A flexible control method and system for automobile welding process

Abstract

The present invention relates to the field of automation control technology, specifically to a flexible control method and system for automobile welding process, comprising the following steps: according to the geometric information of automobile workpiece, by analyzing the positions of multiple welding spots on the target automobile workpiece, considering the intersection and conflict of multiple robot paths, calculating the path planning of the welding robot, adjusting the movement trajectory and speed parameters of the robot, and generating the welding path configuration. In the present invention, by simulating the interactive actions of multiple robot arms, the path intersection and conflict between the robot arms are prevented, the downtime in production is reduced, and the material waste caused by welding errors is reduced. By identifying the weld boundary and comparing it with the standard model, the welding accuracy is optimized and the changes of various models and designs are matched, the consistency of welding quality is improved, and by predicting the welding completion time and comparing it with the preset schedule, the welding workflow is adjusted, the efficiency of human-machine collaboration is optimized, and the production flexibility and economic benefits are improved.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to a flexible control method and system for automobile welding process. Background Art

[0002] The field of automation control technology focuses on the use of control systems and information technology to operate machines and equipment to reduce dependence on manpower, including industrial production and machinery manufacturing. It is applied to multiple fields such as energy management, logistics systems and environmental monitoring. By using a variety of sensors to monitor physical and environmental parameters, combined with controllers and actuators, the production process can be monitored and precisely adjusted in real time to improve production efficiency, ensure product quality, reduce costs, and optimize the use of resources and energy. Combined with real-time data analysis and machine learning algorithms, it optimizes operating strategies and improves long-term reliability and efficiency.

[0003] Among them, the flexible control method of the automobile welding process is used for adaptive and adjustable welding on the automobile production line. By adjusting the welding parameters, including current, pressure and speed, in real time, it can match the changing needs of various models and designs, and optimize the welding quality and efficiency in various production scenarios. By realizing flexible control, the production line can handle diversified production tasks and adapt to market and technological changes, thereby improving production flexibility and economic benefits, and ensuring the consistency of welding quality and the operating efficiency of the production line.

[0004] Traditional automobile welding process control methods still have shortcomings in the flexible control of automobile welding processes. They lack sufficient flexibility and adaptability in optimizing the robot arm path and preventing conflicts during the welding process, resulting in poor production efficiency and material utilization. Real-time welding quality monitoring and adjustment rely on post-testing rather than real-time adjustment during the process, which increases uncertainty in the production process and affects the stability and consistency of welding quality. In terms of human-machine collaboration, they cannot effectively synchronize the work rhythm of humans and robots, resulting in waste of resources and production delays, affecting the economic benefits and market response speed of the production line, and lacking adaptability when facing diversified market demands. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a flexible control method and system for automobile welding process.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a flexible control method for automobile welding process, comprising the following steps:

[0007] S1: Based on the geometric information of the automotive workpiece, by analyzing the positions of multiple welding spots on the target automotive workpiece, considering the intersection and conflict of multiple robot paths, calculating the path planning of the welding robot, adjusting the movement trajectory and speed parameters of the robot, and generating the welding path configuration;

[0008] S2: Based on the welding path configuration, real-time monitoring and acquisition of welding position images, identification of weld boundaries, recording of weld spatial positioning and morphological data, and generation of weld feature data;

[0009] S3: According to the weld feature data, identify key features of the weld and compare them with a standard weld model, record the consistency information of weld quality and size, and generate a weld point offset identification result;

[0010] S4: Based on the welding point offset recognition result, the welding process is monitored in real time, the motion trajectory and welding speed information of the robot are analyzed, the welding completion time is predicted, and the welding process record information is generated;

[0011] S5: According to the welding process record information, the time information in the manual task flow is monitored in real time, and compared with the preset schedule, the time parameters of the workflows of multiple welding robots are adjusted, and the workflow coordination results are generated;

[0012] S6: Based on the workflow coordination result, by analyzing the position and quantity information of multiple welding points, identifying the deviation and quantity anomalies, verifying the welding results, and generating the automobile welding control results.

[0013] As a further solution of the present invention, the welding path configuration includes robot movement trajectory information, movement speed information and welding sequence data, the weld feature data includes weld width information, weld length information and weld point geometric feature information, the weld point offset identification result includes weld point position deviation data, angle error identification record and spacing offset information, the welding process record information includes real-time welding speed, welding angle adjustment record and estimated completion time information, the workflow coordination result includes robot task start time, operation interval time information and time adjustment amount, and the automobile welding control result includes weld point qualification rate, number of missed welds and welding quality score.

[0014] As a further solution of the present invention, according to the geometric information of the automobile workpiece, by analyzing the positions of multiple welding points on the target automobile workpiece, considering the intersection and conflict of multiple robot arm paths, calculating the path planning of the welding robot, adjusting the movement trajectory and speed parameters of the robot, the steps of generating the welding path configuration are specifically as follows:

[0015] S101: Analyze the geometric information of the target automobile workpiece according to the geometric information of the automobile workpiece, identify the spatial coordinates of multiple welding points, and generate geometric analysis data;

[0016] S102: Based on the geometric analysis data, using the A-star pathfinding algorithm, simulating the movement paths of multiple robotic arms in the working area, considering the intersection of multiple robotic arm paths, calculating the path planning of the robotic arms, and generating a robotic arm simulation result;

[0017] S103: Based on the robot arm simulation results, the moving trajectories and speed parameters of the multiple robots are adjusted to optimize the speed and efficiency of the welding operation and generate a welding path configuration.

[0018] As a further solution of the present invention, the A-star pathfinding algorithm is according to the formula:

[0019]

[0020] Calculate the optimal movement path of the robot arm, where is the total cost function of the welding path, From the starting point to the vertex The known actual cost of From the vertex The heuristic estimate of the cost to the target, is the weight coefficient of the heuristic cost, is the weight coefficient of the robot arm joint angle, is the current angle of the robot arm joint, is the weight coefficient of the robot's moving speed, is the moving speed of the robot arm, is the weight coefficient of the robot arm workload, is the workload of the robot arm, is the weight coefficient of working density, is the working density of the robot arm, It is the identifier of the node currently considered in path planning.

[0021] As a further solution of the present invention, based on the welding path configuration, the steps of real-time monitoring and collecting welding position images, identifying weld boundaries, recording the spatial positioning and morphological data of the weld, and generating weld feature data are specifically as follows:

[0022] S201: Based on the welding path configuration, real-time monitoring and recording of the welding image of the robot during the welding process, adjusting the image contrast to enhance the visibility of the weld, and obtaining welding image processing data;

[0023] S202: Based on the welding image processing data, edge detection is performed on the welding image, the weld boundary is identified, and the spatial position information of the weld is recorded to generate a weld position information record;

[0024] S203: Based on the weld position information record, the weld geometry is analyzed to identify the width, depth and length of the target weld, record key features of the geometry, and generate weld feature data.

[0025] As a further solution of the present invention, the steps of identifying key features of the weld and comparing them with the standard weld model based on the weld feature data, recording the consistency information of the weld quality and size, and generating the weld offset identification result are specifically as follows:

[0026] S301: Based on the weld feature data, identify key features of the weld by analyzing weld geometry information, and generate key feature information;

[0027] S302: Based on the key feature information, compare the key features of multiple welds with a preset standard weld model and identify deviations, record weld offset information, and generate weld quality comparison results;

[0028] S303: According to the weld spot quality comparison result, the deviation degree of multiple weld spots is identified, the consistency of weld quality and size is calculated, and a weld spot deviation identification result is generated.

[0029] As a further solution of the present invention, based on the welding point offset recognition result, the welding process is monitored in real time, the motion trajectory and welding speed information of the robot are analyzed, the welding completion time is predicted, and the steps of generating welding process record information are specifically as follows:

[0030] S401: Based on the welding point offset recognition result, the motion trajectory information of the welding robot is monitored in real time, the welding start and end time of multiple welding points are recorded, and a welding time record is generated;

[0031] S402: predicting the welding completion time of multiple welding points according to the welding time record, comparing the prediction result with the preset timetable of the production plan, calculating the time deviation, and generating the time deviation analysis result;

[0032] S403: Based on the time deviation analysis result, the welding speed and pause time of the robot are adjusted to optimize the consistency between the welding process and the preset schedule, and the welding process record information is generated.

[0033] As a further solution of the present invention, according to the welding process record information, the time information in the manual task flow is monitored in real time, and compared with the preset schedule, the time parameters of multiple welding robot workflows are adjusted, and the steps of generating workflow coordination results are specifically as follows:

[0034] S501: According to the welding process record information, the start and end time of multiple processes in the manual task are collected in real time, and compared with the preset task process schedule, the time deviation is identified, and the time comparison result is generated;

[0035] S502: Based on the time comparison result and according to the time deviation, the workflow time parameters that need to be adjusted for the welding robot are calculated, including the start delay and the task interval, to obtain the task time adjustment parameters;

[0036] S503: According to the task time adjustment parameters, the welding rhythm and task sequence of the welding robot are adjusted and updated to optimize the coordination efficiency between humans and robots in the welding task process and generate a workflow coordination result.

[0037] As a further solution of the present invention, based on the workflow coordination result, by analyzing the position and quantity information of multiple welding points, identifying the offset and quantity anomalies, verifying the welding results, and generating the automobile welding control results are specifically as follows:

[0038] S601: According to the workflow coordination result, the position and quantity information of the welding points on the automobile workpiece are collected and recorded to generate welding point monitoring record data;

[0039] S602: Analyze the welding point data according to the welding point monitoring record data, identify position offset and quantity abnormality, record the welding points of wrong welding and missed welding, and obtain welding point abnormality information;

[0040] S603: Based on the abnormal welding spot information, the welding quality of the automobile workpiece is analyzed, the welding quality score is calculated, and the automobile welding control result is generated.

[0041] A flexible control system for automobile welding process, the flexible control system for automobile welding process is used to execute the above-mentioned flexible control method for automobile welding process, the system comprises:

[0042] The work path planning module analyzes the positions of multiple welding spots on the target workpiece according to the geometric information of the automobile workpiece, considers the path intersection and conflict of multiple robotic arms, adjusts the robot's movement trajectory and speed parameters, and generates movement trajectory parameters;

[0043] The welding image analysis module monitors and collects images of the welding position in real time based on the movement trajectory parameters, records the spatial positioning and morphological data of the weld, identifies the key features of the weld, and compares them with the standard weld model to generate consistency analysis results;

[0044] The work process monitoring module monitors the welding process in real time based on the consistency analysis result, analyzes the robot's motion trajectory and welding speed information, predicts the welding completion time, and generates completion time prediction information;

[0045] The human-machine work coordination module monitors the time information in the manual task process in real time according to the completion time prediction information, compares it with the preset schedule, adjusts the time parameters of the workflows of multiple welding robots, and generates work list adjustment information;

[0046] The welding quality verification module verifies the welding results based on the work list adjustment information by analyzing the position and quantity information of multiple welding points to generate the automobile welding control results.

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

[0048] In the present invention, by simulating the interactive actions of multiple robotic arms, path intersections and conflicts between robotic arms are prevented, downtime in production is reduced, and material waste due to welding errors is reduced. By identifying weld boundaries and comparing them with standard models, welding accuracy is optimized and changes in various vehicle models and designs are matched, thereby improving the consistency of welding quality. By predicting the welding completion time and comparing it with a preset schedule, the welding workflow is adjusted, the efficiency of human-machine collaboration is optimized, and production flexibility and economic benefits are improved. 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 flexible control method for automobile welding process, comprising the following steps:

[0059] S1: Based on the geometric information of the automotive workpiece, by analyzing the positions of multiple welding spots on the target automotive workpiece, considering the intersection and conflict of multiple robot paths, calculating the path planning of the welding robot, adjusting the movement trajectory and speed parameters of the robot, and generating the welding path configuration;

[0060] S2: Based on the welding path configuration, real-time monitoring and acquisition of welding position images, identification of weld boundaries, recording of weld spatial positioning and morphological data, and generation of weld feature data;

[0061] S3: Based on the weld feature data, identify the key features of the weld and compare them with the standard weld model, record the consistency information of the weld quality and size, and generate the weld point offset identification result;

[0062] S4: Based on the weld point offset recognition results, the welding process is monitored in real time, the robot's motion trajectory and welding speed information are analyzed, the welding completion time is predicted, and the welding process record information is generated;

[0063] S5: Based on the welding process record information, the time information in the manual task flow is monitored in real time, and compared with the preset schedule, the time parameters of multiple welding robot workflows are adjusted, and the workflow coordination results are generated;

[0064] S6: Based on the workflow coordination results, the position and quantity information of multiple welds are analyzed, the offset and quantity anomalies are identified, the welding results are verified, and the automotive welding control results are generated.

[0065] The welding path configuration includes the robot movement trajectory information, movement speed information and welding sequence data; the weld feature data includes weld width information, weld length information and weld point geometric feature information; the weld point offset recognition results include weld point position deviation data, angle error recognition records and spacing offset information; the welding process record information includes real-time welding speed, welding angle adjustment records and estimated completion time information; the workflow coordination results include the robot task start time, operation interval time information and time adjustment amount; the automobile welding control results include the qualified rate of welds, the number of missed welds and the welding quality score.

[0066] See also Figure 2 According to the geometric information of the automobile workpiece, by analyzing the positions of multiple welding points on the target automobile workpiece, considering the intersection and conflict of multiple robot paths, calculating the path planning of the welding robot, adjusting the movement trajectory and speed parameters of the robot, the specific steps of generating the welding path configuration are as follows:

[0067] S101: Analyze the geometric information of the target automobile workpiece according to the geometric information of the automobile workpiece, identify the spatial coordinates of multiple welding points, and generate the geometric analysis data in detail;

[0068] In sub-step S101, according to the geometric information of the automobile workpiece, the geometric information of the target automobile workpiece is analyzed, the spatial coordinates of multiple welding points are identified, the three-dimensional model data of the workpiece is collected, the key welding point positions are extracted from the three-dimensional model using image processing technology, and the relative position between each welding point is calculated using the Euclidean distance, and the distance calculation formula is used for calculation, which is: ,in, is the three-dimensional space coordinate of welding point 1, is the three-dimensional coordinate of solder joint 2, It is the Euclidean distance between weld points, identifies the positions of multiple weld points in the workpiece, improves the positioning accuracy of the welding robot arm, and generates geometric analysis data.

[0069] S102: Based on the geometric analysis data, the A-star pathfinding algorithm is used to simulate the moving paths of multiple robotic arms in the working area, and the path planning of the robotic arms is calculated considering the intersection of the paths of multiple robotic arms. The specific process of generating the robotic arm simulation result is as follows;

[0070] In sub-step S102, based on the geometric analysis data, the A-star pathfinding algorithm is used to simulate the movement paths of multiple robotic arms in the working area. The geometric analysis data is used as input to determine the starting position of the robotic arms and the target position of the welding points. The A-star pathfinding algorithm is used to consider the intersection problem of the robotic arm paths, optimize the path to avoid conflicts, and combine the increase of the cost function to evaluate the efficiency and safety of the path, optimize the movement efficiency of the robotic arms, improve the safety and efficiency of the overall operation, and generate the robotic arm simulation results.

[0071] A star pathfinding algorithm, according to the formula:

[0072]

[0073] Calculate the optimal movement path of the robot arm, where is the total cost function of the welding path, which is used to represent the path from the starting point to the vertex The estimated total cost of the path through the goal, From the starting point to the top The known actual cost is used to measure the length of the path traveled, From the vertex A heuristic estimate of the cost to the goal, used to estimate the remaining path length, is the weight coefficient of the heuristic cost, used to adjust The impact on total cost, is the weight coefficient of the robot arm joint angle, which is used to adjust the impact of joint angle changes on the path cost. is the current angle of the robot arm joint, which is used to indicate the position change of the robot arm joint during the welding process. is the weight coefficient of the robot's moving speed, which is used to adjust the impact of speed changes on the path cost. is the moving speed of the robot arm, which is used to indicate the speed of the robot arm during the welding process. is the weight coefficient of the robot workload, which is used to adjust the impact of the workload on the path cost. is the workload of the robot arm, which is used to indicate the load borne by the robot arm during the welding process. is the weight coefficient of work density, which is used to adjust the impact of the number of work points on the path cost. is the working density of the robot arm, which is used to indicate the number of welding points operated by the robot arm per unit time. It is the identifier of the node currently being considered in path planning and is used as a reference point in path calculation.

[0074] The specific execution process of the formula is as follows:

[0075] Through the real-time monitoring system, the current position, speed, workload and joint angle parameters of multiple robotic arms are obtained. According to the real-time data on site, the A-star path-finding algorithm is used to calculate the optimal path from each welding point to the next welding point. The parameters are substituted into the formula to calculate the total cost of each welding point. , used to identify the optimal welding route.

[0076] S103: Based on the robot arm simulation results, the moving trajectories and speed parameters of the multiple robots are adjusted to optimize the speed and efficiency of the welding operation, and the specific process of generating the welding path configuration is as follows;

[0077] In sub-step S103, based on the robot arm simulation results, the movement trajectory and speed parameters of multiple robots are adjusted, and the speed and trajectory of each robot are dynamically adjusted according to the simulation results using a feedback control strategy to ensure the uniformity and accuracy of the welding work. The speed of the robot is adjusted using the trajectory optimization formula, which is: ,in, is the current speed, is the time adjustment factor, It is the adjustment coefficient used to control the increase or decrease of speed. Generate welding path configurations for new speeds that increase the speed and efficiency of welding operations while maintaining weld quality.

[0078] See also Figure 3Based on the welding path configuration, the welding position image is monitored and collected in real time, the weld boundary is identified, the spatial positioning and morphological data of the weld are recorded, and the steps for generating weld feature data are as follows:

[0079] S201: Based on the welding path configuration, the welding image of the robot during the welding process is monitored and recorded in real time, the image contrast is adjusted to enhance the visibility of the weld, and the specific process of obtaining the welding image processing data is as follows;

[0080] In sub-step S201, based on the welding path configuration, the welding image of the robot during the welding process is monitored and recorded in real time, a high-resolution camera is set to continuously monitor the welding area, and the image contrast is adjusted using image enhancement technology to enhance the visibility of the weld and make the weld clearer in the image. The contrast adjustment formula is used for image processing, and the formula is: ,in, represents the pixel intensity in the original image, is the average image intensity, is the base of the natural logarithm and is used to adjust the sensitivity of the contrast adjustment. It is the adjusted contrast value that improves the quality of the welding image, making the weld details easier to identify and analyze, and obtaining welding image processing data.

[0081] S202: Based on the welding image processing data, edge detection is performed on the welding image, the weld boundary is identified, and the spatial position information of the weld is recorded. The specific process of generating the weld position information record is as follows;

[0082] In sub-step S202, based on the welding image processing data, edge detection is performed on the welding image to identify the weld boundary. The processed image is analyzed using an edge detection algorithm to determine the spatial position of the weld. The Sobel edge detection formula is used for image analysis. The formula is: ,in, is the gradient of the image in the x direction, is the gradient of the image in the y direction, It is the result gradient amplitude, which identifies the boundary of the weld, improves the accuracy and efficiency of weld identification, and generates weld position information records.

[0083] S203: Based on the weld position information record, the weld geometry is analyzed, the width, depth and length of the target weld are identified, key features of the geometry are recorded, and the process of generating weld feature data is as follows;

[0084] In sub-step S203, based on the weld position information record, the weld geometry is analyzed, and the recorded weld position information is used to determine the weld width, depth and length through geometric calculations, and the key features of these geometric shapes are recorded. A simple geometric formula is used to calculate the size of the weld, and the formula is: ,in, For the The weld width is measured. For the The weld depth is measured. For the The weld length measured is the number of measurements, The key geometric features of the weld are recorded and analyzed for the average width, depth and length of the weld, respectively, to generate weld feature data.

[0085] See also Figure 4 ,According to the weld feature data, the key features of the weld are identified and compared with the standard weld model, and the consistency information of the weld quality and size is recorded. The specific steps for generating the weld offset identification result are as follows:

[0086] S301: Based on the weld feature data, by analyzing the weld geometry information, identifying the key features of the weld, and generating the key feature information in detail;

[0087] In sub-step S301, based on the weld feature data, the key features of the weld are identified by analyzing the weld geometry information, and the weld feature data is processed by numerical methods to extract the width, depth and length information of the weld. The significance of each feature is evaluated by setting a threshold and comparing the standard deviation to obtain a standardized feature vector. The formula is: ,in, is the original eigenvalue of the weld, is the mean of the eigenvalues, is the standard deviation of the eigenvalues, It is a standardized feature vector that optimizes the identifiability of the key features of the weld and generates key feature information.

[0088] S302: Based on the key feature information, the key features of multiple welds are compared with a preset standard weld model and deviations are identified, the weld offset information is recorded, and the process of generating weld quality comparison results is specifically as follows;

[0089] In sub-step S302, based on the key feature information, the key features of multiple welds are compared with the preset standard weld model and the deviation is identified. The cosine similarity is used to calculate the consistency of each weld feature with the standard model and identify the offset of the weld. The formula is: ,in, is the key feature vector of the solder joint, are the eigenvectors of the standard model, is the number of elements in the eigenvector, It is the calculated cosine similarity, which indicates the consistency between the solder joint and the standard model, records the solder joint offset information, and generates the solder joint quality comparison result.

[0090] S303: According to the weld spot quality comparison result, the deviation degree of multiple weld spots is identified, the consistency of weld quality and size is calculated, and the process of generating weld spot deviation identification result is specifically as follows;

[0091] In sub-step S303, according to the weld quality comparison results, the deviation degree of multiple welds is identified, the consistency of weld quality and size is calculated, and the statistical distribution of the deviation of each weld is calculated using the standard deviation formula to measure the overall quality consistency of the weld. The formula is: ,in, It is The quality deviation of each solder joint, is the average value of all solder joint quality deviations, is the total number of solder joints, Indicates the standard deviation of the weld deviation, evaluates the consistency of weld quality, quantitatively evaluates the weld deviation, and generates weld offset identification results.

[0092] See also Figure 5 Based on the weld point offset recognition results, the welding process is monitored in real time, the robot's motion trajectory and welding speed information are analyzed, the welding completion time is predicted, and the steps for generating welding process record information are as follows:

[0093] S401: Based on the welding point offset recognition result, the motion trajectory information of the welding robot is monitored in real time, the welding start and end time of multiple welding points are recorded, and the specific process of generating the welding time record is as follows;

[0094] In sub-step S401, based on the weld offset recognition results, the motion trajectory information of the welding robot is monitored in real time, sensors and monitoring systems are set to capture the welding start and end times of each weld, and the timestamp function is used to record the exact time of each welding operation, which will include the start and stop time of each weld activity, so as to realize the recording and analysis of the welding robot efficiency and welding operation duration, and generate welding time records.

[0095] S402: predicting the welding completion time of multiple welding points according to the welding time record, comparing the prediction result with the preset timetable of the production plan, calculating the time deviation, and generating the time deviation analysis result. The specific process is as follows;

[0096] In sub-step S402, the welding completion time of multiple welding points is predicted based on the welding time record, and the prediction result is compared with the preset schedule of the production plan. The expected completion time of each welding point is predicted by linear regression analysis, and the deviation from the preset schedule is calculated. The formula is: ,in, is the predicted weld completion time, is the preset production plan time, It is the time deviation that helps the production management team adjust and optimize the welding operation process and generate time deviation analysis results.

[0097] S403: Based on the time deviation analysis result, the welding speed and the pause time of the robot are adjusted to optimize the consistency between the welding process and the preset schedule, and the specific process of generating the welding process record information is as follows;

[0098] In sub-step S403, based on the time deviation analysis results, the welding speed and pause time of the robot are adjusted. Dynamic adjustments are made according to the time deviation results to optimize the consistency between the welding process and the preset schedule. The adjustment formula can be described as: ,in, is the original welding speed, is the adjustment coefficient, and the adjustment range is determined according to the time deviation. is the time deviation, It is a new welding speed that adjusts the robot's operation according to the actual production situation, ensures that the welding activities are synchronized with the preset schedule, and generates welding process record information.

[0099] See also Figure 6 ,According to the welding process record information, the time information in the manual task flow is monitored in real time, and compared with the preset schedule, the time parameters of multiple welding robot workflows are adjusted, and the steps for generating workflow coordination results are as follows:

[0100] S501: According to the welding process record information, the start and end time of multiple processes in the manual task are collected in real time, and compared with the preset task process schedule, the time deviation is identified, and the specific process of generating the time comparison result is as follows;

[0101] In sub-step S501, according to the welding process record information, the start and end time of multiple processes in the manual task are collected in real time, and a time tracking system is set to record the actual execution time of each task, and compare it with the preset task process schedule to identify the existing time deviation. The formula is: ,in, is the time when the task is actually completed, is the preset task flow time, It is the time deviation of tasks, identifies time efficiency issues in human and machine collaboration, and generates time comparison results.

[0102] S502: Based on the time comparison result and the time deviation, the workflow time parameters that need to be adjusted for the welding robot are calculated, including the start delay and the task interval, and the specific process of obtaining the task time adjustment parameters is as follows;

[0103] In sub-step S502, based on the time comparison result and the time deviation, the workflow time parameters that need to be adjusted for the welding robot are calculated, including the start delay and the task interval. By analyzing the time deviation data, the operating parameters of the robot are optimized and adjusted to match the actual production speed. The adjustment formula is: ,in, are the original workflow time parameters, including start delay and task interval, is the adjustment factor, which determines how much adjustment to make based on the time deviation. is the time deviation of the task, It is the adjusted workflow time parameter, which ensures that the workflow of the welding robot is synchronized with the actual production demand and obtains the task time adjustment parameter.

[0104] S503: adjusting parameters according to task time, adjusting and updating the welding rhythm and task sequence of the welding robot, optimizing the coordination efficiency between humans and robots in the welding task process, and generating workflow coordination results;

[0105] In sub-step S503, the welding robot's welding beat and task sequence are adjusted and updated according to the task time adjustment parameters, and the adjustment parameters are used. Update the robot's task sequence and beat, optimize the coordination efficiency between humans and machines, and optimize the smoothness and coordination efficiency of human and robot actions in the welding task process. The formula is: ,in, is the original welding cycle or task sequence, is the adjustment factor, affecting the amplitude of the beat or sequence adjustment. is the task time adjustment parameter obtained from S502, It is the updated welding rhythm or task sequence. Through this adjustment, the coordination efficiency of manual and robot in the welding task process is optimized, and the workflow coordination result is generated.

[0106] See also Figure 7 ,Based on the workflow coordination results, by analyzing the location and quantity information of multiple welds, identifying the offset and quantity anomalies, and verifying the welding results, the steps for generating the automotive welding control results are as follows:

[0107] S601: According to the workflow coordination result, the process of collecting and recording the location and quantity information of the welds on the automobile workpiece and generating the weld monitoring record data is as follows;

[0108] In sub-step S601, based on the workflow coordination results, the location and quantity information of the welds on the automotive workpiece are collected and recorded. Through image acquisition sensors and image processing software, the accurate location and quantity of each weld are captured and recorded in real time to ensure that the location and quantity information of the welds are accurately recorded and to generate weld monitoring record data.

[0109] S602: Analyze the welding point data according to the welding point monitoring record data, identify the position offset and quantity abnormality, record the welding points of wrong welding and missed welding, and obtain the welding point abnormality information in detail;

[0110] In sub-step S602, the solder joint data is analyzed according to the solder joint monitoring record data to identify positional offset and quantity anomalies. The position and number of each solder joint are compared using image analysis technology and geometric verification methods to identify solder joints with positional offset or inconsistent quantity. The formula is: ,in, is the coordinate of the actual solder joint, are the ideal and predetermined weld point coordinates, Indicate position deviation, identify and record wrongly welded and missed welds, and obtain abnormal weld information.

[0111] S603: Based on the abnormal welding spot information, the welding quality of the automobile workpiece is analyzed, the welding quality score is calculated, and the process of generating the automobile welding control result is specifically as follows;

[0112] In sub-step S603, based on the abnormal information of the weld spot, the welding quality of the automobile workpiece is analyzed, and the welding quality score is calculated. The welding quality of the workpiece is evaluated by combining the abnormal data of the weld spot using a statistical analysis method. The formula is: ,in, It is The weight factor of each solder joint is determined according to the importance of the solder joint location. It is Deviation value of solder joints, is the total number of solder joints, It is a welding quality score that evaluates welding quality and generates automotive welding control results.

[0113] See also Figure 8 , a flexible control system for automobile welding process, the flexible control system for automobile welding process is used to execute the above-mentioned flexible control method for automobile welding process, the system comprises:

[0114] The work path planning module analyzes the positions of multiple welding spots on the target workpiece according to the geometric information of the automobile workpiece, considers the path intersection and conflict of multiple robotic arms, adjusts the robot's movement trajectory and speed parameters, and generates movement trajectory parameters;

[0115] The welding image analysis module monitors and collects images of the welding position in real time based on the movement trajectory parameters, records the spatial positioning and morphological data of the weld, identifies the key features of the weld, and compares them with the standard weld model to generate consistency analysis results;

[0116] The work process monitoring module monitors the welding process in real time based on the consistency analysis results, analyzes the robot's motion trajectory and welding speed information, predicts the welding completion time, and generates completion time prediction information;

[0117] The human-machine work coordination module monitors the time information in the manual task process in real time based on the completion time prediction information, compares it with the preset schedule, adjusts the time parameters of multiple welding robot workflows, and generates work list adjustment information;

[0118] The welding quality verification module adjusts the information based on the work list, verifies the welding results by analyzing the position and quantity information of multiple welds, and generates automobile welding control results.

[0119] 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 flexible control method for automobile welding process, characterized in that: The following steps are involved: According to the geometric information of the automobile workpiece, by analyzing the positions of multiple welding points on the target automobile workpiece, considering the intersection and conflict of multiple robot arm paths, calculating the path planning of the welding robot, adjusting the robot's movement trajectory and speed parameters, and generating the welding path configuration; Based on the welding path configuration, real-time monitoring and acquisition of welding position images, identification of weld boundaries, recording of weld spatial positioning and morphological data, and generation of weld feature data; According to the weld feature data, key features of the weld are identified and compared with a standard weld model, consistency information of weld quality and size is recorded, and a weld point offset identification result is generated; Based on the welding spot offset recognition result, the welding process is monitored in real time, the motion trajectory and welding speed information of the robot are analyzed, the welding completion time is predicted, and the welding process record information is generated; According to the welding process record information, the time information in the manual task flow is monitored in real time, and compared with the preset timetable, the time parameters of multiple welding robot workflows are adjusted, and the workflow coordination results are generated; Based on the workflow coordination result, by analyzing the position and quantity information of multiple welding points, identifying the deviation and quantity anomaly, verifying the welding result, and generating the automobile welding control result; The welding path configuration includes robot movement trajectory information, movement speed information and welding sequence data, the weld feature data includes weld width information, weld length information and weld spot geometry feature information, the weld spot offset identification result includes weld spot position deviation data, angle error identification record and spacing offset information, the welding process record information includes real-time welding speed, welding angle adjustment record and estimated completion time information, the workflow coordination result includes robot task start time, operation interval time information and time adjustment amount, and the automobile welding control result includes weld spot qualification rate, number of missed weld spots and welding quality score; According to the geometric information of the automobile workpiece, by analyzing the positions of multiple welding points on the target automobile workpiece, considering the intersection and conflict of multiple robot paths, calculating the path planning of the welding robot, adjusting the movement trajectory and speed parameters of the robot, the specific steps of generating the welding path configuration are as follows: According to the geometric information of the automobile workpiece, the geometric information of the target automobile workpiece is analyzed, the spatial coordinates of multiple welding points are identified, and geometric analysis data is generated; Based on the geometric analysis data, an A-star pathfinding algorithm is used to simulate the movement paths of multiple robotic arms in the working area, considering the intersection of multiple robotic arm paths, calculating the path planning of the robotic arms, and generating robotic arm simulation results; Based on the robot arm simulation results, the movement trajectories and speed parameters of the multiple robots are adjusted to optimize the speed and efficiency of the welding operation and generate a welding path configuration; The A-star pathfinding algorithm is based on the formula: Calculate the optimal movement path of the robot arm, where is the total cost function of the welding path, From the starting point to the vertex The known actual cost of From the vertex The heuristic estimate of the cost to the target, is the weight coefficient of the heuristic cost, is the weight coefficient of the robot arm joint angle, is the current angle of the robot arm joint, is the weight coefficient of the robot's moving speed, is the moving speed of the robot arm, is the weight coefficient of the robot arm workload, is the workload of the robot arm, is the weight coefficient of working density, is the working density of the robot arm, It is the identifier of the node currently considered in path planning.

2. The flexible control method for automobile welding process according to claim 1, characterized in that: Based on the welding path configuration, the steps of real-time monitoring and collecting welding position images, identifying weld boundaries, recording the spatial positioning and morphological data of the weld, and generating weld feature data are as follows: Based on the welding path configuration, the welding image of the robot during the welding process is monitored and recorded in real time, the image contrast is adjusted to enhance the visibility of the weld, and the welding image processing data is obtained; Based on the welding image processing data, edge detection is performed on the welding image to identify the weld boundary, and the spatial position information of the weld is recorded to generate a weld position information record; Based on the weld position information record, the weld geometry is analyzed, the width, depth and length of the target weld are identified, key features of the geometry are recorded, and weld feature data is generated.

3. The flexible control method for automobile welding process according to claim 1, characterized in that: According to the weld feature data, the key features of the weld are identified and compared with the standard weld model, the consistency information of the weld quality and size is recorded, and the steps of generating the weld offset identification result are specifically as follows: Based on the weld feature data, by analyzing the weld geometry information, identifying the key features of the weld, and generating key feature information; Based on the key feature information, the key features of multiple welds are compared with a preset standard weld model and deviations are identified, weld offset information is recorded, and weld quality comparison results are generated; According to the weld spot quality comparison result, the deviation degree of multiple weld spots is identified, the consistency of weld quality and size is calculated, and the weld spot deviation identification result is generated.

4. The flexible control method for automobile welding process according to claim 1, characterized in that: Based on the welding spot offset recognition result, the welding process is monitored in real time, the motion trajectory and welding speed information of the robot are analyzed, the welding completion time is predicted, and the steps of generating welding process record information are specifically as follows: Based on the weld point offset recognition result, the motion trajectory information of the welding robot is monitored in real time, the welding start and end time of multiple weld points are recorded, and a welding time record is generated; Predicting the welding completion time of multiple welding points based on the welding time record, comparing the prediction result with the preset timetable of the production plan, calculating the time deviation, and generating the time deviation analysis result; Based on the time deviation analysis results, the welding speed and pause time of the robot are adjusted to optimize the consistency of the welding process and the preset schedule, and the welding process record information is generated.

5. The flexible control method for automobile welding process according to claim 1, characterized in that: According to the welding process record information, the time information in the manual task flow is monitored in real time, and compared with the preset schedule, and the time parameters of multiple welding robot workflows are adjusted to generate the workflow coordination results. The specific steps are: According to the welding process record information, the start and end time of multiple processes in the manual task are collected in real time, and compared with the preset task process schedule, the time deviation is identified, and the time comparison result is generated; Based on the time comparison result, according to the time deviation, the workflow time parameters that need to be adjusted for the welding robot are calculated, including the start delay and the task interval, to obtain the task time adjustment parameters; According to the task time adjustment parameters, the welding rhythm and task sequence of the welding robot are adjusted and updated, the coordination efficiency between humans and robots in the welding task process is optimized, and a workflow coordination result is generated.

6. The flexible control method for automobile welding process according to claim 1, characterized in that: Based on the workflow coordination result, by analyzing the position and quantity information of multiple welding points, identifying the offset and quantity anomalies, and verifying the welding results, the steps of generating the automobile welding control results are specifically as follows: According to the workflow coordination result, the position and quantity information of the welding spots on the automobile workpiece are collected and recorded to generate welding spot monitoring record data; Analyze the welding point data according to the welding point monitoring record data, identify the position deviation and quantity abnormality, record the welding points of wrong welding and missed welding, and obtain the welding point abnormality information; Based on the abnormal welding spot information, the welding quality of the automobile workpiece is analyzed, the welding quality score is calculated, and the automobile welding control result is generated.

7. A flexible control system for automobile welding process, characterized in that: According to the flexible control method for automobile welding process according to any one of claims 1 to 6, the system comprises: The work path planning module analyzes the positions of multiple welding spots on the target workpiece according to the geometric information of the automobile workpiece, considers the path intersection and conflict of multiple robotic arms, adjusts the robot's movement trajectory and speed parameters, and generates movement trajectory parameters; The welding image analysis module monitors and collects images of the welding position in real time based on the movement trajectory parameters, records the spatial positioning and morphological data of the weld, identifies the key features of the weld, and compares them with the standard weld model to generate consistency analysis results; The work process monitoring module monitors the welding process in real time based on the consistency analysis result, analyzes the robot's motion trajectory and welding speed information, predicts the welding completion time, and generates completion time prediction information; The human-machine work coordination module monitors the time information in the manual task process in real time according to the completion time prediction information, compares it with the preset schedule, adjusts the time parameters of the workflows of multiple welding robots, and generates work list adjustment information; The welding quality verification module verifies the welding results based on the work list adjustment information by analyzing the position and quantity information of multiple welding points to generate the automobile welding control results.

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