1013 results about "Welding defect" patented technology

The invention relates to the technical field of material nondestructive testing, and discloses a membrane structure welding seam defect detection method based on image recognition, which is used for solving the problems of low defect segmentation accuracy and incapability of effectively recognizing internal defects caused by continuous image gray change of lap welding seams of unequal-thickness flexible materials in a traditional method. The method comprises the following steps: firstly, collecting an original image of the lap weld of the unequal-thickness flexible material, carrying out gray conversion, analyzing thickness gradient distribution, adjusting a gray value, carrying out region segmentation to lock a weld range, extracting potential defect edge features to form a candidate region, and carrying out classified verification to confirm internal defects. Aiming at the problem of low defect segmentation accuracy caused by continuous change of image gray in the prior art, the method improves the defect identification precision through segmented mapping and boundary tracking logic, and is suitable for membrane structure engineering quality control.

The invention relates to the technical field of nondestructive testing, and discloses a steel structure weld defect ultrasonic detection and identification system. A data acquisition module of the system acquires an original ultrasonic signal of a steel structure welding seam through ultrasonic detection equipment and acquires geometric attribute data of the welding seam; the model construction module constructs a welding seam three-dimensional digital model based on the data; a feature extraction module performs feature mining on the three-dimensional digital model and extracts a weld defect feature index set; the difference analysis module carries out deviation calculation on the characteristic index set and a reference index set in a standard welding seam characteristic database, and an abnormal area is identified; the risk assessment module calculates a defect sensitivity index according to the abnormal region in combination with real-time environmental parameters, and assesses a defect risk level; and the report generation module formulates a detection scheme according to the defect risk level, generates a detection instruction, executes ultrasonic scanning, collects performance data and generates a defect detection report. The system has the advantages of high detection precision, high reliability, automatic and standardized process and the like.

The invention relates to a defect positioning method based on fusion of weld defect features and trajectory tracking data, and belongs to the technical field of weld defect detection and positioning. The method comprises the following steps: capturing welding seam track dynamic data and defect feature data, constructing a dynamic coordinate system based on a welding seam initial feature point, and establishing double-data-set reference mapping; performing multi-physics field interference decoupling correction on the trajectory data, and performing cross-modal feature purification and core feature consistency verification on the defect data; converting the preprocessed data into a feature form adaptive to fusion, and constructing a welding process-defect formation mechanism association network to regulate and control fusion weight; and finally, reconstructing a three-dimensional dynamic contour of the welding seam, calling dynamic positioning logic to position the defect, and outputting a result carrying the process-defect causal confidence coefficient. The positioning precision is improved through multi-dimensional data fusion and mechanism association, and technical support is provided for welding quality management and control.

The invention discloses a welding robot path planning system cooperating with industrial vision and collision detection, and relates to the technical field of robot automatic welding, the welding robot path planning system comprises an upper computer, the upper computer is in communication connection with the following modules: an environment sensing module used for collecting welding environment information in real time, comprising three-dimensional point cloud data of a workpiece and obstacle information; and the data processing and fusion module is used for fusing the visual identification result and the obstacle detection data. By integrating industrial visual recognition and collision detection technologies, the dynamic change of the welding environment can be sensed in real time, an accurate environment model is constructed based on three-dimensional point cloud data, compared with a traditional welding robot with a preset path, the welding track can be dynamically adjusted, welding defects caused by environment interference are avoided, and meanwhile the welding efficiency is improved. In combination with an assembly error compensation mechanism, the deviation between a theoretical model and an actual workpiece can be corrected, the welding position precision is ensured, and the high-precision ship manufacturing requirement is met.

The invention relates to a steel structure engineering welding quality defect analysis method based on voiceprint monitoring, and the method comprises the steps: carrying out the multi-channel voiceprint synchronous collection, time-frequency feature fusion, wavelet packet analysis and Mel-frequency cepstral coefficient extraction for a plurality of defect features fused in voiceprint data in a welding process; a hierarchical semantic concept space and a dynamic causal relationship generation model are established in combination with a welding physical knowledge base, a causal knowledge graph is constructed, causal association between semantic concepts is deduced through a gating circulation unit and a graph neural network, anti-fact disturbance and path aggregation analysis is carried out on a causal graph structure, and a result is obtained. And finally, defect category probability output and causal traceability graph visual display are realized. According to the scheme, the accuracy, traceability and result interpretability of welding defect recognition are effectively improved, and data support is provided for intelligent diagnosis and continuous model optimization in the welding process.

The invention discloses a machine learning-based weld defect intelligent identification system, relates to the technical field of weld defect intelligent identification, solves the technical problems of multi-modal data fusion precision and robustness optimization and defect shielding or overlapping feature deficiency, and provides a machine learning-based weld defect intelligent identification method based on PSNR dynamic parameter adjustment and gradient weight optimization. The limitation of existing fixed parameter denoising is solved, the edge feature retention rate of cracks, air holes and other defects is improved, the omission ratio is reduced, improved DeepLabv3 + segmentation semantic masks are introduced and mapped to point cloud voxels, geometric + semantic double-attribute enhanced point clouds are formed, the defect area positioning accuracy is improved, and through a cross-modal attention module, the defect area positioning accuracy is improved. Weights are dynamically distributed according to illumination intensity and workpiece materials, feature waste caused by fixed weights is avoided, depth mutation and a shielding area with semantic defects are positioned by utilizing depth information of enhanced point cloud, real overlapping and projection overlapping can be effectively distinguished by combining an improved Poisson fusion algorithm, and the overlapping defect recognition accuracy is improved.

The invention relates to the field of image recognition, in particular to an intelligent weld defect detection method based on machine vision, and the method comprises the steps: carrying out the collection and feature preparation of a weld region image, and obtaining a pixel point basic gray feature data set; performing trend prediction comparison on the local gray profile of the pixel point to obtain the deviation degree of the local gray profile; performing unit vector aggregation analysis on a pixel point neighborhood gradient direction to obtain a local gradient structure disorder degree; multiplicative modulation is carried out on the deviation degree of the local gray profile and the disorder degree of the local gradient structure to obtain a distance measurement function of structure perception; a weld defect recognition result is obtained by performing clustering analysis on a distance metric function of structure perception, so that the problem of missing detection caused by the fact that benign heterogeneous points and malignant defect points cannot be distinguished by the Euclidean distance in existing weld defect detection is solved.

The invention relates to the technical field of data processing, and discloses a building construction quality real-time monitoring method and system based on a sensor network. The method comprises the steps of constructing a sensor grid through hydration heat gradient mapping, recognizing welding defects based on acoustic emission spectrum texture and wavelet packet decomposition, obtaining a quality situation by adopting maintenance age weight time-varying fusion, performing multi-scale anomaly detection by applying a residual attention mechanism, and dynamically adjusting a threshold value to generate an intervention strategy in combination with a working condition switching trigger. And intelligent construction quality monitoring is realized. Through the multi-scale feature extraction and cross-modal data fusion technology, the accuracy and real-time performance of construction quality monitoring are remarkably improved.

The invention discloses a high-strength steel welding defect nondestructive detection and identification method based on multi-modal data fusion. The method comprises the following steps: welding data acquisition: acquiring a two-dimensional image and three-dimensional point cloud data of a high-strength steel welding part to form a data pair; performing data space alignment: generating a space-aligned image-depth map data pair; feature extraction and fusion: obtaining fusion features with spatial geometric information and two-dimensional visual information through feature extraction and fusion; defect identification and classification: identifying defect pixels, decoding and recovering space information of a defect area, calculating the three-dimensional size of the defect, taking the feature information associated with a connected domain of each pixel with the defect and the three-dimensional size as input, and automatically classifying defect categories through a pre-trained full-connection neural network classifier. According to the method, the welding defects of the high-strength steel can be accurately identified and accurately and quantitatively analyzed.

The invention discloses a spiral steel pipe weld defect detection device applied to X-ray detection, and relates to the field of steel pipe weld detection, the spiral steel pipe weld defect detection device comprises a base and a movable platform mounted at the upper end of the base, and the upper end of the base is provided with a steel pipe; a stirring roller and a lifting roller are rotationally installed at the upper end of the movable platform and located below the steel pipe fitting. According to the spiral steel pipe welding seam defect detection device applied to X-ray detection, high-pressure wind power is transmitted to the flow dividing pipeline through the pressurizing air outlet by the air blower, and the transmitted high-pressure air carries pressure to penetrate through an opening gap in the surface of the lifting roller to impact on the surface of a steel pipe; the cleaning brush at the upper end is driven to move back and forth in a reciprocating manner in cooperation with limiting of a movable plate and a limiting rod, so that surface residual impurities generated by contact of a pressurizing air outlet and a steel pipe fitting are subjected to auxiliary guiding and removing work, and flaw detection can be more accurate.

The invention discloses a surface-mounted element welding defect detection method based on transfer learning and SimAM-SASPPF-YOLOv8, and belongs to the technical field of industrial automation and computer vision. The problems that in the prior art, a traditional surface-mounted element welding defect detection method cannot process complex defects, and the number of defect samples is small, so that the generalization ability is limited, and the algorithm detection precision is low are solved. The method comprises the following steps: establishing a PCBA defect detection data set and a PCBA element detection data set; a SimAM-SASPPF-YOLOv8 network structure is established, and transfer learning pre-training based on element detection is carried out; introducing a maximum mean value difference loss function to carry out defect detection transfer learning training on the PCBA defect detection data set; and performing defect detection and identification on the PCBA defect detection data set through the trained network model. The method improves the detection precision and the detection speed, and can be applied to surface-mounted element welding defect detection.

The invention relates to an X-ray weld defect intelligent detection method based on weld line perception and reversible domain mapping, which comprises the following steps: collecting a gray X-ray image of a weld region, and carrying out joint labeling on a weld center line track and a defect mask to construct a standardized training data set; designing a weld line sensing hybrid neural network architecture; performing end-to-end training on the network by using a joint loss function formed by detection task loss and center line consistency constraint to realize collaborative optimization of defect category discrimination and spatial positioning; and deploying the trained model in an automatic welding quality detection system, performing forward reasoning on X-ray images acquired in real time, and continuously outputting defect detection results with accurate category, position and form information in combination with a post-processing strategy of weld direction clustering. According to the invention, stable and accurate identification of slender and weak-texture weld defects under the condition of bent welds is realized, and automation and reliability of nondestructive detection of welds are remarkably improved.

The invention discloses a weld defect identification method based on a dense connection convolutional network model, and the method specifically comprises the following steps: S1, constructing a dense connection convolutional network model, and embedding a coordinate attention module behind a transition layer of a convolutional network; s2, data acquisition and processing: acquiring an RGB image of the welding seam through an industrial camera, constructing a data set of the image, and performing image enhancement and standardization processing; s3, performing hyper-parameter optimization, and performing global optimization on the constructed model by adopting a Bayesian optimization algorithm; s4, performing model training and verification, and training a dense connection convolutional network model by using the optimized hyper-parameter combination; and S5, defect identification: inputting a to-be-detected welding seam image into the trained dense connection convolutional network model, and outputting a defect category and a positioning result. According to the method, the transition layer of the convolutional network is embedded into the coordinate attention module, so that the convolutional network model more accurately positions the welding seam position, and the detail features of the welding seam are extracted.

The invention discloses a reflective steel pipe weld defect identification method based on a multimode spectrum chromatography technology, and provides a detection process of multiband reflection data acquisition, denoising and normalization, multi-region feature distribution modeling, sparse reconstruction separation abnormal response, multi-scale contrast weighted enhancement, significant layered imaging and defect classification and discrimination. According to the method, through the measures of automatic partitioning, environment and material dynamic acquisition, self-adaptive parameter optimization and the like, high-reflection interference is effectively suppressed, the distinguishing between defects and backgrounds is remarkably enhanced, and the detection sensitivity and the positioning precision of the small and micro defects are improved.

The invention relates to the technical field of industrial automatic detection and machine vision, and discloses a steel pipe welding seam defect detection method and system based on machine vision, and the method comprises the steps: obtaining original image data of a bent welding seam area, and extracting a preliminary contour point set; analyzing curvature identification key turning points of the initial contour point set; carrying out geometric segmentation and fitting on the contour according to the key turning points, and constructing a complete boundary point sequence containing a starting point and an ending point; according to the complete boundary point sequence, dividing areas and carrying out defect classification to obtain a defect positioning result; tracking a defect boundary from the defect positioning result, and determining a final evaluation range. According to the method, the technical problem of low steel pipe weld defect detection precision in the prior art is solved through accurate geometric analysis of the complex weld contour.

The invention discloses a welding defect detection and automatic repair welding system, which comprises a repair welding execution module, a welding point cloud extraction module, a welding defect extraction module and a defect identification and classification module, and is characterized in that the welding point cloud extraction module is in signal connection with the repair welding execution module; complete point cloud data of a welding area are visually collected through the repair welding execution module, a target area is accurately extracted through the welding point cloud extraction module, and then efficient extraction and contour positioning of welding defects are completed through the welding defect extraction module based on point cloud feature analysis. Accurate identification and multi-type classification of defects are achieved through a defect identification and classification module, finally, six-dimensional coordinates of repair welding track points under a camera coordinate system are obtained through a repair welding instruction calculation module, and then a hand-eye calibration matrix, a tool calibration matrix and a transformation matrix between a wrist joint coordinate system and a base coordinate system are combined, so that the repair welding precision is improved. And through multi-coordinate system collaborative conversion, the six-dimensional pose of the repair welding track point of the mechanical arm welding gun is solved, and equipment is driven to complete repair welding operation.

The invention discloses a lithium battery laser welding detection method, and belongs to the technical field of nondestructive testing. The method comprises the following steps: acquiring a three-dimensional path point sequence of a to-be-detected welding seam; controlling the transient thermal excitation unit to apply transient thermal excitation along the path; a high-speed multispectral imaging unit is used for synchronously collecting the thermal radiation attenuation process of the weld joint under at least two different spectral bands, and a space-time spectrum data cube is formed; and finally, inputting the data cube into an artificial intelligence model for defect identification. The system correspondingly comprises a three-dimensional visual sensor, a transient thermal excitation unit, a high-speed multispectral imaging unit and a synchronous control and data processing unit. According to the method, different types of welding defects such as air holes and incomplete fusion can be effectively distinguished by analyzing the heat diffusion characteristic difference under different spectral bands, the interference of uneven surface emissivity is inhibited, and the online detection accuracy and reliability of the three-dimensional complex welding seam are improved.

The invention discloses a graphite equipment weld defect intelligent detection and positioning system based on multispectral imaging, and particularly relates to the field of welding, and the system is characterized in that a multiband image acquisition unit acquires spectral image data of multiple bands and transmits the spectral image data to a multispectral data storage library; the spectral parameter analysis unit extracts characteristic parameters of the weld joint area and establishes a spectral characteristic parameter library; a spectrum abnormity identification unit locates a suspected defect area of spectrum abnormity; the weld defect positioning unit marks defect boundaries and ranges; the defect identification and grading unit calculates a defect severity index; the element component analysis unit is used for detecting the suspected defect position sample and analyzing the types and contents of trace elements in the suspected defect position sample; the defect and component data are integrated through the weld quality comprehensive evaluation unit, the quality grade and the rectification suggestion are generated, the problem that the defect cause cannot be positioned in the prior art is solved, data support is provided for follow-up maintenance and process optimization, and potential safety hazards of equipment operation and maintenance are reduced.

The invention relates to a weld defect intelligent identification method and system based on multi-modal fusion. The method comprises the following steps: acquiring multi-modal data of the same welding seam area to obtain a multi-modal image set, enhancing a feature map of each modal image in the multi-modal image set through a preset feature extraction network to obtain an enhanced feature, and extracting a multi-scale depth feature of the enhanced feature; on this basis, cross-modal feature alignment is realized by using deformable convolution, and a physical constraint mechanism is introduced to generate a fusion feature map; candidate defect areas are generated based on the fusion features, feature vectors and position information of the candidate defect areas are extracted, and a defect relation graph is constructed; relational reasoning optimization feature representation is carried out through a graph neural network, collaborative judgment of defect types, positions and incidence relations is achieved, the identification accuracy of a symbiotic defect group in a complex industrial scene is remarkably improved, physical relevance between defects is reliably quantified, and the anti-interference capacity is enhanced.

The embodiment of the invention discloses a sealing nail welding defect detection method, electronic equipment and a storage medium. The method comprises the following steps: acquiring a welding image of a sealing nail at a liquid injection hole of the lithium battery; the welding image is input into a target defect detection model for welding defect detection, and a model output result is obtained; the target defect detection model is used for dynamically extracting features and carrying out welding defect detection based on the dynamically extracted features; and determining a welding quality detection result of the sealing nail based on a model output result. Through the technical scheme of the embodiment of the invention, the defect detection of the welding part of the sealing nail can be accurately and conveniently realized, the manual detection is avoided, and the detection efficiency and accuracy of the welding defect of the sealing nail are improved.

The invention relates to the technical field of welding, in particular to an intelligent temperature control stainless steel pipe rail type welding workstation which comprises a main body, a longitudinal moving assembly is arranged at the top of the main body, and a transverse moving assembly is arranged at the top of the longitudinal moving assembly; transverse movement of the welding mechanism is precisely controlled through the first motor, the first lead screw and the first threaded block, extremely high position precision can be achieved, it is guaranteed that the welding head can be accurately aligned with a welding seam, dependence on manual adjustment is reduced, and operators only need to set parameters on a control interface; according to the welding device, the manual operation time is shortened, the labor intensity is reduced, the stable moving speed and position of the welding head in the longitudinal direction can be kept through the longitudinal moving system driven by the second motor, the stability of welding parameters can be guaranteed, and welding defects can be reduced through the stable welding parameters.

The present disclosure provides a battery welding defect detection system, method, device, and storage medium, wherein the system includes: the acquisition processing module, configured to acquire the welding image of the battery welding object and pre-process the welding image to obtain the processed welding image; the defect statistic module, configured to classify and count the processed welding image based on the target detection model to obtain the plurality of battery candidate frames; and the detection analysis module, configured to obtain the corresponding battery detection result according to the preset threshold and the battery candidate frames by the target detection model. The present disclosure performs the defect defection on the processed battery welding image based on the target detection model, thereby significantly improving the detection efficiency and the accuracy of the results, and at the same time effectively reducing the production cost.

The invention relates to the technical field of weld pattern recognition, and discloses a weld pattern recognition data processing method and system, and the method comprises the steps: obtaining pre-scanning data of a weld region before main scanning, analyzing the surface interference type and interference degree, and dynamically generating a main scanning strategy according to the actual interference condition. The scanning parameters of the line laser contourgraph and / or the scanning path of the robot are / is adjusted. According to the method, the limitation that in the prior art, due to diversified workpiece surface conditions, a fixed data processing algorithm fails is overcome, the quality and efficiency of automatic welding are remarkably improved, and the welding defect caused by misrecognition or key geometric feature loss is avoided.

The invention relates to the technical field of nondestructive testing and intelligent manufacturing, and provides a weld defect intelligent identification system based on multi-source data fusion. The system synchronously obtains a visual image, a three-dimensional point cloud and ultrasonic array data of a welding seam through a multi-source data acquisition module; a hidden danger density field construction module generates surface and internal hidden danger density fields; the cross-space association and defect topology extraction module associates and binds high-density areas meeting spatial continuity and gradient consistency conditions in the two density fields into a cross-space association cluster through density gradient consistency analysis and a preset topology mode template, and generates a defect sign vector; and finally, the defect decision module outputs the defect type, position, size and confidence coefficient based on the sign vector. According to the method, multi-source heterogeneous data is innovatively unified to hidden danger density field characterization, collaborative, accurate and explainable recognition of internal and external defects of the weld joint is achieved through topology analysis of a cross-space correlation cluster, and the limitation of a traditional single detection means is overcome.

The invention discloses a few-sample-oriented welding defect identification method and platform, and relates to the technical field of welding defect identification, and the method comprises the following steps: collecting and marking an original welding image, collecting a molten pool gray scale image with an optical filter in real time through a visual system in a welding process, and adjusting and generating various defect images in combination with process parameters; the actual welding defect is used as a label; the welding original image is preprocessed, wherein welding pool area cutting, data enhancement operation and standardized data division are included; performing staged training on a welding defect identification model, extracting spatial features through a backbone network, performing cross fusion on horizontal and vertical features through a multi-dimensional feature fusion module to enhance molten pool positioning, and generating high-order statistical features through a Brownian distance covariance matrix module; the method comprises the following steps: identifying defects in an actual welding process, constructing a prototype welding network space based on a meta-learning strategy, generating class prototype vectors of various defects, realizing classification of query images through similarity measurement, and dynamically adjusting process parameters.

The invention provides a pipeline weld defect detection system and method based on SimAM-YOLOv5s, and the system comprises a pipeline image data acquisition module, a pipeline image data preprocessing module, a data set generation module, a pipeline weld defect detection model construction module, a pipeline weld defect detection model training module, and a pipeline weld defect detection module. The method comprises the following steps: acquiring pipeline image data, formulating a welding seam quality detection judgment standard, preprocessing the pipeline image data, generating a data set based on the preprocessed pipeline image data, constructing a pipeline welding seam defect detection model, and training the pipeline welding seam defect detection model. And inputting the collected pipeline image data into the trained pipeline weld defect detection model to obtain a detection result. According to the invention, the quality of the welding seam can be effectively detected, and the quality of the welding seam can be classified, so that related management personnel can know the field safety condition.

The invention discloses a reactor internal CW plate assembly automatic welding system and a welding method thereof.The system comprises a bottom frame system, a rack, a welding mechanism, a welding head and a displacement mechanism, the welding position is adjusted through a bottom frame and the rack, the workpiece posture is adjusted through the displacement mechanism, and precise welding is achieved in cooperation with a double-wire welding gun and a laser locating system; the welding method comprises the steps of workpiece pre-deformation fixing, temporary accessory supporting, welding system three-dimensional calibration, ship type posture adjustment, double-wire symmetrical welding, real-time monitoring and the like. According to the method, through anti-deformation tool pre-bending design, double-wire pulse TIG welding and automatic motion control, welding deformation of the thin-wall component is effectively restrained, the quality consistency of welding seams is improved, the welding period of the welding seams is greatly shortened, the defect rate of the welding seams is reduced to 1.2%, and the requirement for high-precision manufacturing of nuclear-grade components is met.

The invention relates to the technical field of pressure-bearing structure welding defect identification, and discloses a pressure-bearing structure welding defect automatic identification and risk prediction method and system, and the method comprises the steps: obtaining an initial defect feature data set, carrying out the serialization analysis according to the initial defect feature data set, determining the initial defect extension trend, and carrying out the risk prediction of the initial defect extension trend; carrying out stress response analysis according to the defect initial expansion trend to obtain a defect stress response sequence, carrying out microscopic difference extraction analysis based on the defect stress response sequence to obtain a defect microcrack expansion trend, and carrying out time sequence prediction processing according to the defect microcrack expansion trend to obtain an expansion prediction vector; and carrying out characteristic evolution analysis and fatigue life calculation on the extended prediction vector, determining a potential failure time point, and carrying out risk index fusion calculation according to the potential failure time point to obtain a final failure risk prediction report. The method can solve the problem of insufficient dynamic monitoring in the prior art.

The invention discloses an automatic welding device for an AP reactor type nuclear power CV electrical penetration assembly insertion plate, which comprises a positioning mechanism, the positioning mechanism is provided with a manual screwing handle and a welding track, the positioning mechanism is used for being mounted in a sleeve, the outer side of the sleeve is fixedly connected with the CV penetration assembly insertion plate, and the outer side of the CV penetration assembly insertion plate is provided with a CV containment; the welding trolley comprises a chassis structure, the chassis structure is installed on the welding track, a welding gun height adjusting mechanism is installed on the chassis structure, a welding gun horizontal swing adjusting mechanism is installed on the welding gun height adjusting mechanism, a welding gun angle swing mechanism is installed on the welding gun horizontal swing adjusting mechanism, and a welding gun and a molten pool monitoring camera are installed on the welding gun angle swing mechanism. According to the method, the labor intensity of welders is relieved, high dependence on high-skill welders is reduced, the technical implementation threshold is lowered, and welding defects possibly caused by human factors are effectively avoided.

The invention relates to the technical field of defect identification, in particular to an AI-based welding defect identification method and system, which comprises the following steps: acquiring regional image data to generate a welding seam region mask image and extracting a welding seam center line to calculate tangential and normal vector field data; adjusting the initial offset vector through projection and weighted synthesis to generate a multidirectional anisotropic feature map, calculating the difference between a defect topology Euler number and a predicted value to obtain a topology constraint loss value, and finally inputting the feature map into a support vector machine to screen confidence so as to judge the defect category. According to the method, anisotropic adjustment is conducted by establishing tangential and normal vector fields of a welding seam and guiding feature extraction offset vectors so as to adapt to the trend and structure of the welding seam, and in combination with topological relation constraints of defect connected components and cavities, the model learns a defect deep structure instead of a surface profile; therefore, the identification precision and robustness of complex and high-directivity welding defects are enhanced, and misjudgment caused by form diversity is avoided.