Welding path planning method based on machine vision

Abstract

The invention discloses a welding path planning method based on machine vision, and relates to the technical field of welding robotics.The method comprises the steps that sample data of various groove types are collected, classified and stored, a groove type database is established, and an extraction condition template is defined; groove section data are obtained through the machine vision technology, a three-dimensional model is constructed after grouping, welding seam, material and heat affected zone characteristics are analyzed, a welding sequence principle is determined, and a comprehensive welding path is generated; key parameters in the welding process are monitored in real time, an early warning mechanism is set, and the optimal path is dynamically adjusted; updating a database according to the path planning efficiency value, comprehensively analyzing candidate paths, and recommending an optimal welding path; the extraction condition template is dynamically adjusted, the precision, adaptability and efficiency of welding path planning can be improved, and the welding quality and stability are ensured; and a plurality of different cross section data are analyzed, the comprehensive welding path is determined, and various different cross section data characteristics can be met at the same time.

Description

Technical Field

[0001] The invention relates to the technical field of welding robots, and in particular to a welding path planning method based on machine vision. Background Art

[0002] In the welding industry, the development and application of welding robots have been realized in standardized welding workshops, and they are applied in more and more scenarios to achieve more efficient, safer and more standardized welding. The core tube in the construction industry is in the form of box-shaped steel, and the crossbeam is in the form of H-shaped steel. The thickness of the steel structure is relatively thick, the groove is relatively deep, and the degree of groove standardization is relatively low. For the deeper grooves in building steel structures, multi-layer and multi-pass welding is usually used. The application of welding robots in multi-layer and multi-pass welding requires the completion of the groove weld bead planning and welding path planning before welding begins. The introduction of machine vision technology provides strong support for the application in multi-layer and multi-pass welding. Through high-resolution cameras and advanced image processing algorithms, the shape, size and surface state of the groove are captured and analyzed in real time to ensure the accuracy and consistency of the welding process. The existing welding path planning method cannot guarantee the welding quality well in the face of non-standard grooves.

[0003] The Chinese invention patent with application number 202210985990.3 is a method, device, electronic device and storage medium for planning a groove welding path. It collects multiple sets of groove data and extracts key information from the multiple sets of groove data based on preset extraction conditions to form multiple sets of groove section data; for each groove section data, the number of welding layers and the number of welding passes of each welding layer of the groove section are calculated using preset weld parameters; the position coordinates of each weld pass are marked in each groove section, and the groove welding path is planned based on the weld passes with the same position coordinates in each groove section.

[0004] In the existing patent technology, cross-sectional data analysis is performed on irregular grooves to plan welding paths. However, for irregular grooves, the cross-sectional data obtained will also have obvious differences. When faced with cross-sectional data with different characteristics, it is impossible to accurately plan the path based on the position coordinates. Therefore, the existing patent technology ignores the differentiated characteristics of different cross-sectional data, resulting in the inability to extract key information based on preset extraction conditions, reducing the accuracy of cross-sectional data analysis and affecting the accuracy of welding path planning. Summary of the invention

[0005] The present application provides a welding path planning method based on machine vision, which solves the problem in the prior art of ignoring the differentiated characteristics of data with different cross-sections and reducing the accuracy of welding path planning. It achieves the technical effect of accurately identifying the differentiated characteristics of data with different cross-sections and improving the accuracy of welding path planning.

[0006] The present application provides a welding path planning method based on machine vision, the method comprising:

[0007] S100: Collect and classify and store sample data of various groove types, establish a groove type database, and define an extraction condition template for each groove type;

[0008] S200: Identify the current groove type, select the corresponding extraction condition template, obtain the groove section data, group the groove section data according to different characteristics, establish a three-dimensional model according to multiple groups of groove section data, generate a comprehensive welding path, and generate a welding analysis report according to the actual welding effect;

[0009] S300: According to the welding analysis report, the path planning situation of each groove type is obtained, the path planning efficiency value is generated, and the groove type database is updated;

[0010] Wherein, step S200 includes:

[0011] S210: acquiring groove section data based on machine vision technology, including a two-dimensional image and relative position information, and preprocessing the acquired groove section data to obtain standard section data;

[0012] S220: setting a grouping strategy to group the standard cross-section data, selecting a reconstruction strategy for each group of cross-section data according to its characteristics, and constructing a three-dimensional model using a surface reconstruction algorithm based on the reconstruction strategy for each group of cross-section data;

[0013] S230: Analyze weld characteristics, material characteristics, and heat-affected zone sensitivity characteristics based on a three-dimensional model to determine welding sequence principles;

[0014] S240: Generate a preliminary welding path corresponding to each cross-section group according to each group of cross-section data and the welding sequence principle, and merge the preliminary welding paths of each cross-section group to form a comprehensive welding path.

[0015] Furthermore, the grouping strategy is to group the data according to the shape similarity of different sections and the characteristic nodes between different sections. For the two sections with the highest shape similarity, the trajectory trends of their adjacent nodes are analyzed, and the adjacent nodes with the same trajectory are selected as characteristic nodes to group the groove section data.

[0016] Furthermore, the trajectory trend refers to the welding angle passing through the node when welding between different cross sections. If the welding angles are consistent, the trajectories are judged to be the same; if the welding angles change, the trajectories are judged to be different.

[0017] Further, step S240 includes:

[0018] S241: Obtain each group of cross-sectional data, extract key feature points respectively, and obtain the influence value of each group of cross-sectional data according to the number of key feature points, compare the influence value with the influence threshold, and if the influence value is greater than the difference threshold, mark it, and generate preliminary welding paths respectively according to the marking;

[0019] S242: generating an optional path group by fusing the preliminary welding path and the annotations respectively, setting evaluation indicators to score each optional path, and selecting the one with the highest comprehensive score as the comprehensive welding path.

[0020] Furthermore, the influence value is used to evaluate the influence of the key feature point on the welding path, and the influence threshold is set according to all current influence values. All influence values ​​are arranged in descending order, and the average value of the first 20% of the influence values ​​is selected as the influence threshold.

[0021] Furthermore, step S240 also includes:

[0022] S243: Real-time monitoring of key parameters and conditions in the welding process, processing and analysis of real-time monitoring data, setting warning thresholds, and triggering warnings when the monitoring values ​​exceed the warning thresholds;

[0023] S244: Based on the warning information, determine whether the welding path needs to be changed. If it needs to be changed, select a new optimal path for switching based on the optional path group and the comprehensive score.

[0024] Furthermore, the warning threshold is pre-set based on historical experimental data, and different monitoring values ​​are set for changes in the monitoring data. Different changes have different impacts. The greater the impact, the greater the monitoring value. The monitoring value that changes in real time is compared with the warning threshold.

[0025] Furthermore, step S240 also includes:

[0026] S245: setting the maximum number of adjustment times, monitoring the number of path adjustment times in real time, and further judging the subsequent non-welding condition if the maximum number of adjustment times is reached, generating a demand value, and performing path adjustment if the demand value is greater than the demand threshold; and not performing path adjustment if the demand value is not greater than the demand threshold;

[0027] The demand value is set according to the influence of the current welding path on the subsequent unwelded part. The greater the influence, the greater the demand.

[0028] Furthermore, step S300 also includes:

[0029] S310: selecting actual path planning efficiency values ​​under multiple paths, matching new groove shapes and cross-section information with data in a database, and generating matching values;

[0030] S320: setting a matching threshold, selecting welding paths corresponding to groove types with matching values ​​greater than the matching threshold as candidate paths, and performing a comprehensive analysis on the candidate paths;

[0031] S330: Based on the comprehensive analysis results of the candidate paths, the final recommended welding path is selected and the groove type database is updated.

[0032] Furthermore, step S320 also includes:

[0033] S321: Setting analysis and evaluation indicators, generating a comprehensive evaluation value 1 for candidate paths with switching nodes; the evaluation indicators include switching time, operation efficiency, and actual operation difficulty value; the switching time includes robot repositioning time and welding parameter adjustment time;

[0034] S322: Evaluate the overall time consumption, welding efficiency and welding quality of the common candidate path to generate a second comprehensive evaluation value;

[0035] S323: Select the highest candidate path as the recommended welding path according to the comprehensive evaluation value 1 and the comprehensive evaluation value 2 respectively.

[0036] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0037] By defining basic extraction condition templates for different types of grooves and dynamically adjusting them according to actual conditions to adapt to changes in specific groove characteristics, the accuracy and efficiency of welding path planning can be improved; multiple different cross-sectional data are analyzed to determine the comprehensive welding path, and a comprehensive welding path plan that meets multiple different cross-sectional characteristics is generated, which achieves the effect of simultaneously meeting multiple different cross-sectional data characteristics and improving the overall welding quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the overall process of a welding path planning method based on machine vision in an embodiment of the present invention;

[0039] Figure 2 Schematic diagram of step S200 in a welding path planning method based on machine vision in an embodiment of the present invention;

[0040] Figure 3 Schematic diagram of the process of step S240 in a welding path planning method based on machine vision in an embodiment of the present invention;

[0041] Figure 4 Schematic diagram of step S300 in a welding path planning method based on machine vision in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0044] Embodiment 1: Figure 1 As shown, a welding path planning method based on machine vision, the method comprising:

[0045] S100: Collect and classify and store sample data of various groove types, establish a groove type database, and define an extraction condition template for each groove type.

[0046] In some embodiments, the sample data includes basic parameters and key parameters, the basic parameters including the bottom width of the groove, which affects the formation of the molten pool and the width of the weld during welding; the thickness of the groove, which determines the heat input and the number of welding layers required during welding, and directly affects the welding quality and efficiency; the angle between the right-angled side and the hypotenuse of the groove, which is used to describe the shape of the groove, affects the molten pool flow and the weld shape during welding, and is used to design the welding process and select the welding angle; the depth of the groove is the vertical distance from the material surface to the bottom of the groove, which determines the required penetration depth and the strength of the weld during welding; the length of the groove is the total length of the groove along the welding direction, which affects the heat distribution and the continuity of the weld during welding, and is used to design the welding sequence and select the welding speed. The key parameters include groove contour line, which is a continuous curve describing the shape and size of the groove. The actual contour is obtained by scanning the groove through machine vision technology, and the contour line of the irregular groove is accurately matched and identified; groove area, which is an auxiliary parameter used to verify the shape and size of the groove; irregularity index, which is a quantitative index used to describe the degree of deviation between the groove shape and the standard shape (such as X-type, V-type, U-type, etc.); local feature points, which are representative points of the groove, such as inflection points, extreme points, etc., are used as auxiliary parameters to determine the exact location of the irregular groove. According to the sample data, statistical analysis is performed on different types of grooves, classified and stored to form a groove type database, and extraction condition templates are defined for different types of grooves.

[0047] In some embodiments, a set of basic extraction condition templates are defined for each groove type to guide the extraction of key information. When extracting cross-sectional data for groove types with different regular changes, the extraction conditions need to be adjusted to ensure efficiency and accuracy. The extraction conditions are dynamically adjusted to adapt to specific groove feature changes. The extraction condition template includes edge detection thresholds, filtering parameters, and feature extraction algorithms. Different thresholds are set according to different groove types to accurately identify the edges of the grooves; appropriate filtering algorithms and parameters are selected to smooth the image and remove noise interference; according to the shape characteristics of the groove, a suitable algorithm (such as Hough transform, contour extraction, etc.) is selected to extract the coordinate information of key points. For example, X-type groove: the shape of the groove is similar to the letter "X", with two symmetrical bevels and a narrow bottom width, which is suitable for butt welding of thick plates; its key parameters are the bottom width of the groove, the thickness of the groove, and the angle between the right-angled side of the groove and the bevel (usually two equal acute angles); a higher threshold should be selected for edge detection to highlight the straight line features of the bevel. At the same time, considering the possible shadows or reflections at the bottom of the groove, the threshold range can be appropriately adjusted to ensure accurate identification of the bottom edge. The Hough transform algorithm detects the straight line segment of the bevel, and the contour extraction algorithm obtains the overall contour. V-shaped groove: The shape of the groove is similar to the letter "V", with a sharp bottom and two bevels, which is suitable for welding medium and thick plates; its key parameters are the groove bottom width, groove thickness, and the angle between the right-angled side of the groove and the bevel (an acute angle). A medium threshold is selected to balance the recognition effect of the bevel and the bottom. Morphological operations are used to refine the edges and improve the recognition accuracy. Mean filtering is selected with smaller parameters to retain the detailed information of the bevel and the bottom. The edge detection algorithm obtains the preliminary outline, the corner detection algorithm locates the intersection position, and the Hough transform algorithm detects the bevel straight line segment. U-shaped groove: The shape of the groove is similar to the letter "U", with a wide and smooth bottom, which is suitable for welding thin plates or welding that requires a large penetration depth; its key parameters are the groove bottom width, groove thickness, and groove arc radius. A lower threshold is selected to capture the curve characteristics of the bottom. At the same time, in order to avoid interference from bottom shadows or reflections, a dynamic threshold or adaptive threshold algorithm is used to optimize the recognition effect. Median filtering is selected with moderate parameters to smooth the image and remove noise interference. The edge detection algorithm obtains the preliminary contour, the contour fitting algorithm fits the bottom arc curve, and the contour extraction algorithm obtains the overall contour. Three typical groove types are selected above for illustration. The feature extraction algorithms used in the above are all algorithm models well known to people in this field. The actual groove data is used for training and specific parameters are adjusted to obtain an accurate model for extraction.

[0048] In some embodiments, the local feature point can be used as a feature node to distinguish different sections. When a local feature point is identified, the section data of the local feature point is intercepted. When the next different local feature point is identified, the section data is intercepted again. Obtaining the section data in the middle range can accurately analyze the data differences between different sections.

[0049] S200: Identify the current groove type, select the corresponding extraction condition template, obtain the groove section data, group the groove section data according to different characteristics, establish a three-dimensional model based on multiple groups of groove section data, generate a comprehensive welding path, and generate a welding analysis report based on the actual welding effect.

[0050] S300: According to the welding analysis report, the path planning situation of each groove type is obtained, the path planning efficiency value is generated, and the groove type database is updated.

[0051] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0052] This application defines basic extraction condition templates for different types of grooves, and dynamically adjusts them according to actual conditions to adapt to specific changes in groove characteristics, thereby achieving the effect of improving the accuracy and efficiency of welding path planning.

[0053] Embodiment 2: In embodiment 1, basic extraction condition templates are defined for different types of grooves, and a groove type database is set. This embodiment makes further improvements on the above basis.

[0054] like Figure 2 As shown, step S200 includes:

[0055] S210: acquiring groove section data based on machine vision technology, including a two-dimensional image and relative position information, and preprocessing the acquired groove section data to obtain standard section data;

[0056] S220: setting a grouping strategy to group the standard cross-section data, selecting a reconstruction strategy for each group of cross-section data according to its characteristics, and constructing a three-dimensional model using a surface reconstruction algorithm based on the reconstruction strategy for each group of cross-section data;

[0057] S230: Analyze weld characteristics, material characteristics, and heat-affected zone sensitivity characteristics based on the 3D model to determine the welding sequence principle;

[0058] In some embodiments, the groove section data is obtained based on machine vision technology, and a high-resolution industrial camera, a light source system, and an image processing calculation unit are used to calibrate the camera, determine the camera's internal and external parameters, the internal parameters include focal length, optical center, and distortion coefficient, and the external parameters are the position and posture of the camera in the world coordinate system; record the camera position and posture information at each acquisition, calculate the relative position information, and use it to describe the spatial relationship between different cross-sectional data; obtain a two-dimensional image of the groove section, including the shape, size, and state information of the groove. The obtained groove section data is preprocessed, and the median filtering method is used to remove noise in the image. The image contrast is enhanced by histogram equalization to make the groove contour clearer. The edge detection algorithm is used to extract the edge contour of the groove. According to the camera calibration results, the relative position information obtained is calibrated to ensure the accuracy of the position information, and the position information in the camera coordinate system is converted to the world coordinate system.

[0059] In some embodiments, feature information such as shape, size and surface condition is extracted from the preprocessed cross-sectional data, the extracted feature information is analyzed to find cross-sectional data with similar features, and a grouping strategy is formulated based on the analysis results. The grouping strategy should ensure that the data within the group have similar features.

[0060] The grouping strategy is to group different sections according to the shape similarity and the characteristic nodes between different sections. For the two sections with the highest shape similarity, the trajectory trends of their adjacent nodes are analyzed, and the adjacent nodes with the same trajectory are selected as characteristic nodes to group the groove section data. For each group of section data, a reconstruction strategy is selected according to its characteristics, and based on the reconstruction strategy of each group of sections, a surface reconstruction algorithm is used to construct a three-dimensional model.

[0061] The trajectory trend refers to the welding angle passing through the node when welding between different cross sections. If the welding angles are consistent, the trajectories are judged to be the same; if the welding angles change, the trajectories are judged to be different.

[0062] In some embodiments, a shape matching algorithm is used to calculate the similarity between each section, and the sections are divided into several groups according to the shape similarity. In the section group with the highest similarity, the trajectory trends of adjacent nodes are analyzed, and adjacent nodes with the same trajectory are selected as feature nodes according to the trajectory trend. For sections with low shape similarity but possibly similar trajectory trends, cross-group analysis is performed to confirm whether regrouping is needed. The effectiveness of grouping and feature node selection is verified through actual welding tests. The grouping strategy and feature node selection are optimized according to the welding results and feedback, and the shape similarity calculation method and trajectory trend analysis method are continuously optimized to improve the accuracy of grouping and the rationality of feature node selection.

[0063] In some embodiments, the weld characteristics include the number of weld passes and the number of weld layers. When the groove is large, the weld may need to be welded for several layers to form, and each layer is composed of several welds. The several welds here refer to the number of weld passes, and the number of weld layers refers to the number of layers repeatedly welded on the same weld, usually referring to the number of continuous welding times performed in a single weld. The selection of the number of weld layers is mainly determined by factors such as the thickness of the welding structure, the diameter of the welding rod, and the groove size. The weld characteristics also include the position, type (such as butt weld, fillet weld), quantity, and distribution density of the weld. The material characteristics are based on the different requirements of materials of different thicknesses for welding parameters (such as current, voltage, welding speed) and welding sequence. Thick materials need to be preheated or layered welded, while thin materials need to avoid overburning and deformation; the heat-affected zone sensitivity is used to analyze the sensitivity of the material to the welding heat-affected zone to determine the welding sequence and cooling measures to reduce welding deformation. The welding sequence principle is used to determine the welding sequence, reduce welding deformation, and balance welding stress and deformation. Under the premise of ensuring welding quality, optimizing the welding sequence can improve welding efficiency.

[0064] S240: Generate a preliminary welding path corresponding to each cross-section group according to each group of cross-section data and the welding sequence principle, and merge the preliminary welding paths of each cross-section group to form a comprehensive welding path.

[0065] In some embodiments, the preliminary welding path is generated according to the cross-sectional data of each group and the welding sequence principle, and the preliminary welding paths of each cross-sectional group are merged to form a comprehensive welding path. During the merging process, it is necessary to ensure the continuity between the paths, that is, the end path of the previous cross-sectional group and the start path of the next cross-sectional group can smoothly transition, and the preliminary welding path of each cross-sectional group is segmented and identified, and the starting point and end point of each path are marked. The marked characteristic nodes are used as transition nodes to ensure that the angle remains unchanged during the welding process. According to the order and adjacent relationship of the cross-sectional groups, the end path of the previous cross-sectional group is docked with the start path of the next cross-sectional group. When docking, ensure that the spatial position and direction between the path segments are continuous to avoid fractures or dislocations. At the junction of the path segments, a smooth transition process is performed. If there is no transition node, a curve fitting method is used to generate a smooth transition path. All docked and processed path segments are combined to generate a complete comprehensive welding path.

[0066] In some embodiments, the integrated welding path is adjusted and improved to improve welding efficiency and quality. The kinematic constraints of the welding robot are analyzed, including joint angle limits and end effector speed limits. According to the constraints, the integrated welding path is adjusted to ensure that the robot can weld accurately and smoothly according to the planned path. Joint space planning and speed planning methods are used to meet the kinematic constraints. The integrated welding path is reviewed, unnecessary turning points and pauses are identified and deleted, and the path is simplified by path clipping and merging methods. When simplifying the path, it should be ensured that the welding quality and the smoothness of the robot movement are not affected. The simplified welding path is smoothed to reduce vibration and impact during welding. According to the characteristics and requirements of the welding path, the corresponding welding process parameters are matched. The optimized welding path is actually tested for welding. According to the test results and feedback, the path merging and optimization process is verified. If problems or deficiencies are found, return to the corresponding steps for correction and optimization.

[0067] In this embodiment, the groove cross-section data is obtained by machine vision technology, and preprocessed and grouped to build an accurate three-dimensional model. The comprehensive welding path generated based on the three-dimensional model ensures the accuracy and continuity of the path. By analyzing the characteristics of the weld, material and heat-affected zone, a reasonable welding sequence principle is determined.

[0068] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0069] The present application analyzes multiple different cross-sectional data, determines a comprehensive welding path, and generates a comprehensive welding path plan that satisfies multiple different cross-sectional characteristics, thereby achieving the effect of simultaneously satisfying multiple different cross-sectional data characteristics and improving the overall welding quality.

[0070] Embodiment 3: In the above embodiment, a plurality of different cross-section data are analyzed to determine a comprehensive welding path. This embodiment makes further improvements on the above basis.

[0071] like Figure 3 As shown, step S240 includes:

[0072] S241: Obtain each group of cross-sectional data, extract key feature points respectively, and obtain the influence value of each group of cross-sectional data according to the number of key feature points, compare the influence value with the influence threshold, and if the influence value is greater than the difference threshold, mark it, and generate preliminary welding paths according to the marking.

[0073] S242: generating an optional path group by fusing the preliminary welding path and the annotations respectively, setting evaluation indicators to score each optional path, and selecting the one with the highest comprehensive score as the comprehensive welding path.

[0074] In some embodiments, key feature points are extracted respectively according to each set of cross-sectional data, and the key feature points include cross-sectional shape change points, size mutation points, and material thickness change points; the cross-sectional shape change points are located at positions where the cross-sectional geometry changes significantly, such as from a straight line to a curve, or from one curve to another, and are used to assist in determining parameters that need to be adjusted during welding, such as welding angle and speed; the size mutation points represent positions where cross-sectional dimensions (such as width and height) change significantly, affecting heat distribution and weld quality during welding; the material thickness change points are uneven thickness of cross-sectional materials that can affect penetration depth and heat-affected zone during welding, and affect weld strength. The above key feature points can be identified and analyzed using image processing design software for cross-sectional data, or automatically detected and marked by detection algorithms for shape, size, and thickness change points, which will not be elaborated here.

[0075] The influence value is used to evaluate the influence of the key feature point on the welding path. The influence threshold is set according to all current influence values. All influence values ​​are arranged in descending order, and the average value of the first 20% of the influence values ​​is selected as the influence threshold.

[0076] In some embodiments, an influence value is generated for each group of cross-sectional data according to the number of key feature points, and compared with the influence threshold. If it is greater than the influence threshold, it is marked, indicating that it has a significant impact on the welding path. The marked feature points are used as important reference factors for generating the preliminary welding path. The preliminary welding paths are generated according to the marked situation. If three are marked, the preliminary welding path will be generated according to the marked situation. The preliminary welding path and the mark are fused to generate an optional path group. Each optional path is preferentially satisfied with one of the marks. The number of paths in the optional path group is consistent with the number of marks. For example, a groove contains three different cross-sectional groups, two of which are marked, and three preliminary welding paths are generated respectively. When merging, the welding requirements of one of the marks are first met to generate an optional path. Similarly, the welding requirements of another mark are met to generate another optional path, that is, two optional paths are generated to form an optional path group. In the above optional path generation process, the quality and requirements of the overall welding still need to be considered. The difference between the two optional paths is that there is a deviation in the welding method and requirements selected when welding at the marked cross-sectional position.

[0077] In some embodiments, evaluation indicators are set to score each optional path, and the evaluation indicators include welding materials, speed, temperature, and specific equipment conditions. The score is estimated based on the specific implementation of each optional path. All evaluation indicators are integrated and standardized to generate a unified score value. The one with the highest comprehensive score is selected as the comprehensive welding path and is used as the preferred welding path.

[0078] S243: Real-time monitoring of key parameters and conditions in the welding process, processing and analysis of real-time monitoring data, setting warning thresholds, and triggering warnings when the monitoring values ​​exceed the warning thresholds.

[0079] S244: Based on the warning information, determine whether the welding path needs to be changed. If it needs to be changed, select a new optimal path for switching based on the optional path group and the comprehensive score.

[0080] In some embodiments, key parameters and conditions in the welding process are monitored in real time, and detection equipment such as sensors and cameras are used to monitor key parameters and conditions in the welding process in real time, such as welding current, voltage, welding speed, weld quality, welding deformation, etc. The real-time monitoring data is processed and analyzed to extract key information for evaluating the applicability of the current welding conditions and paths. Based on the real-time monitoring results and the early warning mechanism, it is determined whether the current welding path is applicable.

[0081] The warning threshold is pre-set based on historical experimental data, and different monitoring values ​​are set for changes in the monitoring data. Different changes have different impacts. The greater the impact, the greater the monitoring value. The monitoring value that changes in real time is compared with the warning threshold.

[0082] In some embodiments, when the welding conditions change significantly (such as excessive welding deformation, substandard weld quality, etc.), the monitoring value is large, and if it is greater than the warning threshold, the welding path needs to be adjusted. According to the optional path group and the comprehensive score, a new optimal path is selected for switching.

[0083] S245: setting the maximum number of adjustment times, monitoring the number of path adjustment times in real time, and further judging the subsequent non-welding condition if the maximum number of adjustment times is reached, generating a demand value, and performing path adjustment if the demand value is greater than the demand threshold; and not performing path adjustment if the demand value is not greater than the demand threshold;

[0084] The demand value is set according to the influence of the current welding path on the subsequent unwelded part. The greater the influence, the greater the demand.

[0085] In some embodiments, in order to ensure welding efficiency and quality, it is necessary to pre-set the maximum number of adjustments, monitor the number of path adjustments in real time, and if the maximum number of adjustments is reached, further determine the subsequent unwelded state and generate a demand value, which is set according to the impact of the current welding path on the subsequent unwelded part. The greater the impact, the greater the demand. The adverse impact that the current welding path may cause to the subsequent welding part is evaluated, and the demand threshold is pre-set based on historical experimental data. If the demand value is greater than the demand threshold, the path adjustment is performed; if the demand value is not greater than the demand threshold, the path adjustment is not performed.

[0086] In this embodiment, by extracting key feature points and generating a preliminary welding path, and then fusing and generating an optional path group for evaluation, it is ensured that the welding path can more accurately adapt to different cross-sectional data and welding requirements; by real-time monitoring of key parameters and conditions in the welding process, and setting an early warning mechanism, the welding path can be adjusted in time when the welding conditions change significantly, avoiding welding failures and quality problems; by setting evaluation indicators to score the optional paths, the path with the highest comprehensive score is selected as the comprehensive welding path, ensuring the optimization of welding efficiency and quality. At the same time, the maximum number of adjustments and the influence of subsequent unwelded conditions are considered in the path adjustment process, further improving the overall welding effect.

[0087] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0088] This application achieves the effect of improving the accuracy and adaptability of the welding path by extracting key feature points and generating a preliminary welding path, and then fusing them to generate an optional path group for evaluation; real-time monitoring of key parameters and conditions in the welding process, and setting up an early warning mechanism, which can adjust the welding path in time when the welding conditions change significantly, thereby achieving the effect of dynamically adjusting the optimal path.

[0089] Embodiment 4: In the above embodiment, the effect of dynamically adjusting the optimal path is achieved. This embodiment makes further improvements on the above basis.

[0090] like Figure 4 As shown, step S300 also includes:

[0091] S310: selecting actual path planning efficiency values ​​under multiple paths, matching new groove shapes and cross-section information with data in a database, and generating matching values;

[0092] S320: setting a matching threshold, selecting welding paths corresponding to groove types with matching values ​​greater than the matching threshold as candidate paths, and performing a comprehensive analysis on the candidate paths;

[0093] S330: Based on the comprehensive analysis results of the candidate paths, the final recommended welding path is selected and the groove type database is updated.

[0094] In some embodiments, the actual path planning efficiency value is a comprehensive indicator used to quantitatively evaluate the pros and cons of welding paths under specific groove types. It takes into account multiple factors such as welding time, material consumption, welding quality, etc., and is obtained through a standardized calculation method. When the new groove shape is matched with the data in the database, a value is calculated based on factors such as shape similarity, size difference, and material properties. The higher the matching value, the more similar the new groove is to a groove type in the database, and the path planning of this type is selected. A matching threshold is pre-set, for example, set to 95%. If the matching value is greater than 95%, the corresponding welding path under the groove type is selected as a candidate path, and then the candidate path is comprehensively analyzed. Based on the comprehensive analysis results of the candidate path, the final recommended welding path is selected and the groove type database is updated.

[0095] In some embodiments, if there is a switching node (i.e., a point where switching between different paths is required) in the candidate path, further analysis is performed, and step S320 further includes:

[0096] S321: Setting analysis and evaluation indicators, generating a comprehensive evaluation value 1 for candidate paths with switching nodes; the evaluation indicators include switching time, operation efficiency, and actual operation difficulty value; the switching time includes robot repositioning time and welding parameter adjustment time;

[0097] S322: Evaluate the overall time consumption, welding efficiency and welding quality of the common candidate path to generate a second comprehensive evaluation value;

[0098] S323: Select the highest candidate path as the recommended welding path according to the comprehensive evaluation value 1 and the comprehensive evaluation value 2 respectively.

[0099] In some embodiments, the robot repositioning time refers to the time required for the robot to reposition to a new welding position when switching from one welding node to another. The welding parameter adjustment time is due to the fact that different welding nodes may require different welding parameters (such as current, voltage, welding speed, etc.), and this part of time is used to adjust the welding equipment to adapt to new welding conditions. The operating efficiency is the overall speed of the robot when performing a welding task, including the moving speed and welding speed, as well as the idle time ratio during the task execution process. The actual operation difficulty value is used to evaluate the complexity of the welding path, such as the tortuosity of the path, whether it is necessary to change the posture frequently, etc., considering the increase in welding difficulty and possible risks. The comprehensive evaluation value 1 is to standardize each evaluation index, convert the value of each index to the interval [0, 1], assign weights to each evaluation index according to the specific requirements of the welding task, set weights according to historical experimental data, multiply the standardized index value by the corresponding weight, and then sum it to obtain the comprehensive evaluation value 1.

[0100] In some embodiments, the overall time consumption refers to the total time from the start to the end of welding, including welding time and non-welding time (such as movement and waiting time). The welding efficiency refers to the amount of welding completed per unit time, which is calculated by dividing the welding length or area by the total time consumption. The welding quality is evaluated based on the quality of the finished product after welding, such as the uniformity of the weld, whether there are defects, etc. For the overall time consumption and welding efficiency, their values ​​are used directly; for welding quality, it is quantified by expert scoring or the score given by quality inspection equipment. Similarly, a weight is assigned to each evaluation indicator according to the importance of the welding task. Each indicator value is multiplied by the corresponding weight and then summed to obtain a comprehensive evaluation value of two.

[0101] For candidate paths with switch nodes, select the path with the highest comprehensive evaluation value 1. For common candidate paths, select the path with the highest comprehensive evaluation value 2. The number of the recommended welding paths is one to two.

[0102] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0103] The present application achieves the effect of improving the efficiency and accuracy of welding path planning by generating a path planning efficiency value and updating the database; by conducting a comprehensive analysis of candidate paths, setting analysis and evaluation indicators and comprehensive evaluation values, and according to different welding task requirements and groove type characteristics, the effect of flexibly selecting the optimal welding path is achieved; by updating the groove type database, the effect of improving welding efficiency and quality is achieved.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A welding path planning method based on machine vision, characterized in that: The method comprises: S100: Collect and classify and store sample data of various groove types, establish a groove type database, and define an extraction condition template for each groove type; S200: Identify the current groove type, select the corresponding extraction condition template, obtain the groove section data, group the groove section data according to different characteristics, establish a three-dimensional model according to multiple groups of groove section data, generate a comprehensive welding path, and generate a welding analysis report according to the actual welding effect; S300: According to the welding analysis report, the path planning situation of each groove type is obtained, the path planning efficiency value is generated, and the groove type database is updated; Wherein, step S200 includes: S210: acquiring groove section data based on machine vision technology, including a two-dimensional image and relative position information, and preprocessing the acquired groove section data to obtain standard section data; S220: setting a grouping strategy to group the standard cross-section data, selecting a reconstruction strategy for each group of cross-section data according to its characteristics, and constructing a three-dimensional model using a surface reconstruction algorithm based on the reconstruction strategy for each group of cross-section data; S230: Analyze weld characteristics, material characteristics, and heat-affected zone sensitivity characteristics based on a three-dimensional model to determine welding sequence principles; S240: Generate a preliminary welding path corresponding to each cross-section group according to each group of cross-section data and the welding sequence principle, and merge the preliminary welding paths of each cross-section group to form a comprehensive welding path.

2. A welding path planning method based on machine vision as claimed in claim 1, characterized in that: The grouping strategy is to group the sections according to the shape similarity of different sections and the characteristic nodes between different sections. For the two sections with the highest shape similarity, the trajectory trend of their adjacent nodes is analyzed, and the adjacent nodes with the same trajectory are selected as characteristic nodes to group the groove section data.

3. A welding path planning method based on machine vision as claimed in claim 2, characterized in that: The trajectory trend refers to the welding angle passing through the node when welding between different cross sections. If the welding angles are consistent, the trajectories are judged to be the same; if the welding angles change, the trajectories are judged to be different.

4. A welding path planning method based on machine vision as claimed in claim 1, characterized in that: Step S240 includes: S241: Obtain each group of cross-sectional data, extract key feature points respectively, and obtain the influence value of each group of cross-sectional data according to the number of key feature points, compare the influence value with the influence threshold, and if the influence value is greater than the difference threshold, mark it, and generate preliminary welding paths respectively according to the marking; S242: generating an optional path group by fusing the preliminary welding path and the annotations respectively, setting evaluation indicators to score each optional path, and selecting the one with the highest comprehensive score as the comprehensive welding path.

5. A welding path planning method based on machine vision as claimed in claim 4, characterized in that: The influence value is used to evaluate the influence of the key feature point on the welding path. The influence threshold is set according to all current influence values. All influence values ​​are arranged in descending order, and the average value of the first 20% of the influence values ​​is selected as the influence threshold.

6. A welding path planning method based on machine vision as claimed in claim 1, characterized in that: Step S240 also includes: S243: Real-time monitoring of key parameters and conditions in the welding process, processing and analysis of real-time monitoring data, setting warning thresholds, and triggering warnings when the monitoring values ​​exceed the warning thresholds; S244: Based on the warning information, determine whether the welding path needs to be changed. If it needs to be changed, select a new optimal path for switching based on the optional path group and the comprehensive score.

7. A welding path planning method based on machine vision as claimed in claim 6, characterized in that: The warning threshold is pre-set based on historical experimental data, and different monitoring values ​​are set for changes in the monitoring data. Different changes have different impacts. The greater the impact, the greater the monitoring value. The monitoring value that changes in real time is compared with the warning threshold.

8. A welding path planning method based on machine vision as claimed in claim 1, characterized in that: Step S240 also includes: S245: setting the maximum number of adjustment times, monitoring the number of path adjustment times in real time, and further judging the subsequent non-welding condition if the maximum number of adjustment times is reached, generating a demand value, and performing path adjustment if the demand value is greater than the demand threshold; and not performing path adjustment if the demand value is not greater than the demand threshold; The demand value is set according to the influence of the current welding path on the subsequent unwelded part. The greater the influence, the greater the demand.

9. A welding path planning method based on machine vision as claimed in claim 1, characterized in that: Step S300 also includes: S310: selecting actual path planning efficiency values ​​under multiple paths, matching new groove shapes and cross-section information with data in a database, and generating matching values; S320: setting a matching threshold, selecting welding paths corresponding to groove types with matching values ​​greater than the matching threshold as candidate paths, and performing a comprehensive analysis on the candidate paths; S330: Based on the comprehensive analysis results of the candidate paths, the final recommended welding path is selected and the groove type database is updated.

10. A welding path planning method based on machine vision as claimed in claim 9, characterized in that: Step S320 also includes: S321: Setting analysis and evaluation indicators, generating a comprehensive evaluation value 1 for candidate paths with switching nodes; the evaluation indicators include switching time, operation efficiency, and actual operation difficulty value; the switching time includes robot repositioning time and welding parameter adjustment time; S322: Evaluate the overall time consumption, welding efficiency and welding quality of the common candidate path to generate a second comprehensive evaluation value; S323: Select the highest candidate path as the recommended welding path according to the comprehensive evaluation value 1 and the comprehensive evaluation value 2 respectively.

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