ;

[0173] ;

[0174] All regions are calculated in turn.

[0175] Calculate the correction parameter: Assume , , , ,but

[0176] ;

[0177] Using approximate calculations ,

[0178] ;

[0179] Calculate the final optimization adjustment value:

[0180] Substitute the data from all measurement areas and calculate the final value, for example:

[0181] ;

[0182] The specific value depends on the data of all measurement areas, and the final optimization adjustment plan for suspension stiffness matching is obtained.

[0183] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent monitoring and optimization system for a suspension mounting plate, characterized in that: The system comprises: The multi-scale stiffness monitoring module acquires microscopic lattice structure data, calculates stiffness variation parameters, uses electromagnetic wave measurement data to analyze mesoscopic elastic modulus distribution, combines macroscopic stress measurements to determine stiffness trends, analyzes the impact of driving conditions on stiffness requirements, extracts matching parameters, and obtains stiffness matching coefficients. The fatigue damage prediction module calculates the residual stress change rate based on the stiffness matching coefficient, calls X-ray diffraction and magnetoelasticity measurement data, compares the stress relaxation trend, calculates the correlation parameters between crack growth and residual stress relaxation, and obtains the fatigue damage trend; Based on the fatigue damage trend, the nonlinear stress feedback module obtains stress sensor data, calculates the local stress increase, calls vehicle speed, load, and road surface feedback data, analyzes the impact of transient impact force, calls shape memory alloy and hydraulic actuator response parameters, adjusts the suspension structure, and obtains the stiffness adjustment amplitude; The dynamic load adjustment module calculates the rate of change of the suspension force state based on the stiffness adjustment amplitude, calls the wheel ground pressure and deformation data, compares the load mutation amplitude during high-speed steering and emergency braking, calculates the load change under dynamic working conditions, and obtains the dynamic load change parameter; The multi-scale stiffness monitoring module includes: The micro-lattice characteristic extraction submodule obtains the micro-lattice structure data of the suspension mounting plate, extracts the metal lattice arrangement state parameters, calculates the correlation parameters between the lattice arrangement state and the stiffness change, filters the lattice arrangement data, and obtains the lattice arrangement characteristic parameters; The mesoscopic elastic modulus analysis submodule calls the electromagnetic wave measurement data, analyzes the mesoscopic elastic modulus distribution characteristics, calculates the regional elastic modulus change rate based on the lattice arrangement characteristic parameters, obtains the elastic modulus distribution state, and generates the elastic modulus trend under loading state in combination with the macroscopic stress measurement data; The stiffness matching coefficient calculation submodule calls the elastic modulus trend of the loading state, obtains wheel ground reaction data, vehicle acceleration data, and suspension displacement rate data, calculates the impact of driving state changes on stiffness requirements, analyzes the deviation between the current suspension stiffness characteristics and the stiffness target, and extracts stiffness matching parameters using the formula: ； Obtain stiffness matching parameters under each state through calculation and obtain stiffness matching coefficient; in, represents the stiffness matching coefficient, represents the elastic modulus of the i-th region, represents the ground reaction force in the i-th area, represents the acceleration of the i-th region, represents the suspension displacement rate of the i-th region, represents the total number of measurement areas; The dynamic load adjustment module includes: The stress state calculation submodule obtains torque sensor data and acceleration measurement data based on the stiffness adjustment amplitude, calculates the rate of change of the real-time stress state of the suspension, performs numerical differentiation operation on the measurement data using a differentiated time step, extracts the torque change trend, and obtains the rate of change of the stress state; The stiffness requirement coupling analysis submodule uses the force state change rate to analyze the wheel ground contact pressure data and deformation data, extracts the change pattern of the deformation growth rate under the condition of differentiated stiffness adjustment, establishes the coupling relationship between the deformation growth rate and the stiffness requirement through dynamic mechanical equations, compares the stiffness requirements under differentiated working conditions, and obtains the stiffness requirement coupling parameters; The load change calculation submodule calls the stiffness requirement coupling parameter, compares the load mutation amplitude under high-speed steering and emergency braking, and calculates the load change under dynamic working conditions using the formula: ； Calculate and obtain the load change trend under different dynamic working conditions and obtain the dynamic load change parameters; in, represents the dynamic load variation parameter, represents the energy distribution coefficient of the mth region, represents the angular velocity of the mth region, represents the base angular velocity, represents the stiffness of the mth region, represents the damping coefficient of the mth region, represents the speed of the mth region, Represents the base speed, represents the load mass of the mth region, represents the load angle of the mth region, represents the load change rate of the mth region, Represents the current time, represents the base time, Represents the total number of measurement areas.

2. The suspension mounting plate intelligent monitoring and optimization system according to claim 1, characterized in that: The stiffness matching coefficient includes the stiffness requirement influence, the stiffness characteristic deviation, and the stiffness matching parameter; the fatigue damage trend includes the residual stress relaxation rate, the crack growth rate, and the fatigue damage characteristic area; the stiffness adjustment amplitude includes the local stress increase, the transient impact force influence coefficient, and the morphological adjustment requirement; the dynamic load change parameters include the load change amount, the deformation acceleration rate, and the load mutation amplitude.

3. The intelligent monitoring and optimization system for suspension mounting plates according to claim 1, characterized in that: The fatigue damage prediction module includes: The residual stress monitoring submodule obtains residual stress data of the suspension mounting plate based on the stiffness matching coefficient, measures the residual stress distribution using X-ray diffraction, cross-validates the measurement results using a magnetoelastic stress measurement method, calculates the residual stress change rate under differentiated stress states, and uses a discrete time series method to partition the residual stress changes under differentiated loading states. The stress change trend is established in the time dimension to obtain the residual stress change rate. The crack growth analysis submodule calls the residual stress change rate, retrieves the fatigue damage characteristic area of ​​the suspension mounting plate, obtains fatigue crack growth rate data, analyzes the correspondence between the crack growth direction and the stress concentration area, calculates the crack growth rate based on the crack morphology and crack tip stress intensity factor in the stress concentration area, obtains the crack growth characteristics under differentiated stress levels, and obtains the crack growth trend; The damage trend calculation submodule calls the crack growth trend, compares the relationship between the residual stress relaxation trend and the load action time, and calculates the correlation parameter between the residual stress relaxation rate and the crack growth using the formula: ； Calculate and obtain the fatigue damage rate under different load action times and obtain the fatigue damage trend; in, Represents fatigue damage trend, represents the residual stress relaxation rate in the jth region, represents the crack growth rate of the jth region, represents the crack length of the jth region, represents the stress intensity factor of the jth region, Represents the total number of measurement areas.

4. The suspension mounting plate intelligent monitoring and optimization system according to claim 1, characterized in that: The nonlinear stress feedback module includes: The local stress increase calculation submodule obtains stress sensor data based on the fatigue damage trend, divides the stress area and extracts local stress data, calculates stress change curves under different working conditions, screens stress mutation points, and calculates local stress increase values ​​to obtain local stress increase values; The impact force impact analysis submodule uses the local stress increase value and combines the vehicle speed, load and road surface feedback data to calculate the peak value and duration of the transient impact force. It analyzes the influence coefficient of the impact force on the suspension deformation through the dynamic load model to obtain the impact response coefficient. The stiffness adjustment calculation submodule calls the impact response coefficient to obtain the adjustment response parameters of the shape memory alloy and the micro hydraulic actuator, compares the shape adjustment requirements under differentiated force conditions, and calculates the adjustment amount of the local structure of the suspension mounting plate using the formula: ； Obtaining the stiffness adjustment requirement of the suspension mounting plate through calculation and obtaining the stiffness adjustment range; in, Represents the stiffness adjustment range, represents the actuator response force in the kth region, represents the angle change of the shape memory alloy in the kth region, represents the thickness of the suspension mounting plate in the kth region, represents the adjustment torque of the kth region, represents the morphological correction parameter of the kth region, represents the local load force in the kth region, represents the length of the force arm of the kth region, Represents the total number of measurement areas.

5. The suspension mounting plate intelligent monitoring and optimization system according to claim 1, characterized in that: The system also includes an intelligent stiffness optimization module; The intelligent stiffness optimization module obtains data on adjustable elastic composite materials and electromagnetic variable stiffness materials, analyzes the elastic modulus adjustment range, calls calculation parameters, matches the stiffness change rate with the load requirements, and obtains an optimized adjustment plan; The optimization and adjustment scheme includes intelligent stiffness optimization adjustment value, stiffness matching strategy, and elastic modulus adjustment calculation parameters.

6. The suspension mounting plate intelligent monitoring and optimization system according to claim 5, characterized in that: The intelligent stiffness optimization module includes: The material elastic parameter comparison submodule obtains the material data of the adjustable elastic composite material and the electromagnetic variable stiffness based on the dynamic load variation parameters, compares the elastic modulus variation range of the two types of materials under different stress levels, extracts the corresponding adjustment boundary values, and obtains the material elastic adjustment parameters by loading the elastic response data under different temperature and magnetic field strength conditions; The stiffness change matching analysis submodule calls the material elastic adjustment parameters, analyzes the correlation between the stiffness change rate and the load matching, calculates the suspension deformation after the stiffness adjustment, extracts the stiffness adjustment coefficient, derives the stiffness change trend through differentiated loads, and obtains the stiffness matching optimization parameters; The intelligent stiffness adjustment calculation submodule calls the stiffness matching optimization parameters and adjusts the mounting plate stiffness matching strategy using the formula: ； Calculate and obtain the intelligent stiffness optimization adjustment value, and obtain the suspension stiffness matching optimization adjustment plan; in, Represents the intelligent stiffness optimization adjustment value, represents the elastic modulus of the rth region, represents the material adjustment factor for region r, represents the change in deformation of the rth region, represents the base deformation, represents the stiffness matching correction parameter of the rth region, represents the stiffness adjustment rate of the rth region, Represents the current time, represents the base time, represents the load matching parameter of the rth region, represents the mass distribution factor of the rth region, represents the force height of the rth region, Represents the total number of measurement areas.

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