Quality detection system and method for automobile bumper

Abstract

The invention relates to the technical field of automobile part quality detection, in particular to a quality detection system and method for an automobile bumper. The method comprises the following steps: acquiring surface data of an automobile bumper, generating point cloud data, identifying surface defects of the automobile bumper according to the point cloud data, and dividing the surface of the automobile bumper into a plurality of detection sub-regions according to defect types; detecting mechanical properties, paint film quality and material components of the automobile bumper, outputting test result data, and performing block chain evidence storage on the test result data; fusing the surface defect and the test result, judging a current automobile bumper quality detection result, and outputting the result; the system comprises a defect area division module, a test result acquisition module and a data fusion module. Through the mode, fusion analysis of multi-source data is realized, and the comprehensiveness of quality evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile parts quality inspection, and in particular to a quality inspection system and method for automobile bumpers. Background Art

[0002] As a key component for vehicle safety and visual appeal, the quality of a car bumper directly impacts its collision safety, weather resistance, and market value. Traditional inspection methods primarily rely on manual visual inspection, which suffers from significant drawbacks: low inspection efficiency, requiring area-by-area manual inspection, and a single bumper inspection taking up to 2-4 hours. The inspection is also susceptible to operator experience and fatigue, resulting in a high missed detection rate of 15-20%. Furthermore, inspection accuracy is insufficient, making it difficult to manually identify subtle defects such as microcracks (<0.5mm) and orange peel texture on the paint surface. Furthermore, key indicators such as material toughness and impact resistance cannot be quantified.

[0003] The introduction of automated equipment in existing technologies can solve the above shortcomings. However, the current use of automated equipment for testing focuses on single detection optimization and lacks the fusion analysis of multi-source data, resulting in one-sided quality assessment. Summary of the Invention

[0004] The purpose of the present invention is to provide a quality inspection system and method for automobile bumpers, aiming to solve the technical problems in the prior art that the focus is on single inspection optimization and the lack of fusion analysis of multi-source data leads to one-sided quality assessment.

[0005] To achieve the above object, the present invention adopts a quality inspection method for automobile bumpers, comprising the following steps: Acquire the surface data of the car bumper, generate point cloud data, identify the surface defects of the car bumper based on the point cloud data, and divide the surface of the car bumper into multiple detection sub-areas according to the defect type; Car bumpers are tested for mechanical properties, paint film quality, and material composition, and the test results are output and stored on the blockchain. Integrate surface defects and test results to determine the current car bumper quality inspection results and output them.

[0006] Among them, in the steps of acquiring automobile bumper surface data, generating point cloud data, identifying automobile bumper surface defects based on the point cloud data, and dividing the automobile bumper surface into multiple detection sub-areas according to defect types: Perform laser scanning on the car bumper, generate point cloud data based on the scan data, and perform denoising and filtering on the point cloud data; Identify surface cracks, color differences, and chipping defects on car bumpers based on point cloud data, and output defect identification data and defect types.

[0007] Among them, after the steps of identifying surface cracks, color difference, and chipping defects of the automobile bumper based on the point cloud data and outputting the defect identification data and defect type: The car bumper is divided into multiple inspection sub-areas according to the defect type and severity, and each inspection sub-area is assigned a unique identifier.

[0008] After dividing the vehicle bumper into multiple inspection sub-areas based on defect type and severity and assigning unique identifiers to the inspection sub-areas: Generate defect density heatmaps independently for each region.

[0009] Among them, in the steps of testing the mechanical properties, paint film quality, and material composition of the automobile bumper, outputting the test result data, and storing the test result data on the blockchain: Conduct static deformation and dynamic impact tests on automobile bumpers to obtain yield strength and elongation at break. Also, capture the bumper's energy absorption and deformation process, calculate energy absorption efficiency, and obtain mechanical performance test data. Carry out thickness, adhesion and color difference tests on automobile bumpers to obtain paint film quality test data; Select the suspicious area, identify the recycled materials and additives in the suspicious area, and obtain material composition test data.

[0010] Among them, in the steps of testing the mechanical properties, paint film quality, and material composition of the automobile bumper, outputting the test result data, and storing the test result data on the blockchain: The mechanical properties test data, paint film quality test data, and material composition test data are received and packaged respectively to generate a unique digital fingerprint.

[0011] Among them, after receiving the mechanical properties test data, paint film quality test data, and material composition test data, packaging them, and generating a unique digital fingerprint: The digital fingerprint is stored on the blockchain platform for blockchain evidence storage.

[0012] Among them, in the step of fusing surface defects and test results, judging the current automobile bumper quality test results, and outputting them: Extract feature data of surface defects and test results, and fuse the feature data to obtain the current comprehensive quality score of the car bumper; Establish the first threshold and the second threshold, judge the comprehensive quality score, the first threshold, the second threshold, and output the current car bumper quality inspection result.

[0013] Among them, in the steps of setting the first threshold and the second threshold, judging the comprehensive quality score, the first threshold, the second threshold, and outputting the current automobile bumper quality inspection result: If the comprehensive quality score is greater than or equal to the first threshold, the current automobile bumper quality test result is qualified; If the comprehensive quality score is less than the first threshold and greater than or equal to the second threshold, the current vehicle bumper quality inspection result is to be repaired; If the comprehensive quality score is less than the second threshold, the current automobile bumper quality inspection result is unqualified.

[0014] The present invention also provides a quality inspection system for automobile bumpers, comprising a defect area division module, a test result acquisition module, and a data fusion module; wherein: The defect area division module is used to obtain automobile bumper surface data, generate point cloud data, identify automobile bumper surface defects based on the point cloud data, and divide the automobile bumper surface into multiple detection sub-areas according to the defect type; The test result acquisition module is used to test the mechanical properties, paint film quality, and material composition of the automobile bumper, output the test result data, and store the test result data on the blockchain; The data fusion module is used to fuse surface defects and test results, determine the current automobile bumper quality test results, and output them.

[0015] A quality inspection system and method for automobile bumpers of the present invention adopts the defect area division module, the test result acquisition module, and the data fusion module to perform the following steps: obtaining automobile bumper surface data, generating point cloud data, identifying automobile bumper surface defects based on the point cloud data, and dividing the automobile bumper surface into multiple inspection sub-areas according to the defect type; performing mechanical property, paint film quality, and material composition tests on the automobile bumper respectively, outputting the test result data, and storing the test result data on the blockchain; fusing the surface defects and the test results, judging the current automobile bumper quality inspection result, and outputting it; through the above method, the fusion analysis of multi-source data is realized, and the comprehensiveness of the quality assessment is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1The present invention is a flowchart of the steps of the quality inspection method for automobile bumpers.

[0018] Figure 2 It is a step flow chart of S100 of the present invention.

[0019] Figure 3 It is a step flow chart of S200 of the present invention.

[0020] Figure 4 It is a step flow chart of S300 of the present invention.

[0021] Figure 5 The diagram is a schematic structural diagram of a quality inspection system for automobile bumpers according to the present invention.

[0022] Figure 6 It is a structural principle diagram of the electronic device of the present invention.

[0023] 401-defect area division module, 402-test result acquisition module, 403-data fusion module. DETAILED DESCRIPTION

[0024] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0025] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0027] See also Figures 1 to 4 The present invention provides a quality inspection method for automobile bumpers, comprising the following steps: S100: Acquire surface data of a car bumper, generate point cloud data, identify surface defects of the car bumper based on the point cloud data, and divide the surface of the car bumper into multiple detection sub-areas according to the defect type.

[0028] In this embodiment, the surface data of the car bumper is acquired, point cloud data is generated, surface defects of the car bumper are identified based on the point cloud data, and the surface of the car bumper is divided into multiple detection sub-areas according to the defect type. The specific process is as follows: S101: Perform laser scanning on the car bumper, generate point cloud data based on the scan data, and perform denoising and filtering on the point cloud data; S102: Identify surface cracks, color difference, and chipping defects on the car bumper based on the point cloud data, and output defect identification data and defect type; S103: Divide the vehicle bumper into multiple inspection sub-areas based on defect type and severity, and assign unique identifiers to the inspection sub-areas; S104: Generate a defect density heat map independently for each region.

[0029] In the above process, laser scanning uses a high-precision laser scanner to perform a full-scale scan of the car bumper. During scanning, a laser transmitter emits a laser beam onto the bumper surface. The reflected light is received by a receiver, which calculates the round-trip time or phase difference of the laser beam to accurately obtain the three-dimensional coordinate information of each point on the bumper surface. For example, for a typical car bumper size (1.5-2 meters long and 0.5-0.8 meters wide), the scanner can complete the entire surface scan in 1-2 minutes, acquiring coordinate data for millions of points.

[0030] Point cloud data generation: The 3D coordinate information of each scanned point is integrated to generate a point cloud data model. This model accurately describes the geometric shape and spatial position of the bumper surface in the form of discrete points, providing basic data for subsequent defect identification.

[0031] Denoising and filtering: Due to environmental interference (such as dust and light reflections) during the scanning process, point cloud data may contain noise points. A statistical filtering algorithm is used to perform statistical analysis on the neighborhood of each point, removing noise points that deviate from the neighborhood mean by more than a certain threshold. Furthermore, a Gaussian filter algorithm is used to smooth the point cloud data, eliminating minor surface fluctuations and burrs, thereby improving data quality. For example, after filtering, the noise level of point cloud data can be reduced from an initial 5% to less than 1%.

[0032] Defect Recognition Algorithm: Preprocessed point cloud data is fed into a pretrained deep learning model (such as a modified PointNet++ network). This model is trained on a large number of labeled bumper defect samples (including cracks, color differences, and chipping), automatically learning the characteristic patterns of defects. During the recognition process, the model extracts and classifies each point to determine whether it is a defect and the defect type.

[0033] Defect Identification Data Output: For each identified defect, the system records its location coordinates, size, defect type, and other information, and generates a defect identification data file. For example, for a crack with a length of 5mm and a width of 0.2mm, the data file will record its starting and ending point coordinates, crack direction, and the defect type identifier "crack."

[0034] Regional division is based on the following: The bumper surface is divided into multiple sub-regions based on defect type (e.g., cracks caused by impact damage, chipping caused by manufacturing process issues, color difference caused by paint problems) and severity (mild, moderate, severe). For example, an area with multiple cracks and a relatively concentrated distribution is designated as one sub-region; a single chipping defect is centered in a smaller sub-region.

[0035] Unique identifier assignment: Each detection sub-region is assigned a unique identifier, such as "Region-001" or "Region-002". This identifier is used for subsequent independent analysis and processing of each sub-region, facilitating data management and traceability.

[0036] Heatmap calculation principle: For each inspection sub-region, the number of defects within the region is counted and the defect density is calculated based on the sub-region's area. The defect density is mapped to a color gradient, typically transitioning from green (low defect density) to red (high defect density).

[0037] Heatmap Generation and Display: Using visualization software (such as MATLAB or Python's Matplotlib library), a heatmap of defect density is generated for each inspection sub-area based on the calculated defect density. Heatmaps provide an intuitive graphical representation of defect distribution within each sub-area, allowing operators to quickly understand the defect status of the bumper surface. For example, red areas in the heatmap indicate a high number of defects, requiring specific attention and further inspection.

[0038] S200: Conduct mechanical property, paint film quality, and material composition tests on automobile bumpers, output test result data, and store the test result data on the blockchain.

[0039] In this implementation, the car bumper is tested for mechanical properties, paint film quality, and material composition, and the test results are output and stored on the blockchain. The specific process is as follows: S201: Perform static deformation and dynamic impact tests on the car bumper to obtain yield strength and elongation at break. The bumper's energy absorption and deformation process is captured to calculate energy absorption efficiency and obtain mechanical performance test data. S202: Perform thickness, adhesion, and color difference tests on the automobile bumper to obtain paint film quality test data; S203: Select the suspicious area, identify the recycled materials and additives in the suspicious area, and obtain material composition test data; S204: Receive mechanical property test data, paint film quality test data, and material composition test data respectively, encapsulate them, and generate a unique digital fingerprint; S205: The digital fingerprint is stored on the blockchain platform for blockchain evidence storage.

[0040] In the above process, static deformation testing involves performing a tensile test on the bumper using a universal material testing machine. The bumper sample is secured to the machine's fixture, and tensile force is applied at a constant rate (e.g., 5 mm / min) until the sample yields or breaks. A force-displacement curve is recorded during the test, from which the yield strength (the stress at which the material begins to undergo significant plastic deformation) and elongation at break (the ratio of the elongation at break to the original length) are derived. For example, the standard yield strength of a certain model of bumper is 25 MPa. If the measured value is below 22 MPa, there may be quality issues.

[0041] Dynamic impact testing: This test simulates the conditions of a low-speed collision, using a drop-weight impact tester. The bumper is mounted on a test bench, and the height and weight of the drop-weight are adjusted so that it strikes the bumper at a specific speed (e.g., 15 km / h). A high-speed camera (capable of recording at over 1000 fps) captures the bumper's energy absorption and deformation during the impact. Image analysis software then calculates the bumper's energy absorption efficiency (EAE). The standard EAE value is generally ≥55%. A measured value below 50% indicates insufficient energy absorption performance.

[0042] Thickness Testing: Use a film thickness gauge (such as an eddy current film thickness gauge or a magnetic film thickness gauge, depending on the bumper material) to measure the paint film thickness. Measure the film thickness at multiple points (generally no less than 10) on the bumper surface and calculate the average. Compare this to the manufacturer's standard for film thickness (usually 80-120μm). If the deviation exceeds 15%, the film thickness is considered unacceptable.

[0043] Adhesion testing: The cross-hatch method, in accordance with ASTM D3359, assesses the bond strength between the paint film and the substrate. A crosshatch cutter is used to create a grid with a specified spacing (e.g., 1 mm) on the paint film surface. Tape is then applied to the grid, quickly removed, and the paint film observed for any signs of detachment. The adhesion grade is determined based on the extent of the detachment; a detachment greater than 15% is considered insufficient adhesion.

[0044] Color difference testing: Use a spectrophotometer to measure the color parameters of the bumper paint film (such as L, a, and b* values), compare them with a standard color sample, and calculate the color difference ΔE value. Generally, ΔE is required to be ≤ 3.0. If ΔE is greater than 3.0, it means that the paint film color is significantly different from the standard and requires full spray treatment.

[0045] Suspicious Area Selection: Based on the surface defect detection results and abnormalities in the mechanical properties test, suspicious areas are selected on the bumper. For example, areas with significantly lower strength than the standard in the mechanical properties test, or areas with abnormal color or texture on the surface, are considered suspicious areas for further inspection.

[0046] Recycled material and additive identification: X-ray fluorescence (XRF) is used to quickly screen suspicious areas to preliminarily determine whether the material contains recycled materials or specific additives. Suspected components are further analyzed using Fourier transform infrared spectroscopy (FTIR) to determine their chemical structure and composition. For example, if testing reveals that the recycled content in a particular area exceeds the standard 5%, the bumper may have quality issues.

[0047] Detection data packaging and digital fingerprint generation, data reception and integration: receive mechanical properties test data, paint film quality test data and material composition test data respectively, integrate these data in a unified data format to form a complete detection data set.

[0048] Data encapsulation and digital fingerprint generation: The integrated test data set is encapsulated using an encryption algorithm (such as the SHA-256 algorithm) to generate a unique digital fingerprint. This digital fingerprint serves as a unique identifier for the test data and can be used for data integrity verification and traceability.

[0049] Blockchain platform selection: Choose a suitable blockchain platform (such as Hyperledger Fabric, Ethereum, etc.) and deploy and configure nodes according to the platform's requirements.

[0050] Data storage on-chain: The generated digital fingerprint is uploaded to the blockchain platform. Through the blockchain's distributed ledger and encryption technology, the test data is guaranteed to be tamper-proof and traceable. For example, on the blockchain platform, the digital fingerprint of each test data is recorded in a block and linked to the previous block through a hash value, forming a complete blockchain.

[0051] S300: Integrate surface defects and test results to determine the current car bumper quality test results and output them.

[0052] In this embodiment, the surface defects and test results are integrated to determine the current car bumper quality test results and output them. The specific process is as follows: S301: Extracting feature data of surface defects and test results, and fusing the feature data to obtain a comprehensive quality score of the current automobile bumper; S302: Establish a first threshold and a second threshold, determine the comprehensive quality score, the first threshold, and the second threshold, and output the current automobile bumper quality inspection result.

[0053] Furthermore, in the step of setting the first threshold and the second threshold, determining the comprehensive quality score, the first threshold, the second threshold, and outputting the current automobile bumper quality inspection result: If the comprehensive quality score is greater than or equal to the first threshold, the current automobile bumper quality test result is qualified; If the comprehensive quality score is less than the first threshold and greater than or equal to the second threshold, the current vehicle bumper quality inspection result is to be repaired; If the comprehensive quality score is less than the second threshold, the current automobile bumper quality inspection result is unqualified.

[0054] In the above process, feature data extraction involves extracting key feature data from surface defect inspection data (such as defect type, defect density, and defect location) and test result data (mechanical performance indicators, paint film quality indicators, material composition indicators, etc.). For example, the average defect density of each sub-region is extracted from the defect density heat map as a surface defect feature, and the yield strength, elongation at break, and energy absorption efficiency are extracted from the mechanical performance test data as mechanical performance features.

[0055] Data Fusion Method: The extracted feature data are fused using a weighted average method. Different weight coefficients are assigned based on the degree of influence of each feature on the bumper quality. For example, the weight coefficient for surface defects can be set to 0.4, the weight coefficient for mechanical properties to 0.35, the weight coefficient for paint film quality to 0.15, and the weight coefficient for material composition to 0.1. The weighted average calculation yields a comprehensive quality score for the vehicle bumper.

[0056] Threshold setting: Based on the quality standards and actual needs of the car bumper, set the first and second thresholds. For example, the first threshold can be set to 80 points, and the second threshold can be set to 60 points.

[0057] Result judgment and output: Compare the calculated quality comprehensive score with the first threshold and the second threshold: If the comprehensive quality score is greater than or equal to the first threshold (S≥80), the current automobile bumper quality inspection result is qualified, a "qualified" report is output, and it can be recommended to continue using it.

[0058] If the comprehensive quality score is less than the first threshold and greater than or equal to the second threshold (60≤S<80), the current car bumper quality inspection result is to be repaired, and a "to be repaired" report is output, with detailed description of the parts that need to be repaired and repair suggestions (such as local painting, reinforcement treatment, etc.).

[0059] If the overall quality score is less than the second threshold (S < 60), the current bumper quality test result is considered unqualified, and an "unqualified" report is output with a recommendation to replace the bumper. The quality test result report is also stored in the database for subsequent query and traceability.

[0060] Corresponding to the aforementioned embodiment of the quality inspection method for automobile bumpers, the present application also provides an embodiment of a quality inspection system for automobile bumpers.

[0061] Figure 5 FIG. 1 is a block diagram of a quality inspection system for automobile bumpers according to an exemplary embodiment. Figure 5 The system may include: a defect area division module 401, a test result acquisition module 402, and a data fusion module 403; wherein: The defect area division module 401 is used to obtain automobile bumper surface data, generate point cloud data, identify automobile bumper surface defects based on the point cloud data, and divide the automobile bumper surface into multiple detection sub-areas according to the defect type; The test result acquisition module 402 is used to test the mechanical properties, paint film quality, and material composition of the automobile bumper, output the test result data, and store the test result data in a blockchain; The data fusion module 403 is used to fuse the surface defects and the test results, determine the current automobile bumper quality test results, and output them.

[0062] In this embodiment, the defect area division module 401 obtains the surface data of the automobile bumper, generates point cloud data, identifies the surface defects of the automobile bumper based on the point cloud data, and divides the surface of the automobile bumper into multiple detection sub-areas according to the defect type; the test result acquisition module 402 performs mechanical property, paint film quality, and material composition tests on the automobile bumper respectively, outputs the test result data, and stores the test result data in the blockchain; the data fusion module 403 fuses the surface defects and test results, determines the current automobile bumper quality inspection results, and outputs them; through the above method, the fusion analysis of multi-source data is realized, and the comprehensiveness of the quality assessment is improved.

[0063] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0064] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0065] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned quality inspection method for automobile bumpers. Figure 6 As shown in the figure, a hardware structure diagram of a quality inspection system for automobile bumpers provided by an embodiment of the present invention is provided, in which any device with data processing capability is provided. Figure 6 In addition to the processor, memory, and network interface shown, any device with data processing capabilities in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0066] Accordingly, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the quality inspection method for automobile bumpers as described above. The computer-readable storage medium can be an internal storage unit of any device with data processing capabilities described in any of the aforementioned embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium can also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and can also be used to temporarily store data that has been output or is to be output.

[0067] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed in this application.

[0068] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A quality inspection method for automobile bumpers, characterized in that: The steps include: Acquire the surface data of the car bumper, generate point cloud data, identify the surface defects of the car bumper based on the point cloud data, and divide the surface of the car bumper into multiple detection sub-areas according to the defect type; Car bumpers are tested for mechanical properties, paint film quality, and material composition, and the test results are output and stored on the blockchain. Integrate surface defects and test results to determine the current car bumper quality inspection results and output them.

2. The quality inspection method for automobile bumpers according to claim 1, characterized in that: In the steps of acquiring automobile bumper surface data, generating point cloud data, identifying automobile bumper surface defects based on the point cloud data, and dividing the automobile bumper surface into multiple detection sub-areas according to defect types: Perform laser scanning on the car bumper, generate point cloud data based on the scan data, and perform denoising and filtering on the point cloud data; Identify surface cracks, color differences, and chipping defects on car bumpers based on point cloud data, and output defect identification data and defect types.

3. The quality inspection method for automobile bumpers according to claim 2, characterized in that: After identifying surface cracks, color difference, and chipping defects on the car bumper based on point cloud data and outputting defect identification data and defect types: The car bumper is divided into multiple inspection sub-areas according to the defect type and severity, and each inspection sub-area is assigned a unique identifier.

4. The quality inspection method for automobile bumpers according to claim 3, characterized in that: After dividing the car bumper into multiple inspection sub-areas based on defect type and severity and assigning unique identifiers to the inspection sub-areas: Generate defect density heatmaps independently for each region.

5. The quality inspection method for automobile bumpers according to claim 1, characterized in that: In the steps of testing the mechanical properties, paint film quality, and material composition of the automobile bumper, outputting the test result data, and storing the test result data on the blockchain: Conduct static deformation and dynamic impact tests on automobile bumpers to obtain yield strength and elongation at break. Also, capture the bumper's energy absorption and deformation process, calculate energy absorption efficiency, and obtain mechanical performance test data. Carry out thickness, adhesion and color difference tests on automobile bumpers to obtain paint film quality test data; Select the suspicious area, identify the recycled materials and additives in the suspicious area, and obtain material composition test data.

6. The quality inspection method for automobile bumpers according to claim 5, characterized in that: In the steps of testing the mechanical properties, paint film quality, and material composition of the automobile bumper, outputting the test result data, and storing the test result data on the blockchain: The mechanical properties test data, paint film quality test data, and material composition test data are received and packaged respectively to generate a unique digital fingerprint.

7. The quality inspection method for automobile bumpers according to claim 6, characterized in that: After receiving the mechanical properties test data, paint film quality test data, and material composition test data, packaging them, and generating a unique digital fingerprint: The digital fingerprint is stored on the blockchain platform for blockchain evidence storage.

8. The quality inspection method for automobile bumpers according to claim 1, characterized in that: In the step of integrating surface defects and test results, judging the current car bumper quality test results, and outputting them: Extract feature data of surface defects and test results, and fuse the feature data to obtain the current comprehensive quality score of the car bumper; Establish the first threshold and the second threshold, judge the comprehensive quality score, the first threshold, the second threshold, and output the current car bumper quality inspection result.

9. The quality inspection method for automobile bumpers according to claim 8, characterized in that: In the steps of setting the first threshold and the second threshold, determining the comprehensive quality score, the first threshold, the second threshold, and outputting the current automobile bumper quality inspection result: If the comprehensive quality score is greater than or equal to the first threshold, the current automobile bumper quality test result is qualified; If the comprehensive quality score is less than the first threshold and greater than or equal to the second threshold, the current vehicle bumper quality inspection result is to be repaired; If the comprehensive quality score is less than the second threshold, the current automobile bumper quality inspection result is unqualified.

10. A quality inspection system for automobile bumpers, applied to the quality inspection method for automobile bumpers according to claim 1, characterized in that: It includes defect area division module, test result acquisition module and data fusion module; among which: The defect area division module is used to obtain automobile bumper surface data, generate point cloud data, identify automobile bumper surface defects based on the point cloud data, and divide the automobile bumper surface into multiple detection sub-areas according to the defect type; The test result acquisition module is used to test the mechanical properties, paint film quality, and material composition of the automobile bumper, output the test result data, and store the test result data on the blockchain; The data fusion module is used to fuse surface defects and test results, determine the current automobile bumper quality test results, and output them.

Images