4643 results about "Welding joint" patented technology

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 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, 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 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 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 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 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.

The invention discloses an intelligent life prediction and optimization system and method for a steam turbine rotor welded joint. The system comprises a multi-source data acquisition module, a digital twin modeling module, a health state evaluation and life prediction module, a risk early warning module and an operation collaborative optimization module. The multi-source data acquisition module acquires the multi-dimensional physical quantity of the rotor welding joint in real time. The digital twin modeling module establishes a high-fidelity virtual model and realizes real-time synchronization and correction of a physical entity and a digital model. And the health state evaluation and life prediction module is used for calculating a damage accumulation rate and a health index based on fusion of a physical model and an LSTM neural network so as to realize residual life estimation. And the risk early warning module performs graded early warning according to the dynamic trend of the health state. And the operation collaborative optimization module adaptively adjusts operation parameters and optimizes the unit efficiency based on a reinforcement learning algorithm. All the modules are interconnected through an industrial network to form a closed loop, and real-time monitoring, intelligent evaluation and active service life management of the rotor welding joint are achieved.

The invention relates to the technical field of metal welding automation control, in particular to a metal welding seam automatic tracking welding system based on machine vision. Comprising a data acquisition module used for acquiring initial three-dimensional geometric parameters of a welding seam and monitoring welding process parameters including welding current, arc voltage and welding speed; the thermal deformation prediction module is used for solving a weld joint thermal deformation displacement vector; the dynamic deviation evaluation module is used for calculating a dynamic deviation index; the risk grading module is used for judging the trajectory deviation risk as safety, first-level risk or second-level risk; the defect tendency prediction module is used for calculating an incomplete fusion index and a hot crack index; the comprehensive risk assessment module is used for determining a comprehensive process risk factor; and the track correction module is used for generating a corrected welding gun target track so as to carry out closed-loop correction control on the welding process. According to the method, the problem of trajectory deviation caused by thermal hysteresis and dynamic deformation is solved, and the tracking precision and stability of the welding path are remarkably improved.

An inductor coil welding equipment includes a feeding system, a solid-state welding device, and a laser welding device. The laser welding device is installed behind the solid-state welding device, and the feeding system is used to sequentially send the inductor coil and the conductive plates to the solid-state welding device and the laser welding device. The solid-state welding device is used to pre-weld and fix the inductor coil to the conductive plates, forming a welded inductance conductive plate. Laser welding is used for performing enhancement welding at a welding joint between the inductor coil and the conductive plate.

The invention discloses a digital manufacturing method and system for a bracket node in a steel structure column, and relates to the related field of digital manufacturing of building components, and the method comprises the steps: collecting three-dimensional point cloud data of a bracket node connection region in a target steel structure column; stress concentration analysis is conducted in the vertical direction and the horizontal direction, and a key welding point set and a bolt hole site set are marked; lightweight design parameters of the bracket supporting structure are introduced, load stability analysis is conducted, and first assembly parameters are configured; traversing M bracket nodes in the target steel structure column to obtain a first assembly parameter to an Mth assembly parameter, and configuring a digital manufacturing instruction sequence in combination with the three-dimensional point cloud data; and inputting the data into a numerical control machining center, and driving multi-axis linkage equipment to manufacture and manage the bracket node. The technical problems that existing bracket joint manufacturing is low in efficiency and insufficient in precision and stability are solved, and the technical effects that the manufacturing efficiency is improved, the manufacturing precision and stability are improved, and powerful support is provided for energy-saving building material production are achieved.

The invention discloses a method and device for predicting the fatigue life of a high-temperature alloy vacuum electron beam welding structure, and belongs to the technical field of aero-engines. The method comprises the following steps: acquiring fatigue experiment data of a target high-temperature alloy standard sample; extracting the maximum area of an initial defect, a defect shape factor and crack source position information; obtaining a stress intensity factor range considering defect morphological characteristics and a fatigue crack propagation threshold value of a material structure containing initial defects, and correcting a damage tolerance theoretical formula to obtain simulation data; fatigue test data and simulation data are fused to form an initial data set, a fatigue limit is calculated and expanded to the data set, the expanded data set serves as input, training is conducted through a support vector regression method, hyper-parameters are determined through Bayesian optimization, and a fatigue life prediction result of the high-temperature alloy vacuum electron beam welding structure is output. The method provided by the invention can well consider the influence of different morphology defects on the fatigue performance of the welded joint.

The present invention belongs to the field of steel structure building construction detection. Disclosed are a prefabricated steel structure welding data transmission system based on digital twin, and a method. The prefabricated steel structure welding data real-time transmission system based on digital twin comprises an unmanned aerial vehicle, which is used for flying to a high point of a prefabricated steel structure, wherein an RFID tag positioning identifier is provided at the top of the unmanned aerial vehicle, and is used for identifying RFID tags attached to edges of welding positions. By using an image registration and superposition module, an image, which is processed in real time, is matched and superposed with ultrasonic feedback array points having array point coordinate information; and by means of a result generation module, defective parts are marked on a matched and superposed image which has array point coordinate information, and continuous welding seam length data, height difference data between adjacent array points and a lack-of-welding length value are attached, such that a welding seam quality inspection error is avoided, and the defect situation of each welding point can be effectively uploaded to a computer for checking, thereby avoiding the quality problem of a steel structure building that is caused by an error of a welder.

The invention discloses a method for reducing pores by repair welding of internal defects of a thin-wall aluminum alloy casting. The method comprises the following steps: confirming and marking X-ray positions of the internal defects of the aluminum alloy casting; internal defects at the marked position are cleaned, the defects are required to be completely eliminated, and the cleaning position is smooth and has a certain groove angle; cleaning oil stains and oxidation films at the repair welding position of the casting; preheating the whole casting; welding a reverse support and adding a blowing protection tool; manual argon arc welding filling surfacing is carried out, arc surfacing is carried out from the periphery to the center, and excess weld metal is reserved; removing redundant weld joint materials of surfacing bulges of the casting; x-ray detection is conducted on the repaired casting, whether the pore defects generated after surfacing repair can meet related requirements or not is emphatically confirmed, and if pores gathered on the surface layer of a weld joint exist, the pores can be discharged through a hot pressing method. According to the method, through hole internal defect cleaning, reverse supporting, blowing protection increasing and arc drawing surfacing methods are adopted for conducting surfacing repairing on the internal defects of the casting, the quality of aluminum alloy casting products is guaranteed, and the rejection rate and the production cost are reduced.

The invention relates to the technical field of titanium alloy welding deformation control, in particular to a device and method for controlling titanium alloy welding deformation, and the method comprises the following specific steps: S1, preparation before welding: determining a reversible deformation device according to a welding seam form of a titanium alloy part, and carrying out assembly and spot welding; the assembled titanium alloy welding part is fixed to a reversible deformation device, and it is ensured that the part does not deform in the welding process; s2, a welding process; a thermocouple is placed on the surface of a weld joint, a local heating belt and heat preservation cotton are laid, and the heating belt, the thermocouple and local heating control equipment are connected for heat treatment; and S4, measurement of welding deformation is carried out. According to the method, a complete and easy-to-operate technological process is constructed by combining pre-welding anti-deformation tool fixing and post-welding local heating treatment technologies, and the problem of welding deformation with considerable numerical values and complex forms generated by multiple welding procedures of a large complex titanium alloy structure is solved.

The invention provides a strengthening and toughening control method for a high-strength titanium alloy welded joint, and relates to the technical field of metal material welding, the strengthening and toughening control method comprises the following steps: S1, during welding, multi-layer and multi-channel gradient heat input is adopted, the heat input quantity of front-channel welding is Q1, the heat input quantity of rear-channel welding is Q2, and Q1 is greater than Q2; s2, after welding, cooling a welding area by stages, adopting inert gas forced convection cooling in a primary cooling stage, adopting water mist pulse cooling when the welding area is cooled to a medium temperature stage, and adopting natural air cooling when the welding area is cooled to a low temperature stage; through the synergistic effect of multi-layer multi-channel gradient heat input and staged cooling, the microstructure and performance of the high-strength titanium alloy welded joint can be effectively regulated and controlled, the structure gradient regulation and control of the titanium alloy welded joint are achieved, the tensile strength, the ductility, the fracture toughness and the creep property of the welded joint are improved, meanwhile, the process can be simplified, and deformation is reduced.

The invention provides a welding control method, device and system for a welding robot, and the method comprises the steps: collecting the point cloud data of a to-be-welded workpiece in real time, and obtaining a first point cloud; constructing an encoder based on sparse convolution, and at least combining the encoder, a decoder and a filter into an instance segmentation model; processing the first point cloud by adopting an instance segmentation model to obtain a plurality of weld instance objects; extracting a plurality of skeleton points of each welding seam instance object through a point cloud skeleton extraction algorithm to obtain a welding point of each welding seam; all the welding points of all the welding seams are connected, connecting lines between the welding points are smoothed, target welding tracks of all the welding seams are obtained, operation parameters of all the welding points in the target welding tracks are determined, and a welding gun is controlled to weld all the welding seams in sequence according to the target welding tracks and the operation parameters. The method solves the problems that in the prior art, a welding track generation method is low in precision, and the requirement for large-scale automatic welding cannot be met.

The invention provides a teaching-free welding robot dynamic energy-saving path planning method and system, and relates to the technical field of industrial robos.The method comprises the steps that 1, a workpiece is scanned through a laser vision device, weld joint coordinates, obstacle information and workpiece thermal deformation data are obtained, weld joint feature points are extracted, and the weld joint feature points are obtained; establishing a mapping relation between a workpiece coordinate system and a robot base coordinate system; step 2, based on the mapping relationship, in combination with a thermal deformation prediction mechanism and sensor feedback data, implanting a dynamic response node in the initial path, calculating path point offset by using the feedback data, and generating an anti-offset path; and step 3, aiming at the anti-offset path, constructing a multi-dimensional collaborative evaluation mechanism, and carrying out synchronous adjustment and comprehensive balance on the path length, the energy consumption of the whole welding process and the welding quality parameters so as to obtain a multi-dimensional collaborative evaluation result. Weld joint deformation, clamp offset and obstacle displacement are sensed in real time, the path is dynamically planned, and the flexibility of path planning is improved.

The invention provides a method for detecting the quality of a foundation steel bar welded joint, and belongs to the technical field of steel bar welded joint quality detection.The method comprises the steps that a stress distribution matrix is obtained through ultrasonic detection equipment, and a fatigue distribution matrix is established in combination with a fatigue analysis algorithm; a stress fatigue correlation analysis method and a bipartite graph maximum matching algorithm are used to construct a stress fatigue correlation matrix, a damage evaluation deep learning model based on a Transform architecture is used to generate a damage distribution matrix, and a time sequence prediction algorithm is used to consider a fatigue center change effect to establish a damage expansion trend matrix. A damage prediction matrix is deduced through a damage prediction deep learning model of a bidirectional long-short-term memory network, and finally, a quality detection result matrix is comprehensively evaluated and output by utilizing a game model, a multi-objective optimization algorithm and a gating weighting function, so that the technical problem that the quality detection accuracy of the steel bar welded joint is not enough is solved.

The invention discloses a weld structure sample fatigue life prediction method considering microstructure characteristics, which comprises the following steps of: acquiring microstructure crystal characteristics of a weld joint area of a weld structure sample; establishing a finite element model of a representative volume element considering microstructure crystal characteristics of the welding joint area; acquiring material parameters of the weld joint structure sample; and calculating the representative volume element finite element model containing the microstructure crystal characteristics of the welding joint area through the crystal plasticity constitutive model, and combining with the improved fatigue life prediction model to obtain the final fatigue life prediction value of the weld joint structure sample. Based on the improved fatigue life prediction model, the fatigue life of the weld joint structure sample can be predicted more accurately.

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 discloses a niobium-zirconium alloy and nickel-based alloy vacuum brazing process and a part, and belongs to the technical field of vacuum brazing. According to the process, a vacuum brazing furnace is adopted for brazing, and BNi-2 brazing filler metal is used; the process comprises the following steps that S1, a welding assembly structure formed by a niobium-zirconium alloy part and a nickel-based alloy part is assembled, and BNi-2 brazing filler metal is arranged at the matching position of the welding assembly structure; s2, staged heating is carried out on the welding assembly structure under the preset vacuum degree of the vacuum brazing furnace, and the staged heating comprises a foreign matter removing heating stage, a brazing filler metal solid diffusion heating stage and a brazing filler metal melting wetting heating stage which are sequentially carried out; and S3, staged cooling is conducted on the welded assembly structure after welding is completed, the staged cooling comprises a furnace cooling stage and a gas quenching cooling stage which are conducted in sequence, according to the scheme, defect control over the niobium-zirconium alloy and nickel-based alloy welding joint can be effectively achieved, and the strength and sealing performance of the welding joint are guaranteed.

The invention discloses an automobile light-weight aluminum part connecting process and equipment based on laser welding, and relates to the technical field of automobile aluminum part laser welding, the equipment is based on a base body, a man-machine interaction control screen is arranged at the front end of the base body, and a driving motor and a threaded rod are arranged on the side wall of the base body; a lateral protection metal plate is fixed to the top end of the tool, a parameter adjusting plate is arranged on one side of the tool, a rotary station driving motor and a self-adaptive clamping tool are fixed to the parameter adjusting plate, an anti-scratching gasket is arranged on the inner wall of the tool in the circumferential direction, and the outer side of the tool is connected with a feeding air cylinder through a linkage lug base. The air cylinder is connected with a pressure controllable extrusion table through a first linkage lug base and a force transmission bending arm. A main bearing support is further welded to a base body, a laser welding head is installed on the main bearing support through a jacking air cylinder, a linear push rod and a fine adjustment lifting table, adjustment stability is guaranteed in cooperation with a guide structure, aluminum parts are prevented from being damaged through flexible clamping, precision is improved through multi-dimensional adjustment, and the precision welding requirement is met.

The invention discloses a heat exchanger welding seam image enhancement processing method, and belongs to the technical field of image processing, and the method comprises the steps: S1, collecting a heat exchanger welding seam original image through a preset collection module, and converting the original image into a grayscale image; s2, a processing module is preset, the gray level image is segmented based on a preset dynamic threshold value, and a welding seam area image and a base metal area image are generated; s3, in the welding seam area image, generating a first enhanced image, a second enhanced image and a third enhanced image through a preset generation module; and S4, extracting a reference edge contour of the weld joint area image from the base metal area image, performing pixel-level fusion on the first enhanced image, the second enhanced image and the third enhanced image, and outputting a weld joint image. According to the method, the weld joint area is divided into the highlight area, the dark area and the middle area to be processed respectively, weld pool texture loss caused by strong reflection is effectively restrained, and the problem that details are lost in a highlight scene in an existing method is solved.

According to the laser welding monitoring system, the material type and welding information of the to-be-machined workpiece are determined through the workpiece determining strategy, and corresponding welding data are indexed in a welding database to be set as welding parameters according to the material type and welding position information of the to-be-machined workpiece; the pre-welding device is driven to weld the pre-welded workpiece to the surface of a workpiece to be machined; an image, collected by a visual camera, of a pre-welding point is obtained to serve as a pre-welding image, the pre-welding image comprises images generated when different tensile forces act on a pre-welding workpiece, and the pre-welding image is analyzed according to a welding seam change strategy to obtain welding seam defect changes of the pre-welding point under the different acting forces; a welding quality value is obtained according to the welding seam defect change of the welding point and the tensile strength through a tensile value calculation formula, the welding quality is judged according to welding quality parameters, and if the welding quality is lower than a threshold value, a parameter adjusting command is output; and a parameter adjusting command is obtained, and the welding parameters are adjusted.