A fatigue test method and system for automobile seat cushion frame

Abstract

The present invention relates to the field of seat cushion frame detection technology, and in particular to a method and system for automobile seat cushion frame fatigue testing. The method comprises the following steps: obtaining automobile seat cushion frame sample information data; screening key parts of the frame for the automobile seat cushion frame sample information data to obtain key part screening data for the frame; collecting sensor data for the automobile seat cushion frame sample based on the key part screening data for the frame to obtain stress sensor collection data and deformation sensor collection data; designing a loading mode for the automobile seat cushion frame sample information data to generate automobile seat cushion frame fatigue loading mode data. The present invention improves the accuracy and reliability of automobile seat cushion frame fatigue testing by accurately screening key parts of the frame, designing a true loading mode, conducting in-depth damage detection, life prediction, and material optimization.

Description

Technical Field

[0001] The invention relates to the technical field of seat cushion frame detection, and in particular to a fatigue test method and system for an automobile seat cushion frame. Background Art

[0002] Early automotive seat testing methods mainly relied on static loading and simple structural strength tests, which failed to fully simulate the complex dynamic loads that seats are subjected to during actual driving. With the advancement of technology, especially the introduction of computer simulation technology and dynamic fatigue test equipment, seat frame fatigue testing has gradually developed towards higher accuracy and realistic working condition simulation. The use of vibration platforms and multi-axis loading systems can more realistically simulate various dynamic stresses during vehicle driving and evaluate the fatigue damage of seat frames during long-term use. With the advancement of material science and manufacturing processes, more and more lightweight and high-strength materials are being used in automotive seat frames, which requires the methods and standards of fatigue testing to be continuously updated to adapt to the characteristics of new materials and structural designs. However, early fatigue test loading modes failed to fully simulate the dynamic loads in actual use. At the same time, traditional test methods only focused on external damage and lacked in-depth evaluation of internal damage, resulting in low accuracy and reliability of automotive seat frame fatigue tests. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for fatigue testing of a car seat cushion frame to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for fatigue testing of a car seat frame is provided, the method comprising the following steps:

[0005] Step S1: Acquire information data of a car seat frame sample; screen key parts of the car seat frame sample information data to obtain key part screening data; perform sensor data collection on the car seat frame sample based on the key part screening data to obtain stress sensor collection data and deformation sensor collection data;

[0006] Step S2: Design a loading mode for the sample information data of the car seat frame to generate fatigue loading mode data of the car seat frame; perform cross-sectional scanning on the key parts screening data of the frame according to the fatigue loading mode data of the car seat frame to generate a cross-sectional view of the car seat frame; perform fatigue test frame damage detection on the cross-sectional view of the car seat frame to generate internal damage detection data of the seat frame;

[0007] Step S3: Based on the long-term loading change data of the automobile seat frame, frame life modeling is performed to generate frame life modeling data; based on the frame life modeling data, frame life prediction is performed on the seat frame internal damage detection data to generate frame life prediction data; based on the frame life prediction data, frame material optimization is performed on the seat frame internal damage detection data to generate seat frame material optimization data;

[0008] Step S4: Screen the optimal automobile seat cushion frame material based on the seat cushion frame material optimization data to generate the optimal automobile seat cushion frame material data; perform frame life optimization visualization on the frame life prediction data based on the optimal automobile seat cushion frame material data to generate an automobile seat cushion frame fatigue test report.

[0009] The present invention ensures that the test data is concentrated on the key areas that affect the durability of the frame by obtaining detailed information of the seat cushion frame sample and screening the key parts of the frame, avoiding unnecessary irrelevant data collection. The use of sensors to collect stress and deformation data provides more accurate and comprehensive stress distribution information, laying the foundation for subsequent fatigue analysis and damage assessment. A fatigue loading mode that is more in line with actual use is designed to simulate the dynamic load in real driving, so that the test is more in line with actual working conditions. Through cross-sectional scanning and fatigue test frame damage detection based on the loading mode, the micro-damage inside the frame can be captured in detail, helping to discover potential structural weaknesses and providing more accurate data for subsequent life prediction. Through the frame life modeling based on long-term loading change data, the fatigue life of the seat cushion frame can be predicted, and accurate life prediction data can be provided to designers. This step can not only detect fatigue failure in advance, but also optimize the frame material according to the life prediction results, improve the frame structure and materials, thereby improving the durability and comfort of the seat frame. Through the optimal material screening based on material optimization data, it is ensured that the most suitable material is used to enhance the fatigue tolerance of the seat cushion frame. Life optimization visualization can intuitively display the optimized skeleton life data, allowing designers to quickly understand the improvement effect and provide guidance for product development. The fatigue test report finally generated provides a reliable basis for subsequent product improvement, quality control and production. Therefore, the present invention improves the accuracy and reliability of the fatigue test of the automobile seat cushion skeleton by accurately screening the key parts of the skeleton, designing the real loading mode, in-depth damage detection, life prediction and material optimization.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire automobile seat cushion frame sample information data;

[0012] Step S12: Screening key parts of the frame of the automobile seat cushion sample information data to obtain key parts screening data of the frame, wherein the key parts screening of the frame includes support beam screening and welding point screening;

[0013] Step S13: deploying stress sensors and deformation sensors on the car seat cushion skeleton sample based on the skeleton key part screening data, thereby obtaining stress sensor deployment data and deformation sensor deployment data;

[0014] Step S14: setting the collection time consistency for the stress sensor deployment data and the deformation sensor deployment data to obtain a standard sensor collection time interval; collecting data for the stress sensor deployment data and the deformation sensor deployment data based on the standard sensor collection time interval to obtain stress sensor collection data and deformation sensor collection data.

[0015] The present invention can accurately identify the parts that are critical to the structural strength and durability by screening the support beams and welding points of the car seat frame samples. This screening process helps to improve the accuracy and reliability of subsequent sensor deployment, ensures that monitoring is only performed at key locations, and avoids unnecessary waste of resources. Deploying stress sensors and deformation sensors based on the screening data of key parts of the skeleton helps to accurately capture the stress and deformation characteristics of the car seat frame during use. This can better reflect the dynamic changes of the seat frame under different working conditions and improve the effectiveness of data acquisition. By setting the consistency of the acquisition time of the stress sensor and the deformation sensor, the data of the two sensors are ensured to be collected synchronously, the problem of data inconsistency caused by time difference is avoided, and the reliability and comparability of the data are improved. The standardized acquisition time interval helps to improve the time resolution of the data and ensure that key dynamic information is not lost during real-time monitoring. The precisely deployed stress and deformation sensors can provide comprehensive real-time data, reflecting the stress, deformation changes and their interactions of the car seat frame under various working conditions. This provides high-quality data support for subsequent analysis and can better evaluate the performance and life of the seat frame.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: Designing a loading mode for the sample information data of the automobile seat cushion frame to generate fatigue loading mode data of the automobile seat cushion frame, wherein the fatigue loading mode data of the automobile seat cushion frame includes a static loading mode, a dynamic loading mode and a random loading mode;

[0018] Step S22: performing a long-period loading variation analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the static loading mode, the dynamic loading mode and the random loading mode, and generating long-period loading variation data of the automobile seat cushion frame;

[0019] Step S23: performing cross-sectional scanning of key parts of the frame according to the long-term loading variation data of the frame of the automobile seat cushion, and generating a cross-sectional view of the frame of the automobile seat cushion;

[0020] Step S24: performing image preprocessing on the cross-sectional view of the frame part of the automobile cushion to generate a cross-sectional view of the frame part of the standard automobile cushion, wherein the image preprocessing includes image denoising, curve filtering, image brightness enhancement and image grayscale value transformation; performing fatigue test frame damage detection on the cross-sectional view of the frame part of the standard automobile cushion to generate seat cushion frame internal damage detection data.

[0021] The present invention can simulate the fatigue load of the car seat frame under different use conditions by designing static loading mode, dynamic loading mode and random loading mode. These loading modes cover a variety of scenarios from static state to dynamic working condition to sudden load, providing comprehensive reference data for subsequent fatigue analysis, ensuring the comprehensiveness and accuracy of the analysis. Based on the data collected by stress sensors and deformation sensors of different loading modes, long-term loading change analysis can be carried out to deeply understand the fatigue and damage trends of the car seat frame during long-term use. This analysis provides an important basis for fatigue life prediction, helps to identify potential structural weaknesses, and then optimizes the design and prolongs the service life. According to the long-term loading change data, the key parts of the skeleton are scanned in section, and the parts with the most concentrated fatigue load can be accurately located. This process provides a detailed part cross-section diagram, which helps to identify potential problems inside the seat frame, such as local stress concentration and fatigue damage, which is crucial to improving the safety and reliability of the skeleton. Through image preprocessing technologies such as image denoising, curve filtering, image brightness enhancement, and image grayscale value transformation, the generated standard car seat frame part cross-section diagram is ensured to have high quality and high precision. This provides clear and accurate image data for subsequent fatigue test frame damage detection, making damage detection more accurate and reliable. By performing fatigue test frame damage detection on the cross-section of the standard car seat frame, the damaged parts of the frame during fatigue testing can be effectively identified. This process helps to detect potential damage early, facilitates targeted improvements and optimization, and improves the durability and safety of the frame.

[0022] Preferably, step S21 includes:

[0023] Divide the sample information data of the automobile seat frame into loading modes to generate the automobile seat frame test loading mode, wherein the automobile seat frame test loading mode includes a static test loading mode, a dynamic test loading mode and a random test load mode;

[0024] Based on the static test loading mode, a hydraulic loading device is used to apply constant pressure to the car seat cushion frame, and the stress distribution of the key parts of the frame is measured by collecting data through stress sensors, so as to generate stress distribution measurement data of the key parts of the frame; the static load-bearing capacity of the stress distribution measurement data of the key parts of the frame is evaluated to generate a static loading mode;

[0025] Based on the dynamic test loading mode, the dynamic loading equipment is used to apply periodic loads to the car seat frame, and the stress sensor is used to collect data to analyze the stress-strain curve of the dynamic loading equipment to generate the stress-strain curve data of the car seat frame; the vibration working condition combination performance evaluation is performed on the stress-strain curve data of the car seat frame to generate the dynamic loading mode;

[0026] Based on the random test load mode, the load waveform of the automobile seat cushion frame sample information data is extracted to obtain the load waveform extraction data; the load waveform extraction data is subjected to random waveform generation by the loading device to obtain the random load waveform data; the frame performance is evaluated on the random load waveform data to generate the random load mode.

[0027] The present invention divides the loading mode of the sample information data of the car seat frame into static test loading mode, dynamic test loading mode and random test load mode, and can fully simulate the stress conditions of the car seat frame under different working conditions. Different loading modes are respectively for static, dynamic and irregular load scenarios, ensuring the comprehensiveness of the test and covering the various stress states encountered by the car seat frame in actual use. Based on the static loading mode, a hydraulic loading device is used to apply constant pressure to the seat frame, and stress distribution measurement is performed on the key parts of the frame by a stress sensor, which helps to accurately identify the weak parts and bearing capacity of the frame. Through the static bearing capacity evaluation, the durability and stability of the frame under constant pressure can be evaluated, providing data support for design and material selection, and optimizing the structural design. In the dynamic loading mode, a dynamic loading device is used to apply periodic loads, and stress-strain curve analysis is performed by collecting data through a stress sensor, so that the response of the car seat frame under periodic loads can be understood in detail. This provides strong support for evaluating the performance of the frame under dynamic working conditions such as vibration and impact, especially under frequently stressed working conditions, which is very critical for predicting material fatigue and life. By evaluating the combined performance of the stress-strain curve of the car seat frame under vibration conditions, we can fully understand the performance of the frame under vibration conditions. This process helps to analyze the impact of loads of different frequencies and amplitudes on the frame structure, and further guides the optimization of vibration reduction, seismic resistance and other performance during the design process. By extracting load waveforms based on random test load patterns and generating random load waveform data, it is possible to simulate the working state of the frame under non-ideal, random stress conditions. The random load pattern is closer to the complex working conditions encountered during actual use, and provides more realistic data support for the evaluation of the strength, durability and other performance of the seat frame.

[0028] Preferably, step S22 includes the following steps:

[0029] Step S221: performing stress-deformation slow-changing trend analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the static loading mode to generate stress-deformation slow-changing trend data; extracting load stability parameters from the stress-deformation slow-changing trend data to obtain static mode time series change data;

[0030] Step S222: Calculate the stress-deformation phase difference of the stress sensor collected data and the deformation sensor collected data based on the dynamic loading mode to obtain stress-deformation phase difference data; extract energy dissipation index of the automobile seat cushion frame sample information data according to the stress-deformation phase difference data to obtain dynamic mode time series change data;

[0031] Step S223: performing stress-deformation random characteristic analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the random load mode to generate stress-phase change random characteristic data; extracting peak information from the stress-phase change random characteristic data to obtain random load mode time series change data;

[0032] Step S224: Integrate the static mode time series change data, the dynamic mode time series change data and the random load mode time series change data to generate the long-period loading change data of the automobile seat cushion frame.

[0033] The present invention can understand the changing trend of stress and deformation of the seat frame under static loading in detail by analyzing the stress-deformation slow-changing trend of data collected by stress sensors and deformation sensors based on the static loading mode. This helps to reveal how the stress and deformation of various parts of the frame gradually change over time under a constant load, thereby providing the fatigue evolution of the frame under a static state. By extracting the load stability parameter of the stress-deformation slow-changing trend data, the bearing capacity and stability of the seat frame under the static loading mode can be quantified. This parameter helps to determine whether the seat frame can maintain sufficient stability under long-term use to avoid structural instability or fatigue fracture. Based on the data of the dynamic loading mode, the stress-deformation phase difference calculation is performed to analyze the energy dissipation of the frame under dynamic working conditions. Energy dissipation is an important factor affecting the fatigue performance of materials. Therefore, by extracting the energy dissipation index, the fatigue resistance of the automobile seat frame under dynamic loading can be evaluated. This analysis helps to reveal the stress response and deformation behavior of the frame under working conditions such as vibration and impact, and optimize the design to improve seismic resistance and durability. By performing stress-deformation random characteristic analysis based on random load pattern data, the stress and deformation changes of the cushion frame under random loads can be simulated more realistically. This provides important data support for fatigue and damage assessment of the frame under complex and irregular loads, especially under unforeseen load conditions, and can evaluate the reliability and durability of the frame. By extracting peak information from stress-phase change random characteristic data, the maximum stress and deformation changes of the frame under random load patterns can be revealed, helping to identify potential fatigue points and damage areas. The extraction of peak information helps to promptly discover structural weaknesses, thereby optimizing the design and enhancing the reliability of the structure.

[0034] Preferably, the fatigue test skeleton damage detection of the cross-section of the standard automobile seat cushion skeleton part includes:

[0035] Performing part section area segmentation on the standard automobile seat frame part section diagram to generate a seat frame part core area segmentation diagram and a seat frame part edge area segmentation diagram;

[0036] Perform edge crack detection on the edge area segmentation map of the seat cushion frame to generate a crack edge image of the seat cushion frame; perform morphological marking on the crack edge image of the seat cushion frame to generate edge crack detection data of the seat cushion frame;

[0037] Perform hole identification on the core area segmentation map of the seat cushion frame to generate hole data of the core area of ​​the seat cushion frame; perform irregular hole marking on the core area segmentation map of the seat cushion frame based on the hole data of the core area of ​​the seat cushion frame to generate the core hole defect detection data of the automobile seat cushion frame;

[0038] The detection data of the edge crack of the automobile seat cushion frame and the core hole defect of the automobile seat cushion frame are integrated to obtain the internal damage detection data of the seat cushion frame.

[0039] The present invention can clearly separate the core area and edge area of ​​the skeleton by segmenting the section area of ​​the standard automobile seat frame. This segmentation process provides a clear area division for subsequent damage detection, making the detection process more accurate and helping to improve the accuracy of damage identification. By performing edge crack detection on the edge area segmentation map of the seat frame, tiny cracks at the edge of the skeleton can be identified, especially cracks in high stress or fatigue areas. This detection process can timely discover potential structural damage, avoid crack expansion and skeleton damage during long-term use, thereby improving the safety and durability of the product. The morphological marking method is used for crack detection, so that the recognition of the crack edge image is clearer and more accurate, especially when there is noise or interference in the image. This method can effectively eliminate noise interference, accurately mark the position and morphology of the crack, generate edge crack detection data, and provide reliable basic data for subsequent analysis. Hole discrimination is performed on the core area of ​​the seat frame, and the position of the hole or defect in the skeleton structure can be accurately identified. For the holes in the core area, further marking and defect detection can help determine their impact on the strength and stability of the skeleton. Early identification of hole defects enables timely repair or improvement measures to be taken to prevent damage to the frame due to structural weakness. By marking the holes in the core area with irregular holes, irregularly shaped hole defects can be identified. These irregular holes often have a more serious impact on the load-bearing capacity of the frame, so this method can effectively determine the potential threat of these special defects to the performance of the cushion frame, helping engineers optimize the design and avoid hidden risks. The edge crack detection data of the automobile seat cushion frame and the core area hole defect detection data are integrated to generate complete internal damage detection data. This data integration helps to fully understand the damage of the seat cushion frame, and combined with the defect information in different areas, it provides an accurate assessment of the overall health of the frame, providing an important basis for subsequent structural optimization and quality control.

[0040] Preferably, step S3 comprises the following steps:

[0041] Step S31: constructing a stress-life curve based on the long-period loading change data of the automobile seat cushion frame to generate a stress-life curve of the seat cushion frame;

[0042] Step S32: Performing skeleton life modeling on the seat cushion skeleton stress-life curve based on the seat cushion skeleton internal damage detection data to generate skeleton life modeling data; performing skeleton life influence correlation analysis on the seat cushion skeleton stress-life curve based on the skeleton life modeling data to generate skeleton life influence factors;

[0043] Step S33: predicting the life of the seat cushion frame based on the frame life influencing factors to generate frame life prediction data;

[0044] Step S34: Use the frame life prediction data to classify the fatigue damage area levels of the seat cushion frame internal damage detection data to generate the seat cushion frame fatigue damage area level data; use the seat cushion frame fatigue damage area level data to optimize the grade area skeleton material of the skeleton key parts screening data to generate the seat cushion frame material optimization data.

[0045] The present invention can reflect the fatigue life of the cushion frame under different stress levels in detail by constructing the stress-life curve of the cushion frame based on the long-cycle loading change data. The stress-life curve is the core tool in fatigue analysis, which provides an accurate theoretical basis for further evaluating the durability of the material and the service life of the frame. This curve can help engineers predict the life and failure mode of the frame under different loads and use conditions, thereby improving the reliability of product design. By applying internal damage detection data to the frame life modeling, a more accurate life prediction can be achieved. By combining the stress-life curve with the actual damage data, the frame life model can more accurately describe the impact on the frame during actual use. This modeling method helps understand the life characteristics of the frame under fatigue load and provides data support for improved design. By performing life impact correlation analysis on the frame life modeling data, the key factors affecting the frame life can be identified and quantified. For example, factors such as frame material, loading method, fatigue damage distribution, etc. will affect its service life. Through the analysis of these influencing factors, designers can be helped to identify the key factors affecting product durability at an early stage and take corresponding optimization measures. Life prediction based on the frame life influencing factors can effectively estimate the service life of the cushion frame. This prediction provides a scientific basis for product maintenance, replacement cycle determination, and quality control. Through this prediction, failure points and life critical values ​​can be identified in advance, thus avoiding unexpected failures of the frame during use.

[0046] Preferably, step S33 includes the following steps:

[0047] Step S331: quantifying the life damage of the seat cushion frame internal damage detection data according to the frame life influencing factor to generate seat cushion frame internal damage quantification data;

[0048] Step S332: Divide the seat cushion frame internal damage quantification data into data sets to generate a model training set and a model test set; perform model training on the model training set using a multi-layer feedforward neural network algorithm to generate a frame life prediction pre-model;

[0049] Step S333: Use the model test set to perform model optimization iteration on the skeleton life prediction pre-model to generate a skeleton life prediction model; import the seat cushion skeleton internal damage detection data into the skeleton life prediction model to perform skeleton life prediction and generate skeleton life prediction data.

[0050] The present invention can convert complex damage data into quantifiable indicators by quantifying the life damage of the internal damage detection data of the cushion frame according to the influencing factors of the frame life. This quantification process not only helps to identify fatigue damage inside the frame, but also accurately evaluates the impact of each damage point on the overall life. This is crucial for accurately predicting the service life of the frame, helps to discover potential safety hazards in advance, and provides data support for subsequent design optimization. By dividing the data set of the damage quantification data into a model training set and a model test set, the model overfitting can be effectively avoided and the generalization ability of the model can be ensured. The model training set is used to train the neural network, and the model test set is used to verify the model performance, thereby ensuring the accuracy and robustness of the frame life prediction model. By using a multi-layer feedforward neural network algorithm for model training, efficient modeling of complex nonlinear relationships can be achieved. The neural network can automatically identify and learn the complex relationship between damage data and life, thereby generating an accurate frame life prediction pre-model. This method can process a large amount of complex input data, extract potential laws, and provide strong support for life prediction. By optimizing and iterating the life prediction pre-model using the model test set, the accuracy and stability of the prediction model can be further improved. Each optimization iteration enables the model to gradually adjust parameters, reduce prediction errors, and improve the performance of the model in practical applications. The resulting skeleton life prediction model will have stronger prediction capabilities and higher accuracy. Importing the internal damage detection data of the cushion skeleton into the optimized skeleton life prediction model can achieve an accurate prediction of the skeleton life. This prediction not only helps designers understand the service life of the skeleton, but also provides valuable information about future damage or failure modes. This helps to carry out maintenance and replacement in advance, avoid sudden failures of the skeleton during use, and improve product safety and reliability.

[0051] Preferably, step S4 comprises the following steps:

[0052] Step S41: performing a cyclic frame fatigue test based on the seat cushion frame material optimization data, and synchronously recording the seat cushion frame material optimization data to obtain a seat cushion frame material optimization experimental data set;

[0053] Step S42: Screen the optimal automobile seat cushion frame material for the seat cushion frame material optimization experimental data set to generate optimal automobile seat cushion frame material data; perform frame life optimization visualization on the frame life prediction data based on the optimal automobile seat cushion frame material data, thereby generating an automobile seat cushion frame fatigue test report.

[0054] The present invention can verify the performance of the optimized material in actual use by performing a cyclic skeleton fatigue test based on the seat frame material optimization data. Synchronous recording of experimental data not only helps to evaluate the actual performance of different materials under fatigue loading, but also provides data support for further material improvement. The construction of the experimental data set provides a comprehensive basis for subsequent analysis and optimization. By screening the optimal car seat frame material for the experimental data set, the best material can be accurately selected from a large number of candidate materials. This screening process is based on a comprehensive evaluation of multiple factors such as fatigue performance, durability, strength and toughness to ensure that the final selected material has the best performance in terms of improving the life of the frame, reducing weight and reducing costs. By optimizing the skeleton life prediction data based on the optimal material data, the service life of the seat frame can be extended by optimizing the performance of the material. The choice of material has an important influence on the fatigue life, stiffness, wear resistance and other aspects of the skeleton. Through this optimization process, the stability and durability of the seat frame in long-term use can be ensured. By visually generating a test report for the fatigue test of the car seat frame by optimizing the skeleton life, the experimental data and optimization results can be clearly presented in the form of charts and data. This report not only provides engineers with comparative data before and after the seat frame optimization, but also provides clear support for decision-making. The report can include detailed information such as frame life, material properties, fatigue test curves, etc., which helps guide subsequent design and production.

[0055] In this specification, a car seat frame fatigue test system is provided, which is used to perform the above-mentioned car seat frame fatigue test method. The car seat frame fatigue test system includes:

[0056] The data acquisition module is used to obtain the information data of the automobile seat cushion frame sample; screen the key parts of the frame of the automobile seat cushion frame sample information data to obtain the key parts screening data of the frame; based on the key parts screening data of the frame, perform sensor data collection on the automobile seat cushion frame sample to obtain stress sensor collection data and deformation sensor collection data;

[0057] The test design module is used to design the loading mode of the automobile seat frame sample information data to generate the fatigue loading mode data of the automobile seat frame; perform cross-sectional scanning on the key parts screening data of the frame according to the fatigue loading mode data of the automobile seat frame to generate the cross-sectional view of the automobile seat frame; perform fatigue test frame damage detection on the cross-sectional view of the automobile seat frame to generate the internal damage detection data of the seat frame;

[0058] The life prediction module is used to model the life of the frame based on the long-term loading change data of the automobile seat frame and generate the frame life modeling data; to predict the life of the frame based on the internal damage detection data of the seat frame according to the frame life modeling data and generate the frame life prediction data; to optimize the frame material based on the internal damage detection data of the seat frame using the frame life prediction data and generate the seat frame material optimization data;

[0059] The material screening module is used to screen the optimal automobile seat cushion frame material based on the seat cushion frame material optimization data and generate the optimal automobile seat cushion frame material data; based on the optimal automobile seat cushion frame material data, the frame life prediction data is optimized and visualized, thereby generating a car seat cushion frame fatigue test report.

[0060] The beneficial effect of the present invention is that by screening the key parts of the information data of the car seat frame sample, the most important parts (such as support points, bearing points, etc.) can be accurately identified, providing a clear research focus for the subsequent stress and deformation analysis. The stress and deformation sensor data collection based on the screening data of the key parts of the skeleton provides accurate mechanical response data, providing real and fine input for fatigue performance analysis and structural optimization. Through scientific loading mode design, fatigue loading mode data is generated, which can simulate various working conditions that the car seat frame is subjected to in actual use, ensuring that the design meets the long-term high-intensity use requirements. Fatigue test detection of the cross-section of the skeleton part can timely identify the potential damage area inside the seat frame. This effectively avoids fatigue failure of the skeleton after long-term use and improves the safety and comfort of the seat. The skeleton life modeling generated based on long-term loading change data can not only accurately reflect the fatigue behavior of the skeleton under different working conditions, but also predict its long-term durability. Through the analysis of life prediction data, the fatigue life bottleneck can be identified in advance. Combining skeleton life prediction with internal damage detection further improves the accuracy of fatigue damage prediction, helps to optimize skeleton design, and prolongs the service life of the seat. By optimizing the skeleton material based on the life prediction data, the most suitable material can be selected according to different fatigue loading modes and operating conditions. This process helps to improve the performance of the material, reduce damage accumulation, and further extend the service life of the skeleton. By analyzing the material optimization data, the optimal seat cushion skeleton material can be finally screened out, which not only optimizes the strength and durability of the material, but also reduces the overall production cost and improves the cost performance of the product. Therefore, the present invention improves the accuracy and reliability of the fatigue test of the automobile seat cushion skeleton by accurately screening the key parts of the skeleton, designing the real loading mode, in-depth damage detection, life prediction and material optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of the steps of a fatigue test method for a car seat cushion frame;

[0062] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0063] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0064] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0065] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0066] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0067] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0068] To achieve this, please refer to Figures 1 to 3 , a fatigue test method for a car seat frame, the method comprising the following steps:

[0069] Step S1: Acquire information data of a car seat frame sample; screen key parts of the car seat frame sample information data to obtain key part screening data; perform sensor data collection on the car seat frame sample based on the key part screening data to obtain stress sensor collection data and deformation sensor collection data;

[0070] Step S2: Design a loading mode for the sample information data of the car seat frame to generate fatigue loading mode data of the car seat frame; perform cross-sectional scanning on the key parts screening data of the frame according to the fatigue loading mode data of the car seat frame to generate a cross-sectional view of the car seat frame; perform fatigue test frame damage detection on the cross-sectional view of the car seat frame to generate internal damage detection data of the seat frame;

[0071] Step S3: Based on the long-term loading change data of the automobile seat frame, frame life modeling is performed to generate frame life modeling data; based on the frame life modeling data, frame life prediction is performed on the seat frame internal damage detection data to generate frame life prediction data; based on the frame life prediction data, frame material optimization is performed on the seat frame internal damage detection data to generate seat frame material optimization data;

[0072] Step S4: Screen the optimal automobile seat cushion frame material based on the seat cushion frame material optimization data to generate the optimal automobile seat cushion frame material data; perform frame life optimization visualization on the frame life prediction data based on the optimal automobile seat cushion frame material data to generate an automobile seat cushion frame fatigue test report.

[0073] The present invention ensures that the test data is concentrated on the key areas that affect the durability of the frame by obtaining detailed information of the seat cushion frame sample and screening the key parts of the frame, avoiding unnecessary irrelevant data collection. The use of sensors to collect stress and deformation data provides more accurate and comprehensive stress distribution information, laying the foundation for subsequent fatigue analysis and damage assessment. A fatigue loading mode that is more in line with actual use is designed to simulate the dynamic load in real driving, so that the test is more in line with actual working conditions. Through cross-sectional scanning and fatigue test frame damage detection based on the loading mode, the micro-damage inside the frame can be captured in detail, helping to discover potential structural weaknesses and providing more accurate data for subsequent life prediction. Through the frame life modeling based on long-term loading change data, the fatigue life of the seat cushion frame can be predicted, and accurate life prediction data can be provided to designers. This step can not only detect fatigue failure in advance, but also optimize the frame material according to the life prediction results, improve the frame structure and materials, thereby improving the durability and comfort of the seat frame. Through the optimal material screening based on material optimization data, it is ensured that the most suitable material is used to enhance the fatigue tolerance of the seat cushion frame. Life optimization visualization can intuitively display the optimized skeleton life data, allowing designers to quickly understand the improvement effect and provide guidance for product development. The fatigue test report finally generated provides a reliable basis for subsequent product improvement, quality control and production. Therefore, the present invention improves the accuracy and reliability of the fatigue test of the automobile seat cushion skeleton by accurately screening the key parts of the skeleton, designing the real loading mode, in-depth damage detection, life prediction and material optimization.

[0074] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a process flow of a fatigue test method for a car seat frame of the present invention. In this example, the fatigue test method for a car seat frame includes the following steps:

[0075] Step S1: Acquire information data of a car seat frame sample; screen key parts of the car seat frame sample information data to obtain key part screening data; perform sensor data collection on the car seat frame sample based on the key part screening data to obtain stress sensor collection data and deformation sensor collection data;

[0076] In an embodiment of the present invention, sample information of a car seat frame is obtained. The sample information includes but is not limited to the physical properties of the seat frame such as size, shape, material type, and weight. High-precision three-dimensional scanning can be performed by 3D scanners, laser rangefinders, and other equipment to obtain detailed skeleton geometry data. A 3D model of the car seat frame is established based on the collected sample information. This model will include the various components of the frame and their connection relationships, and can display the deformation area of ​​the frame under different load conditions. Using the 3D model of the car seat frame, combined with engineering analysis (such as finite element analysis or static analysis), the area where the bearing stress or deformation is most concentrated is identified. The key parts are usually connection points, support points, bearing points, etc., especially the parts with greater force. According to the analysis results, the key parts are screened out from the 3D model of the car seat frame to generate "skeleton key part screening data". These data include information such as the position, size, and stress distribution of each key part. According to the screened key parts of the skeleton, a suitable sensor type is selected. For example, a stress sensor is used to measure the stress distribution borne by the skeleton, and a deformation sensor is used to measure the deformation of the skeleton. Sensors can be selected from piezoelectric stress sensors, strain gauges, fiber optic sensors, etc., and arranged at key locations. Stress sensors are installed at key locations to monitor stress changes in these locations under different load conditions in real time. The sensors will output stress data in real time and record it through a data acquisition system. Deformation sensors are installed at key locations to monitor the deformation of the frame under load. Deformation sensors include strain gauges or laser rangefinders, etc. These sensors will record the deformation amount, especially the degree of deformation of each key location. The data of the stress sensor and the deformation sensor are collected synchronously to generate stress sensor collection data and deformation sensor collection data. These data will provide a basis for subsequent performance analysis of the cushion frame.

[0077] Step S2: Design a loading mode for the sample information data of the car seat frame to generate fatigue loading mode data of the car seat frame; perform cross-sectional scanning on the key parts screening data of the frame according to the fatigue loading mode data of the car seat frame to generate a cross-sectional view of the car seat frame; perform fatigue test frame damage detection on the cross-sectional view of the car seat frame to generate internal damage detection data of the seat frame;

[0078] In an embodiment of the present invention, a loading mode is designed according to the stress conditions of the car seat frame in actual use. These stress scenarios include but are not limited to vertical pressure, lateral pressure, vibration, impact, etc., which the seat cushion is subjected to during driving and riding. Using calculation tools such as finite element analysis (FEA), the deformation and stress distribution of the skeleton under different loads are simulated according to the material properties and geometric shape of the skeleton sample. The loading mode should include multiple different working conditions, covering conventional use, high load, vibration frequency, long-term fatigue, etc. According to the analysis results, the loading mode is quantified into a series of load-time curves. Each curve represents a different loading condition, such as maximum load, minimum load and frequency. Based on the loading simulation results, the fatigue loading mode data of the car seat frame is generated. The data contains stress, vibration, pressure and other data under different working conditions, which will be used for further cross-sectional scanning and damage detection. According to the "skeleton key part screening data" generated in the previous step, the key parts of the skeleton that are subjected to greater stress or deformation are selected as the scanning area. These areas are usually parts where the force is concentrated, such as connection points, support points, etc. The car seat frame is scanned in sections using technologies such as CT scanning, laser scanning or X-ray scanning. This process can accurately measure the cross-sectional shape, stress distribution, internal structure, and cracks or defects of each key part. The scan results are processed and analyzed to generate a cross-sectional view of the car seat frame. The cross-sectional view can show the deformation, stress, and microstructural changes of the frame under different loading modes. Based on the fatigue loading mode data, a fatigue test is performed using a test bench. During the test, the stress changes of the seat frame in long-term use are simulated, and repeated load cycles are simulated by mechanical loading. This process can be performed in a variety of ways such as stretching, compression, bending, and torsion. During the test, the damage progress inside the frame is monitored in real time, including cracks, fatigue fractures, material aging, etc. Strain sensors, optical sensors, ultrasonic detection and other technologies are used to detect the damaged area inside the frame. The frame damage data obtained in the fatigue test is compared with the cross-sectional view to evaluate the distribution of frame damage under different loading modes. The changes in the cross-sectional view (such as cracks, deformation, fractures, etc.) are combined with the damage data to reveal the damage evolution process of the frame under fatigue load. According to the damage detection results, the "seat frame internal damage detection data" is generated. This data includes the fatigue damage degree of each key part, the damage type (such as cracks, fatigue fractures, material damage, etc.) and the location and scope of the damage.

[0079] Step S3: Based on the long-term loading change data of the automobile seat frame, frame life modeling is performed to generate frame life modeling data; based on the frame life modeling data, frame life prediction is performed on the seat frame internal damage detection data to generate frame life prediction data; based on the frame life prediction data, frame material optimization is performed on the seat frame internal damage detection data to generate seat frame material optimization data;

[0080] In an embodiment of the present invention, the stress, strain, vibration and other data of the frame are collected through the actual use environment of the car seat frame, especially the repeated loading conditions in a long period of time. By simulating the load changes of the seat frame in actual use, a long-period loading data set is constructed, and these change data can be collected through experimental testing, finite element simulation or long-term monitoring based on field use data. Based on the long-period loading change data, fatigue life theory (such as Miner's rule, Basquin's rule, etc.) is applied to model the frame life. By analyzing factors such as stress peak, stress range, and number of cycles in the loading data, a mathematical model is established to evaluate the fatigue life of the car seat frame under different working conditions. Common fatigue life models include SN curves (stress-life curves), strain life models, and damage accumulation models. Based on actual data, a linear or nonlinear fatigue life prediction model can be established to deduce the relationship between the accumulated damage and life of the frame under different working conditions. According to the fatigue life theory and model adopted, the long-period loading change data is input into the life prediction model to generate the frame life modeling data. These data contain the expected life, damage distribution, and failure occurrence time of the frame under different loading conditions. The "seat cushion frame internal damage detection data" generated in step S2 is used in combination with the frame life modeling data. The damage detection data contains information such as cracks, deformation, material fatigue, etc. of the frame under fatigue loading, while the life modeling data provides the estimated results of the frame life under different working conditions. Use the existing frame life modeling data to predict the remaining life of the frame. Combined with the damage detection data, the health status of the frame can be evaluated in real time, and the life prediction model can be corrected according to the damage that has occurred. For example, if microcracks or deformations have appeared in certain key parts of the frame, the life prediction should take into account the impact of these existing damages. The remaining service life of the frame in the current damage state is predicted through a cumulative damage model (such as Miner's law) or based on a modified SN curve, and the expected fatigue life is calculated in combination with the dynamic loading change data. Based on the combination of damage detection data and frame life modeling data, frame life prediction data is generated. The data includes information such as the remaining life of the frame in the current state, damage evolution trend, failure mode, etc. According to the skeleton life prediction data, set the goal of material optimization: that is, by selecting suitable materials or improving the performance of materials, extend the service life of the skeleton and minimize the damage of the skeleton under different loading conditions (such as crack extension, plastic deformation, etc.). According to the characteristics of fatigue performance, strength, durability, etc., choose suitable materials for skeleton optimization. You can choose to use high-strength alloys, composite materials, fatigue-resistant materials, etc., or use specially treated materials such as heat treatment and surface strengthening to improve the fatigue strength of the skeleton. Compare the performance of different materials and select the best material combination or improvement plan to ensure that the material can maintain good performance under long-term load.According to the life prediction data, the skeleton design is optimized, including material replacement, thickness change, surface treatment, etc., to improve the skeleton's fatigue resistance. According to the skeleton material optimization requirements, structural optimization is also required, such as adding support points, strengthening stress-bearing parts, improving connection methods, etc., in order to give full play to the advantages of the material and improve overall durability and service life.

[0081] Step S4: Screen the optimal automobile seat cushion frame material based on the seat cushion frame material optimization data to generate the optimal automobile seat cushion frame material data; perform frame life optimization visualization on the frame life prediction data based on the optimal automobile seat cushion frame material data to generate an automobile seat cushion frame fatigue test report.

[0082] In the embodiment of the present invention, all materials are analyzed based on the seat frame material optimization data generated in the previous step (such as fatigue strength, tensile strength, high temperature resistance, corrosion resistance, etc. of different materials). The analysis content includes the mechanical properties, durability, production cost, processing technology and compatibility with the existing car seat design of the material. A multi-objective optimization method is adopted to comprehensively consider multiple factors such as the strength, fatigue resistance, corrosion resistance, cost, etc. of the material to set the optimization target. The priorities of different indicators are sorted by weighted methods (such as AHP hierarchy analysis method, TOPSIS method, etc.) to achieve the optimal material selection. A set of scientific material screening standards is established according to requirements such as fatigue life, stress concentration resistance, rigidity and toughness of the material. For example, the performance of each material under different working conditions can be determined by calculating parameters such as the SN curve, fatigue strength coefficient, crack growth rate, etc. of the material. Based on the above standards, the most satisfactory material is selected from multiple candidate materials. This process includes a comprehensive comparison of traditional metal materials (such as steel, aluminum alloy) and advanced composite materials (such as carbon fiber, glass fiber composite materials). After selecting the optimal material, experimental verification is carried out. Through small-scale sample production and fatigue testing, the performance of the selected materials under actual loading conditions is verified. The optimal car seat frame material data and the frame life prediction data are integrated to generate a comprehensive frame life optimization model. This model combines the performance of the optimal material (such as fatigue resistance, strength, etc.) with the frame's workload, use environment and other factors to further optimize the frame's life. Use visualization tools (such as Matplotlib, ParaView, ANSYS Workbench, etc.) to visualize the optimized frame life data to show the impact of different materials on the frame life prediction. Design a fatigue test plan based on the optimized frame life prediction and material data. Determine the test loading mode, frequency, number of cycles, etc. to ensure that the test can simulate actual use. The test includes static loading test, dynamic cyclic loading test, vibration test, etc. Obtain actual data through laboratory fatigue tests to verify the impact of optimized materials on the frame life. Compare the stress, strain, deformation and damage data obtained during the test with the prediction model to verify the accuracy of the life prediction.

[0083] Preferably, step S1 comprises the following steps:

[0084] Step S11: Acquire automobile seat cushion frame sample information data;

[0085] Step S12: Screening key parts of the frame of the automobile seat cushion sample information data to obtain key parts screening data of the frame, wherein the key parts screening of the frame includes support beam screening and welding point screening;

[0086] Step S13: deploying stress sensors and deformation sensors on the car seat cushion skeleton sample based on the skeleton key part screening data, thereby obtaining stress sensor deployment data and deformation sensor deployment data;

[0087] Step S14: setting the collection time consistency for the stress sensor deployment data and the deformation sensor deployment data to obtain a standard sensor collection time interval; collecting data for the stress sensor deployment data and the deformation sensor deployment data based on the standard sensor collection time interval to obtain stress sensor collection data and deformation sensor collection data.

[0088] In an embodiment of the present invention, samples of automobile seat frame of different models, different materials and different structural forms are selected according to design requirements. Samples may include traditional metal frames, composite frames, etc. Ensure that the selected samples cover a variety of situations under the target working conditions (such as different loads, service life, fatigue test requirements, etc.). Use high-precision measuring equipment (such as 3D scanner, CT scanner, etc.) to obtain the 3D geometric data of the frame, and the data may include information such as the size, shape, structure, welding position, and support beam position of the frame. Obtain complete skeleton information through high-precision scanning or digitization technology. The acquired data is usually stored in the form of CAD model, point cloud data, or mesh model. The data should contain detailed information of each part of the frame and its key parts. Determine the criticality of the support beam part in the frame according to the function and stress of the frame. The support beam usually bears a large load and is susceptible to fatigue damage. Therefore, special attention should be paid to its structure, material, thickness, etc., and the key parts of the support beam can be determined by simulation or analysis. The welding point of the automobile seat frame is the location where the force is concentrated and the high-risk area for fatigue damage. Use finite element analysis (FEA) or other simulation methods to analyze the stress concentration of the welding part and screen out the welding points that have the greatest impact on fatigue life. Through the above screening, a set of key parts data is obtained, including support beams, welding points, etc. These data will serve as the basis for the subsequent deployment of stress sensors and deformation sensors. Stress sensors need to select stress sensors suitable for the material and working range of the car seat frame, usually strain gauge sensors, optical fiber sensors or micro pressure sensors. The sensor should have sufficient sensitivity and response speed to accurately measure the stress of key parts of the frame. Deformation sensors are mainly used to monitor the deformation of the frame under load, and select suitable sensors (such as capacitive and strain gauge sensors) to ensure the accuracy and stability of deformation monitoring. According to the key parts of the frame screened in step S12, stress sensors and deformation sensors are deployed in high stress areas such as support beams and welding points. Sensor deployment needs to fully consider the measurement range and installation position of the sensor to ensure that the stress and deformation of the frame can be monitored in real time. Install the sensor at the selected frame part. During the installation process, it is necessary to ensure that the sensor is in good contact with the frame surface to avoid measurement errors. For stress sensors, they need to be fixed by welding, pasting, punching, etc.; for deformation sensors, they need to be installed using adhesives or mechanical fixation. After installation, the sensor is calibrated to ensure its measurement accuracy. The calibration process involves comparing the sensor with a standard load to ensure that its measurement error is within an acceptable range. The sensor deployment data includes information such as sensor type, deployment location, sensor measurement range, sensitivity, etc. The deployment data can be stored as a sensor technical specification file as the basis for subsequent data collection and analysis.

[0089] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0090] Step S21: Designing a loading mode for the sample information data of the automobile seat cushion frame to generate fatigue loading mode data of the automobile seat cushion frame, wherein the fatigue loading mode data of the automobile seat cushion frame includes a static loading mode, a dynamic loading mode and a random loading mode;

[0091] Step S22: performing a long-period loading variation analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the static loading mode, the dynamic loading mode and the random loading mode, and generating long-period loading variation data of the automobile seat cushion frame;

[0092] Step S23: performing cross-sectional scanning of key parts of the frame according to the long-term loading variation data of the frame of the automobile seat cushion, and generating a cross-sectional view of the frame of the automobile seat cushion;

[0093] Step S24: performing image preprocessing on the cross-sectional view of the frame part of the automobile cushion to generate a cross-sectional view of the frame part of the standard automobile cushion, wherein the image preprocessing includes image denoising, curve filtering, image brightness enhancement and image grayscale value transformation; performing fatigue test frame damage detection on the cross-sectional view of the frame part of the standard automobile cushion to generate seat cushion frame internal damage detection data.

[0094] In an embodiment of the present invention, the stress condition of the car seat in a static state is simulated by a static loading mode. By analyzing the design and skeleton structure of the seat, the type of static load applied is determined, for example: uniformly distributed load (such as pressure on the seat cushion) or concentrated load (such as pressure at the connection between the backrest and the seat). The data of the static loading mode includes load size, loading direction, loading position, etc. The dynamic loading mode simulates the dynamic force on the car seat during driving, which is mainly caused by the acceleration, braking, turning and other operations of the vehicle. By simulating the vibration and impact force of the seat under different working conditions, the parameters of the dynamic loading mode are determined, such as vehicle speed, turning angle, impact force size, frequency, etc. The random load mode takes into account the complex loads that occur under different driving conditions, such as uneven road surface, vehicle load changes, etc. By using random signal generation technology (such as a pseudo-random number generator or a random signal based on experimental data), different types of loads on the car seat are simulated. The data of this mode usually includes load amplitude, frequency and random distribution characteristics. The data of the static loading mode, dynamic loading mode and random loading mode are integrated into a comprehensive loading mode, which contains detailed parameters and simulation scenarios in each mode. These data will serve as the basis for the fatigue test of the skeleton, providing parameters such as loading frequency and loading amplitude under different loading conditions. Collect the collected data from the stress sensor and deformation sensor in step S13, including stress change curves, deformation change curves, etc. These data usually involve the stress and deformation response of the skeleton under various loading conditions. In the actual working environment, the seat cushion skeleton undergoes long-term and periodic loading. Use finite element analysis (FEA) and fatigue analysis algorithms to analyze the collected stress and deformation data to simulate the deformation and stress distribution of the skeleton in multiple loading cycles. By comparing the sensor data under different loading modes, the stress and deformation change laws of the skeleton under long-term loading are analyzed, and the numerical data and trends of long-term loading changes are generated. Based on the data of the key parts of the skeleton (such as support beams, welding points, etc.) screened in step S12, these parts are scanned in detail. These parts are usually subjected to large stresses and are prone to fatigue damage under long-term loading. Use high-precision scanning equipment, such as industrial CT scanners, 3D laser scanners, etc., to perform cross-sectional scanning on key parts. Cross-sectional scanning can provide detailed geometric data of key parts of the skeleton and detect structural defects inside the parts. Generate cross-sectional images of key parts of the skeleton based on the scan data. These images contain information such as the cross section, size, and shape of the parts, which can help engineers analyze the stress distribution and damage points of the skeleton. Remove noise from the cross-sectional images of the skeleton parts to remove image noise introduced by scanning instrument errors or environmental interference. High-pass filtering, low-pass filtering, mean filtering and other methods can be used for denoising. Filter the edges and curves in the image to reduce the impact of errors and noise on the analysis results.Common curve filtering methods include Gaussian filtering, edge detection (such as Sobel filtering), etc. To enhance the brightness and contrast of the image, the key parts of the image are more prominent, which is convenient for subsequent analysis. Histogram equalization or adaptive brightness enhancement methods can be used. The gray value of the image is processed to adjust the brightness and contrast of the image to make it clearer, which is helpful for subsequent damage detection. Common gray value transformation methods include gamma transformation, logarithmic transformation, etc. Use image processing algorithms (such as texture analysis, morphological analysis, edge detection, deep learning, etc.) to detect potential damage to key parts of the skeleton. By analyzing the damage features such as cracks, corrosion, and welding point detachment in the skeleton cross-section, the degree of fatigue damage is evaluated. By detecting the damage features such as fracture and crack extension in the skeleton, the spatial position, area, morphology and other information of the damage are extracted. These features can be used for subsequent damage assessment and life prediction.

[0095] Preferably, step S21 includes:

[0096] Divide the sample information data of the automobile seat frame into loading modes to generate the automobile seat frame test loading mode, wherein the automobile seat frame test loading mode includes a static test loading mode, a dynamic test loading mode and a random test load mode;

[0097] Based on the static test loading mode, a hydraulic loading device is used to apply constant pressure to the car seat cushion frame, and the stress distribution of the key parts of the frame is measured by collecting data through stress sensors, so as to generate stress distribution measurement data of the key parts of the frame; the static load-bearing capacity of the stress distribution measurement data of the key parts of the frame is evaluated to generate a static loading mode;

[0098] Based on the dynamic test loading mode, the dynamic loading equipment is used to apply periodic loads to the car seat frame, and the stress sensor is used to collect data to analyze the stress-strain curve of the dynamic loading equipment to generate the stress-strain curve data of the car seat frame; the vibration working condition combination performance evaluation is performed on the stress-strain curve data of the car seat frame to generate the dynamic loading mode;

[0099] Based on the random test load mode, the load waveform of the automobile seat cushion frame sample information data is extracted to obtain the load waveform extraction data; the load waveform extraction data is subjected to random waveform generation by the loading device to obtain the random load waveform data; the frame performance is evaluated on the random load waveform data to generate the random load mode.

[0100] In the embodiment of the present invention, a hydraulic loading device is used to apply a uniform constant pressure based on a static test loading mode. During the loading process, the hydraulic device will apply a set pressure to simulate the situation where the car seat cushion bears the weight of the human body. The hydraulic loading device can provide accurate constant pressure to ensure the controllability of the loading conditions. During the loading process, a stress sensor is used to measure the stress distribution of key parts of the skeleton, especially in stress concentration parts such as support beams and welding points. The stress sensor will monitor and record the stress data of each point in real time. The stress distribution diagram of the skeleton can be drawn through the data obtained by the sensor to evaluate its stress condition. Based on the data collected by the sensor, stress distribution data of key parts of the skeleton are generated. These data will be used to evaluate the stress condition of the skeleton under static load and provide a basis for subsequent optimization design. Using the stress distribution measurement data, the static load-bearing capacity of the skeleton is evaluated. By calculating the maximum stress value of key parts of the skeleton (such as support beams, welding points, etc.), combined with the yield strength, tensile strength and other performance parameters of the material, the load-bearing capacity of the skeleton under static load is evaluated. According to the evaluation results, a static loading mode is generated, including the size of the static load, the loading position, the stress distribution of the skeleton, etc., to ensure that the skeleton can withstand the static load in actual use. Based on the dynamic test loading mode, a periodic load is applied to the frame of the car seat cushion through a dynamic loading device. This process simulates the situation in which the frame is subjected to periodic vibration due to factors such as uneven road surface, turning, and braking during the driving of the car. During the dynamic loading process, stress sensors and deformation sensors are used to collect stress and strain data of the frame. Through real-time monitoring of stress and strain signals, the stress-strain curve of the frame is drawn to reflect the mechanical response characteristics of the frame under dynamic loading. By analyzing the stress and strain data of the frame, complete stress-strain curve data is generated. These curves can reflect the elastic zone, yield zone, and failure zone of the frame under dynamic loading, providing data support for subsequent dynamic performance evaluation. Based on the stress-strain curve data in the dynamic loading mode, a combined performance evaluation of vibration conditions is performed. Considering the influence of different frequencies, amplitudes, and load cycles on the frame, the dynamic performance of the frame is evaluated by comprehensively analyzing the stress-strain response under each condition. Through the evaluation of the vibration condition, complete data of the dynamic loading mode is generated. These data will describe the mechanical behavior of the frame under dynamic loading, including indicators such as stress distribution, vibration response, and fatigue life. In the random test loading mode, data of different load waveforms are first extracted according to actual driving data or road conditions. These load waveforms reflect the stress of the seat frame under different dynamic conditions, such as acceleration, braking, and turning. The extracted load waveforms are processed and randomly generated by the loading device to obtain different random load waveforms. These waveforms simulate the random load conditions encountered by the car seat frame under complex driving conditions. The performance of the frame is evaluated using the random load waveform data.This includes simulating fatigue behavior and damage expansion during long-term use, and evaluating the fatigue life and durability of the skeleton. Based on the evaluation results of random load waveform data, random load pattern data are generated. These data will be used to further analyze the performance of the skeleton under complex working conditions and provide a basis for optimizing the design.

[0101] Preferably, step S22 includes the following steps:

[0102] Step S221: performing stress-deformation slow-changing trend analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the static loading mode to generate stress-deformation slow-changing trend data; extracting load stability parameters from the stress-deformation slow-changing trend data to obtain static mode time series change data;

[0103] Step S222: Calculate the stress-deformation phase difference of the stress sensor collected data and the deformation sensor collected data based on the dynamic loading mode to obtain stress-deformation phase difference data; extract energy dissipation index of the automobile seat cushion frame sample information data according to the stress-deformation phase difference data to obtain dynamic mode time series change data;

[0104] Step S223: performing stress-deformation random characteristic analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the random load mode to generate stress-phase change random characteristic data; extracting peak information from the stress-phase change random characteristic data to obtain random load mode time series change data;

[0105] Step S224: Integrate the static mode time series change data, the dynamic mode time series change data and the random load mode time series change data to generate the long-period loading change data of the automobile seat cushion frame.

[0106] In an embodiment of the present invention, the relationship between stress and deformation during loading is analyzed by using data collected by stress sensors and deformation sensors in a static loading mode. The stress-deformation curve reflects the deformation behavior of the skeleton material during static loading. The stress and deformation data are subjected to a slow-changing trend analysis. By continuously observing the data points, the changing trend of stress and deformation over time is identified, especially the slow change or plastic deformation characteristics of the material under long-term load. This analysis can reveal the stress-deformation response of the skeleton under long-term loading and help understand the fatigue behavior of the skeleton material. Based on the above analysis, stress-deformation slow-changing trend data are generated. These data can help evaluate the static deformation behavior of the skeleton during long-term use and provide a basis for design optimization. According to the stress-deformation slow-changing trend data, the stability parameters of the load are further extracted. These parameters include the stability of stress and deformation, the stress-deformation hysteresis during loading, etc., which reveal whether the skeleton material will have stability problems under long-term loading. By extracting the load stability parameters, the time series change data in the static mode are obtained. These data represent the stress and deformation change process of the skeleton under static loading over time, reflecting the stress stability of the skeleton during actual use. Based on the data collected by the stress sensor and deformation sensor in the dynamic loading mode, the phase difference between stress and deformation is analyzed. The stress and deformation data in the dynamic loading mode usually have a certain time delay, which is called the phase difference. By calculating the phase difference of the stress and deformation signals, the elasticity and hysteresis characteristics of the skeleton during the dynamic loading process can be understood. The phase difference of the stress and deformation data is calculated to obtain the stress-deformation phase difference data. These data reveal the response characteristics of the skeleton under dynamic loading, especially the behavior under vibration and cyclic loading. Based on the stress-deformation phase difference data, the energy dissipation characteristics of the skeleton are analyzed. In dynamic loading, the skeleton material will dissipate part of the energy due to internal friction. By calculating the energy loss in each loading cycle, the energy dissipation characteristics of the skeleton can be quantified. Combined with the stress-deformation phase difference data, the energy dissipation index is extracted to generate the dynamic mode time series change data. These data reflect the energy loss, damping characteristics and fatigue behavior of the skeleton under the dynamic loading mode. Based on the data collected by the stress sensor and deformation sensor in the random loading mode, the random characteristics of stress and deformation are analyzed. The random load mode simulates complex and unpredictable load conditions. By analyzing the random relationship between stress and deformation, the dynamic response of the skeleton under random load can be revealed. Using statistical methods such as autocorrelation analysis and frequency domain analysis, the random characteristics of stress and deformation data are analyzed to reveal the random fluctuations and correlations between stress and deformation. Based on the above analysis, the random characteristic data of stress-deformation are generated. These data show the random deformation behavior of the skeleton under complex and irregular loads. Peak extraction is performed on the random characteristic data of stress-deformation.Analyze the maximum stress and maximum deformation of the skeleton under random loads, and extract the peak information in the random waveform. These peak information reflects the performance of the skeleton under extreme loads. Based on the peak information, generate random load mode time series change data. These data are helpful to evaluate the durability and fatigue life of the skeleton under random complex loads. The time series change data under the above three modes are integrated to form a complete skeleton long-term loading change data set. This data set contains the stress, deformation and performance response information of the skeleton under different loading modes. Generate the final "automotive seat frame long-term loading change data" based on the integrated data. This data comprehensively reflects the behavior of the skeleton under long-term, dynamic and complex loads, and helps with fatigue analysis, performance evaluation and optimization design.

[0107] Preferably, the fatigue test skeleton damage detection of the cross-section of the standard automobile seat cushion skeleton part includes:

[0108] Performing part section area segmentation on the standard automobile seat frame part section diagram to generate a seat frame part core area segmentation diagram and a seat frame part edge area segmentation diagram;

[0109] Perform edge crack detection on the edge area segmentation map of the seat cushion frame to generate a crack edge image of the seat cushion frame; perform morphological marking on the crack edge image of the seat cushion frame to generate edge crack detection data of the seat cushion frame;

[0110] Perform hole identification on the core area segmentation map of the seat cushion frame to generate hole data of the core area of ​​the seat cushion frame; perform irregular hole marking on the core area segmentation map of the seat cushion frame based on the hole data of the core area of ​​the seat cushion frame to generate the core hole defect detection data of the automobile seat cushion frame;

[0111] The detection data of the edge crack of the automobile seat cushion frame and the core hole defect of the automobile seat cushion frame are integrated to obtain the internal damage detection data of the seat cushion frame.

[0112] In an embodiment of the present invention, the cross-sectional view of the frame of the automobile seat cushion is segmented by using an image processing algorithm, such as threshold segmentation or a segmentation method based on deep learning. The segmentation process divides the cross-sectional view into a core area and an edge area. The core area is usually the main structural part of the seat cushion frame, and the edge area contains cracks or other defects. A crack detection algorithm (such as Canny edge detection, Sobel operator or a crack detection model based on deep learning) is used to detect cracks in the edge area. The crack edge information in the image is extracted, and the morphology and distribution of the cracks are determined using image analysis techniques (such as edge tracking, fracture line calibration, etc.). Morphological operations such as expansion, corrosion, opening and closing operations, etc. are applied to the crack edge image to enhance the crack characteristics. The cracks are clearly marked by a morphological marking method to highlight the shape and position of the cracks. Based on image segmentation, the hole position in the core area is analyzed. Common methods include image thresholding, morphological analysis, etc. A connected region analysis algorithm (such as Flood-fill or connected component analysis) is used to identify the hole area and determine its morphology and size. For suspected hole areas, further hole feature analysis is performed, such as hole depth, width, shape, etc. Further morphological analysis is performed on the holes in the core area to determine whether they are regular circles or ellipses, or whether they have irregular edges. Use feature extraction algorithms to analyze the shape, area, boundary complexity, etc. of the holes to identify irregular holes. Mark irregular holes to emphasize the impact of these holes on the skeleton structure. Integrate crack edge detection data with core hole defect data. Integration methods include visualizing damage information on different parts of the skeleton through image synthesis, annotation merging, and other methods. Through data fusion, a comprehensive skeleton internal damage detection map is generated. This map summarizes the distribution of edge cracks, core holes, and other defects, providing a comprehensive structural damage view for subsequent fatigue analysis.

[0113] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0114] Step S31: constructing a stress-life curve based on the long-period loading change data of the automobile seat cushion frame to generate a stress-life curve of the seat cushion frame;

[0115] Step S32: Performing skeleton life modeling on the seat cushion skeleton stress-life curve based on the seat cushion skeleton internal damage detection data to generate skeleton life modeling data; performing skeleton life influence correlation analysis on the seat cushion skeleton stress-life curve based on the skeleton life modeling data to generate skeleton life influence factors;

[0116] Step S33: predicting the life of the seat cushion frame based on the frame life influencing factors to generate frame life prediction data;

[0117] Step S34: Use the frame life prediction data to classify the fatigue damage area levels of the seat cushion frame internal damage detection data to generate the seat cushion frame fatigue damage area level data; use the seat cushion frame fatigue damage area level data to optimize the grade area skeleton material of the skeleton key parts screening data to generate the seat cushion frame material optimization data.

[0118] In the embodiment of the present invention, by using the common SN curve (stress-life curve) method, the stress level under different loading conditions is first extracted according to the long-cycle loading data. Under different stress levels, the fatigue life (in cycles) of the cushion frame is inferred using fatigue test or experimental data. Methods such as mine damage theory and Ramel curve can be used to describe the fatigue damage behavior of materials under different load conditions. The curve obtained by fitting reflects the fatigue life of the frame under different stress levels. The internal damage detection data obtained from crack detection, hole analysis, stress distribution, etc. are combined with the stress-life curve to perform stress-damage-life modeling. By analyzing the damage of the skeleton part (for example, crack depth, hole area, crack propagation rate, etc.), adjusting the stress-life curve, and establishing a more accurate fatigue life prediction model, finite element analysis (FEA) can be used to simulate the stress distribution of the cushion frame under actual working conditions, and life modeling is performed in combination with experimental data. The fatigue limit, fatigue strength and fatigue toughness of the material are introduced to adjust the model parameters. According to the skeleton life modeling data, the key factors affecting the skeleton life, such as material properties, loading mode, structural defects, process, etc., are analyzed. Use multiple regression analysis, principal component analysis (PCA) or other data mining techniques to identify influencing factors with a high correlation with fatigue life. For example, analyze the impact of crack size, number of holes, and stress concentration points on fatigue life. Analyze the skeleton structure of different parts separately to reveal the contribution of each part to the overall life of the skeleton. Use the skeleton life prediction data to classify the internal damage area of ​​the cushion skeleton, including light damage area, moderate damage area, severe damage area, etc. Classify based on indicators such as damage depth, location, crack growth trend, and material properties. Use image processing technology or finite element analysis simulation to evaluate the impact of the damage area on the skeleton life and determine the degree of damage in different areas. According to the fatigue damage area grade data, optimize the materials of key parts of the skeleton. Focus on high damage level areas and select materials that are more resistant to fatigue and crack growth. High-strength alloys, composite materials, or advanced plastics can be selected to improve the material properties of the damaged area. By optimizing the fatigue performance of the material, the overall life of the skeleton is improved, especially in high-load or high-stress areas. Evaluate the fatigue limit, crack growth resistance, corrosion resistance, etc. of different materials, and optimize the material configuration of key parts.

[0119] Preferably, step S33 includes the following steps:

[0120] Step S331: quantifying the life damage of the seat cushion frame internal damage detection data according to the frame life influencing factor to generate seat cushion frame internal damage quantification data;

[0121] Step S332: Divide the seat cushion frame internal damage quantification data into data sets to generate a model training set and a model test set; perform model training on the model training set using a multi-layer feedforward neural network algorithm to generate a frame life prediction pre-model;

[0122] Step S333: Use the model test set to perform model optimization iteration on the skeleton life prediction pre-model to generate a skeleton life prediction model; import the seat cushion skeleton internal damage detection data into the skeleton life prediction model to perform skeleton life prediction and generate skeleton life prediction data.

[0123] In an embodiment of the present invention, the internal damage detection data is quantified by combining the factors affecting the life of the skeleton (such as loading mode, material fatigue limit, damage location, etc.). The main steps include: extracting key features from internal damage data such as crack size, crack depth, hole area, crack propagation speed, etc. The influence of each damage is evaluated according to the weight of the influencing factor (such as material type, damage area location, damage level, etc.). Based on the type and degree of damage, the skeleton life is corrected to obtain specific damage quantification data. The damage quantification data is randomly divided into a training set and a test set. Usually 70%-80% of the data is used for training, and the remaining data is used for testing. Ensure the diversity and representativeness of the data when dividing. Standardize and normalize the damage quantification data to ensure that the data is at the same scale to avoid the dimensional differences of different features affecting the model training. Select an appropriate multi-layer feedforward neural network architecture, including multiple hidden layers, to capture complex nonlinear relationships. Each hidden layer uses a ReLU activation function, and the output layer uses a linear activation function to adapt to the life prediction task. The input layer includes various features of the cushion skeleton damage quantification data (such as crack size, hole depth, etc.). The output layer provides a skeleton life prediction value. The back propagation algorithm (BP algorithm) and gradient descent method are used to optimize the network parameters, minimize the prediction error, and train a preliminary skeleton life prediction pre-model. The trained skeleton life prediction pre-model is optimized using the model test set. It mainly includes: comparing the model prediction results with the actual measured life data (such as test life) to evaluate the accuracy and precision of the model. According to the test set results, the hyperparameters of the neural network (such as learning rate, number of layers, number of neurons in each layer, etc.) and regularization parameters are adjusted to iteratively optimize the model to reduce overfitting and underfitting. Use a suitable loss function (such as mean square error) for optimization to ensure that the model maintains a high generalization ability during training and testing. The optimized skeleton life prediction model is applied to the internal damage detection data of the cushion skeleton. Through the input of the model, the remaining life of the cushion skeleton under different damage states is predicted. The prediction results may include: fatigue life prediction of each key part, expected life of the overall skeleton, potential failure time, etc.

[0124] Preferably, step S4 comprises the following steps:

[0125] Step S41: performing a cyclic frame fatigue test based on the seat cushion frame material optimization data, and synchronously recording the seat cushion frame material optimization data to obtain a seat cushion frame material optimization experimental data set;

[0126] Step S42: Screen the optimal automobile seat cushion frame material for the seat cushion frame material optimization experimental data set to generate optimal automobile seat cushion frame material data; perform frame life optimization visualization on the frame life prediction data based on the optimal automobile seat cushion frame material data, thereby generating an automobile seat cushion frame fatigue test report.

[0127] In an embodiment of the present invention, a cyclic loading experiment is performed using a fatigue testing machine to simulate fatigue load conditions in a real working environment. Loading conditions include static load, dynamic load, periodic variable load, etc. Different material samples are selected for experiments, and different loading modes (such as constant load, pulse load, etc.) are set according to the experimental plan. In each loading stage, the stress, deformation, crack extension and other data of the material are recorded. The material is loaded periodically, and the stress-strain curve, crack extension, deformation characteristics, etc. at each stage of the experiment are recorded. During the fatigue experiment, the key information such as stress, deformation, and cracks of the material are monitored in real time and synchronously recorded by stress sensors and deformation sensors. The experimental data includes stress response data, deformation response data, crack extension data, and loading and unloading conditions of each cycle of the experiment. Based on fatigue test data, the following screening criteria are used to evaluate materials: fatigue limit: fatigue limit of each material (i.e., the maximum cyclic stress that the material can withstand), fatigue life: the number of cycles that each material can withstand under specified loading conditions, crack growth rate: the rate of crack growth of the material under a certain load, elastic modulus and plastic properties: elastic and plastic behavior of the material under fatigue load, temperature and environmental influence: consider the fatigue performance of the material under different temperatures, humidity, and corrosive environments. Use statistical methods (such as regression analysis and variance analysis) to evaluate the experimental data in multiple dimensions, and select the optimal material based on the comprehensive fatigue performance of the material. Select the optimal material that meets the requirements of long fatigue life, low crack growth rate, and excellent elastic and plastic properties, generate the optimal car seat frame material data, and list the specific performance data and applicable scenarios of different materials. Based on the optimal material data and frame life prediction data, generate a visual chart of frame life optimization through data visualization tools (such as Matplotlib, Tableau, etc.). Summarize the material screening results, fatigue test data, life optimization analysis, etc., and generate a comprehensive car seat frame fatigue test report.

[0128] In this specification, a car seat frame fatigue test system is provided, which is used to perform the above-mentioned car seat frame fatigue test method. The car seat frame fatigue test system includes:

[0129] The data acquisition module is used to obtain the information data of the automobile seat cushion frame sample; screen the key parts of the frame of the automobile seat cushion frame sample information data to obtain the key parts screening data of the frame; based on the key parts screening data of the frame, perform sensor data collection on the automobile seat cushion frame sample to obtain stress sensor collection data and deformation sensor collection data;

[0130] The test design module is used to design the loading mode of the automobile seat frame sample information data to generate the fatigue loading mode data of the automobile seat frame; perform cross-sectional scanning on the key parts screening data of the frame according to the fatigue loading mode data of the automobile seat frame to generate the cross-sectional view of the automobile seat frame; perform fatigue test frame damage detection on the cross-sectional view of the automobile seat frame to generate the internal damage detection data of the seat frame;

[0131] The life prediction module is used to model the life of the frame based on the long-term loading change data of the automobile seat frame and generate the frame life modeling data; to predict the life of the frame based on the internal damage detection data of the seat frame according to the frame life modeling data and generate the frame life prediction data; to optimize the frame material based on the internal damage detection data of the seat frame using the frame life prediction data and generate the seat frame material optimization data;

[0132] The material screening module is used to screen the optimal automobile seat cushion frame material based on the seat cushion frame material optimization data and generate the optimal automobile seat cushion frame material data; based on the optimal automobile seat cushion frame material data, the frame life prediction data is optimized and visualized, thereby generating a car seat cushion frame fatigue test report.

[0133] The beneficial effect of the present invention is that by screening the key parts of the information data of the car seat frame sample, the most important parts (such as support points, bearing points, etc.) can be accurately identified, providing a clear research focus for the subsequent stress and deformation analysis. The stress and deformation sensor data collection based on the screening data of the key parts of the skeleton provides accurate mechanical response data, providing real and fine input for fatigue performance analysis and structural optimization. Through scientific loading mode design, fatigue loading mode data is generated, which can simulate various working conditions that the car seat frame is subjected to in actual use, ensuring that the design meets the long-term high-intensity use requirements. Fatigue test detection of the cross-section of the skeleton part can timely identify the potential damage area inside the seat frame. This effectively avoids fatigue failure of the skeleton after long-term use and improves the safety and comfort of the seat. The skeleton life modeling generated based on long-term loading change data can not only accurately reflect the fatigue behavior of the skeleton under different working conditions, but also predict its long-term durability. Through the analysis of life prediction data, the fatigue life bottleneck can be identified in advance. Combining skeleton life prediction with internal damage detection further improves the accuracy of fatigue damage prediction, helps to optimize skeleton design, and prolongs the service life of the seat. By optimizing the skeleton material based on the life prediction data, the most suitable material can be selected according to different fatigue loading modes and operating conditions. This process helps to improve the performance of the material, reduce damage accumulation, and further extend the service life of the skeleton. By analyzing the material optimization data, the optimal seat cushion skeleton material can be finally screened out, which not only optimizes the strength and durability of the material, but also reduces the overall production cost and improves the cost performance of the product. Therefore, the present invention improves the accuracy and reliability of the fatigue test of the automobile seat cushion skeleton by accurately screening the key parts of the skeleton, designing the real loading mode, in-depth damage detection, life prediction and material optimization.

[0134] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0135] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A fatigue test method for a car seat frame, characterized in that: The following steps are involved: Step S1: obtaining information data of a car seat frame sample; screening key parts of the car seat frame sample information data to obtain screening data of key parts of the frame; Based on the screening data of key parts of the skeleton, sensor data of the car seat frame sample is collected to obtain stress sensor data and deformation sensor data; Step S2: Designing a loading mode for the automobile seat cushion frame sample information data to generate fatigue loading mode data for the automobile seat cushion frame; According to the fatigue loading mode data of the car seat frame, the key parts of the frame are screened for cross-section scanning to generate a cross-section diagram of the car seat frame; fatigue test the cross-section diagram of the car seat frame to detect the damage of the frame and generate the internal damage detection data of the seat frame; Step S3: Based on the long-term loading change data of the automobile seat frame, frame life modeling is performed to generate frame life modeling data; based on the frame life modeling data, frame life prediction is performed on the seat frame internal damage detection data to generate frame life prediction data; based on the frame life prediction data, frame material optimization is performed on the seat frame internal damage detection data to generate seat frame material optimization data; Step S4: Screening the optimal automobile seat cushion frame material based on the seat cushion frame material optimization data to generate optimal automobile seat cushion frame material data; Based on the optimal car seat frame material data, the frame life prediction data is optimized and visualized to generate a car seat frame fatigue test report.

2. The automobile seat cushion frame fatigue test method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire automobile seat cushion frame sample information data; Step S12: Screening key parts of the frame of the automobile seat cushion sample information data to obtain key parts screening data of the frame, wherein the key parts screening of the frame includes support beam screening and welding point screening; Step S13: deploying stress sensors and deformation sensors on the car seat cushion skeleton sample based on the skeleton key part screening data, thereby obtaining stress sensor deployment data and deformation sensor deployment data; Step S14: setting the collection time consistency for the stress sensor deployment data and the deformation sensor deployment data to obtain a standard sensor collection time interval; collecting data for the stress sensor deployment data and the deformation sensor deployment data based on the standard sensor collection time interval to obtain stress sensor collection data and deformation sensor collection data.

3. The automobile seat cushion frame fatigue test method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Designing a loading mode for the sample information data of the automobile seat cushion frame to generate fatigue loading mode data of the automobile seat cushion frame, wherein the fatigue loading mode data of the automobile seat cushion frame includes a static loading mode, a dynamic loading mode and a random loading mode; Step S22: performing a long-period loading variation analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the static loading mode, the dynamic loading mode and the random loading mode, and generating long-period loading variation data of the automobile seat cushion frame; Step S23: performing cross-sectional scanning of key parts of the frame according to the long-term loading variation data of the frame of the automobile seat cushion, and generating a cross-sectional view of the frame of the automobile seat cushion; Step S24: performing image preprocessing on the cross-sectional view of the frame part of the automobile cushion to generate a cross-sectional view of the frame part of the standard automobile cushion, wherein the image preprocessing includes image denoising, curve filtering, image brightness enhancement and image grayscale value transformation; performing fatigue test frame damage detection on the cross-sectional view of the frame part of the standard automobile cushion to generate seat cushion frame internal damage detection data.

4. The automobile seat cushion frame fatigue test method according to claim 3, characterized in that: Step S21 includes: Divide the sample information data of the automobile seat frame into loading modes to generate the automobile seat frame test loading mode, wherein the automobile seat frame test loading mode includes a static test loading mode, a dynamic test loading mode and a random test load mode; Based on the static test loading mode, a hydraulic loading device is used to apply constant pressure to the car seat cushion frame, and the stress distribution of the key parts of the frame is measured by collecting data through stress sensors, so as to generate stress distribution measurement data of the key parts of the frame; the static load-bearing capacity of the stress distribution measurement data of the key parts of the frame is evaluated to generate a static loading mode; Based on the dynamic test loading mode, the dynamic loading equipment is used to apply periodic loads to the car seat frame, and the stress sensor is used to collect data to analyze the stress-strain curve of the dynamic loading equipment to generate the stress-strain curve data of the car seat frame; the vibration working condition combination performance evaluation is performed on the stress-strain curve data of the car seat frame to generate the dynamic loading mode; Based on the random test load mode, the load waveform of the automobile seat cushion frame sample information data is extracted to obtain the load waveform extraction data; the load waveform extraction data is subjected to random waveform generation by the loading device to obtain the random load waveform data; the frame performance is evaluated on the random load waveform data to generate the random load mode.

5. The automobile seat cushion frame fatigue test method according to claim 3, characterized in that: Step S22 includes the following steps: Step S221: performing stress-deformation slow-changing trend analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the static loading mode to generate stress-deformation slow-changing trend data; extracting load stability parameters from the stress-deformation slow-changing trend data to obtain static mode time series change data; Step S222: Calculate the stress-deformation phase difference of the stress sensor collected data and the deformation sensor collected data based on the dynamic loading mode to obtain stress-deformation phase difference data; extract energy dissipation index of the automobile seat cushion frame sample information data according to the stress-deformation phase difference data to obtain dynamic mode time series change data; Step S223: performing stress-deformation random characteristic analysis on the data collected by the stress sensor and the data collected by the deformation sensor based on the random load mode to generate stress-phase change random characteristic data; extracting peak information from the stress-phase change random characteristic data to obtain random load mode time series change data; Step S224: Integrate the static mode time series change data, the dynamic mode time series change data and the random load mode time series change data to generate the long-period loading change data of the automobile seat cushion frame.

6. The automobile seat cushion frame fatigue test method according to claim 3, characterized in that: Fatigue test of standard car seat frame section diagram, frame damage detection includes: Performing part section area segmentation on the standard automobile seat frame part section diagram to generate a seat frame part core area segmentation diagram and a seat frame part edge area segmentation diagram; Perform edge crack detection on the edge area segmentation map of the seat cushion frame to generate a crack edge image of the seat cushion frame; perform morphological marking on the crack edge image of the seat cushion frame to generate edge crack detection data of the seat cushion frame; Perform hole identification on the core area segmentation map of the seat cushion frame to generate hole data of the core area of ​​the seat cushion frame; perform irregular hole marking on the core area segmentation map of the seat cushion frame based on the hole data of the core area of ​​the seat cushion frame, thereby generating core hole defect detection data of the automobile seat cushion frame; The detection data of the edge crack of the automobile seat cushion frame and the detection data of the core hole defect of the automobile seat cushion frame are integrated to obtain the internal damage detection data of the seat cushion frame.

7. The automobile seat cushion frame fatigue test method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: constructing a stress-life curve based on the long-period loading change data of the automobile seat cushion frame to generate a stress-life curve of the seat cushion frame; Step S32: Performing skeleton life modeling on the seat cushion skeleton stress-life curve based on the seat cushion skeleton internal damage detection data to generate skeleton life modeling data; performing skeleton life influence correlation analysis on the seat cushion skeleton stress-life curve based on the skeleton life modeling data to generate skeleton life influence factors; Step S33: predicting the life of the seat cushion frame based on the frame life influencing factors to generate frame life prediction data; Step S34: Use the frame life prediction data to classify the fatigue damage area levels of the seat cushion frame internal damage detection data to generate the seat cushion frame fatigue damage area level data; use the seat cushion frame fatigue damage area level data to optimize the grade area skeleton material of the skeleton key parts screening data to generate the seat cushion frame material optimization data.

8. The automobile seat cushion frame fatigue test method according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: quantifying the life damage of the seat cushion frame internal damage detection data according to the frame life influencing factor to generate seat cushion frame internal damage quantification data; Step S332: Divide the seat cushion frame internal damage quantification data into data sets to generate a model training set and a model test set; perform model training on the model training set using a multi-layer feedforward neural network algorithm to generate a frame life prediction pre-model; Step S333: Use the model test set to perform model optimization iteration on the skeleton life prediction pre-model to generate a skeleton life prediction model; import the seat cushion skeleton internal damage detection data into the skeleton life prediction model to perform skeleton life prediction and generate skeleton life prediction data.

9. The automobile seat cushion frame fatigue test method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a cyclic frame fatigue test based on the seat cushion frame material optimization data, and synchronously recording the seat cushion frame material optimization data to obtain a seat cushion frame material optimization experimental data set; Step S42: Screen the optimal automobile seat cushion frame material for the seat cushion frame material optimization experimental data set to generate optimal automobile seat cushion frame material data; perform frame life optimization visualization on the frame life prediction data based on the optimal automobile seat cushion frame material data, thereby generating an automobile seat cushion frame fatigue test report.

10. A car seat frame fatigue test system, characterized in that: Used to perform the automobile seat cushion frame fatigue test method as claimed in claim 1, the automobile seat cushion frame fatigue test system comprises: The data acquisition module is used to obtain the information data of the automobile seat cushion frame sample; screen the key parts of the frame of the automobile seat cushion frame sample information data to obtain the key parts screening data of the frame; based on the key parts screening data of the frame, perform sensor data collection on the automobile seat cushion frame sample to obtain stress sensor collection data and deformation sensor collection data; The test design module is used to design the loading mode of the automobile seat frame sample information data to generate the fatigue loading mode data of the automobile seat frame; perform cross-sectional scanning on the key parts screening data of the frame according to the fatigue loading mode data of the automobile seat frame to generate the cross-sectional view of the automobile seat frame; perform fatigue test frame damage detection on the cross-sectional view of the automobile seat frame to generate the internal damage detection data of the seat frame; The life prediction module is used to model the life of the frame based on the long-term loading change data of the automobile seat frame and generate the frame life modeling data; to predict the life of the frame based on the internal damage detection data of the seat frame according to the frame life modeling data and generate the frame life prediction data; to optimize the frame material based on the internal damage detection data of the seat frame using the frame life prediction data and generate the seat frame material optimization data; The material screening module is used to screen the optimal automobile seat cushion frame material based on the seat cushion frame material optimization data and generate the optimal automobile seat cushion frame material data; based on the optimal automobile seat cushion frame material data, the frame life prediction data is optimized and visualized, thereby generating a car seat cushion frame fatigue test report.

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