Intelligent welding control method and system for automobile processing

Abstract

The present invention relates to the field of welding control technology, specifically to an intelligent welding control method and system for automobile processing, comprising the following steps: configuring a camera in an automobile welding operation area, adjusting the focal length and exposure, capturing welding images of key parts of the vehicle body structure, performing noise elimination processing, and obtaining a preliminary automobile weld image set. In the present invention, edge enhancement and color correction technology are used to make the image clearer, and structural defects can be accurately marked. By identifying irregular patterns in the image and comparing them with known defect features, welding defects can be effectively recorded, and the accuracy of welding quality control can be improved. Through real-time monitoring of environmental parameters and comprehensive analysis of data, control of the welding process can be enhanced, structural integrity and potential risks can be effectively predicted, and the safety and reliability of the welding process can be improved. Welding parameter adjustment for different vehicle models and material characteristics can improve the adaptability and flexibility of production, significantly optimize production speed and reduce manufacturing costs.

Description

Technical Field

[0001] The present invention relates to the field of welding control technology, and in particular to an intelligent welding control method and system for automobile processing. Background Art

[0002] The field of welding control technology involves the development and implementation of technical methods and systems for managing and optimizing the welding process, aiming to improve the quality and production efficiency of welded joints by accurately controlling welding parameters such as current, voltage, welding speed, and heat input. Intelligent welding control technology uses artificial intelligence and machine learning algorithms to predict and solve potential problems in welding, thereby achieving more stable and automated welding operations, which has an important impact on improving the automation level and manufacturing quality of industrial production lines, especially in the fields of aviation, automobile manufacturing and heavy industry.

[0003] Among them, the intelligent welding control method for automobile processing refers specifically to the development of an intelligent control method for the welding process in automobile manufacturing. The purpose is to monitor and adjust the welding process in real time by integrating advanced sensing technology and intelligent algorithms to ensure the quality and efficiency of the weld, reduce defects, increase production speed, and reduce manufacturing costs. The application of intelligent welding control in automobile manufacturing is very critical, which can ensure the welding quality of vehicle structural components, thus directly affecting the safety and reliability of the product.

[0004] Although the existing technology has achieved precise control of welding parameters, it lacks real-time monitoring and dynamic adjustment capabilities. When encountering differences in material batches or environmental changes, it cannot respond quickly, resulting in inconsistent automotive welding quality. At the same time, the existing technology usually does not integrate advanced image processing and machine learning technologies, which limits the accuracy and efficiency of weld quality monitoring. In actual operation, the lack of real-time feedback and adaptability leads to the failure to identify and handle welding defects in a timely manner, increasing structural weaknesses and seriously affecting the safety and reliability of the vehicle. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent welding control method and system for automobile processing.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an intelligent welding control method for automobile processing, comprising the following steps:

[0007] S1: Place a camera in the automobile welding operation area, adjust the focus and exposure, capture welding images of key parts of the vehicle structure, perform noise elimination, and obtain a preliminary automobile weld image set;

[0008] S2: Based on the preliminary automobile weld image set, adjusting brightness and contrast parameters, drawing weld boundaries using edge enhancement, adjusting the accuracy of automobile weld area visualization through color correction, and generating optimized automobile weld images;

[0009] S3: performing defect recognition on the optimized automobile weld image, comparing irregular patterns in the image with known defect features, recording structural defects occurring during the welding process, and generating welding defect marking record results;

[0010] S4: Real-time monitoring of temperature and smoke environment parameters during welding, setting thresholds to determine whether there is an abnormal state, and conducting a comprehensive analysis based on the welding defect marking record results to predict the structural integrity and potential risks of the weld, and generate abnormal processing and prediction records;

[0011] S5: Based on the abnormal processing and prediction records, the operating parameters of the welding equipment are gradually adjusted, including current adjustment, speed modification and angle optimization, combined with the automobile welding quality requirements, matching the welding needs of different models and material characteristics, and generating adjusted working parameters;

[0012] S6: Apply the adjusted working parameters to perform welding, continuously record the quality indicators during the welding process, analyze the welding data, verify the welding effect in combination with the automobile manufacturing quality standards, and generate an automobile welding quality confirmation record.

[0013] As a further solution of the present invention, the preliminary automobile weld image set includes a focus adjustment image, an exposure effect image and a noise reduction processing image, the optimized automobile weld image includes a brightness optimization image, a contrast enhancement image and a color accurate image, the welding defect marking record result includes a defect type record set, a defect location record and a defect severity record, the abnormal processing and prediction record includes a temperature abnormality analysis result, a smoke monitoring result and a risk warning level, the adjusted working parameters include a current setting result, a welding speed adjustment result and an angle adjustment result, and the automobile welding quality confirmation record includes an automobile welding qualification rating and a welding standard compliance record.

[0014] As a further solution of the present invention, a camera is arranged in the automobile welding operation area, the focus and exposure are adjusted, the welding images of the key parts of the vehicle body structure are captured, and the noise elimination process is performed to obtain the preliminary automobile weld image set. The specific steps are as follows:

[0015] S101: configuring a camera in the automobile welding operation area, adjusting the horizontal and vertical positions of the camera, adjusting the focal length to match the actual width of the weld, synchronously adjusting the exposure parameters to match the light intensity of the workshop, capturing the automobile welding image, and generating a weld focus image set;

[0016] S102: according to the weld focus image set, setting a noise filtering threshold to remove background noise, adjusting color saturation to enhance the contrast between the weld and the background, optimizing the visual effect of the weld image, and generating an optimized weld image set;

[0017] S103: Based on the optimized weld image set, weld features are highlighted, and the geometric shape and position information of the weld are recorded to obtain a preliminary automobile weld image set.

[0018] As a further solution of the present invention, based on the preliminary automobile weld image set, adjusting brightness and contrast parameters, using edge enhancement to draw weld boundaries, and adjusting the accuracy of automobile weld area visualization through color correction, the specific steps of generating an optimized automobile weld image are as follows:

[0019] S201: Based on the preliminary automobile weld image set, adjusting the brightness level of the weld image, optimizing the image contrast to visually separate the weld from the background, distinguishing the weld from the non-weld area, and generating a brightness contrast optimization map;

[0020] S202: performing edge detection and adjusting detection accuracy and intensity according to the brightness contrast optimization map, accurately drawing the automobile weld boundary, and generating a clear weld edge map;

[0021] S203: Based on the weld edge clear image, color correction is performed to adjust the color balance to match the actual weld color, ensure that the color of the weld area is consistent with the actual material, optimize the visual expression and recognition of the weld, and obtain an optimized automobile weld image.

[0022] As a further solution of the present invention, the specific steps of performing defect recognition on the optimized automobile weld image, comparing the irregular pattern in the image with the known defect characteristics, recording the structural defects occurring during the welding process, and generating the welding defect marking record result are as follows:

[0023] S301: Based on the optimized automobile weld image, perform structural comparison, compare each structural pattern in the image with a known defect feature database one by one, identify abnormal areas that are different from the standard pattern, and generate a structural abnormality identification map;

[0024] S302: Using the structural anomaly identification map, drawing marking lines and annotations in all abnormal areas, recording the shape, size and predicted type of each area, including pores and cracks, and generating a detailed annotation map of automobile welding defects;

[0025] S303: Based on the detailed annotation diagram of the automobile welding defects, the annotation information is summarized, the actual position, size and type of the defects are uniformly recorded, and each defect is coded to obtain the welding defect marking record result.

[0026] As a further solution of the present invention, the temperature and smoke environment parameters in the welding process are monitored in real time, a threshold is set to determine whether there is an abnormal state, and a comprehensive analysis is performed in combination with the welding defect mark record results to predict the structural integrity and potential risks of the weld. The specific steps of generating abnormal processing and prediction records are as follows:

[0027] S401: Deploy temperature and smoke sensors in key welding areas of automobiles to collect data in real time, adjust monitoring warning thresholds, automatically record abnormal events when temperature or smoke exceeds the warning threshold, and generate real-time welding environment monitoring records;

[0028] S402: Based on the real-time welding environment monitoring record, a comparative analysis is performed with the welding defect marking record, the welding defect occurrence time is matched with the environmental parameter abnormal time, the correlation between the environmental factor and the defect is calculated, the potential risk factor is identified, and the environment and defect comparative analysis results are generated;

[0029] S403: Using the environment and defect comparison analysis results, comprehensively evaluate the safety and structural integrity of the automobile welding area, mark the risky weld area, predict and record potential risks, and obtain abnormal processing and prediction records.

[0030] As a further solution of the present invention, the correlation between the calculation environment factor and the defect generation is calculated according to the formula:

[0031]

[0032] Calculate the weighted correlation coefficient ,in, is the specific value of the environmental parameter, is a numerical representation of the severity of the defect. is the weight of each data point, is the total number of data points, After weighting and The sum of the products of the corresponding data points, For all weighted The sum of values, For all weighted The sum of values, For all weighted The sum of squares of the values, For all weighted The sum of the squares of the values.

[0033] As a further solution of the present invention, based on the abnormal processing and prediction records, the operating parameters of the welding equipment are gradually adjusted, including current adjustment, speed modification and angle optimization, combined with the automobile welding quality requirements, matching the welding needs of different models and material properties, and generating the adjusted working parameters in the specific steps of:

[0034] S501: Based on the abnormal processing and prediction records, potential risks in the records are evaluated, current parameters of the welding equipment are adjusted to ensure that the current setting matches the material characteristics and vehicle model requirements, and a current adjustment record is generated;

[0035] S502: modifying the speed setting of the welding equipment according to the current adjustment record, adjusting the welding speed according to the different vehicle models and material characteristics, optimizing the uniformity and efficiency of the welding process, and generating a speed adjustment record;

[0036] S503: Based on the current adjustment record and the speed adjustment record, the welding angle is adjusted, the welding angle is set according to the weld position and structural requirements, the weld joint quality is optimized, and the adjusted working parameters are obtained.

[0037] As a further solution of the present invention, the adjusted working parameters are used for welding, the quality indicators in the welding process are continuously recorded, the welding data are analyzed, and the welding effect is verified in combination with the automobile manufacturing quality standards. The specific steps of generating the automobile welding quality confirmation record are as follows:

[0038] S601: Based on the adjusted working parameters, start the welding equipment, monitor the key parameters of the welding process in real time, record the quality data of each welding point, and generate a welding process monitoring record;

[0039] S602: After each welding cycle, the collected quality data is analyzed according to the welding process monitoring record to evaluate whether the quality index of each welding point meets the automobile manufacturing standard, identify quality deviations, and generate automobile welding quality evaluation results;

[0040] S603: Utilizing the welding quality assessment result, comprehensively analyzing the automobile welding effect, marking qualified and unqualified welding points, recording all relevant quality information, and obtaining the automobile welding quality confirmation record.

[0041] An intelligent welding control system for automobile processing, comprising:

[0042] The image processing module configures the camera to capture the welding images of the key parts of the vehicle body structure, adjusts the brightness and contrast parameters, uses edge enhancement to draw the weld boundary, adjusts the visualization effect of the automobile weld area, and generates an optimized automobile weld image;

[0043] The defect recognition module performs structural comparison and identifies abnormal areas that are different from the standard pattern based on the optimized automobile weld image, draws marking lines and annotations in all abnormal areas, summarizes the annotation information, uniformly records and encodes each defect, and obtains the welding defect marking record result;

[0044] The abnormal monitoring module deploys temperature and smoke sensors, adjusts monitoring warning thresholds, records abnormal events, compares and analyzes them with the welding defect mark records, comprehensively evaluates the safety and structural integrity of the automobile welding area, predicts and records potential risks, and obtains abnormal processing and prediction records;

[0045] The parameter adjustment module adjusts the current parameters of the welding equipment based on the abnormal processing and prediction records, adjusts the welding speed according to the different vehicle models and material characteristics, sets the welding angle according to the weld position and structural requirements, and obtains the adjusted working parameters;

[0046] The quality assessment module monitors the key parameters of the welding process in real time based on the adjusted working parameters, analyzes the collected quality data, evaluates whether the quality indicators of each weld point meet the automobile manufacturing standards, marks qualified and unqualified weld points, and obtains the automobile welding quality confirmation record.

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

[0048] In the present invention, edge enhancement and color correction technology are used to make the image clearer and accurately mark structural defects. By identifying irregular patterns in the image and comparing them with known defect features, welding defects can be effectively recorded and the accuracy of welding quality control can be improved. Through real-time monitoring of environmental parameters and comprehensive analysis of data, control over the welding process can be enhanced, structural integrity and potential risks can be effectively predicted, and the safety and reliability of the welding process can be improved. The welding parameters can be adjusted according to different vehicle models and material characteristics, thereby improving the adaptability and flexibility of production, significantly optimizing production speed and reducing manufacturing costs. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0050] Figure 2 A diagram showing the steps for obtaining a preliminary automobile weld image set according to the present invention;

[0051] Figure 3 This is a diagram of the steps for obtaining the optimized automobile weld image of the present invention;

[0052] Figure 4 A diagram showing the steps for obtaining the welding defect marking and recording results of the present invention;

[0053] Figure 5A diagram showing the steps for obtaining the abnormality processing and prediction records of the present invention;

[0054] Figure 6 A diagram showing the steps for obtaining the adjusted working parameters of the present invention;

[0055] Figure 7 A diagram showing the steps for obtaining the automobile welding quality confirmation record of the present invention;

[0056] Figure 8 It is a system module diagram 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 , an intelligent welding control method for automobile processing, comprising the following steps:

[0059] S1: Place a camera in the automobile welding operation area, adjust the focus and exposure, capture welding images of key parts of the vehicle structure, perform noise elimination, and obtain a preliminary automobile weld image set;

[0060] S2: Based on the preliminary automobile weld image set, adjust the brightness and contrast parameters, use edge enhancement to draw the weld boundary, adjust the accuracy of automobile weld area visualization through color correction, and generate optimized automobile weld images;

[0061] S3: Defect recognition is performed on the optimized automobile weld image, the irregular pattern in the image is compared with the known defect characteristics, the structural defects occurring during the welding process are recorded, and the welding defect marking record results are generated;

[0062] S4: Monitor the temperature and smoke environment parameters in real time during the welding process, set thresholds to determine whether there is an abnormal state, conduct a comprehensive analysis based on the welding defect marking record results, predict the structural integrity and potential risks of the weld, and generate abnormal processing and prediction records;

[0063] S5: Based on the abnormal processing and prediction records, the operating parameters of the welding equipment are gradually adjusted, including current adjustment, speed modification and angle optimization. Combined with the automotive welding quality requirements, the welding needs of different models and material characteristics are matched to generate the adjusted working parameters;

[0064] S6: Apply the adjusted working parameters for welding, continuously record the quality indicators during the welding process, analyze the welding data, verify the welding effect in accordance with the automobile manufacturing quality standards, and generate automobile welding quality confirmation records.

[0065] The preliminary automobile weld image set includes focus adjustment images, exposure effect images and noise reduction processing images; the optimized automobile weld images include brightness optimization images, contrast enhancement images and color accurate images; the welding defect marking record results include defect type record set, defect location record and defect severity record; the abnormality handling and prediction records include temperature abnormality analysis results, smoke monitoring results and risk warning levels; the adjusted working parameters include current setting results, welding speed adjustment results and angle adjustment results; the automobile welding quality confirmation records include automobile welding qualification ratings and welding standard compliance records.

[0066] See also Figure 2 , the specific steps of S1 are:

[0067] S101: configuring a camera in the automobile welding operation area, adjusting the horizontal and vertical positions of the camera, adjusting the focal length to match the actual width of the weld, synchronously adjusting the exposure parameters to match the light intensity of the workshop, capturing the automobile welding image, and generating a weld focus image set;

[0068] First, the camera is configured in the automobile welding operation area to monitor the actual width of the weld in real time. By controlling and adjusting the horizontal and vertical positions of the camera, the weld is ensured to be completely presented in the center of the image. Then, according to the characteristics of the weld, the focal length of the camera is adjusted to make the weld image clear. By increasing or decreasing the aperture size to match the light intensity of the workshop, the exposure time and ISO sensitivity of the camera are adjusted to adapt to different ambient light conditions, and a targeted weld focus image set is generated.

[0069] S102: according to the weld focus image set, setting a noise filtering threshold to remove background noise, adjusting color saturation to enhance the contrast between the weld and the background, optimizing the visual effect of the weld image, and generating an optimized weld image set;

[0070] The weld focus image set is processed by image processing software. First, the noise filtering threshold is set, and the adaptive noise filtering algorithm is used to remove random noise and interference in the image. The filtering strength is automatically adjusted by comparing the color difference of adjacent pixels. Then the color saturation is adjusted, and the color contrast between the weld and the background is enhanced through linear transformation to make the weld more prominent visually. Through this series of optimizations, an optimized weld image set with better visual effects is generated.

[0071] S103: Based on the optimized weld image set, the weld features are highlighted, the geometric shape and position information of the weld are recorded, and a preliminary automobile weld image set is obtained;

[0072] For the optimized weld image set, edge detection techniques such as the Sobel operator are used to enhance the image edges and extract the exact position and shape of the weld. Subsequently, geometric parameters such as the width, length and curvature of the weld are recorded through geometric modeling tools, and the precise position of the weld on the car body is calculated based on the camera positioning data. After the data is aggregated, a preliminary automobile weld image set with practical application value is formed, providing basic data for subsequent quality control and automated inspection.

[0073] See also Figure 3 , the specific steps of S2 are:

[0074] S201: Based on the preliminary automobile weld image set, adjust the brightness level of the weld image, optimize the image contrast to visually separate the weld from the background, distinguish the weld from the non-weld area, and generate a brightness contrast optimization map;

[0075] Image processing technology is used to adjust the brightness of each image in the preliminary automotive weld image set. The method of adjusting the brightness is to modify the image histogram and linearly stretch the pixel values ​​in the weld area, making the weld part brighter and the background relatively darker, thereby enhancing the overall contrast of the image. Through this adjustment, the weld and non-weld areas are effectively separated visually, and the stretching parameters are automatically adjusted according to the difference in the average brightness of the weld and the brightness of the background, generating a brightness contrast optimized image with better visual effects.

[0076] S202: performing edge detection and adjusting detection accuracy and intensity according to the brightness contrast optimization map, accurately drawing the automobile weld boundary, and generating a clear weld edge map;

[0077] After obtaining the brightness contrast optimization map, the Canny edge detection algorithm is used to extract the edge of the weld. The algorithm controls the accuracy and strength of edge detection through two thresholds: the low threshold is used to detect the initial response of the edge, and the high threshold is used to confirm the real edge. By properly adjusting these two thresholds, it is possible to minimize false detection while ensuring edge clarity. The algorithm iteratively optimizes the detection parameters to ensure accurate drawing of the weld boundary and generate a clear weld edge map.

[0078] S203: Based on the weld edge clear image, color correction is performed to adjust the color balance to match the real weld color, ensure that the color of the weld area is consistent with the actual material, optimize the weld visual expression and recognition, and obtain the optimized automobile weld image;

[0079] Based on the weld edge clarity image, the color correction step is to adjust the RGB color channels of the image so that the color of the weld area matches the color of the actual weld material. This process involves calculating the color deviation and gradually adjusting the gain coefficient of each color channel to achieve the best color balance. The software automatically compares the color of the weld area with the preset material color library, accurately adjusts the color balance, and ensures that the color of the weld area truly restores the original color of the material. This optimization step significantly improves the accuracy and recognition of the visual expression of the weld, generating an optimized automotive weld image.

[0080] See also Figure 4 , the specific steps of S3 are:

[0081] S301: Based on the optimized automobile weld image, perform structural comparison, compare each structural pattern in the image with the known defect feature database one by one, identify abnormal areas that are different from the standard pattern, and generate a structural abnormality identification map;

[0082] The optimized automotive weld image is loaded on the image processing platform. A structural matching algorithm is used, which extracts structural features (such as linearity, angle, texture, etc.) from the image and compares them with templates in a database of known defect features. This comparison process involves calculating the similarity index between the image features and each standard defect pattern in the database, and identifying areas that are significantly different from the standard pattern by setting a similarity threshold. Whenever abnormal structures are found, the algorithm automatically marks these areas and generates a structural anomaly identification map, showing all weld areas that do not conform to the standard pattern.

[0083] S302: Using the structural anomaly identification map, draw marking lines and annotations in all abnormal areas, record the shape, size and predicted type of each area, including pores and cracks, and generate a detailed annotation map of automobile welding defects;

[0084] After obtaining the structural anomaly identification map, the next step is to use the graphic marking tool to draw marking lines and add annotations in each abnormal area of ​​the map. In this process, the operator or the automated system will record the specific shape, size and predicted defect type (such as pores, cracks, etc.) of each abnormal area based on the image analysis results. Through this detailed image annotation, a detailed annotation map of automotive welding defects is generated, which shows the location and characteristics of each defect in detail, providing accurate reference information for further quality control and repair.

[0085] S303: Based on the detailed annotation diagram of automobile welding defects, the annotation information is summarized, the actual position, size and type of the defects are uniformly recorded, and each defect is coded to obtain the welding defect marking record result;

[0086] On the basis of the detailed annotation diagram, the defect data is summarized and recorded. This step involves integrating all defect information in the diagram and uniformly recording the actual location, size and type of each defect. In order to facilitate subsequent management and tracking, each type of defect will be assigned a unique code. After the information is summarized, the welding defect marking record results are formed, which not only provides a basis for quality assurance on the production line, but also provides valuable data support for future welding process improvements.

[0087] See also Figure 5 , the specific steps of S4 are:

[0088] S401: Deploy temperature and smoke sensors in key welding areas of automobiles to collect data in real time, adjust monitoring warning thresholds, automatically record abnormal events when temperature or smoke exceeds the warning threshold, and generate real-time welding environment monitoring records;

[0089] Temperature and smoke sensors are installed in key welding areas of the car. These sensors can monitor and collect environmental data in real time. Warning thresholds, such as upper limits of temperature and smoke concentration, are set through monitoring software to monitor environmental safety. When the measured data exceeds these set thresholds, the system automatically triggers an alarm and records this abnormal event. The recorded data includes timestamps, temperature readings, smoke concentrations, and corresponding camera images, forming real-time welding environment monitoring records to provide data support for subsequent analysis.

[0090] S402: Based on the real-time welding environment monitoring records, a comparative analysis is performed with the welding defect marking records, the welding defect occurrence time is matched with the environmental parameter abnormal time, the correlation between the environmental factors and the defect generation is calculated, the potential risk factors are identified, and the environment and defect comparative analysis results are generated;

[0091] The correlation between the calculation environment factors and the occurrence of defects is calculated according to the formula:

[0092]

[0093] Calculate the weighted correlation coefficient ,in, is the specific value of the environmental parameter, is a numerical representation of the severity of the defect. is the weight of each data point, which is determined by the rate of change of the environmental parameters and the time proximity of the defect occurrence. is the total number of data points, After weighting and The sum of the products of the corresponding data points, For all weighted The sum of values, For all weighted The sum of values, For all weighted The sum of squares of the values, For all weighted The sum of the squares of the values.

[0094] The execution process is as follows:

[0095] First, each data point is assigned a weight based on its sensitivity to changes in environmental parameters and its proximity to the time of defect occurrence. These weights are then used to adjust the and value, generating a weighted and The data set is then used to calculate the weighted , , , and , and finally the weighted correlation coefficient is calculated using the formula .

[0096] S403: Using the results of the environment and defect comparison analysis, comprehensively evaluate the safety and structural integrity of the automobile welding area, mark the risky weld area, predict and record potential risks, and obtain abnormal processing and prediction records;

[0097] The safety and structural integrity of the automotive welding area are comprehensively evaluated using the results of the environmental and defect comparison analysis. Based on the analysis results, the potential risk weld areas are clearly marked, and the potential risk types and the problems they may cause are recorded. This marking and recording process not only helps to correct or strengthen the welding process in a timely manner, but also provides a basis for formulating future preventive measures. The generated abnormal handling and prediction records will be used for further safety assessment and quality control.

[0098] See also Figure 6 , the specific steps of S5 are:

[0099] S501: Based on the abnormal processing and prediction records, evaluate the potential risks in the records, adjust the current parameters of the welding equipment, ensure that the current setting matches the material characteristics and vehicle model requirements, and generate current adjustment records;

[0100] According to the abnormal handling and prediction records, various potential risk factors mentioned in the records are analyzed, such as the welding quality that may be affected by excessively high ambient temperature. Based on these analysis results, the current parameters of the welding equipment are adjusted to adapt to different material characteristics and vehicle model requirements. The specific operation of current adjustment includes calculating the resistivity and thermal conductivity of the material, and adjusting the current according to the relevant physical properties to ensure a moderate welding temperature and avoid overheating or insufficient welding. Through this series of calculations and adjustments, a detailed current adjustment record is generated, which includes the current setting value and adjustment basis required for each material and vehicle model.

[0101] S502: modify the speed setting of the welding equipment according to the current adjustment record, adjust the welding speed according to the different vehicle models and material characteristics, optimize the uniformity and efficiency of the welding process, and generate a speed adjustment record;

[0102] After the current parameter adjustment is completed, the speed setting of the welding equipment is further modified according to the current adjustment record. Taking into account the different requirements of welding speed for different vehicle models and material characteristics, such as thicker materials require slower welding speeds to ensure weld penetration, the welding speed is optimized. Through the analysis of the welding process simulation, the equipment speed is adjusted to achieve uniformity of the weld and improve welding efficiency, and a speed adjustment record is generated, which records in detail the speed adjustment values ​​and reasons for different vehicle models and materials.

[0103] S503: Comprehensively adjusting the current adjustment record and the speed adjustment record, adjusting the welding angle, setting the welding angle according to the weld position and structural requirements, optimizing the weld joint quality, and obtaining the adjusted working parameters;

[0104] The current adjustment record and speed adjustment record are integrated to adjust the welding angle. According to the position and structural requirements of the weld, the welding angle is accurately set to optimize the joint quality of the weld. For example, for a fillet weld, the angle needs to be adjusted to ensure that the welding material can be evenly distributed on both sides of the weld. The adjustment is achieved by calculating the welding torque and heat distribution model to ensure that each weld can achieve the best welding effect. Through these detailed parameter adjustments, the adjusted working parameter records are finally obtained, which provides a scientific basis for ensuring the consistency and efficiency of welding quality on the production line.

[0105] See also Figure 7 , the specific steps of S6 are:

[0106] S601: Based on the adjusted working parameters, start the welding equipment, monitor the key parameters of the welding process in real time, record the quality data of each welding point, and generate a welding process monitoring record;

[0107] According to the previously adjusted working parameters, the welding equipment is started for production. During the welding process, key parameters such as current, voltage, welding speed and temperature are monitored in real time through sensors embedded in the system. The real-time data of the parameters will be automatically recorded by the system, and the stability and consistency of each weld will be evaluated. Through the data acquisition module, a welding process monitoring record is generated, which describes in detail the performance of each weld under different working conditions, providing basic data for subsequent quality analysis.

[0108] S602: According to the welding process monitoring records, after each welding cycle, the collected quality data is analyzed to evaluate whether the quality index of each welding point meets the automobile manufacturing standards, identify quality deviations, and generate automobile welding quality evaluation results;

[0109] After each welding cycle, the system automatically analyzes the collected quality data. This analysis process includes calculating the quality indicators such as strength, uniformity and integrity of each weld, and comparing the indicators with automotive manufacturing standards. The set algorithm automatically identifies welds that do not meet the standards and calculates the quality deviation. In addition, statistical methods such as control chart technology are used during the analysis process to monitor the stability of the process. The generated automotive welding quality assessment results record the qualified status of each weld and its deviation value in detail.

[0110] S603: Utilize the welding quality assessment results to comprehensively analyze the automobile welding effect, mark qualified and unqualified welding points, record all relevant quality information, and obtain the automobile welding quality confirmation record;

[0111] Using the welding quality assessment results, further comprehensive analysis is conducted to not only mark the qualified and unqualified status of each weld, but also analyze the causes of unqualified welds, such as material defects, improper equipment adjustment or operating errors. All quality information, including the location, type, cause of deviation and corrective measures of the weld, will be recorded in the automotive welding quality confirmation record. This record is not only an important document for production quality control, but also provides a detailed basis for improving production processes and improving product quality.

[0112] See also Figure 8 , an intelligent welding control system for automobile processing, comprising:

[0113] The image processing module configures the camera to capture the welding images of the key parts of the vehicle body structure, adjusts the brightness and contrast parameters, uses edge enhancement to draw the weld boundary, adjusts the visualization effect of the automobile weld area, and generates an optimized automobile weld image;

[0114] The defect recognition module performs structural comparison based on the optimized automobile weld image to identify abnormal areas that are different from the standard pattern, draws marking lines and annotations in all abnormal areas, summarizes the annotation information, uniformly records and encodes each defect, and obtains the welding defect marking record results;

[0115] The abnormal monitoring module deploys temperature and smoke sensors, adjusts monitoring warning thresholds, records abnormal events, compares and analyzes them with welding defect mark records, comprehensively evaluates the safety and structural integrity of the automobile welding area, predicts and records potential risks, and obtains abnormal processing and prediction records;

[0116] The parameter adjustment module adjusts the current parameters of the welding equipment based on abnormal processing and prediction records, adjusts the welding speed according to different vehicle models and material characteristics, sets the welding angle according to the weld position and structural requirements, and obtains the adjusted working parameters;

[0117] The quality assessment module monitors the key parameters of the welding process in real time based on the adjusted working parameters, analyzes the collected quality data, evaluates whether the quality indicators of each weld point meet the automobile manufacturing standards, marks qualified and unqualified weld points, and obtains the automobile welding quality confirmation record.

[0118] 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. An intelligent welding control method for automobile processing, characterized in that: The following steps are involved: S1: Place a camera in the automobile welding operation area, adjust the focus and exposure, capture welding images of key parts of the vehicle structure, perform noise elimination, and obtain a preliminary automobile weld image set; S2: Based on the preliminary automobile weld image set, adjusting brightness and contrast parameters, drawing weld boundaries using edge enhancement, adjusting the accuracy of automobile weld area visualization through color correction, and generating optimized automobile weld images; S3: performing defect recognition on the optimized automobile weld image, comparing irregular patterns in the image with known defect features, recording structural defects occurring during the welding process, and generating welding defect marking record results; S4: Real-time monitoring of temperature and smoke environment parameters during welding, setting thresholds to determine whether there is an abnormal state, and conducting a comprehensive analysis based on the welding defect marking record results to predict the structural integrity and potential risks of the weld, and generate abnormal processing and prediction records; S5: Based on the abnormal processing and prediction records, the operating parameters of the welding equipment are gradually adjusted, including current adjustment, speed modification and angle optimization, combined with the automobile welding quality requirements, matching the welding needs of different models and material characteristics, and generating adjusted working parameters; S6: Apply the adjusted working parameters to perform welding, continuously record the quality indicators during welding, analyze welding data, verify the welding effect in combination with automobile manufacturing quality standards, and generate automobile welding quality confirmation records; The specific steps of configuring a camera in the automobile welding operation area, adjusting the focus and exposure, capturing welding images of key parts of the vehicle body structure, and performing noise elimination processing to obtain a preliminary automobile weld image set are as follows: Configure a camera in the automobile welding operation area, adjust the horizontal and vertical positions of the camera, adjust the focal length to match the actual width of the weld, and simultaneously adjust the exposure parameters to match the light intensity in the workshop, capture the automobile welding image, and generate a weld focus image set; According to the weld focus image set, a noise filtering threshold is set to remove background noise, color saturation is adjusted to enhance the contrast between the weld and the background, the visual effect of the weld image is optimized, and an optimized weld image set is generated; Based on the optimized weld image set, the weld features are highlighted, the geometric shape and position information of the weld are recorded, and a preliminary automobile weld image set is obtained; Based on the preliminary automobile weld image set, the brightness and contrast parameters are adjusted, the weld boundary is drawn using edge enhancement, and the accuracy of automobile weld area visualization is adjusted by color correction. The specific steps of generating an optimized automobile weld image are as follows: Based on the preliminary automobile weld image set, adjusting the brightness level of the weld image, optimizing the image contrast to perform visual separation of the weld from the background, distinguishing the weld from the non-weld area, and generating a brightness contrast optimization map; According to the brightness contrast optimization map, edge detection is performed and the detection accuracy and intensity are adjusted to accurately draw the boundaries of automobile welds and generate a clear weld edge map; Based on the weld edge clear image, color correction is performed to adjust the color balance to match the real weld color, ensure that the color of the weld area is consistent with the actual material, optimize the weld visual expression and recognition, and obtain an optimized automobile weld image; The specific steps of performing defect recognition on the optimized automobile weld image, comparing the irregular pattern in the image with known defect features, recording the structural defects occurring during the welding process, and generating the welding defect marking record result are as follows: Based on the optimized automobile weld image, performing structural comparison, comparing each structural pattern in the image with a known defect feature database one by one, identifying abnormal areas that are different from the standard pattern, and generating a structural abnormality identification map; Using the structural anomaly identification map, marking lines and annotations are drawn in all abnormal areas, and the shape, size and predicted type of each area, including pores and cracks, are recorded to generate a detailed annotation map of automobile welding defects; Based on the detailed annotation diagram of automobile welding defects, the annotation information is summarized, the actual position, size and type of the defects are uniformly recorded, and each defect is coded to obtain the welding defect marking record result; The temperature and smoke environment parameters in the welding process are monitored in real time, and thresholds are set to determine whether there is an abnormal state. Combined with the welding defect marking record results, a comprehensive analysis is performed to predict the structural integrity and potential risks of the weld. The specific steps for generating abnormal processing and prediction records are as follows: Deploy temperature and smoke sensors in key welding areas of automobiles to collect data in real time, adjust monitoring warning thresholds, automatically record abnormal events when temperature or smoke exceeds the warning threshold, and generate real-time welding environment monitoring records; Based on the real-time welding environment monitoring records, a comparative analysis is performed with the welding defect marking records, the welding defect occurrence time is matched with the environmental parameter abnormal time, the correlation between environmental factors and defect generation is calculated, potential risk factors are identified, and the environment and defect comparative analysis results are generated; By using the environment and defect comparison analysis results, the safety and structural integrity of the automobile welding area are comprehensively evaluated, the risky weld area is marked, potential risks are predicted and recorded, and abnormal processing and prediction records are obtained; The correlation between the calculation environment factors and the occurrence of defects is calculated according to the formula: Calculate the weighted correlation coefficient ,in, is the specific value of the environmental parameter, is a numerical representation of the severity of the defect. is the weight of each data point, is the total number of data points, After weighting and The sum of the products of the corresponding data points, For all weighted The sum of values, For all weighted The sum of values, For all weighted The sum of squares of the values, For all weighted The sum of squares of the values; Based on the abnormal processing and prediction records, the operating parameters of the welding equipment are gradually adjusted, including current adjustment, speed modification and angle optimization. Combined with the automotive welding quality requirements, the welding needs of different models and material characteristics are matched. The specific steps for generating the adjusted working parameters are as follows: Based on the abnormal processing and prediction records, potential risks in the records are evaluated, current parameters of the welding equipment are adjusted to ensure that the current settings match the material characteristics and vehicle model requirements, and current adjustment records are generated; According to the current adjustment record, modify the speed setting of the welding equipment, adjust the welding speed according to the difference in vehicle type and material characteristics, optimize the uniformity and efficiency of the welding process, and generate a speed adjustment record; The welding angle is adjusted based on the current adjustment record and the speed adjustment record, and the welding angle is set according to the weld position and structural requirements to optimize the weld joint quality and obtain the adjusted working parameters.

2. The intelligent welding control method for automobile processing according to claim 1 is characterized in that: The preliminary automobile weld image set includes a focus adjustment image, an exposure effect image and a noise reduction processing image, the optimized automobile weld image includes a brightness optimization image, a contrast enhancement image and a color accurate image, the welding defect marking record result includes a defect type record set, a defect location record and a defect severity record, the abnormal processing and prediction record includes a temperature abnormality analysis result, a smoke monitoring result and a risk warning level, the adjusted working parameters include a current setting result, a welding speed adjustment result and an angle adjustment result, and the automobile welding quality confirmation record includes an automobile welding qualification rating and a welding standard compliance record.

3. The intelligent welding control method for automobile processing according to claim 1 is characterized in that: The specific steps of applying the adjusted working parameters to weld, continuously recording the quality indicators during welding, analyzing welding data, verifying the welding effect in combination with automobile manufacturing quality standards, and generating automobile welding quality confirmation records are as follows: Based on the adjusted working parameters, the welding equipment is started, key parameters of the welding process are monitored in real time, quality data of each welding point is recorded, and a welding process monitoring record is generated; According to the welding process monitoring records, after each welding cycle, the collected quality data is analyzed to evaluate whether the quality index of each welding point meets the automobile manufacturing standards, identify quality deviations, and generate automobile welding quality evaluation results; The welding quality evaluation result is used to comprehensively analyze the automobile welding effect, mark qualified and unqualified welding points, record all relevant quality information, and obtain the automobile welding quality confirmation record.

4. An intelligent welding control system for automobile processing, characterized in that: According to an intelligent welding control method for automobile processing according to any one of claims 1 to 3, the system comprises: The image processing module configures the camera to capture the welding images of the key parts of the vehicle body structure, adjusts the brightness and contrast parameters, uses edge enhancement to draw the weld boundary, adjusts the visualization effect of the automobile weld area, and generates an optimized automobile weld image; The defect recognition module performs structural comparison and identifies abnormal areas that are different from the standard pattern based on the optimized automobile weld image, draws marking lines and annotations in all abnormal areas, summarizes the annotation information, uniformly records and encodes each defect, and obtains the welding defect marking record result; The abnormal monitoring module deploys temperature and smoke sensors, adjusts monitoring warning thresholds, records abnormal events, compares and analyzes them with the welding defect mark records, comprehensively evaluates the safety and structural integrity of the automobile welding area, predicts and records potential risks, and obtains abnormal processing and prediction records; The parameter adjustment module adjusts the current parameters of the welding equipment based on the abnormal processing and prediction records, adjusts the welding speed according to the different vehicle models and material characteristics, sets the welding angle according to the weld position and structural requirements, and obtains the adjusted working parameters; The quality assessment module monitors the key parameters of the welding process in real time based on the adjusted working parameters, analyzes the collected quality data, evaluates whether the quality indicators of each weld point meet the automobile manufacturing standards, marks qualified and unqualified weld points, and obtains the automobile welding quality confirmation record.

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