A quality detection system and method for automobile bumpers

Abstract

The present application relates to the technical field of automobile parts quality detection, and particularly relates to a quality detection system and method for automobile bumpers; the method comprises the following steps: acquiring surface data of the 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 multiple detection sub-regions according to defect types; the mechanical properties, paint film quality and material composition of the automobile bumper are detected respectively, test result data is output, and the test result data is stored in a block chain; the surface defects and the test result data are fused to determine the quality detection result of the current automobile bumper and output; the system comprises a defect area division module, a test result acquisition module and a data fusion module; through the above method, fusion analysis of multi-source data is realized, and the comprehensiveness of quality evaluation is improved.

Description

Technical Field

[0001] This invention relates to the field of automotive parts quality inspection technology, and in particular to a quality inspection system and method for automotive bumpers. Background Technology

[0002] As a key component for vehicle safety and appearance, the quality of a car bumper directly affects its collision safety, weather resistance, and market value. Traditional inspection methods mainly rely on manual visual inspection, which has the following significant drawbacks: low inspection efficiency, requiring manual inspection of each area, with a single bumper inspection taking 2-4 hours, and easily affected by operator experience and fatigue, resulting in a missed inspection rate as high as 15%-20%. Insufficient inspection accuracy, as it is difficult for humans to identify minute defects such as microcracks (<0.5mm) and orange peel texture in the paint, and it is impossible to quantitatively assess key indicators such as material toughness and impact resistance.

[0003] Existing technologies that introduce automated equipment can solve the above-mentioned shortcomings, but current automated equipment testing focuses on single-detection optimization and lacks fusion analysis of multi-source data, resulting in one-sided quality assessment. Summary of the Invention

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

[0005] To achieve the above objectives, the present invention provides a quality inspection method for automobile bumpers, comprising the following steps:

[0006] Acquire surface data of the car bumper, generate point cloud data, identify surface defects of the car bumper based on the point cloud data, and divide the car bumper surface into multiple detection sub-regions according to the defect type.

[0007] The mechanical properties, paint film quality, and material composition of the car bumper are tested respectively, the test results data are output, and the test results data are stored on the blockchain.

[0008] By combining surface defects and test results, the current quality inspection result of the car bumper is determined and output.

[0009] The steps include: acquiring surface data of the car bumper, generating point cloud data, identifying surface defects of the car bumper based on the point cloud data, and dividing the car bumper surface into multiple detection sub-regions according to the defect type:

[0010] The car bumper is laser scanned, point cloud data is generated based on the scan data, and the point cloud data is then denoised and filtered.

[0011] This tool identifies surface defects such as cracks, color differences, and chipping on car bumpers using point cloud data, and outputs defect identification data and defect types.

[0012] After identifying surface defects such as cracks, color differences, and chipping on car bumpers using point cloud data, and outputting defect identification data and defect types:

[0013] Based on the type and severity of the defect, the car bumper is divided into multiple inspection sub-areas, and each inspection sub-area is assigned a unique identifier.

[0014] After dividing the car bumper into multiple inspection sub-regions based on defect type and severity, and assigning each sub-region a unique identifier:

[0015] Generate a defect density heatmap independently for each region.

[0016] The process includes conducting tests on the mechanical properties, paint film quality, and material composition of the car bumper, outputting test results data, and storing the test results data using blockchain technology.

[0017] Static deformation and dynamic impact tests were performed on the car bumper to obtain yield strength and elongation at break. The energy absorption deformation process of the bumper was captured, the energy absorption efficiency was calculated, and mechanical performance test data were obtained.

[0018] Thickness, adhesion, and color difference tests were performed on the car bumpers to obtain paint film quality test data.

[0019] Select the suspicious area, identify the recycled materials and additives in the suspicious area, and obtain material composition test data.

[0020] The process includes conducting tests on the mechanical properties, paint film quality, and material composition of the car bumper, outputting test results data, and storing the test results data using blockchain technology.

[0021] It receives mechanical property test data, paint film quality test data, and material composition test data respectively, and encapsulates them to generate a unique digital fingerprint.

[0022] The process includes receiving mechanical property test data, paint film quality test data, and material composition test data, encapsulating them, and generating a unique digital fingerprint.

[0023] Digital fingerprints are stored on a blockchain platform for blockchain-based evidence preservation.

[0024] Among them, in the step of integrating surface defects and test results to determine the current quality inspection result of the car bumper and outputting the result:

[0025] 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;

[0026] Set a first threshold and a second threshold, judge the overall quality score, the first threshold, and the second threshold, and output the current car bumper quality inspection result.

[0027] Among them, in the steps of setting the first threshold and the second threshold, judging the overall quality score, the first threshold, the second threshold, and outputting the current car bumper quality inspection result:

[0028] If the overall quality score is greater than or equal to the first threshold, the current car bumper quality inspection result is qualified;

[0029] If the overall quality score is less than the first threshold but greater than or equal to the second threshold, the current quality inspection result for the car bumper is "to be repaired".

[0030] If the overall quality score is less than the second threshold, the current quality inspection result of the car bumper is unqualified.

[0031] This invention also provides a quality inspection system for automobile bumpers, including a defect area division module, a test result acquisition module, and a data fusion module; wherein:

[0032] The defect area division module is used to acquire surface data of the car bumper, generate point cloud data, identify defects on the surface of the car bumper based on the point cloud data, and divide the surface of the car bumper into multiple detection sub-regions according to the defect type.

[0033] The test result acquisition module is used to perform mechanical performance, paint film quality, and material composition tests on the car bumper, output test result data, and store the test result data on the blockchain.

[0034] The data fusion module is used to fuse surface defects and test results, determine the current quality inspection result of the car bumper, and output the result.

[0035] This invention discloses a quality inspection system and method for automobile bumpers, comprising a defect area division module, a test result acquisition module, and a data fusion module, performing the following steps: acquiring surface data of the automobile bumper, generating point cloud data, identifying surface defects of the automobile bumper based on the point cloud data, and dividing the automobile bumper surface into multiple inspection sub-regions according to the defect type; performing mechanical performance, paint film quality, and material composition tests on the automobile bumper, outputting test result data, and storing the test result data on a blockchain; fusing surface defects and test results to determine the current quality inspection result of the automobile bumper, and outputting it; through the above methods, the fusion analysis of multi-source data is achieved, improving the comprehensiveness of quality assessment. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the steps in the quality inspection method for automobile bumpers of the present invention.

[0038] Figure 2 This is a flowchart of steps S100 of the present invention.

[0039] Figure 3 This is a flowchart of steps S200 of the present invention.

[0040] Figure 4 This is a flowchart of steps S300 of the present invention.

[0041] Figure 5 This is a schematic diagram of the structural principle of the quality inspection system for automobile bumpers of the present invention.

[0042] Figure 6 This is a schematic diagram of the electronic device of the present invention.

[0043] 401 - Defect Area Division Module, 402 - Test Result Acquisition Module, 403 - Data Fusion Module. Detailed Implementation

[0044] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0045] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also 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 includes any or all possible combinations of one or more of the associated listed items.

[0046] 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 one another. 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 "when," "when," or "in response to determination."

[0047] Please see Figures 1-4 This invention provides a quality inspection method for automobile bumpers, comprising the following steps:

[0048] S100: Acquire surface data of the car bumper, generate point cloud data, identify surface defects of the car bumper based on the point cloud data, and divide the car bumper surface into multiple detection sub-regions according to the defect type.

[0049] In this embodiment, 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 car bumper surface is divided into multiple detection sub-regions according to the defect type. The specific process is as follows:

[0050] S101: Perform laser scanning on the car bumper, generate point cloud data based on the scanning data, and perform noise reduction and filtering on the point cloud data;

[0051] S102: Identify surface defects such as cracks, color differences, and chipping on car bumpers based on point cloud data, and output defect identification data and defect types;

[0052] S103: Based on the type and severity of the defect, divide the car bumper into multiple inspection sub-areas and assign a unique identifier to each inspection sub-area;

[0053] S104: Generates a defect density heatmap independently for each region.

[0054] In the above process, laser scanning involves using a high-precision laser scanner to perform a full-range scan of the car bumper. During the scan, a laser emitter emits a laser beam onto the bumper surface, and the reflected light is received by a receiver. By calculating the round-trip time or phase difference of the laser beam, the three-dimensional coordinate information of each point on the bumper surface is accurately obtained. For example, for a common-sized car bumper (1.5-2 meters long and 0.5-0.8 meters wide), the scanner can complete the scan of the entire surface within 1-2 minutes, acquiring coordinate data for millions of points.

[0055] Point cloud data generation: The three-dimensional coordinate information of each point obtained from the scan is integrated to generate a point cloud data model. This model accurately describes the geometry and spatial position of the bumper surface in the form of discrete points, providing basic data for subsequent defect identification.

[0056] Noise Reduction and Filtering: Due to environmental interference (such as dust and light reflection) during the scanning process, noisy points may exist in the point cloud data. A statistical filtering algorithm is used to perform statistical analysis on the neighborhood of each point, removing noisy points that deviate from the neighborhood mean by more than a certain threshold. Simultaneously, a Gaussian filtering algorithm is used to smooth the point cloud data, eliminating minor surface undulations and spikes, thus improving data quality. For example, after filtering, the noise level of the point cloud data can be reduced from the initial 5% to less than 1%.

[0057] Defect identification algorithm: Preprocessed point cloud data is input into a pre-trained deep learning model (such as an improved PointNet++ network). This model is trained on a large number of labeled bumper defect samples (including cracks, color differences, chipping, etc.) and can automatically learn the feature patterns of defects. During the identification process, the model extracts features and classifies each point to determine whether it belongs to a defect point and to identify the defect type.

[0058] Defect identification data output: For identified defects, information such as their location coordinates, size, and defect type is recorded, and a defect identification data file is generated. For example, for a crack with a length of 5mm and a width of 0.2mm, the data file will record in detail its starting point coordinates, ending point coordinates, crack direction, and the defect type identifier "crack".

[0059] The criteria for area division are as follows: Based on the type of defect (such as cracks caused by impact damage, chipping caused by manufacturing process problems, and color difference caused by painting problems) and its severity (mild, moderate, severe), the surface of the car bumper is divided into multiple inspection sub-areas. For example, an area with multiple cracks and a relatively concentrated distribution of cracks is divided into one sub-area; for a single chipping defect, a smaller sub-area is divided with that chipping defect as the center.

[0060] Unique identifier assignment: Each detected 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.

[0061] Heatmap calculation principle: For each detection sub-region, the number of defect points within that region is counted, and the defect density is calculated by combining this with the area of ​​the sub-region. The defect density is then mapped onto a color gradient, typically using a color transition from green (low defect density) to red (high defect density).

[0062] Heatmap Generation and Display: Using visualization software (such as MATLAB, Python's Matplotlib library, etc.), an independent defect density heatmap is generated for each inspection sub-region based on the calculated defect density. The heatmap displays the defect distribution of each sub-region in an intuitive graphical way, allowing operators to quickly understand the defect status of the bumper surface. For example, in the heatmap, a red area indicates that there are more defects in that area, requiring focused attention and further inspection.

[0063] S200: Performs tests on the mechanical properties, paint quality, and material composition of car bumpers, outputs test result data, and stores the test result data on the blockchain.

[0064] In this embodiment, the mechanical properties, paint film quality, and material composition of the car bumper are tested, the test results are output, and the test results are stored on a blockchain for notarization. The specific process is as follows:

[0065] S201: Perform static deformation and dynamic impact tests on car bumpers to obtain yield strength and elongation at break, capture the energy absorption deformation process of the bumper, calculate energy absorption efficiency, and obtain mechanical performance test data.

[0066] S202: Conduct thickness, adhesion, and color difference tests on the car bumper to obtain paint film quality test data;

[0067] S203: Select suspicious areas, identify recycled materials and additives in the suspicious areas, and obtain material composition test data;

[0068] S204: Receives mechanical property test data, paint film quality test data, and material composition test data respectively, encapsulates them, and generates a unique digital fingerprint;

[0069] S205: Store the digital fingerprint on the blockchain platform for blockchain-based evidence preservation.

[0070] In the above process, static deformation detection involves performing a tensile test on the car bumper using a universal testing machine. The bumper sample is fixed on the fixture of the testing machine, and a tensile force is applied at a certain rate (e.g., 5 mm / min) until the sample yields or fractures. The force-displacement curve during the test is recorded, and 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 fracture to the original length) are obtained from the curve. For example, if the standard yield strength of a certain model of car bumper is 25 MPa, a measured value lower than 22 MPa may indicate a quality problem.

[0071] Dynamic Impact Testing: Simulating low-speed collisions of a car, a drop hammer impact testing machine is used to conduct dynamic impact tests on the bumper. The bumper is mounted on a test bench, and the height and weight of the drop hammer are adjusted to cause it to impact the bumper at a certain speed (e.g., 15 km / h). A high-speed camera (with a shooting frequency of over 1000 fps) is used to capture the energy absorption deformation process of the bumper during the impact. Image analysis software is used to calculate the bumper's energy absorption efficiency (EAE). The standard EAE value is generally required to be ≥55%. If the measured value is lower than 50%, it indicates that the bumper's energy absorption performance is insufficient.

[0072] Thickness Inspection: The paint film thickness is measured using a film thickness gauge (such as an eddy current film thickness gauge or a magnetic film thickness gauge, selected according to the bumper material). Multiple measurement points (generally no less than 10) are selected on the bumper surface, and the paint film thickness at each point is measured, and the average value is calculated. The average value is compared with the original manufacturer's specified paint film thickness standard (usually 80~120μm). If the deviation exceeds the tolerance by more than 15%, the paint film thickness is deemed unqualified.

[0073] Adhesion testing: The adhesion strength between the paint film and the substrate is assessed using the cross-cut test according to ASTM D3359. A grid with a specified spacing (e.g., 1 mm) is drawn on the paint film surface using a cross-cut tester. Adhesive tape is then applied to the grid, and the tape is quickly removed. The extent of paint film peeling is observed. The adhesion grade is determined based on the size of the peeling area; a peeling area >15% indicates insufficient adhesion.

[0074] Color difference detection: Use a spectrophotometer to measure the color parameters (such as L, a, b* values) of the bumper paint film, compare them with the standard color sample, and calculate the color difference ΔE value. Generally, ΔE ≤ 3.0 is required. If ΔE > 3.0, it indicates that there is a significant difference between the paint film color and the standard, and a full repaint is required.

[0075] Suspicious Area Selection: Based on the surface defect detection results and anomalies in the mechanical performance testing, suspicious areas are selected on the bumper. For example, areas with strength significantly lower than the standard in the mechanical performance testing, or areas with abnormal color or texture on the surface, are designated as suspicious areas for further testing.

[0076] Identification of Recycled Materials and Additives: X-ray fluorescence spectrometry (XRF) is used for rapid screening of suspicious areas to preliminarily determine whether the material contains recycled materials or specific additives. For suspicious components, Fourier transform infrared spectroscopy (FTIR) is used for further analysis to determine their chemical structure and composition. For example, if the test finds that the proportion of recycled materials in a certain area exceeds the standard limit of 5%, the bumper may have a quality problem.

[0077] Test data encapsulation and digital fingerprint generation, data reception and integration: receive mechanical property test data, paint film quality test data and material composition test data respectively, integrate these data according to a unified data format to form a complete test dataset.

[0078] Data encapsulation and digital fingerprint generation: The integrated detection dataset is encapsulated using an encryption algorithm (such as SHA-256) to generate a unique digital fingerprint. This digital fingerprint serves as a unique identifier for the detection data and can be used for data integrity verification and traceability.

[0079] Blockchain platform selection: Choose a suitable blockchain platform (such as Hyperledger Fabric, Ethereum, etc.) and deploy and configure nodes according to the platform requirements.

[0080] On-chain data storage: The generated digital fingerprints are uploaded to a blockchain platform. Through the distributed ledger and encryption technology of the blockchain, the immutability and traceability of the detection data are ensured. For example, on the blockchain platform, the digital fingerprint of each detection data is recorded in a block and linked with the previous block through a hash value to form a complete blockchain.

[0081] S300: Combines surface defects and test results to determine the current quality inspection results of the car bumper and outputs the results.

[0082] In this embodiment, surface defects and test results are combined to determine the current quality inspection result of the car bumper, and then output the result. The specific process is as follows:

[0083] S301: 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;

[0084] S302: Set the first threshold and the second threshold, judge the overall quality score, the first threshold, and the second threshold, and output the current car bumper quality inspection result.

[0085] Furthermore, in the steps of setting a first threshold and a second threshold, judging the overall quality score, the first threshold, the second threshold, and outputting the current car bumper quality inspection result:

[0086] If the overall quality score is greater than or equal to the first threshold, the current car bumper quality inspection result is qualified;

[0087] If the overall quality score is less than the first threshold but greater than or equal to the second threshold, the current quality inspection result for the car bumper is "to be repaired".

[0088] If the overall quality score is less than the second threshold, the current quality inspection result of the car bumper is unqualified.

[0089] In the above process, feature data extraction involves extracting key feature data from surface defect detection data (such as defect type, defect density, defect location, etc.) 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 heatmap as a surface defect feature; yield strength, elongation at break, and energy absorption efficiency are extracted from the mechanical performance test data as mechanical performance features.

[0090] Data fusion method: A weighted average method is used to fuse the extracted feature data. Different weighting coefficients are assigned to each feature based on its impact on the bumper quality. For example, the weighting coefficient for surface defect features can be set to 0.4, for mechanical performance features to 0.35, for paint film quality features to 0.15, and for material composition features to 0.1. The overall quality score of the car bumper is then calculated through a weighted average.

[0091] Threshold setting: Based on the quality standards and actual needs of the car bumper, establish a first threshold and a second threshold. For example, the first threshold can be set to 80 points, and the second threshold can be set to 60 points.

[0092] Result Judgment and Output: The calculated overall quality score is compared with the first threshold and the second threshold.

[0093] If the overall quality score is greater than or equal to the first threshold (S≥80), the current quality inspection result of the car bumper is qualified, a "qualified" report is output, and it can be recommended to continue using it.

[0094] If the overall quality score is less than the first threshold but greater than or equal to the second threshold (60≤S<80), the current quality inspection result of the car bumper is "to be repaired". A "to be repaired" report will be output, detailing the parts that need to be repaired and repair suggestions (such as local painting, reinforcement, etc.).

[0095] If the overall quality score is less than the second threshold (S<60), the current car bumper quality inspection result is unqualified, an "unqualified" report is output, and replacement of the bumper is recommended. Simultaneously, the quality inspection result report is stored in the database for subsequent querying and traceability.

[0096] Corresponding to the foregoing embodiments of the quality inspection method for automobile bumpers, this application also provides embodiments of a quality inspection system for automobile bumpers.

[0097] Figure 5 This is a block diagram illustrating a quality inspection system for an automobile bumper according to an exemplary embodiment. (Refer to...) Figure 5The system may include: a defect area division module 401, a test result acquisition module 402, and a data fusion module 403; wherein:

[0098] The defect area division module 401 is used to acquire surface data of the car bumper, generate point cloud data, identify defects on the surface of the car bumper based on the point cloud data, and divide the surface of the car bumper into multiple detection sub-regions according to the defect type.

[0099] The test result acquisition module 402 is used to perform mechanical performance, paint film quality and material composition tests on the car bumper, output test result data, and store the test result data on the blockchain.

[0100] The data fusion module 403 is used to fuse surface defects and test results, determine the current quality inspection result of the car bumper, and output the result.

[0101] In this embodiment, the defect area segmentation module 401 acquires surface data of the car bumper, generates point cloud data, identifies surface defects of the car bumper based on the point cloud data, and divides the car bumper surface into multiple detection sub-regions according to the defect type; the test result acquisition module 402 performs mechanical performance, paint film quality, and material composition tests on the car bumper, outputs test result data, and stores the test result data on the blockchain; the data fusion module 403 fuses surface defects and test results, determines the current quality inspection result of the car bumper, and outputs it; through the above methods, the fusion analysis of multi-source data is realized, improving the comprehensiveness of quality assessment.

[0102] Regarding the system in the above embodiments, the specific manner in which each module performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0103] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0104] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the quality inspection method for automobile bumpers as described above. Figure 6 The diagram shown is a hardware structure diagram of any device with data processing capabilities used in a quality inspection system for automobile bumpers, according to an embodiment of the present invention. (Except for...) Figure 6 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0105] Accordingly, this application also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the aforementioned quality inspection method for automobile bumpers. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing 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, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0106] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0107] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A quality inspection method for automobile bumpers, characterized in that, Includes the following steps: Acquire surface data of the car bumper, generate point cloud data, identify surface defects of the car bumper based on the point cloud data, and divide the car bumper surface into multiple detection sub-regions according to the defect type. The mechanical properties, paint film quality, and material composition of the car bumper are tested respectively, the test results data are output, and the test results data are stored on the blockchain. By combining surface defects and test results, the current quality inspection result of the car bumper is determined and output; In the steps of acquiring surface data of a car bumper, generating point cloud data, identifying surface defects of the car bumper based on the point cloud data, and dividing the car bumper surface into multiple detection sub-regions according to the defect type: The car bumper is laser scanned, point cloud data is generated based on the scan data, and the point cloud data is then denoised and filtered. This tool identifies surface defects such as cracks, color differences, and chipping on car bumpers using point cloud data, and outputs defect identification data and defect types. Based on the type and severity of defects, the car bumper is divided into multiple inspection sub-areas, and each inspection sub-area is assigned a unique identifier. Generate a defect density heatmap independently for each region.

2. The quality inspection method for automobile bumpers as described in claim 1, characterized in that, The process involves conducting tests on the mechanical properties, paint film quality, and material composition of the car bumper, outputting the test results data, and storing the test results data using blockchain technology. Static deformation and dynamic impact tests were performed on the car bumper to obtain yield strength and elongation at break. The energy absorption deformation process of the bumper was captured, the energy absorption efficiency was calculated, and mechanical performance test data were obtained. Thickness, adhesion, and color difference tests were performed on the car 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.

3. The quality inspection method for automobile bumpers as described in claim 2, characterized in that, The process involves conducting tests on the mechanical properties, paint film quality, and material composition of the car bumper, outputting the test results data, and storing the test results data using blockchain technology. It receives mechanical property test data, paint film quality test data, and material composition test data respectively, and encapsulates them to generate a unique digital fingerprint.

4. The quality inspection method for automobile bumpers as described in claim 3, characterized in that, After receiving mechanical property test data, paint film quality test data, and material composition test data respectively, and then encapsulating them to generate a unique digital fingerprint: Digital fingerprints are stored on a blockchain platform for blockchain-based evidence preservation.

5. The quality inspection method for automobile bumpers as described in claim 1, characterized in that, In the steps of integrating surface defects and test results, judging the current quality inspection results of the car bumper, and outputting the results: 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; Set a first threshold and a second threshold, judge the overall quality score, the first threshold, and the second threshold, and output the current car bumper quality inspection result.

6. The quality inspection method for automobile bumpers as described in claim 5, characterized in that, In the steps of setting the first and second thresholds, judging the overall quality score, the first and second thresholds, and outputting the current car bumper quality inspection result: If the overall quality score is greater than or equal to the first threshold, the current car bumper quality inspection result is qualified; If the overall quality score is less than the first threshold but greater than or equal to the second threshold, the current quality inspection result for the car bumper is "to be repaired". If the overall quality score is less than the second threshold, the current quality inspection result of the car bumper is unqualified.

7. A quality inspection system for automobile bumpers, applied to the quality inspection method for automobile bumpers as described in claim 1, characterized in that, This includes a defect area segmentation module, a test result acquisition module, and a data fusion module; among which: The defect area division module is used to acquire surface data of the car bumper, generate point cloud data, identify defects on the surface of the car bumper based on the point cloud data, and divide the surface of the car bumper into multiple detection sub-regions according to the defect type. The test result acquisition module is used to perform mechanical performance, paint film quality, and material composition tests on the car bumper, output 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 quality inspection result of the car bumper, and output the result.

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