5852 results about "Welding joint" patented technology

The invention relates to the technical field of welding processing, and discloses a dissimilar metal welding joint temperature field optimization control method and system. Real-time temperature field scanning is conducted on the dissimilar metal welded joint, image segmentation processing is conducted on a temperature field image obtained through scanning, temperature data of a weld joint area, a heat affected area and a base metal area are extracted, and whether the temperature gradient exceeds a preset threshold value or not is judged; aiming at an area with non-uniform distribution of heat input quantity, analyzing the performance of the welding joint under the combined action of thermal stress and mechanical stress, obtaining the difference between residual stress distribution and plastic deformation capacity, and analyzing a stress concentration area by combining with the yield strength of the material; and a mapping relation between the welding process parameters and temperature field distribution is established, and a welding process parameter adjustment prediction model is obtained through historical welding process parameters and corresponding temperature field data training in combination with heat flux density distribution. The problems of uneven heat input, stress concentration and the like in dissimilar metal welding are effectively solved, and the welding quality and efficiency are improved.

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention relates to a multi-product-line-oriented robot welding integration method, device and equipment and a medium, and the method comprises the steps that three-dimensional geometrical characteristics and material attribute data of multiple types of workpieces are collected, and geometric parameters and material mechanical properties of weld joints are extracted; an order demand template is intelligently matched, and initial welding parameters and a clamp configuration scheme are generated; the health state of production equipment is evaluated in real time, and the collision-free movement track of the mechanical arm is planned; dynamically analyzing the joint angle sequence to generate a high-precision servo control signal; the welding process is monitored in real time, and current and molten pool temperature parameters are corrected; and iteratively optimizing the algorithm weight of the process knowledge base to improve the adaptive capacity. Through cooperation of multi-source data fusion and an intelligent algorithm, rapid switching of multiple kinds of workpieces, self-adaptive compensation of the equipment operation state and closed-loop optimization of welding process parameters are achieved, and the equipment utilization rate and the welding quality stability in a flexible production scene are remarkably improved.

The invention relates to the technical field of welding, in particular to a corrosion steel welding cooperative control method and system. Comprising the following steps that welding seam geometric parameters, molten pool dynamic characteristic parameters and welding heat input parameters in the corrosion steel welding process are collected in real time through a multi-dimensional sensor array; constructing a corroded steel welding seam feature space model based on the welding seam geometric parameters, and determining material corrosion grade distribution and mechanical property parameters of a welding seam area in combination with a preset corroded steel material database; a molten pool form evolution prediction model is established through an adaptive Kalman filtering algorithm by utilizing the dynamic characteristic parameters of the molten pool and the welding heat input parameters, and the solidification behavior and the welding seam forming trend of the molten pool are predicted in real time; according to the material corrosion grade distribution, the mechanical property parameters and the molten pool forming trend, a dynamic adjustment strategy of the welding process parameters is generated through a multi-objective optimization algorithm; the reliability and safety of the corrosion steel welding joint can be improved.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention relates to the field of image processing, in particular to a welding quality comprehensive evaluation method and system, and the method comprises the steps: obtaining an ultrasonic image of a welding joint, carrying out the contour detection, and extracting a defect region of the welding joint; gradient features and gray features of pixel points in the defect area are calculated, feature vectors of target pixel points in the defect area are constructed, and the feature vectors serve as the expression degree of the defect area; edge information of the defect area is obtained so as to determine defect types, the defect types comprise cracks and bubbles, the shape and size of a window are set according to the defect types, bilateral filtering processing is carried out, defect information is extracted, and comprehensive evaluation is carried out on the welding quality. According to the method, the appropriate window shape and size are selected according to the defect type, the detail information of the defect is effectively reserved, the definition of the ultrasonic image is remarkably enhanced through the self-adaptive filtering strategy, and therefore the welding quality is better evaluated.

The invention relates to a welding seam path planning method and device, computer equipment and a readable storage medium. The method comprises the steps that workpiece surface point clouds of a to-be-welded workpiece are obtained, the workpiece surface point clouds are clustered, a point cloud clustering cluster is obtained, weld joint end points in the point cloud clustering cluster are detected, the number of the detected weld joint end points is determined, a weld joint exists between the weld joint end points, a first weld joint type of the weld joint is detected according to the number of the end points, and a second weld joint type of the weld joint is determined according to the first weld joint type. And detecting a second welding seam type of the welding seam based on the distance between the target vector between the welding seam end points and the mass center of the preset area where the welding seam is located, and performing welding path planning on the welding seam based on the first welding seam type and the second welding seam type. By adopting the method, the welding path can be accurately planned.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention relates to the field of welding seam detection, in particular to an air conditioner refrigeration copper pipe welding defect detection device which comprises a base, positioning assemblies are symmetrically arranged on the two sides of the upper end of the base, each positioning assembly comprises a shell with a central through hole, a to-be-detected copper pipe horizontally penetrates through the central through hole of the shell, and a fixed cross beam is fixedly arranged between the top ends of the shells. Fixed ring frames are fixedly arranged at the two ends of the bottom of the fixed cross beam, movable ring frames are rotatably mounted in the fixed ring frames, the two movable ring frames are fixedly connected through mounting plates, the number of the mounting plates is four, the mounting plates are distributed in a cross shape, a pressure assembly is arranged on the mounting plate at the top end, and a detection assembly is arranged on the mounting plate at the bottom end. The stress load is gradually applied to the weld joint position of the to-be-detected copper pipe through the pressure assembly, and the deformation process of the weld joint position is monitored in real time in cooperation with the detection assembly, so that the bending strength of the weld joint position of the to-be-detected copper pipe can be dynamically detected in the circumferential direction.

The invention relates to the technical field of welding quality evaluation, in particular to a carbon steel pipe welding quality evaluation system which comprises a welding area three-dimensional scanning module, a welding seam dynamic grid generation module, a welding form change analysis module, a welding seam structure diagram construction module, a welding seam quality diagram convolutional network module and a welding defect intelligent recognition module. According to the invention, through real-time adjustment of the dynamic grid generation module, tiny changes of the geometric shape of the welding seam can be captured, new dimensions are provided for welding quality evaluation through generation of welding form characteristic data and construction of the welding seam structure diagram, and the introduction of the welding seam quality diagram convolution network module can improve the accuracy of welding quality evaluation. Node relations and structural features in a weld joint diagram structure are analyzed through a deep learning technology, an intelligent means is provided for welding quality evaluation, an intelligent welding defect identification module optimizes an identification process, various welding defects can be accurately classified, and a powerful technical support is provided for guarantee of welding quality.

The invention relates to the field of welding, and discloses a non-standard intelligent customized welding system based on vision and surface gradient, which comprises a visual perception module used for iteratively approaching a workpiece from a preset initial height through a self-adaptive high angle shooting exploration mechanism, and combining layered grid division based on a camera view and a progressive multi-angle expansion scanning mode, collecting multi-view three-dimensional point cloud data of the workpiece; and the point cloud processing module is used for performing regional progressive registration on the multi-view three-dimensional point cloud data, implementing threshold constraint based on point cloud curvature characteristics by dynamically adjusting registration step length, and eliminating accumulative errors in combination with pose map optimization. Through self-adaptive high-angle shooting and layered grid scanning, multi-view point cloud can be automatically collected without manually presetting a workpiece model, regional registration and curvature constraint are combined, the workpiece model is constructed, a weld joint structure is automatically extracted based on surface gradient features, traditional manual labeling is replaced, a welding track is generated according to weld joint topology, and the precision is dynamically corrected.

The invention provides welding seam nondestructive testing equipment and method for a high-precision pressure vessel, and relates to the technical field of welding seam detection, the welding seam nondestructive testing equipment comprises a flaw detector main body, a data line, a probe and a fixed ring, two ends of the data line are respectively connected with the flaw detector main body and the probe, a movable frame is slidably mounted on one side of the fixed ring, and an adjustable track is mounted on the fixed ring. According to the nondestructive testing equipment and method for the welding seam of the high-precision pressure container, through the arrangement of the flaw detector main body, the data line, the probe, the fixed ring, the movable frame, the movable frame, the mounting frame, the adjustable track and the buffer structure, the wavelength of the adjustable track can be adjusted according to the width of the welding seam on the pressure container, so that the welding seam area is better covered, and the welding seam detection accuracy is improved. The device is simple and convenient to operate, in addition, it can be ensured that the probe can be better attached to a weld joint to conduct scanning operation, in the scanning process, the effect of buffering and protecting the external force borne by the probe can be well achieved, and damage to the probe is reduced.

The invention discloses an intelligent welding quality evaluation method and system based on vision, and relates to the technical field of welding quality evaluation.The method comprises the steps that the welding process is monitored in real time through multi-mode imaging equipment, weld joint surface texture, molten pool heat distribution and three-dimensional shape information are synchronously obtained, and a three-dimensional voxel model is constructed; establishing a two-stage multi-channel architecture for executing dynamic resource scheduling, and extracting geometric features and deep semantic features in the three-dimensional voxel model; and on the basis of the defect confidence coefficient vector, in combination with the real-time process parameters and the molten pool state, a dynamic evaluation model of the welding quality index is constructed, and time-phased quality scores are calculated and output through stage weighting. According to the method, the welding seam surface texture, molten pool heat distribution and three-dimensional shape information are synchronously collected through multi-mode imaging equipment, a texture-temperature-space coordinate bound three-dimensional voxel model is constructed, and multi-source heterogeneous data fusion analysis of the welding quality is achieved.

The invention discloses a TIG (Tungsten Inert Gas) welding penetration depth self-adaptive control system for a coiled pipe, belongs to the technical field of welding automatic control, and aims to solve the problems of inconsistent penetration depth control and real-time early warning of abnormal processing in TIG welding of the coiled pipe. The method comprises the steps that welding parameters are initialized based on parameters such as the material and the pipe diameter of the coiled pipe, and the target fusion depth is determined; identifying a straight line segment and a curve segment through the panoramic image, marking a target welding point and generating a welding path; determining a local processing space according to welding point position division types and differentiation, and verifying and updating; positioning precursor, current and subsequent points during updating, collecting images to extract multi-modal features, and adaptively updating the target fusion depth based on the geometric change of the subsequent points and the quality features of the precursor points; and during welding, processing quality and intervention abnormity early warning is carried out according to the real-time multi-mode characteristics, the processing parameters and the environment parameters.

The invention discloses a body-in-white welding seam recognition and automatic welding method based on a machine vision technology, particularly relates to the technical field of computer vision and image processing, and is used for solving the problem of welding seam track recognition accuracy caused by insufficient processing capability of an existing three-dimensional vision recognition method on incomplete and uncertain point cloud data. Through the steps of multi-view point cloud acquisition and registration, probabilistic confidence evaluation, region growth of track continuity constraint, multi-track fusion optimization and the like, accurate identification of a body-in-white welding seam track under a complex working condition is realized; firstly, multi-view point cloud data are obtained, probabilistic registration is carried out to generate a confidence evaluation result, then candidate tracks are generated based on confidence weighting and semantic constraint, finally, an optimal track is generated through intelligent optimization and converted into a welding instruction which can be executed by a robot, and the accuracy and robustness of weld joint recognition are effectively improved.

The invention discloses a weld quality intelligent diagnosis system based on deep learning, and relates to the technical field of welding quality detection, and the weld quality intelligent diagnosis system comprises an image quality evaluation module, a feature alignment module, a deviation detection module, a path reconstruction module, a prior enhancement module and a defect identification module, identifying an area of which the signal-to-noise ratio is lower than a preset threshold value, and constructing a noise interference distribution diagram; and the feature alignment module executes a deformable convolution feature alignment operation with a confidence factor adjustment mechanism based on the noise interference distribution diagram to generate an initial space mapping result. Through mechanisms such as image quality perception, robust alignment, deviation detection, self-adaptive reconstruction and prior enhancement, a closed-loop weld joint intelligent diagnosis process is constructed, false alignment errors are effectively inhibited, the multi-modal fusion stability and the defect recognition precision are improved, and the reliability and the intelligent level of the system under complex working conditions are enhanced.

The invention discloses a steel plate welding quality monitoring and controlling method for ship machining, and relates to the technical field of welding control, the method comprises the following steps: monitoring and identifying a target steel plate welding seam through a monitoring and sensing module to obtain a welding seam characteristic sensing data stream, mapping processing analysis is conducted on the weld joint characteristic sensing data flow through a data processing multi-dimensional channel, weld joint defect characteristic parameter information is obtained, quality evaluation is conducted on the weld joint defect characteristic parameter information, a weld joint multi-dimensional evaluation quality result is obtained, control analysis is conducted on the weld joint multi-dimensional evaluation quality result through a steel plate welding strategy space, and a steel plate welding strategy space is obtained. And target welding control parameters are determined, welding control monitoring is carried out based on the target welding control parameters, steel plate welding feedback parameters are obtained, and quality optimization control is carried out on the target welding control parameters. The technical effects that intelligent and efficient monitoring of the welding quality is achieved through multiple nondestructive testing modes, the welding quality monitoring control accuracy and efficiency are improved, and then the ship steel plate manufacturing quality is ensured are achieved.

The invention provides a path planning method of a laser welding head, a medium and a system, and belongs to the technical field of laser welding, the laser welding path planning method provided by the invention comprises the following steps: firstly collecting image information of a to-be-welded part, predicting a welding effect through a deep learning model, and establishing a scientific welding gain matrix evaluation system; a singular value decomposition method is adopted to intelligently segment a welding seam, welding feature vectors are automatically extracted, and a process parameter space is established. And the system constructs a multi-objective optimization function based on the welding quality comprehensive score, and generates an optimized welding path. The welding process image and the quality thermodynamic diagram are collected in real time, the feature similarity with the preset effect is calculated, dynamic optimization of the path is achieved, the intelligent welding path is finally output, the limitation of traditional artificial experience path planning is broken through, and the path planning efficiency is improved. The technical problem that in the prior art, laser welding often depends on manual experience to preset the welding path, and the welding path is not accurate enough is solved.

The invention provides a welding structural part monitoring method and system based on a vibration acoustic method, and relates to the technical field of welding monitoring, and the method comprises the steps: constructing a defect classification model and a defect strategy model, employing an ultrasonic phased array to detect a to-be-detected welding line of a welding structural part, obtaining a first image, inputting the first image into the defect classification model, and obtaining a second image; a second image is obtained, whether welding defects exist in the second image or not is judged, and if not, the welding quality evaluation result of the to-be-detected weld joint is set to be optimal and output; and if yes, inputting the second image into the defect strategy model to obtain a defect repairing strategy of the second image. According to the method, the defects of the welding structural part are monitored in real time, the welding defects are classified on the basis of the defect classification model, then the welding defects are evaluated, the targeted repairing strategy is obtained through the defect strategy model on the basis of the evaluation result, and the purpose that the welding technology is adjusted in time for repairing on the basis of the welding defects occurring in the welding process is achieved.

The invention provides a point cloud welding seam recognition method combining a 2D segmentation model and spatial features, relates to the field of welding automation, and solves the technical problem of low welding seam recognition precision in the prior art. The method comprises the following steps: acquiring point cloud data of the surface of a to-be-identified workpiece by utilizing three-dimensional laser scanning equipment; preprocessing the point cloud data, and projecting the preprocessed point cloud data to a two-dimensional image to obtain two-dimensional data; performing semantic segmentation on the two-dimensional data by adopting a pre-trained image segmentation model, and identifying a welding seam region; back-projecting the weld joint area to a point cloud space, and extracting spatial features of adjacent planes of the weld joint; calculating an included angle and a distance between the adjacent planes based on the spatial characteristics of the adjacent planes, and judging the welding seam type; and according to the welding seam type and the spatial characteristics of the adjacent planes, calculating to obtain end point coordinates of the welding seam. The device is used in the industrial welding automatic production process.

The invention provides a mechanical correction optimization method and device for steel structure welding and electronic equipment, and relates to the technical field of steel structure welding.The mechanical correction optimization method for steel structure welding comprises the steps that a steel structure deformation model is obtained through preset simulation software according to obtained design data and welding parameters of a steel structure; based on the steel structure deformation model, a mechanical correction model of the steel structure is established according to the obtained mechanical correction process data; and the mechanical correction model is optimized through mechanical correction simulation, optimized mechanical correction process data are obtained, and the optimized mechanical correction process data are used for conducting mechanical correction on the welded steel structure. According to the method, the mechanical correction process data are optimized through modeling, then the correction process is controlled according to the optimized mechanical correction process data, welding residual stress and deformation are effectively reduced, the alignment precision and the overall performance of a welded joint are improved, and therefore the welding quality is improved.

The invention relates to the technical field of detection, in particular to a circuit assembly welding spot quality online detection method and system. According to the method, by obtaining the real-time temperature data of each welding point in the welding process, the temperature change of the welding points can be monitored in real time in the welding process so as to adjust the preset welding parameters, and the temperature gradient distribution data of each welding point at different time nodes can be accurately extracted through the dynamic temperature change curve; the welding process can be automatically optimized according to the temperature change of each welding spot through real-time data feedback and analysis, optimal parameter configuration can be carried out according to different welding spots and different working conditions through adjustment, and the welding process can be optimized by analyzing multi-dimensional data such as thermal stress, external dynamic stress and deformation comprehensive indexes of each welding spot. By means of the method, the quality of the welding points can be evaluated more accurately, then automatic welding parameter adjustment is achieved by combining real-time data collection and deformation analysis, the stability and consistency of the quality of the welding points are improved, and the risk of unqualified welding points is reduced.

The invention relates to the technical field of pipe welding, and discloses special welding equipment for an inner pipe connection type heat insulation oil casing, which comprises a welding table, a pipeline clamping mechanism is arranged at the top of the welding table, and the pipeline clamping mechanism comprises two rotating rings. During oil pipe machining, two oil pipes penetrate through the fixing rings to be connected, the electric telescopic rods drive the V-shaped clamping plates to clamp the oil pipes, and the driving motors enable the supporting rollers to support the oil pipes through the gears and the lead screws. The adjusting motor is matched with a related structure, so that the grinding wheel and the arc-shaped grinding piece are attached to oil pipes with different diameters. The grinding motor and the rotating motor work cooperatively to comprehensively grind the oil pipe, during welding, the oil pipe rotates at a constant speed to achieve uniform welding, and after welding, the grinding wheel and the arc-shaped grinding piece clean weld joints. In addition, in the polishing process of the equipment, a cleaning brush is driven to clean a polishing part through transmission of a gear and a chain, the polishing and welding quality is guaranteed, and the machining efficiency is improved.

The invention discloses a thick plate welding residual stress numerical prediction method based on heat-fluid-solid multi-field coupling, which combines computational fluid dynamics with a finite element method, and comprehensively considers the flow characteristic and thermodynamic effect of liquid metal. In the CFD, the influence of metal melting, molten pool flowing, surface tension, buoyancy, heat transfer and fluid flowing on the geometric shape and temperature distribution of the weld joint is simulated, in the FEM module, the geometric shape and temperature difference of the weld joint are calculated based on the CFD, residual stress and deformation are further simulated and analyzed in combination with a life-death unit technology, and through the FEM module, the residual stress and deformation of the weld joint are obtained. The forming mechanism of the convex welding seam, the evolution rule of the thick plate welding residual stress and the influence of the geometrical shape on residual stress concentration are systematically researched.

The invention discloses a ship plane segmented welding multi-robot task distribution system based on digital twinning, and relates to the technical field of ship intelligent manufacturing, and the system comprises a four-dimensional digital twinning body module which is used for collecting workpiece assembly errors, welding seam positions and robot state data in real time; and constructing a twin model comprising a physical engine layer, a process knowledge base and a dynamic topological graph. According to the ship plane segmented welding multi-robot task allocation system based on digital twinning, workpiece assembly errors, robot motion states and welding process rules are fused in real time through the digital twinning body, a weld joint task topological graph is dynamically constructed, and multi-robot task accurate allocation is achieved in combination with an anti-conflict auction algorithm. By means of a space-time gridding cooperation mechanism, motion conflicts between a large gantry mechanism and a small gantry mechanism are effectively solved, and zero interference of a welding path is guaranteed; the local path optimization module adaptively adjusts the posture and the dry extension length of a welding gun for a narrow space, and eliminates the physical collision risk.

The invention discloses a welding seam quality detection method and a welding seam detection system. The detection method comprises the following steps: acquiring an image of a detection area of the welding assembly by adopting a visual identification device; an ultrasonic detection device is adopted for scanning along the weld joint, and the ultrasonic detection area of the detection area is obtained; wherein the ultrasonic detection area is the welding area of the second welding piece and the concave bottom wall; comparing the ultrasonic detection area with an area threshold value; if the ultrasonic detection area is larger than or equal to the area threshold value, it is judged that the weld quality is qualified; if the ultrasonic detection area is smaller than the area threshold value, the welding seam quality is judged to be unqualified. The welding seam quality can be quantitatively evaluated, and the welding seam quality detection precision is improved.

The invention relates to a rib plate welding seam identification method, a storage medium and a computer system.The rib plate point cloud of a workpiece is firstly obtained, a welding seam area is determined, then the mass center of the welding seam area is searched for, downsampling is conducted, and each optimized rib plate point cloud is obtained; on this basis, the rib plate point clouds can be divided into single-class rib plate point clouds and multi-class rib plate point clouds according to the number of the end points of each rib plate point cloud in the step S3, in the subsequent step S4, only one welding seam straight line can be obtained through fitting in the single-class rib plate point clouds, and multiple welding seam straight lines can be obtained through fitting in the multi-class rib plate point clouds; according to the method, redundant welding line straight lines can be prevented from being mistakenly extracted from single-class rib plate point clouds, and essential welding line straight lines can be prevented from being missed from being extracted from multi-class rib plate point clouds, so that the accuracy and precision of the welding line straight lines are improved; on this basis, the step S5 determines the end points of each weld straight line as the start-stop point and the end point of the weld, and a weld line segment is obtained.

The invention discloses a hard alloy cutter welding visual inspection data intelligent management method, and relates to the technical field of welding quality detection, and the method comprises the following steps: obtaining image data in a welding process through a visual inspection system; preprocessing the image data; analyzing defect characteristics of a welding joint area based on an image recognition algorithm, and recognizing a welding quality problem; generating a defect report based on the identified defect features; establishing a welding quality prediction model, and automatically generating a quality evaluation result; and storing the data to the cloud. Through high-resolution image capture and automatic defect analysis based on an image recognition algorithm, defects, such as cracks, air holes and weld irregularity, of a welding joint area can be accurately recognized in real time, the detection precision and efficiency are greatly improved, personal errors are reduced, a welding quality prediction model is established based on a machine learning algorithm, and the welding quality prediction accuracy is improved. Therefore, the quality of the current welding operation is predicted, and the reliability of the detection process is improved.

The invention belongs to the technical field of fusion welding, and particularly relates to a method for planning multi-layer and multi-welding-seam welding parameters according to welding seam feature information, which comprises the following steps: 1) laser emitted by a 3D visual sensor is perpendicular to a welding seam and scans along the direction of the welding seam, and the 3D visual sensor scans welding seam data and transmits the data to a robot controller in real time; (2) the robot controller processes the weld joint data, the whole weld joint is subjected to multi-layer and multi-pass planning calculation, and the number of welding layers and the number of welding passes of each layer are planned; (3) the robot controller calculates the layer number and the channel number of a welding channel needing to be welded currently, a welding gun track coordinate set on a welding path and welding parameters corresponding to welding path points; and (4) the robot controller adjusts the welding track and the welding parameters in real time according to the step (1) to the step (3) in the current welding bead welding process, so that the purpose of multi-layer and multi-pass planning self-adaptive welding of the medium-thickness plate is achieved.