Small electric vehicle body structure lightweight design based on nondestructive testing

Abstract

A small electric vehicle body structure lightweight design method based on nondestructive testing comprises the steps that firstly, lightweight design is conducted on a vehicle frame, and the overall structure is optimized; in the manufacturing process, a nondestructive testing technology is applied, equipment is installed to comprehensively detect the frame, and internal defects and discontinuous positions are found in time. The tiny defects are identified through analysis software, and a basis is provided for optimization design. And according to the detection result, the frame structure performance is evaluated, the defect part is repaired or improved, and the light weight and the safety are improved. And then repairing and improving measures are carried out to ensure that the material performance meets the requirements. Nondestructive testing is carried out again after repairing, whether defects are solved or not is verified, and the quality of the frame is guaranteed. And finally, integrating the data and feeding back the data to a design database to provide reference for subsequent design and manufacturing.

Description

Technical Field

[0001] The invention relates to the field of automobile body structures, and in particular to a lightweight design of a small electric vehicle body structure based on non-destructive testing. Background Art

[0002] With the global emphasis on environmental protection and sustainable development, small electric vehicles have attracted much attention due to their advantages of energy saving and environmental protection. However, electric vehicles face some challenges in their development, among which the performance and safety of the frame are key issues.

[0003] Traditional frame design usually focuses more on strength and stability, and lacks consideration for lightweighting, which leads to a large weight of the whole vehicle, which not only affects the driving range, but also reduces the performance of the vehicle. During the manufacturing process, due to the relatively backward process level and detection methods, defects and discontinuities inside the frame are often difficult to be discovered in time. These potential problems may gradually be exposed during the use of the vehicle, thus causing safety hazards. In addition, the existing frame structure performance evaluation methods are not accurate and comprehensive enough, which makes the repair and improvement measures lack of pertinence and cannot effectively improve the lightweight degree and safety of the frame. At the same time, the data integration and feedback mechanism is not perfect, resulting in the inability to fully utilize the lessons learned in the design and manufacturing process, making it difficult to achieve continuous improvement and optimization. These defects seriously restrict the improvement of the performance and safety of small electric vehicle frames, making electric vehicles face many challenges in market competition. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a lightweight design of a small electric vehicle body structure based on non-destructive testing, and the specific steps are as follows, which are characterized by:

[0005] Step 1: Lightweight design of the frame of a small electric vehicle;

[0006] Step 2: Introduce non-destructive testing technology. During the manufacturing process of small electric vehicle frames, install non-destructive testing equipment to conduct comprehensive testing of frame materials to ensure that internal defects and structural discontinuities can be discovered in a timely manner;

[0007] Step 3: Data analysis and defect identification: using the data obtained by non-destructive testing technology, the analysis software identifies the tiny defects inside the frame, providing a basis for the optimal design of the frame;

[0008] Step 4: Frame evaluation and optimization: Based on the results of nondestructive testing, the structural performance of the frame is evaluated, and the defects found are repaired or improved to improve the lightweight effect and safety of the frame.

[0009] Step 5: Implement repair and improvement measures, make necessary repairs and design adjustments to the frame based on the evaluation results to ensure that the material performance of the frame fully meets the design requirements;

[0010] Step 6: Re-inspection and verification: After the repair and improvement measures are implemented, non-destructive testing is performed again to verify whether the frame defects have been effectively resolved to ensure the quality of the frame;

[0011] Step 7: Data integration and feedback: Integrate the nondestructive testing data, evaluation results and repair records into the frame design database to provide feedback and reference for subsequent frame design and manufacturing.

[0012] The invention is based on the lightweight design of the body structure of a small electric vehicle for non-destructive testing, and the beneficial effects are:

[0013] 1. The present invention uses non-destructive testing technology to timely discover defects and discontinuities inside the frame, and repair and improve them to ensure that the material performance of the frame meets the design requirements, thereby improving the quality and safety of the frame;

[0014] 2. The present invention uses data analysis and defect identification to provide a basis for the optimal design of the frame, and can carry out targeted repairs and design improvements to improve the lightweight effect and safety of the frame, making the frame design more scientific and reasonable;

[0015] 3. The present invention integrates nondestructive testing data, evaluation results and repair records into a database, providing feedback and reference for subsequent frame design and manufacturing, which helps to continuously summarize experience and lessons, achieve continuous improvement, and improve the efficiency and quality of the entire production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the present invention.

[0017] Figure 2 It is a schematic diagram of the optimization analysis of the vehicle connection structure of the present invention.

[0018] Figure 3 It is a cross-sectional view of the steel pipe of the present invention.

[0019] Figure 4 It is a schematic diagram of the frame optimization of the present invention.

[0020] Figure 5 This is the equivalent displacement distribution diagram of the frame structure of the present invention.

[0021] Figure 6 It is the equivalent stress distribution diagram of the frame structure of the present invention. DETAILED DESCRIPTION

[0022] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0023] The present invention proposes a lightweight design of a small electric vehicle body structure based on non-destructive testing. The invention flow chart is as follows: Figure 1 As shown, the steps of the present invention are described in detail below.

[0024] The present invention classifies the two longitudinal beams (①, ②) at the rear of the frame as the secondary main load-bearing area, and the two supporting ribs and longitudinal beams in the middle of the frame and the two cross beams (③, ④, ⑤, ⑥, ⑦) at the rear of the frame as the non-main load-bearing area. Figure 2 shown.

[0025] For the secondary load-bearing area, i.e., rectangular steel tubes ①, ②, and ③, because the location of the structure will be subject to certain loads and sufficient requirements must be met in terms of stiffness and strength, the original 50mm×30mm×6mm rectangular steel tubes are replaced with Φ30mm×5mm round steel tubes. For the non-primary load-bearing area, i.e., rectangular steel tubes ④ and ⑤, because the frequency of being subjected to loads is relatively low and the reserve strength and stiffness are relatively high, 30mm×30mm×6mm rectangular steel tubes are used instead. The other parts remain unchanged, and the specific pipe shapes are as follows: Figure 3 The frame structure optimization results are shown in Figure 4 As shown, the frame material is Q345 steel and the frame mass is 86.864kg, which is 10.414kg less than 97.278kg before optimization, i.e., a weight reduction of 10.71%.

[0026] Under static or uniform speed conditions, the maximum deformation of the optimized frame when subjected to force is 5.9954mm, and the maximum stress after optimization is 90.260MPa. Figure 5 , 6 Compared with the optimization, the overall force of the frame is more uniform. The optimization result is ideal. The data summary and comparison of the frame static analysis are shown in Tables 1 and 2.

[0027] Table 1 Comparison of frame deformation before and after optimization

[0028]

[0029] Table 2 Comparison of stress before and after frame optimization

[0030]

[0031] The modal analysis of the optimized frame is still carried out on its sixth-order natural frequency and vibration mode, and the results show that there is no overlap between the natural frequency distribution and the main excitation of the road surface, and no resonance will be formed. The comparison of the natural frequency before and after the modal analysis is shown in Table 3:

[0032] Table 3 Comparison of natural frequencies before and after frame optimization

[0033]

[0034] Introduce nondestructive testing technology in structural lightweight design, integrate nondestructive testing data, evaluation results and repair records into the frame design database, and provide feedback and reference for subsequent frame design and manufacturing. The specific steps are as follows:

[0035] Step 1: Introduce non-destructive testing technology. During the manufacturing process of small electric vehicle frames, install non-destructive testing equipment to conduct comprehensive testing of frame materials to ensure that internal defects and structural discontinuities can be discovered in a timely manner;

[0036] Step 1.1, install ultrasonic testing equipment, including ultrasonic transmitter and receiver, probes of different frequencies and sizes, coupling agent, and testing instruments for displaying and interpreting echo signals;

[0037] Step 1.2, set the resonant frequency of the detection probe. The frequency calculation formula is as follows:

[0038]

[0039] Where f is the resonant frequency of the probe, c is the propagation speed of ultrasound in the material, ρ is the density of the frame material, L is the probe length, and T is the tension of the probe;

[0040] Step 1.3, fix the probe on the ultrasonic testing equipment and apply coupling agent between the probe and the surface of the frame material to ensure that the ultrasonic wave can be effectively transmitted into the material;

[0041] Step 1.4, the probe moves along the surface of the frame to emit ultrasonic waves. When encountering defects or material interfaces, echoes will be generated. The propagation speed formula of ultrasonic waves in the material is as follows:

[0042] c=f*λ

[0043] Among them, λ is the length of a complete waveform of ultrasonic wave propagating in the material;

[0044] Step 1.5, rough defect analysis, the location and size of the defect are determined by analyzing the time difference t of the echo signal. The depth d of the defect can be calculated by the following formula:

[0045]

[0046] Step 1.6, record the test results. The information will be recorded in the test report for subsequent analysis and frame design optimization.

[0047] Step 2: Data analysis and defect identification: using the data obtained by non-destructive testing technology, the analysis software identifies the tiny defects inside the frame, providing a basis for the optimal design of the frame;

[0048] Step 2.1, data preprocessing, including removing noise, standardizing data, filling missing values, etc.;

[0049] Step 2.2, feature extraction, extracting the peak value, average value, standard deviation, and waveform characteristics of the ultrasonic signal, and using these features as input to the SVM model;

[0050] Step 2.3, SVM multi-classification model training, train an SVM classifier for each category, which separates this category from all other categories. For category k (k = 1, ..., K, K is the total number of categories), train an SVM classifier to label samples of category k as positive and samples of all other categories as negative. For each SVM classifier, use the following optimization problem for training:

[0051]

[0052] Among them, w k and b k is the weight vector and configuration item of category k, x ki is the feature vector of the i-th sample of category k, y ki is the corresponding category label, It means that for all samples i, the propositional expression is valid. Through the Lagrange multiplier method, we can get the dual problem:

[0053]

[0054] Among them, a k is a Lagrange multiplier of type k, a ki is the Lagrange multiplier of the i-th sample of category k, n k is the number of samples of category k, a kj is the Lagrange multiplier of the jth sample of category k, y ki is the category label of the i-th sample of category k, y kj is the class label of the jth sample of class k, x kj is the feature vector of the jth sample of category k;

[0055] Step 2.4, detailed defect analysis, use the trained SVM multi-classification model to classify the ultrasonic signal data recorded in step 1. For a new SVM model input x, each SVM classifier will output a decision value:

[0056] f k (x) = w k ·x+b k

[0057] Among them, f k (x) is the decision value of the SVM classifier of category k for input x, which is used to determine the probability that the input data belongs to category k. Then, the category corresponding to the classifier with the largest positive decision value is selected as the predicted category of x:

[0058]

[0059] in, is the predicted category of the SVM model input x, that is, the frame defect category.

[0060] Step 3: Frame evaluation and optimization: Based on the results of nondestructive testing, the structural performance of the frame is evaluated, and the defects found are repaired or improved to improve the lightweight effect and safety of the frame.

[0061] Step 4: Implement repair and improvement measures, make necessary repairs and design adjustments to the frame based on the evaluation results to ensure that the material performance of the frame fully meets the design requirements;

[0062] Step 5: Re-inspection and verification: After the repair and improvement measures are implemented, non-destructive testing is performed again to verify whether the frame defects have been effectively resolved to ensure the quality of the frame;

[0063] Step 6: Data integration and feedback: Integrate the nondestructive testing data, evaluation results and repair records into the frame design database to provide feedback and reference for subsequent frame design and manufacturing.

[0064] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. The lightweight design of the body structure of a small electric vehicle based on non-destructive testing, the specific steps are as follows, characterized in that: Step 1: Lightweight design of the frame of a small electric vehicle; Step 2: Introduce non-destructive testing technology. During the manufacturing process of small electric vehicle frames, install non-destructive testing equipment to conduct comprehensive testing of frame materials to ensure that internal defects and structural discontinuities can be discovered in a timely manner; Step 3: Data analysis and defect identification: using the data obtained by non-destructive testing technology, the analysis software identifies the tiny defects inside the frame, providing a basis for the optimal design of the frame; Step 4: Frame evaluation and optimization: Based on the results of nondestructive testing, the structural performance of the frame is evaluated, and the defects found are repaired or improved to improve the lightweight effect and safety of the frame. Step 5: Implement repair and improvement measures, make necessary repairs and design adjustments to the frame based on the evaluation results to ensure that the material performance of the frame fully meets the design requirements; Step 6: Re-inspection and verification: After the repair and improvement measures are implemented, non-destructive testing is performed again to verify whether the frame defects have been effectively resolved to ensure the quality of the frame; Step 7: Data integration and feedback: Integrate the nondestructive testing data, evaluation results and repair records into the frame design database to provide feedback and reference for subsequent frame design and manufacturing.

2. The lightweight design of the body structure of a small electric vehicle based on non-destructive testing according to claim 1 is characterized in that: The process of introducing nondestructive testing technology in step 2 is expressed as: Step 2.1, install ultrasonic testing equipment, including ultrasonic transmitter and receiver, probes of different frequencies and sizes, coupling agent, and testing instruments for displaying and interpreting echo signals; Step 2.2, set the resonant frequency of the detection probe. The frequency calculation formula is as follows: Where f is the resonant frequency of the probe, c is the propagation speed of ultrasound in the material, ρ is the density of the frame material, L is the probe length, and T is the tension of the probe; Step 2.3, fix the probe on the ultrasonic testing equipment, and apply coupling agent between the probe and the surface of the frame material to ensure that the ultrasonic wave can be effectively transmitted into the material; Step 2.4, the probe moves along the surface of the frame to emit ultrasonic waves. When encountering defects or material interfaces, echoes will be generated. The formula for the propagation speed of ultrasonic waves in the material is as follows: c=f*λ Among them, λ is the length of a complete waveform of ultrasonic wave propagating in the material; Step 2.5, rough defect analysis, the location and size of the defect are determined by analyzing the time difference t of the echo signal. The depth d of the defect can be calculated by the following formula: Step 2.6, record the test results. The information will be recorded in the test report for subsequent analysis and frame design optimization.

3. The lightweight design of the body structure of a small electric vehicle based on non-destructive testing according to claim 1 is characterized in that: The process of data analysis and defect identification in step 3 is shown as follows: Step 3.1, data preprocessing, including removing noise, standardizing data, filling missing values, etc.; Step 3.2, feature extraction, extracting the peak value, average value, standard deviation, and waveform characteristics of the ultrasonic signal, and using these features as input to the SVM model; Step 3.3, SVM multi-classification model training, train an SVM classifier for each category, which separates this category from all other categories. For category k, k = 1, ..., K, K is the total number of categories, train an SVM classifier to label samples of category k as positive and samples of all other categories as negative. For each SVM classifier, use the following optimization problem for training: Among them, w k and b k is the weight vector and configuration item of category k, x ki is the feature vector of the i-th sample of category k, y ki is the corresponding category label, It means that for all samples i, the propositional expression is valid. Through the Lagrange multiplier method, we can get the dual problem: Among them, a k is a Lagrange multiplier of type k, a ki is the Lagrange multiplier of the i-th sample of category k, n k is the number of samples of category k, a kj is the Lagrange multiplier of the jth sample of category k, y ki is the category label of the i-th sample of category k, y kj is the class label of the jth sample of class k, x kj is the feature vector of the jth sample of category k; Step 3.4, detailed defect analysis, use the trained SVM multi-classification model to classify the ultrasonic signal data recorded in step 2. For a new SVM model input x, each SVM classifier will output a decision value: f k (x)=w k ·x+b k Among them, f k (x) is the decision value of the SVM classifier of category k for input x, which is used to determine the probability that the input data belongs to category k. Then, the category corresponding to the classifier with the largest positive decision value is selected as the predicted category of x: in, is the predicted category of the SVM model input x, that is, the frame defect category.

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