1469 results about "Welding defect" patented technology

The invention relates to the technical field of welding defect identification, and discloses a welding defect identification method and system based on a molten pool image. According to the method, a multispectral high-speed camera is used for collecting a molten pool dynamic image sequence in the welding process, after multi-scale morphological filtering preprocessing is conducted, a fusion feature vector is extracted through a depth separable convolutional network, and then the fusion feature vector is input into a defect classification model adopting a heterogeneous graph neural network architecture to obtain a defect probability distribution matrix. Then, constructing a dynamic sparse optimization model to position defects, generating a defect space coordinate set, and finally, outputting welding defect types and position information through hierarchical verification framework processing. According to the method, the problems of welding image noise interference, complex defect characteristics and the like are effectively solved, the accuracy and reliability of welding defect identification are improved, and powerful technical support is provided for welding quality control.

The invention discloses a mechanical welding seam defect identification method, and relates to the technical field of welding quality intelligent detection and defect identification, and the method comprises the steps: S1, collecting welding seam surface image data, obtaining a standardized image data set, carrying out the feature extraction of the standardized image data set, and obtaining a feature extraction result; the method comprises the following steps: S1, extracting edge contours, texture distribution and gray change features of defects, and generating a high-dimensional feature vector containing space position coordinates and morphological features of the defects, S2, generating a weld quality evaluation database containing defect distribution uniformity and defect severity scores according to edge continuity parameters and gray uniformity parameters in the high-dimensional feature vector; according to the mechanical welding seam defect identification method, accurate identification, classification and positioning of welding seam defects can be achieved, comprehensive evaluation of the welding seam quality is given, and the intelligent level and reliability of welding quality control are effectively improved.

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 the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

The invention relates to the technical field of mechanical engineering, in particular to a three-dimensional simulation design method and system based on an elevator structure. The method comprises the following steps that an elevator structure drawing is obtained; a three-dimensional elevator assembly is constructed according to the elevator structure drawing; lift car static load simulation is conducted according to the three-dimensional elevator assembly, and lift car static load data are obtained; detecting the structural strength of the lift car based on the lift car static load data; evaluating welding defects based on the structural strength of the lift car, and generating welding defect data; identifying a weld weak area according to the welding defect data; determining the abrasion loss of the car structure based on the weld weak area; according to the lift car structure abrasion loss, the lift car bottom plate fracture risk is predicted; carrying out welding optimization design based on the fracture risk of the bottom plate of the lift car to obtain welding optimization design data; and traction and traction simulation is conducted according to the three-dimensional elevator assembly, and traction and traction data are obtained. Based on the mechanical engineering technology, the safety and stability of the elevator structure are improved, and the design precision and efficiency are remarkably improved.

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.

Provided in the present disclosure are a welding quality test method, apparatus and system, and a medium and a program. The test method comprises: receiving a plurality of groups of welding data collected by a plurality of sensors during welding; pre-processing the plurality of groups of welding data, so as to obtain a plurality of key features; inputting the plurality of key features into a test model, such that the test model determines, by means of a knowledge graph, whether there is a welding defect; when it is determined that there is a welding defect, outputting an identification result for indicating the presence of the welding defect; in response to the welding defect in the identification result belonging to a new welding defect type, adding new nodes into the knowledge graph, wherein the new nodes comprise a plurality of new welding process parameter nodes, which correspond to the plurality of key features, and a new welding defect node; and on the basis of the plurality of groups of welding data, establishing an association relationship between the plurality of new welding process parameter nodes and the new welding defect node.

The invention provides a parameter control method and system in the friction stir welding process, and relates to the technical field of welding equipment of metal materials. The method comprises the steps that before friction stir welding is started, working condition information of a to-be-welded workpiece is input into a welding process simulation model for virtual test welding; screening out a plurality of groups of standard process parameter combinations meeting preset welding quality requirements; in the friction stir welding process, the change trend of all the technological parameters is determined according to the real-time technological parameter combination within the first preset time period; and if it is determined that the deviation risk exists in the current welding state within the second preset time period in the future according to the change trend, the standard technological parameter combination with the maximum similarity with the real-time technological parameter combination in the standard technological parameter library is determined as the target technological parameter combination to conduct parameter control adjustment on the welding equipment. By implementing the method, the welding defects caused by sudden change of the process window can be overcome, and the stability of the welding process and the consistency of the welding seam quality are improved.

The invention relates to the technical field of analyzing materials by utilizing an ultrasonic technology, in particular to a pipeline welding seam nondestructive testing method and system based on the ultrasonic technology, and the method comprises the following steps: acquiring echo signals collected by each ultrasonic probe at a welding seam position of a pipeline; analyzing the similarity degree of the time domain local features of the echo signals of each ultrasonic probe and the ultrasonic probe at the adjacent position under the same wavelet transform scale, and determining position similar probes and position similar parameters of each ultrasonic probe; according to the position similar parameters of the ultrasonic probe and the change difference of the echo signals of the ultrasonic probe and the corresponding position similar probe under each scale, determining a correction coefficient under each scale, and correcting a detail coefficient of the echo signals of the ultrasonic probe under each scale; obtaining a reconstruction signal based on the corrected detail coefficient under each scale; and comparing the reconstruction signal of each ultrasonic probe with the standard echo signal to detect the weld defect. By adopting the method, false detection and missing detection of tiny defects can be reduced.

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 welding defect classification method and system based on deep learning, and the method comprises the steps: collecting a welding image, taking the welding image as a training sample in a training set, and carrying out the preprocessing of the welding image in the training set; constructing a feature fusion CNN image recognition and classification model; training a CNN image recognition and classification model by using the training sample to obtain a trained CNN image recognition and classification model; and inputting a welding image to be classified and recognized into the trained CNN image recognition and classification model, and obtaining a defect category to which the welding image belongs through model reasoning. According to the method, the defect identification precision is effectively improved, the feature extraction process is optimized, and the real-time detection capability is ensured.

The invention provides a steel structure welding quality detection method and a storage medium, and the method comprises the steps: generating a composite image # imgabs1 # marked with the type and position of a welding defect based on an unmarked real welding region image # imgabs0 # of a steel structure; the real steel structure welding image # imgabs2 # and the composite image # imgabs3 # marked with the welding defect type and position are combined into a steel structure welding image set # imgabs4 #; performing datamation processing on the steel structure welding image set # imgabs5 # to generate a deformable grid # imgabs6 # matched with the type of the welding defect and a preprocessing image set # imgabs7 #; and inputting the preprocessed image set # imgabs8 # into a pre-trained building engineering steel structure welding defect detection model so as to output welding defect characteristics of a welding seam area. The steel structure welding quality detection method is small in data dependence, high in welding defect recognition robustness and high in intelligent degree, and the intelligent level of constructional engineering steel structure welding quality detection can be improved.

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 discloses a welding spot defect detection method, electronic equipment and a storage medium. The mode relates to the field of image detection, and the method comprises the following steps: obtaining a to-be-detected image of a to-be-detected welding spot; edge detection is carried out on the to-be-detected image, a welding spot edge image of the to-be-detected welding spot is obtained, and the welding spot edge image is used for representing contour features of the to-be-detected welding spot in the image space; parameter extraction is conducted on the welding spot edge image, welding spot shape information of the to-be-detected welding spot is obtained, and the welding spot shape information is used for representing shape features of the to-be-detected welding spot in a parameter space; welding spot defect detection is conducted on the to-be-detected welding spot based on the welding spot shape information, a defect detection result is obtained, and the defect detection result is used for representing whether the to-be-detected welding spot has welding defects or not. According to the invention, the technical problem of low efficiency of detecting the welding spot defect in the prior art is solved.

The invention provides a welding seam defect identification and area quantitative calculation method and system, and relates to the technical field of defect identification, and the method comprises the steps: collecting an array eddy current probe scanning signal to generate a welding seam eddy current C scanning original image, enhancing the image quality through adaptive filtering and sharpening processing, constructing a nonlinear mapping function for normalization, and calculating the welding seam defect. And optimizing the enhancement parameters by using a genetic algorithm. The method can effectively identify the weld defect and realize quantitative area calculation, improves the accuracy and efficiency of weld defect detection, and can be widely applied to the field of industrial welding quality control.

The invention relates to the technical field of automatic detection, and discloses a cantilever crane welding defect detection method and system. The method comprises the following steps: establishing a detection model by adopting historical boom data to position plate positions and angles; establishing a welding seam detection model by utilizing historical welding data to evaluate the welding quality; collecting and preprocessing six-view-angle image data of the boom; inputting the preprocessed images into the two models for respective detection; if the output of the two models is qualified, the welding is passed, otherwise, an alarm is activated. The system comprises a data acquisition module, an image preprocessing module, a cantilever crane detection module, a welding seam detection module and a result judgment module. According to the invention, the automatic and intelligent detection of the welding quality of the cantilever crane is realized, the detection efficiency and accuracy are improved, the welding quality of the cantilever crane is ensured, and the equipment stability and service life are improved.

The precise welding control method for vacuum brazing comprises the steps that the geometrical shape and material characteristics of a workpiece are obtained, modeling processing is conducted in combination with the geometrical shape and the material characteristics, and a high-dimensional tensor and a high-dimensional time sequence are compared and calculated to obtain a deviation matrix; based on the deviation matrix, the brazing heating power and the brazing filler metal supply rate are adjusted, and a real-time control instruction set is generated; constructing a deep reinforcement learning model, performing iterative optimization on the real-time control instruction set based on the deep reinforcement learning model, extracting weld defect characteristics of the workpiece, generating a weld quality score according to the weld defect characteristics, and obtaining a difference value between the weld quality score and a set threshold value; and performing comprehensive optimization on the optimization parameter vector according to the difference value to generate a global optimization parameter set. According to the method, systematic dynamic control over complex geometrical shapes and multi-material workpieces is achieved, the defects of response lag and insufficient regulation and control are overcome, and therefore the process stability and production efficiency of vacuum brazing are greatly improved.

The invention discloses a rotor punching and stacking welding defect identification system based on edge calculation, and relates to the technical field of rotor welding identification, and the rotor punching and stacking welding defect identification system comprises a data acquisition module which is used for carrying out real-time acquisition on smoke dust data generated in the welding process, and the smoke dust data comprises smoke concentration, suspended particulate matter concentration and smoke infrared thermal imaging temperature; the model matching module is used for comparing the smoke concentration, the suspended particulate matter concentration and the smoke infrared thermal imaging temperature with corresponding preset reference models respectively to obtain abnormal data with an abnormal trend; and the judgment module is used for performing time sequence analysis on the abnormal data, calculating a standard deviation and a mean deviation rate based on a sliding window, and generating a corresponding early warning growth value. According to the method, a dynamic alarm value generation and attenuation mechanism is constructed, so that false alarm caused by continuous influence of historical data is avoided, and the real-time response accuracy of the integrated value is improved. And through a pre-judgment mechanism, it is ensured that alarm value attenuation is executed only on the premise that data are stable or no early warning growth trend exists.

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 relates to the technical field of image processing, in particular to an intelligent welding defect positioning and detecting system based on image processing. Obtaining a suspected noise degree according to gradient features and neighborhood gray level distribution features of pixel points in the welding image; obtaining a gray confidence coefficient according to the gray features of the pixel points and the gray difference features of the pixel points and the neighborhood; obtaining a structure confidence coefficient according to the area feature of the connected domain where the pixel point is located, the contour change feature of the edge line of the connected domain and the gradient distribution feature of the edge line in the normal direction; obtaining a denoising coefficient of the pixel point according to the suspected noise degree, the gray level confidence coefficient and the structure confidence coefficient; and adjusting the standard deviation in the Gaussian filtering algorithm according to the de-noising coefficient. The method comprises the following steps: denoising a welding image according to an adaptive standard deviation, and performing image enhancement on the denoised welding image to obtain a to-be-detected image; welding flaw detection is carried out on the to-be-detected image, and the detection accuracy is 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 welding defect detection method based on deep learning. The method comprises the following steps: collecting a welding defect image; carrying out image preprocessing on the image, carrying out classified marking on welding defects in the image and forming a data set; dividing the data set; constructing a weld defect detection model; training the model by using the training set, verifying by using the verification set, calculating the gradient of a loss function and a loss value of the model in each training to model parameters by using training and verification results, and updating the parameters of the model according to the gradient and the learning rate; judging whether the model performance reaches the expectation or reaches the maximum number of training times; and outputting the trained model and evaluating the performance of the trained welding defect detection model by using the verification set. According to the multi-scale convolution attention module, the calculation amount is reduced, the model can more effectively capture the characteristics of the strip-shaped welding defects, and the multi-scale defect detection performance of the model under the complex background is improved.

The invention relates to the field of nondestructive testing, in particular to a flaw detection method for a large steel box girder and a related product, and the flaw detection method comprises the following steps: importing a three-dimensional model of the large steel box girder, and analyzing the three-dimensional model to obtain geometric feature information of the steel box girder; generating a scanning path of the ultrasonic probe based on the geometric feature information; an ultrasonic probe is controlled to scan the large steel box girder according to the scanning path, and ultrasonic echo signals are collected; the ultrasonic echo signals are processed, and the welding defect positions of the steel box girder are recognized and positioned; according to the method, the three-dimensional model of the steel box girder is imported and analyzed, automatic extraction of the detection area and the welding seam information is achieved, the optimized probe scanning path is generated by applying the self-adaptive path planning algorithm, efficient and full-coverage detection of the large complex structural part is achieved, and missing detection is avoided.

The invention discloses an automatic welding method of a reinforcement cage welding robot, and relates to the technical field of welding robots. Based on an improved ResNet-50 multi-mode visual system, the welding point identification error is reduced to 0.1 mm level, and by matching with an SE (3) space coordinate conversion algorithm, the positioning precision reaches + / -0.03 mm (3 sigma); the visual algorithm fusing the deformable convolution and the coordinate attention mechanism still keeps the recognition rate improvement of 23.6% under the illumination fluctuation of 200-1500 lux; the improved RRT algorithm is combined with the self-adaptive B-spline interpolation, so that the motion smoothness of the six-degree-of-freedom mechanical arm is improved by 400%, and the welding defect caused by path sudden change is effectively avoided; motion control of the mechanical arm is optimized through the friction compensation model, and the welding stability is remarkably improved.

The invention belongs to the technical field of laser welding, and provides a method for welding multiple layers of tabs through annular light spot laser, which comprises the following steps: selecting a material to be processed for careful inspection to ensure that the material is qualified; performance parameters of a laser are checked and corrected, the power and frequency of ultrasonic waves are obtained through an intelligent ultrasonic frequency and power matching algorithm, and the to-be-machined material is pre-welded; welding the welding area by adopting an annular light spot; a preset laser power curve is obtained through an intelligent laser power matching algorithm, real-time power is compared with the preset power curve through a measurement method based on an energy meter, and the laser power is adjusted in time; an intelligent path planning algorithm based on computer vision is utilized to automatically generate an optimal galvanometer swing path to optimize a galvanometer path; and after welding is completed, defect type standards are recognized and recalled through the welding defect automatic recognition model based on deep learning.

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.