A visual inspection method and system for automobile welding production line

Abstract

The present invention relates to the field of visual inspection technology, specifically to a visual inspection method and system for an automobile welding production line, comprising the following steps: based on the video data of the automobile welding production line, performing frame segmentation on the original image sequence, extracting key visual information frame by frame, adjusting contrast and brightness using image processing technology, and generating serialized image frames. In the present invention, by using image processing technology, such as contrast and brightness adjustment, a clearer serialized image frame can be generated, the recognition accuracy of the weld boundary can be improved, and the image binarization processing is performed to not only clarify the boundary between the weld and the background, but also enhance the visualization of the weld boundary in the image, making the recognition of abnormal temperature distribution more accurate, and identifying internal inhomogeneities through pixel intensity analysis. The system can more effectively mark and track abnormal points, improve the positioning accuracy of abnormal temperature welds, not only optimize production efficiency, but also improve the overall standard of welding quality.

Description

Technical Field

[0001] The present invention relates to the technical field of visual inspection, and in particular to a visual inspection method and system for an automobile welding production line. Background Art

[0002] Visual inspection technology is a key technology used in industrial automation and is widely used for quality control and inspection in the manufacturing process. It mainly relies on high-resolution cameras, image processing software, and machine learning algorithms to capture images of products or components and then perform real-time analysis to identify defects, measure dimensions, or confirm assembly integrity. Visual inspection systems can improve inspection speed and accuracy, reduce labor costs, and improve overall production efficiency. With the advancement of artificial intelligence and machine learning technologies, the intelligence level of visual inspection systems is also constantly improving, and they can handle more complex inspection tasks and provide more accurate analysis results.

[0003] Among them, the visual inspection method of automobile welding production line refers to the use of visual inspection technology to ensure product quality and safety during the automobile manufacturing process, especially in the welding and assembly links. Specially designed cameras and image processing systems are used to monitor welding points and assembly quality, and to detect welding defects such as cracks, holes or uneven welds in real time. The main purpose is to improve the automation level of the production line, reduce the outflow of defective products, ensure the structural strength and durability of automobile parts, and improve the safety and reliability of products. Manufacturers can find problems in the early stages of production to avoid subsequent costly rework or recalls.

[0004] Although existing visual inspection technology is widely used in the field of industrial automation, especially playing a key role in quality control and inspection, it still shows some shortcomings when dealing with complex or changing production scenarios. For example, conventional visual inspection systems are not robust enough in image processing under lighting changes or background interference, that is, they are not stable and reliable enough in the face of uncertainty and changes, resulting in frequent problems of misidentification or missed detection. Traditional systems require high-performance processor support when analyzing high-speed moving objects in real time, which increases costs. On highly automated automotive welding production lines, even minor inspection delays can lead to reduced production efficiency and increased costs. Existing technologies are still limited in the early identification and precise positioning of welding defects, which to some extent slows down the fault response speed and maintenance efficiency of the production line. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a visual inspection method and system for an automobile welding production line.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a visual inspection method for an automobile welding production line, comprising the following steps:

[0007] S1: Based on the video data of the automobile welding production line, the original image sequence is segmented, key visual information is extracted frame by frame, and the contrast and brightness are adjusted using image processing technology to generate serialized image frames;

[0008] S2: using the serialized image frame to identify the weld boundary of the automobile welding assembly, performing image binarization processing to distinguish the weld from the background, and obtaining a weld boundary image;

[0009] S3: Analyze the non-uniformity of the interior of the automobile welding assembly by using pixel intensity through the weld boundary image, identify abnormal temperature distribution, collect abnormal features and mark them, and track and locate abnormal points to generate calibrated abnormal welds;

[0010] S4: using the calibrated abnormal welds, analyzing the key time points in the automobile welding production line using the timestamp index, and obtaining the time positioning result by comparing the changes in weld quality in differentiated time periods and recording the time when the abnormality occurred;

[0011] S5: Analyze the quality of the automobile welding assembly based on the time positioning result, classify the welding quality according to the shape and heat distribution of the weld, grade the automobile welding assembly according to the quality index, and generate the welding quality grade;

[0012] S6: Using the welding quality grade, adjust and optimize welding parameters, update the current and speed of the welding machine on the production line, match differentiated welding requirements, and obtain a production parameter adjustment plan.

[0013] As a further solution of the present invention, the serialized image frame includes an image with contrast adjustment, an image with frame brightness adjustment, and key visual information of each frame, the weld boundary image includes an image distinguishing the weld boundary and an image comparing the background and the weld, the calibrated abnormal weld includes a weld image marked with abnormal temperature distribution, an analysis image of inhomogeneity within the weld, and an abnormal point tracking and positioning map, the time positioning result includes a timestamp index of a key time point and a time period record of weld quality changes, the welding quality grade includes a weld shape classification result map, a heat distribution analysis result map, and a welding quality grading standard, and the production parameter adjustment plan includes an updated current setting parameter, an adjusted welding speed parameter, and a differentiated welding demand matching plan.

[0014] As a further solution of the present invention, based on the video data of the automobile welding production line, the original image sequence is frame segmented, key visual information is extracted frame by frame, and the contrast and brightness are adjusted by using image processing technology. The specific steps of generating serialized image frames are as follows:

[0015] S101: Perform frame segmentation processing on the video data of the automobile welding production line, extract a target number of frames per second with a fixed time interval, verify the continuity and integrity of the data, and generate a frame sequence image;

[0016] S102: using the frame sequence images, extracting coordinates of the welding points and the contact points of the automobile body in each frame, distinguishing key visual areas by color filtering, and marking visual information to obtain a key visual frame sequence;

[0017] S103: adjusting the contrast and brightness parameters of each frame of the image through the key visual frame sequence, respectively increasing the brightness and contrast of the target amount, optimizing the image quality and visual effect, and generating a serialized image frame.

[0018] As a further solution of the present invention, the serialized image frame is used to identify the weld boundary of the automobile welding assembly, and the image binarization process is performed to distinguish the weld from the background. The steps of obtaining the weld boundary image are specifically as follows:

[0019] S201: using the serialized image frame, adjusting the image to a uniform grayscale range according to the contrast difference between the weld and non-weld areas of the automobile welding production line, verifying the visualization of the weld features in the image, and obtaining a weld highlight image;

[0020] S202: Based on the weld protrusion image, a grayscale threshold is set to perform image binarization, and the weld area is separated from the non-weld area by optimizing the contrast between the weld boundary and the background to obtain a background-distinguished image;

[0021] S203: Using the background to distinguish the image, identify and extract the position of the weld boundary, optimize the continuity and clarity of the boundary line, evaluate and verify the visual recognition effect of the boundary line, and generate a weld boundary image.

[0022] As a further solution of the present invention, the steps of generating and calibrating abnormal welds are specifically as follows:

[0023] S301: using the weld boundary image, evaluating the non-uniformity inside the weld by analyzing the intensity of pixels, recording areas where pixel intensity changes, and marking potential non-uniform areas to obtain a non-uniform weld analysis diagram;

[0024] S302: Based on the uneven weld analysis diagram, edge detection technology is used to identify abnormal temperature areas in the automobile welding production line, record areas that deviate from the standard weld temperature distribution, and process the abnormal temperature distribution to obtain an abnormal temperature weld diagram;

[0025] S303: Using the abnormal temperature weld map, mark the location of abnormal temperature distribution, track and locate the abnormal point, associate the mark with the real-time location of the abnormal area, and generate a calibrated abnormal weld.

[0026] As a further solution of the present invention, the formula of the edge detection technology is as follows:

[0027]

[0028] in, is the gradient amplitude, and are the gradients of the image in the horizontal and vertical directions, respectively. and are the weighting coefficients in the horizontal and vertical directions, is the weighting coefficient of the diagonal gradient, is the gradient in the diagonal direction of the image, is the Gaussian blur coefficient, is the standard deviation, is the base of the natural exponent.

[0029] As a further solution of the present invention, the steps of using the calibrated abnormal welds, analyzing the key time points in the automobile welding production line using the timestamp index, comparing the changes in weld quality in differentiated time periods, recording the time when the abnormality occurred, and obtaining the time positioning result are specifically as follows:

[0030] S401: using the calibration of abnormal welds, assigning a timestamp to each abnormal weld, synchronizing the timestamp with the log of the welding production line, identifying and extracting the time when the abnormality occurred, and obtaining an abnormal timestamp index map;

[0031] S402: Based on the abnormal timestamp index map, analyzing the weld quality near the key time point, comparing the weld image recognition quality change trend in the differentiated time period, analyzing the time interval of the weld quality change, and obtaining a time-differentiated weld quality map;

[0032] S403: Record the time of each quality abnormality through the time-differentiated weld quality map, compare it with the history of the automobile welding production line, mark the time when the abnormality occurs, and generate a time positioning result.

[0033] As a further solution of the present invention, based on the time positioning result, the quality of automobile welding is analyzed, the welding quality is classified according to the shape and heat distribution of the weld, and the automobile welding is graded according to the quality index. The steps of generating the welding quality grade are specifically as follows:

[0034] S501: Analyze the welding quality at the target time point using the time positioning result, evaluate the consistency with the standard quality by monitoring the shape and heat distribution of the weld, and obtain a weld shape and heat distribution analysis diagram;

[0035] S502: Based on the weld shape and heat distribution analysis diagram, the weld is classified using preset quality evaluation indicators, including the integrity, uniformity and regularity of heat distribution of the weld, to obtain a classified welding quality diagram;

[0036] S503: Using the classified welding quality map, the automobile welding is graded according to the quality index, and the quality is prioritized, and each level is verified to meet the associated quality standards, and a welding quality grade is generated.

[0037] As a further solution of the present invention, the welding quality level is used to adjust and optimize welding parameters, and the current and speed of the welding machine on the production line are updated to match the differentiated welding requirements. The steps of obtaining the production parameter adjustment solution are specifically as follows:

[0038] S601: using the welding quality grade, analyzing the welding quality data under the differentiated grade, identifying the welding parameters that need to be adjusted, including current and speed, formulating adjustment requirements for each quality grade, and obtaining a welding parameter requirement analysis table;

[0039] S602: Based on the welding parameter demand analysis table, set the current and speed adjustment plan, the adjustment range is based on the welding quality level requirements, verify that the welding parameters of each level match the quality standards, and obtain a draft welding parameter adjustment plan;

[0040] S603: The draft welding parameter adjustment plan is used to update the current and speed of the welding machines on the production line, and the production parameter adjustment plan is generated by adjusting the settings of the differentiated welding sites to match the real-time changing welding requirements.

[0041] A visual inspection system for an automobile welding production line, the visual inspection system for an automobile welding production line is used to execute the above-mentioned visual inspection method for an automobile welding production line, the system comprising:

[0042] The frame segmentation module segments the original image sequence based on the video data of the automobile welding production line, extracts visual information frame by frame, and adjusts the contrast and brightness of each frame to generate serialized image frames;

[0043] The boundary recognition module recognizes the weld and the background based on the serialized image frame, performs image binarization processing, and generates a weld boundary image;

[0044] The abnormal temperature analysis module uses the weld boundary image to analyze pixel intensity, identify abnormal temperature distribution, collect and mark abnormal features, and track and locate abnormal points to generate calibrated abnormal welds;

[0045] The timestamp index module analyzes the key time points in the automobile welding production line by calibrating the abnormal welds, and obtains the time positioning result by comparing the changes in weld quality and recording the time when the abnormality occurs;

[0046] The welding quality classification module analyzes the weld shape and heat distribution according to the time positioning result, classifies the welding quality, adjusts and optimizes the welding parameters according to differentiated welding requirements, and generates a production parameter adjustment plan.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by performing frame segmentation and frame-by-frame key visual information extraction on the video data of the automobile welding production line, and utilizing image processing techniques, such as contrast and brightness adjustment, a clearer serialized image frame can be generated, thereby improving the recognition accuracy of the weld boundary. Performing image binarization processing not only clarifies the boundary between the weld and the background, but also enhances the visualization of the weld boundary in the image, making the recognition of abnormal temperature distribution more accurate. By identifying internal inhomogeneities through pixel intensity analysis, the system can more effectively mark and track abnormal points, thereby improving the positioning accuracy of abnormal temperature welds. Timestamp index analysis helps capture key time points, compares weld quality changes within differentiated time periods, and increases the accurate record of the time when the abnormality occurs. Quality classification based on weld shape and heat distribution, as well as adjustment of welding parameters, not only optimizes production efficiency, but also improves the overall standard of welding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] See also Figure 1 The present invention provides a technical solution, a visual inspection method for an automobile welding production line, comprising the following steps:

[0059] S1: Based on the video data of the automobile welding production line, the original image sequence is segmented, key visual information is extracted frame by frame, and the contrast and brightness are adjusted using image processing technology to optimize the image quality and generate serialized image frames;

[0060] S2: using serialized image frames to identify the weld boundary of the automobile welding assembly, performing image binarization processing to distinguish the weld from the background, and obtaining a weld boundary image;

[0061] S3: Through the weld boundary image, the pixel intensity is used to analyze the internal non-uniformity of the automobile welding assembly, identify abnormal temperature distribution, collect abnormal features and mark them, and track and locate abnormal points to generate calibrated abnormal welds;

[0062] S4: Use the calibration of abnormal welds and the timestamp index to analyze the key time points in the automobile welding production line. By comparing the changes in weld quality in different time periods, record the time when the abnormality occurs, and obtain the time positioning result;

[0063] S5: Based on the time positioning results, the quality of automobile welding is analyzed, the welding quality is classified according to the shape and heat distribution of the weld, the automobile welding is graded according to the quality indicators, and the welding quality grade is generated;

[0064] S6: Use the welding quality grade to adjust and optimize the welding parameters, update the current and speed of the welding machines on the production line, match the differentiated welding requirements, optimize the production efficiency and quality, and obtain the production parameter adjustment plan.

[0065] The serialized image frames include images with contrast adjustment, images with frame brightness adjustment, and key visual information of each frame. The weld boundary image includes an image of weld boundary distinction and an image of background and weld comparison. The calibrated abnormal welds include weld images marked with abnormal temperature distribution, images of weld internal non-uniformity analysis, and abnormal point tracking and positioning maps. The time positioning results include timestamp indexes of key time points and time period records of weld quality changes. The welding quality grade includes weld shape classification result maps, heat distribution analysis result maps, and welding quality grading standards. The production parameter adjustment plan includes updated current setting parameters, adjusted welding speed parameters, and differentiated welding demand matching plans.

[0066] See also Figure 2 Based on the video data of the automobile welding production line, the original image sequence is segmented, key visual information is extracted frame by frame, and the contrast and brightness are adjusted using image processing technology. The specific steps of generating serialized image frames are as follows:

[0067] S101: Perform frame segmentation processing on the video data of the automobile welding production line, extract a target number of frames per second with a fixed time interval, verify the continuity and integrity of the data, and generate a frame sequence image. The execution process is as follows;

[0068] Frame segmentation of the video data of the automobile welding production line involves segmenting the video at fixed time intervals to ensure that a predetermined number of frames can be extracted per second to capture the continuous actions and key moments on the production line. The key to frame segmentation is to ensure the continuity and integrity of the data and avoid the loss of important information during the extraction process. It can effectively extract static images from dynamic videos and provide a basis for subsequent analysis. It is also necessary to verify the timestamp and order of each frame image to generate a frame sequence image.

[0069] S102: Using the frame sequence image, extracting the coordinates of the welding point and the automobile body contact point in each frame, distinguishing the key visual area through color filtering, and marking the visual information, the execution process of obtaining the key visual frame sequence is as follows;

[0070] Using frame sequence images, extracting key elements for identification and coordinate extraction, it is necessary to identify the contact point position of the welding point and the car body in each frame, and distinguish the key visual area through color filtering technology. Color filtering helps identify welding points and important visual marks for further analysis and processing. Coordinates are extracted to ensure that the movement trajectory and operation points of welding points on the production line can be accurately tracked. The extracted visual information will be used for marking to provide data support for automated monitoring and quality control systems. It is the key to realizing an automated visual monitoring system and is of great significance to improving the efficiency of the production line and the welding quality. The key visual frame sequence is obtained using the formula:

[0071]

[0072] in, are the welding point coordinates, and are the X and Y coordinates of the point where the weld contacts the car body, and Processing correction values, and are the adjustment parameters for the X and Y coordinates, and is the weight factor for coordinate adjustment.

[0073] S103: adjusting the contrast and brightness parameters of each frame of the image through the key visual frame sequence, respectively increasing the brightness and contrast of the target amount, optimizing the image quality and visual effect, and generating the execution flow of the serialized image frame is as follows;

[0074] Through the key visual frame sequence, the contrast and brightness parameters of the image are adjusted to optimize the image quality of the extracted key visual frame sequence. By increasing the brightness and contrast of the target number, the important features in the image can be made more prominent, which is convenient for subsequent analysis and recognition. Adjusting the parameters not only improves the visual effect of the image, but also helps the automation system to perform tasks such as solder joint detection and alignment more accurately. The optimized image is also more user-friendly for operators to monitor the status of the production line, and problems and anomalies can be observed more clearly. By enhancing the quality of each frame of the image, a serialized image frame is generated, and the formula used is:

[0075]

[0076] in, is the adjusted image brightness, is the original image brightness, and To adjust the contrast and brightness parameters, is the sensitivity parameter for contrast adjustment, is the average brightness of the image.

[0077] See also Figure 3 , using serialized image frames, identifying the weld boundary of automobile welding, performing image binarization processing to distinguish the weld from the background, and obtaining the weld boundary image in the following steps:

[0078] S201: using the serialized image frames, adjusting the image to a uniform grayscale range according to the contrast difference between the weld and non-weld areas of the automobile welding production line, verifying the visualization of the weld features in the image, and obtaining the weld highlight image. The execution process is as follows;

[0079] The serialized image frames are used to highlight the weld area on the automobile welding production line by adjusting the grayscale range of the image. The focus is on using the difference in contrast between the weld and non-weld areas to adjust the image to a uniform grayscale range so that the weld position can be more clearly identified. The adjustment helps to enhance the visualization of the weld features, making the weld more obvious in the image, which is convenient for subsequent analysis and processing. The unified grayscale processing of the image not only improves the image quality, but also provides clearer visual input for the automated detection system, improves recognition accuracy and processing speed, and generates a weld highlight image. The formula used is:

[0080]

[0081] in, Represents the grayscale value of the adjusted image, and are the maximum and minimum values ​​of the target grayscale range, is the grayscale value of the original image, is the minimum gray value of the original image, and are the maximum and minimum grayscale values ​​in the original image, respectively.

[0082] S202: Based on the weld highlight image, a grayscale threshold is set to binarize the image, and the weld area is separated from the non-weld area by optimizing the contrast between the weld boundary and the background, and the execution process of obtaining the background distinguishing image is as follows;

[0083] Based on the weld highlight image, binarization processing is performed. The weld area and the non-weld area are separated by setting an appropriate grayscale threshold. The contrast between the weld boundary and the background is optimized by adjusting the threshold to ensure that the weld area is visually clearly separated from the surrounding non-weld area. Binarization is a common technology in image processing. The image is simplified into black and white, with white representing the weld area and black representing the background. This not only simplifies the complexity of the image, but also facilitates subsequent image analysis and feature extraction, such as automatic detection of weld quality, to obtain a background-distinguished image.

[0084] S203: distinguish the image from the background, identify and extract the position of the weld boundary, optimize the continuity and clarity of the boundary line, evaluate and verify the visual recognition effect of the boundary line, and generate the weld boundary image. The execution process is as follows;

[0085] By distinguishing the image from the background, identifying and extracting the position of the weld boundary, it is necessary not only to identify the boundary, but also to optimize the continuity and clarity of the boundary line to ensure that the representation of the weld boundary in the image is accurate. The process of optimizing the boundary line includes adjusting the image processing parameters to improve the visual recognition effect of the boundary, making the boundary more obvious, and facilitating the accurate interpretation of the machine vision system. It is the key to ensure the accuracy of welding quality control, because only by clearly identifying and correctly parsing the weld boundary can the automation equipment accurately perform subsequent welding tasks and generate a weld boundary image. The formula used is:

[0086]

[0087] in, represents the weld boundary image, Representative The image of the weld boundary images, Indicates the clarity of the boundary line. is the weight coefficient for adjusting the clarity effect, Indicates the discontinuity of the boundary line, Represents the total number of images.

[0088] See also Figure 4 ,Through the weld boundary image, the pixel intensity is used to analyze the internal non-uniformity of the automobile welding assembly, identify the abnormal temperature distribution, collect and mark the abnormal features, and track and locate the abnormal points. The steps of generating and calibrating the abnormal welds are as follows:

[0089] S301: Using the weld boundary image, evaluating the non-uniformity inside the weld by analyzing the intensity of pixels, recording the area where the pixel intensity changes, and marking the potential non-uniform area, the execution process of obtaining the non-uniform weld analysis diagram is as follows;

[0090] The weld boundary image is used to analyze the inhomogeneity inside the weld. Through detailed evaluation of the pixel intensity in the image, it is possible to identify the uneven areas that occur during the welding process. The areas are manifested as significant changes in pixel intensity, which is crucial for detecting potential welding defects because the inhomogeneity indicates insufficient material strength or welding technology problems. By recording and marking the changed areas, further quantitative analysis and quality control can be performed to ensure the consistency of the welding process and the structural integrity of the product, and to generate an analysis map of the uneven weld.

[0091] S302: Based on the uneven weld analysis diagram, edge detection technology is used to identify abnormal temperature areas in the automobile welding production line, record areas that deviate from the standard weld temperature distribution, and process the abnormal temperature distribution to obtain the abnormal temperature weld diagram. The execution process is as follows;

[0092] Based on the uneven weld analysis diagram, edge detection technology is used to identify abnormal temperature areas from the uneven weld analysis diagram. It can accurately locate the areas with uneven temperature distribution during welding, which is crucial to ensuring welding quality and avoiding welding defects. Abnormal temperature areas are related to potential welding problems, such as overheating or failure to reach the required welding temperature. By recording the deviation area and comparing it with the standard weld temperature distribution, the welding parameters can be more accurately processed and adjusted to correct the problem and generate an abnormal temperature weld diagram.

[0093] The formula for edge detection technique is as follows:

[0094]

[0095] in, is the gradient amplitude, and are the gradients of the image in the horizontal and vertical directions, respectively. and are the weighting coefficients in the horizontal and vertical directions, is the weighting coefficient of the diagonal gradient, is the gradient in the diagonal direction of the image, is the Gaussian blur coefficient, is the standard deviation, is the base of the natural exponent.

[0096] The execution flow is as follows:

[0097] Calculate the horizontal and vertical gradients of each pixel in the image and , applying weight coefficients to the gradient values and , the weights are automatically adjusted by analyzing the image content to enhance important edge features and introduce diagonal gradients And apply weights , to identify edge directions that are ignored in standard edge detection, apply a Gaussian blur coefficient and standard deviation ,The influence of the gradient value is adjusted by the Gaussian function to smooth the noise and optimize the edge detection results. The optimal value of the parameter is determined by the preliminary analysis of the image, or learned from batch samples using machine learning technology. The entire edge detection process is completed, a more accurate edge intensity map is obtained, thus enhancing the recognition of abnormal temperature areas.

[0098] S303: Through the abnormal temperature weld map, mark the location of abnormal temperature distribution, track and locate the abnormal point, associate the mark with the real-time location of the abnormal area, and generate the execution process of calibrating the abnormal weld as follows;

[0099] Through the abnormal temperature weld map, the location of abnormal temperature distribution is marked, the abnormal point is tracked and located, and the mark is associated with the real-time location of the abnormal area, allowing operators to quickly respond and adjust welding parameters to solve potential quality problems. By accurately calibrating the abnormal area, it can ensure more accurate welding quality control on the production line, effectively reduce the scrap rate and improve product consistency. The formula used to generate calibrated abnormal welds is:

[0100]

[0101] in, represents the normalized difference measure of abnormal temperature distribution in the image, For The observed point Temperature value, Normal temperature value for reference, is the number of measurement points, is the standard deviation of the temperature values, The number of measurement points minus 1.

[0102] See also Figure 5 , the abnormal welds are calibrated, and the key time points in the automobile welding production line are analyzed using the timestamp index. By comparing the changes in weld quality in different time periods and recording the time when the abnormality occurs, the steps to obtain the time positioning results are as follows:

[0103] S401: using calibration abnormal welds, assigning a timestamp to each abnormal weld, synchronizing the timestamp with the log of the welding assembly production line, identifying and extracting the time when the abnormality occurred, and obtaining the abnormal timestamp index map. The execution process is as follows;

[0104] By calibrating abnormal welds, assigning a timestamp to each identified abnormal weld and synchronizing it with the operation log of the welding production line, the specific time point of each abnormality can be accurately tracked, allowing for more in-depth analysis and root cause exploration. By synchronizing the timestamp with the production line log, the time of each welding abnormality can be accurately identified and extracted, which not only helps the operation team to quickly locate the problem, but also helps to perform preventive maintenance and operation optimization, and generate an abnormal timestamp index map. The formula used is:

[0105]

[0106] in, Represents the abnormal timestamp index map, Representative timestamp of the abnormal welds, is the weight coefficient for adjusting the impact of time synchronization, Indicates time synchronization. Represents the time record of the production log, Indicates the time deviation, Represents the total number of abnormal welds.

[0107] S402: Based on the abnormal timestamp index map, analyze the weld quality near the key time point, compare the weld image recognition quality change trend in the differentiated time period, analyze the time interval of the weld quality change, and obtain the time-differentiated weld quality map. The execution process is as follows;

[0108] Based on the abnormal timestamp index map, the weld quality near the key time points is analyzed. By comparing the weld images in different time periods, the quality change trend can be identified to ensure continuous optimization of welding quality. By analyzing the time interval of weld quality changes, the problems and fluctuations in the production process can be better understood, helping the team predict potential risk points and take measures to improve or adjust them, generating a time-differentiated weld quality map.

[0109] S403: Record the time of each quality abnormality through the time-differentiated weld quality map, compare it with the history of the automobile welding production line, mark the time when the abnormality occurred, and generate the execution process of the time positioning result as follows;

[0110] Through the time-differentiated weld quality map, the specific time of each quality anomaly is recorded and compared with the historical data of the automobile welding production line. By marking the time when the anomaly occurred, the root cause of the problem can be tracked and located more accurately, which is crucial for understanding the periodic problems and random failures that occur on the production line. By associating the abnormal time with the actual operation situation, the production process can be optimized to ensure that the problem is identified and solved in time, and the time positioning result is generated. The formula used is:

[0111]

[0112] in, For the time positioning result, is the current quality abnormality time point, is the historical data time point, Record values ​​for abnormal quality. is the weight coefficient of historical data.

[0113] See also Figure 6 Based on the time positioning results, the quality of automobile welding is analyzed, the welding quality is classified according to the shape and heat distribution of the weld, and the automobile welding is graded according to the quality indicators. The specific steps for generating welding quality grades are as follows:

[0114] S501: Using the time positioning result, analyzing the welding quality at the target time point, and evaluating the consistency with the standard quality by monitoring the shape and heat distribution of the weld, the execution process of obtaining the weld shape and heat distribution analysis diagram is as follows;

[0115] Using time positioning results to analyze welding quality at a specific time point, including detailed monitoring of weld shape and heat distribution to evaluate consistency with standard quality, quality issues that affect product structural integrity and performance, such as irregular weld shape or uneven heat distribution, can be identified. It is essential to ensure that each welding operation meets high quality standards, and can help the production team adjust welding parameters or processes in a timely manner to prevent quality deviations from affecting product performance, and generate weld shape and heat distribution analysis diagrams using the formula:

[0116]

[0117] in, is the weld quality analysis value, and are the weld shape and heat distribution coefficient, and is the shape and heat distribution coefficient of the standard mass, is the standard quality deviation.

[0118] S502: Based on the weld shape and heat distribution analysis diagram, the preset quality evaluation index is applied to classify the welding, including the integrity, uniformity and regularity of the heat distribution of the weld, and the execution process of obtaining the classified welding quality diagram is as follows;

[0119] Based on the weld shape and heat distribution analysis diagram, the preset quality evaluation indicators are used to classify the welding, including evaluating the integrity, uniformity and regularity of the weld heat distribution, improving the quality control of the welding process, and ensuring that the welding process meets the predetermined quality standards through quantitative analysis of various aspects of the weld. It not only helps the operation team understand the quality status of each weld, but also provides a basis for subsequent process improvements to generate a classified welding quality diagram. The formula used is:

[0120]

[0121] in, To classify the welding quality value, is the weld integrity value, is the weld uniformity value, is the regularity value of heat distribution, is the evaluation weight coefficient.

[0122] S503: By classifying the welding quality diagram, the automobile welding is graded according to the quality index, and the quality priority is sorted, and each level is verified to meet the associated quality standards. The execution process of generating the welding quality level is as follows;

[0123] By classifying the welding quality map, grading the automotive welding according to the quality indicators, and prioritizing the quality, it is possible to effectively verify whether each level meets the associated quality standards, which is essential to ensure the quality consistency of the entire production line. Through accurate quality grading, the welding process can be better managed, quality problems can be prevented, and each welded part can be ensured to meet or exceed the expected performance standards. The welding quality grade is generated using the formula:

[0124]

[0125] in, is the welding quality grade, is the welding quality grading value, is the priority sorting coefficient, is the compliance standard threshold, is the consistency coefficient of historical data.

[0126] See also Figure 7 , using the welding quality level, adjusting and optimizing the welding parameters, updating the current and speed of the welding machines on the production line, matching the differentiated welding requirements, and obtaining the production parameter adjustment plan. The specific steps are:

[0127] S601: Using welding quality levels, analyzing welding quality data under differentiated levels, identifying welding parameters that need to be adjusted, including current and speed, formulating adjustment requirements for each quality level, and obtaining the welding parameter requirement analysis table. The execution process is as follows;

[0128] Using welding quality levels, analyze welding quality data under differentiated levels, including identifying welding parameters that need to be adjusted, such as current and speed. By analyzing the welding data corresponding to each quality level in detail, the key factors affecting welding quality can be identified, and specific adjustment requirements can be formulated for each level to improve welding quality and production efficiency, because it allows the operating team to adjust the settings of the welding machine in a targeted manner to adapt to the specific requirements of different quality levels and generate a welding parameter demand analysis table.

[0129] S602: Based on the welding parameter demand analysis table, set the current and speed adjustment plan, the adjustment range is based on the welding quality level requirements, verify that the welding parameters of each level match the quality standards, and obtain the draft welding parameter adjustment plan. The execution process is as follows;

[0130] Based on the welding parameter demand analysis table, set the current and speed adjustment plan. The scope of the adjustment plan is determined according to the specific requirements of the welding quality level. It is the key to ensure that the welding parameters match the quality standards. Through accurate parameter settings, the welding quality and the overall efficiency of the production line can be significantly improved. Setting reasonable current and speed not only affects the welding quality, but also affects the production cost and equipment life. Generate a draft welding parameter adjustment plan.

[0131] S603: The current and speed of the welding machines on the production line are updated through the draft welding parameter adjustment plan, and the settings of the differentiated welding sites are adjusted to match the real-time changing welding requirements. The execution process of generating the production parameter adjustment plan is as follows;

[0132] Through the draft welding parameter adjustment plan, the current and speed of the welding machines on the production line are updated, and the settings of the differentiated welding stations are adjusted to match the real-time changing welding needs. It is crucial to ensure that the welding process adapts to changes in production needs. Effectively adjusting parameters can reduce quality problems and improve production efficiency. By implementing adjustments, the production line can respond to different production challenges more flexibly and generate production parameter adjustment plans.

[0133] See also Figure 8 , a visual inspection system for an automobile welding production line, the visual inspection system for an automobile welding production line is used to execute the above-mentioned visual inspection method for an automobile welding production line, the system comprises:

[0134] The frame segmentation module segments the original image sequence based on the video data of the automobile welding production line, extracts visual information frame by frame, and adjusts the contrast and brightness of each frame to generate serialized image frames;

[0135] The boundary recognition module identifies the weld and the background based on the serialized image frames, performs image binarization processing, and generates a weld boundary image;

[0136] The abnormal temperature analysis module uses the weld boundary image to analyze pixel intensity, identify abnormal temperature distribution, collect and mark abnormal features, and track and locate abnormal points to generate calibrated abnormal welds;

[0137] The timestamp index module calibrates abnormal welds and analyzes the key time points in the automobile welding production line. It compares the changes in weld quality, records the time when the abnormality occurs, and obtains the time positioning result.

[0138] The welding quality classification module analyzes the weld shape and heat distribution according to the time positioning results, classifies the welding quality, adjusts and optimizes the welding parameters according to differentiated welding requirements, and generates a production parameter adjustment plan.

[0139] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A visual inspection method for an automobile welding production line, characterized in that: The following steps are involved: Based on the video data of the automobile welding production line, the original image sequence is segmented, key visual information is extracted frame by frame, and the contrast and brightness are adjusted using image processing technology to generate serialized image frames; The serialized image frames are used to identify the weld boundary of the automobile welding assembly, and image binarization processing is performed to distinguish the weld from the background to obtain a weld boundary image; By using the weld boundary image, the pixel intensity is used to analyze the non-uniformity inside the automobile welding assembly, the abnormal temperature distribution is identified, the abnormal features are collected and marked, and the abnormal points are tracked and located to generate a calibrated abnormal weld; The abnormal welds are calibrated as described above, and the key time points in the automobile welding production line are analyzed using the timestamp index. By comparing the changes in weld quality in different time periods, the time when the abnormality occurs is recorded, and the time positioning result is obtained; Based on the time positioning result, the quality of the automobile welding is analyzed, the welding quality is classified according to the shape and heat distribution of the weld, the automobile welding is graded according to the quality index, and the welding quality grade is generated; Using the welding quality grade, adjusting and optimizing welding parameters, updating the current and speed of welding machines on the production line, matching differentiated welding requirements, and obtaining a production parameter adjustment plan; The steps of analyzing the unevenness of the interior of the automobile welding assembly by using pixel intensity through the weld boundary image, identifying abnormal temperature distribution, collecting and marking abnormal features, and tracking and locating abnormal points to generate and calibrate abnormal welds are as follows: Using the weld boundary image, evaluating the internal non-uniformity of the weld by analyzing the intensity of pixels, recording areas where pixel intensity changes, and marking potential non-uniform areas to obtain a non-uniform weld analysis diagram; Based on the uneven weld analysis diagram, edge detection technology is used to identify abnormal temperature areas in the automobile welding production line, record areas that deviate from the standard weld temperature distribution, and process the abnormal temperature distribution to obtain an abnormal temperature weld diagram; By using the abnormal temperature weld map, the position of abnormal temperature distribution is marked, the abnormal point is tracked and located, and the mark is associated with the real-time position of the abnormal area to generate a calibrated abnormal weld; The formula for the edge detection technique is as follows: in, is the gradient amplitude, and are the gradients of the image in the horizontal and vertical directions, respectively. and are the weighting coefficients in the horizontal and vertical directions, is the weighting coefficient of the diagonal gradient, is the gradient in the diagonal direction of the image, is the Gaussian blur coefficient, is the standard deviation, is the base of the natural exponent.

2. The visual inspection method for automobile welding production line according to claim 1 is characterized in that: The serialized image frames include contrast-adjusted images, frame brightness-adjusted images, and key visual information of each frame; the weld boundary image includes an image distinguishing the weld boundary and an image comparing the background and the weld; the calibrated abnormal weld includes a weld image marked with abnormal temperature distribution, an image for analyzing the inhomogeneity within the weld, and an abnormal point tracking and positioning map; the time positioning result includes a timestamp index of a key time point and a time period record of weld quality changes; the welding quality grade includes a weld shape classification result map, a heat distribution analysis result map, and a welding quality grading standard; the production parameter adjustment plan includes an updated current setting parameter, an adjusted welding speed parameter, and a differentiated welding demand matching plan.

3. The visual inspection method for automobile welding production line according to claim 1 is characterized in that: Based on the video data of the automobile welding production line, the original image sequence is segmented, key visual information is extracted frame by frame, and the contrast and brightness are adjusted using image processing technology. The specific steps to generate serialized image frames are as follows: Perform frame segmentation processing on the video data of the automobile welding production line, extract the target number of frames per second with a fixed time interval, verify the continuity and integrity of the data, and generate frame sequence images; The frame sequence images are used to extract coordinates of the welding points and the contact points of the automobile body in each frame, distinguish key visual areas by color filtering, and mark visual information to obtain a key visual frame sequence; Through the key visual frame sequence, the contrast and brightness parameters of each frame of the image are adjusted to increase the brightness and contrast of the target amount respectively, optimize the image quality and visual effect, and generate a serialized image frame.

4. The automobile welding production line visual inspection method according to claim 1 is characterized in that: The steps of using the serialized image frame to identify the weld boundary of the automobile welding assembly, performing image binarization processing to distinguish the weld from the background, and obtaining the weld boundary image are specifically as follows: The serialized image frames are used to adjust the image to a uniform grayscale range according to the contrast difference between the weld and non-weld areas of the automobile welding production line, verify the visualization of the weld features in the image, and obtain a weld highlight image; Based on the weld protrusion image, a grayscale threshold is set to binarize the image, and the weld area is separated from the non-weld area by optimizing the contrast between the weld boundary and the background to obtain a background-distinguished image; The background is distinguished by the image, the position of the weld boundary is identified and extracted, the continuity and clarity of the boundary line are optimized, the visual recognition effect of the boundary line is evaluated and verified, and the weld boundary image is generated.

5. The visual inspection method for automobile welding production line according to claim 1 is characterized in that: The steps of using the calibrated abnormal welds, analyzing the key time points in the automobile welding production line using the timestamp index, comparing the changes in weld quality in differentiated time periods, recording the time when the abnormality occurred, and obtaining the time positioning result are as follows: Using the calibration method for abnormal welds, assigning a timestamp to each abnormal weld, synchronizing the timestamp with the log of the welding production line, identifying and extracting the time when the abnormality occurred, and obtaining an abnormal timestamp index map; Based on the abnormal timestamp index map, the weld quality near the key time point is analyzed, the quality change trend of the weld image recognition in the differentiated time period is compared, the time interval of the weld quality change is analyzed, and a time-differentiated weld quality map is obtained; The time of each quality anomaly is recorded through the time-differentiated weld quality map, and compared with the history of the automobile welding production line, the time when the anomaly occurred is marked, and a time positioning result is generated.

6. The automobile welding production line visual inspection method according to claim 1 is characterized in that: Based on the time positioning results, the quality of automobile welding is analyzed, the welding quality is classified according to the shape and heat distribution of the weld, and the automobile welding is graded according to the quality indicators. The specific steps of generating the welding quality grade are as follows: The time positioning result is used to analyze the welding quality at the target time point, and the consistency with the standard quality is evaluated by monitoring the shape and heat distribution of the weld, so as to obtain a weld shape and heat distribution analysis diagram; Based on the weld shape and heat distribution analysis diagram, the welds are classified using preset quality evaluation indicators, including the integrity, uniformity and regularity of heat distribution of the welds, to obtain a classified welding quality diagram; Through the classified welding quality map, the automobile welding is graded according to the quality index, and the quality priority is sorted, and each level is verified to meet the associated quality standards to generate the welding quality grade.

7. The visual inspection method for automobile welding production line according to claim 1 is characterized in that: The welding quality level is used to adjust and optimize welding parameters, update the current and speed of welding machines on the production line, match differentiated welding requirements, and obtain the production parameter adjustment plan in the following steps: Using the welding quality grades, analyzing the welding quality data under the differentiated grades, identifying the welding parameters that need to be adjusted, including current and speed, formulating adjustment requirements for each quality grade, and obtaining a welding parameter requirement analysis table; Based on the welding parameter demand analysis table, set the current and speed adjustment plan, the adjustment range is based on the requirements of the welding quality level, verify that the welding parameters of each level match the quality standards, and obtain a draft welding parameter adjustment plan; Through the draft welding parameter adjustment plan, the current and speed of the welding machines on the production line are updated, and by adjusting the settings of differentiated welding sites to match the real-time changing welding requirements, a production parameter adjustment plan is generated.

8. A visual inspection system for an automobile welding production line, characterized in that: According to the automobile welding production line visual inspection method according to any one of claims 1 to 7, the system comprises: The frame segmentation module segments the original image sequence based on the video data of the automobile welding production line, extracts visual information frame by frame, and adjusts the contrast and brightness of each frame to generate serialized image frames; The boundary recognition module recognizes the weld and the background based on the serialized image frame, performs image binarization processing, and generates a weld boundary image; The abnormal temperature analysis module uses the weld boundary image to analyze pixel intensity, identify abnormal temperature distribution, collect and mark abnormal features, and track and locate abnormal points to generate calibrated abnormal welds; The timestamp indexing module analyzes the key time points in the automobile welding production line by calibrating the abnormal welds, and obtains the time positioning result by comparing the changes in weld quality and recording the time when the abnormality occurs; The welding quality classification module analyzes the weld shape and heat distribution according to the time positioning result, classifies the welding quality, adjusts and optimizes the welding parameters according to differentiated welding requirements, and generates a production parameter adjustment plan.

Images