14648 results about "Welding process" 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 relates to the technical field of welding processing, and discloses a dissimilar metal welding joint temperature field optimization control method and system. Real-time temperature field scanning is conducted on the dissimilar metal welded joint, image segmentation processing is conducted on a temperature field image obtained through scanning, temperature data of a weld joint area, a heat affected area and a base metal area are extracted, and whether the temperature gradient exceeds a preset threshold value or not is judged; aiming at an area with non-uniform distribution of heat input quantity, analyzing the performance of the welding joint under the combined action of thermal stress and mechanical stress, obtaining the difference between residual stress distribution and plastic deformation capacity, and analyzing a stress concentration area by combining with the yield strength of the material; and a mapping relation between the welding process parameters and temperature field distribution is established, and a welding process parameter adjustment prediction model is obtained through historical welding process parameters and corresponding temperature field data training in combination with heat flux density distribution. The problems of uneven heat input, stress concentration and the like in dissimilar metal welding are effectively solved, and the welding quality and efficiency are improved.

The invention relates to a welding quality detection system which comprises a process monitoring layer configured with a multi-mode sensor array and used for collecting original performance data in the welding process in real time; the intelligent analysis layer is provided with a multi-source data fusion module and a defect prediction module, and the multi-source data fusion module adopts an improved D-S evidence theory algorithm to perform fusion processing on the original performance data collected by the process monitoring layer to obtain multi-dimensional performance data; the defect prediction module analyzes and captures abnormal data according to the multi-dimensional performance data, dynamically predicts a defect development trend and outputs defect prediction information; the decision execution layer is provided with a self-adaptive control module and is used for dynamically adjusting welding process parameters according to the defect prediction information output by the intelligent analysis layer; and the data communication bus is used for realizing real-time data interaction and closed-loop feedback control among the process monitoring layer, the intelligent analysis layer and the decision execution layer. The method has the effect of effectively improving the detection precision and the detection efficiency.

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

The invention provides an intelligent high-performance collaborative welding robot system, which belongs to the technical field of welding robots and comprises a multi-degree-of-freedom welding robot body, an intelligent control system, a high-performance welding power supply and auxiliary equipment. A welding process control module firstly obtains three-dimensional information of a workpiece based on visual inspection to construct a welding path; then selecting matched process parameters according to material characteristics and welding seam shapes; establishing a welding error matrix to monitor the welding process in real time; constructing a parameter error adjustment matrix for dynamic adjustment; analyzing parameter fluctuation by adopting a neural network to identify molten pool characteristics; the pre-trained neural network is used for evaluating the welding seam quality; and finally, a multi-robot collaborative welding strategy is integrated to achieve efficient operation, and the technical problem that in the prior art, welding process parameters in a welding robot are difficult to adjust accurately in real time so as to adapt to complex working condition changes is solved.

The embodiment of the invention provides a welding path planning method and device, and the method comprises the steps: carrying out the three-dimensional scanning of a to-be-processed object through a machine vision algorithm, obtaining three-dimensional point cloud data, carrying out the data analysis of the three-dimensional point cloud data through a segmentation algorithm based on deep learning, and carrying out the recognition to obtain geometric features; the geometric features are constructed into an undirected graph, an optimal communication path of a weld joint center line is searched on the graph through a minimum spanning tree algorithm, and a fine three-dimensional model of the machined object is constructed through the optimal communication path; according to the three-dimensional coordinates of the welding seam locating point, the current welding gun posture and the target welding seam length, the optimal locating height, locating depth, inclination angle and offset of a welding gun are determined, and a corresponding welding path plan is generated through a welding process simulation algorithm based on the locating height, the locating depth, the inclination angle and the offset; the precision and efficiency of welding path planning can be effectively improved.

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

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

The invention relates to the field of welding process control, and discloses a pipeline all-position automatic TIG (Tungsten Inert Gas) welding method which comprises the following steps: collecting pipeline geometric parameters, welding position angles and material attribute data in real time through multi-source data fusion; constructing a Gaussian process regression dynamic response model, coupling a nonlinear mapping relationship among the process parameters, the molten pool morphology and the corrosion tendency index, and dynamically adjusting the weight of the model based on the welding position angle; a hierarchical strategy of Bayesian optimization and model prediction control is adopted to generate a parameter solution set meeting the fusion depth constraint and the corrosion threshold value, and the molten pool oscillation frequency is used as feedback to correct the current in real time; the heat accumulation evolution trend is predicted through a hidden Markov chain, and parameter closed-loop migration and trajectory compensation are achieved in combination with molten pool flow field coupling correction. The problems of uneven forming quality and corrosion risk caused by space displacement, dissimilar metal interface effect and dynamic disturbance in all-position welding are solved.

The invention discloses a dissimilar metal laser welding device based on swing light beams and molten pool state monitoring. The dissimilar metal laser welding device aims at improving the welding quality and the joint stability. The device comprises a laser welding head with a light beam swinging function, and the laser welding head can implement nonlinear energy scanning in a welding area according to a preset track type, frequency and amplitude; the acquisition module can synchronously acquire a visual image, an excitation spectrum and an infrared thermal imaging signal of the molten pool at a high frame rate, and extracts interface diffusion and metal mixing characteristics through multi-modal fusion; the calculation module performs dynamic feature modeling according to the collected physical feature information and the swing parameters, and quantifies multi-dimensional state indexes of the welding quality; the control module carries out combined adjustment on the laser power, the welding speed and the swing parameters according to the state indexes, closed-loop feedback control is constructed, and therefore real-time stable regulation and control and defect suppression in the dissimilar metal welding process are achieved.

The invention discloses a multi-mechanical-arm space-time synchronization control method for coiled pipe welding, and belongs to the technical field of automation control, and the method comprises the steps: in a primary laser and TIG hybrid welding execution stage, carrying out real-time data acquisition on a welding area of a workpiece, carrying out real-time deviation detection, and once real-time welding deviation is detected, carrying out time-space synchronization on the welding area of the workpiece; a local correction mechanism is triggered immediately, and deviation is prevented from being accumulated in the single welding process; after one-time welding is completed, the workpiece enters a transition area, global contour reconstruction is conducted on a welded section of the workpiece through three-dimensional scanning equipment, global deviation recognition is conducted, and unprocessed hysteresis deformation and accumulative errors are covered and corrected in real time; on the basis of the analysis result of the global deviation recognition, double correction is conducted on secondary welding, and cooperative control over space track correction and technological parameter adaptation is achieved; and after secondary welding is completed, the correction effect is evaluated through a double-layer evaluation mechanism, and the stability of the correction process is ensured.

The invention provides a thermal deformation intelligent compensation and control method and system in a metal part welding process, and relates to the technical field of metal welding, and the thermal deformation intelligent compensation and control method comprises the following steps: obtaining a three-dimensional model and process parameters, constructing a thermal-mechanical coupling analysis model, and collecting real-time temperature and strain data; thermal deformation is predicted through a dynamic hierarchical graph structure and a bidirectional propagation neural network, optimal welding process parameters are calculated through a recursive optimizer integrating a gradient strategy and an evolution strategy based on thermal deformation deviation, accurate prediction and intelligent compensation of thermal deformation in the welding process can be achieved, and the welding quality and precision are improved.

The invention discloses an intelligent welding forming method and system for a green building steel heating radiator, and the method comprises the following steps: carrying out the surface defect recognition of a steel heating radiator base material based on an AI visual inspection system, recognizing a qualified base material, and automatically matching the type of a welding material from a material database according to the material and thickness parameters of the qualified base material. Welding parameters are intelligently matched through AI visual inspection and a neural network model, a laser and friction stir hybrid welding process is combined, traditional manual operation is replaced, the welding efficiency and precision are improved, and the problems of uneven welding seams and the like are solved; welding data are analyzed in real time through a multi-mode AI model, parameters are dynamically adjusted, intelligent defect recognition and repair welding are achieved in cooperation with 3D visual inspection, and the quality stability is improved through whole-process monitoring; smoke dust is treated through an environment-friendly process, acid pickling is replaced with mechanical rust removal, efficient recycling of materials is achieved through waste recycling, a green manufacturing system is constructed, and the sustainable development requirement of green buildings is met.

The invention discloses a positioning and welding device for truss construction, relates to the technical field of welding, and solves the technical problems that a building truss is lack of effective positioning in the welding process, and the welding efficiency is influenced due to the need of overturning and welding. Comprising a welding table and a welding mechanism located in the direction of one side of the welding table, a fixing mechanism used for guiding and positioning an upper chord and a lower chord is installed on the welding table, a first clamping block used for clamping a first web member and a second clamping block used for clamping a second web member are rotationally installed on one side of the welding table, and a deflection mechanism is further installed on the welding table; the deflection mechanism is used for enabling the first clamping block and the second clamping block to synchronously and reversely deflect. By means of the welding device, the long truss can be positioned and welded section by section, the truss does not need to be overturned in the whole welding process, the welding quality and the welding efficiency can be improved, meanwhile, the web member and the two chord members can be always kept in the attached state in the welding process, and the welding firmness can be improved.

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

The invention relates to the technical field of laser welding, in particular to a laser welding system control method for metal accessories. The method comprises the following steps that automobile industry metal fitting data are obtained, welding demand analysis is conducted according to the automobile industry metal fitting data, and therefore welding demand data are obtained; laser welding feature extraction and resistance spot welding feature extraction are carried out according to the welding demand data, so that laser welding data and resistance spot welding data are obtained; carrying out welding simulation according to the laser welding data and the resistance spot welding data so as to obtain welding simulation data; welding form feature extraction and welding strength feature extraction are carried out according to the welding simulation data, so that welding form data and welding strength data are obtained; and carrying out welding defect detection according to the welding form data and the welding strength data so as to obtain welding defect data. Automatic adjustment and accurate control of the laser welding process are achieved based on the laser welding technology.

The invention belongs to the technical field of laser welding, and discloses a laser welding macro-micro multi-physics field cross-scale bidirectional coupling modeling method, which adopts a diffusion interface theory, considers macro heat flow information to revise micro phase field and solute field evolution equations, considers micro phase quality information to revise macro temperature field and flow field evolution equations, and considers micro heat flow information to revise micro phase field and solute field evolution equations. A macroscopic heat flow-microcosmic phase and mass multi-physics field bidirectional coupling model is innovatively constructed, and macroscopic and microcosmic bidirectional coupling calculation in the same computational domain is achieved. The model can overcome the limitation that a traditional sharp interface is difficult to couple, and a theoretical model basis is provided for precise prediction of macroscopic forming and microstructure evolution in the laser welding process. According to the method, a macroscopic temperature field / flow field-microscopic phase field / solute field bidirectional coupling mechanism is established, a cross-scale data bidirectional collaborative interaction method is provided, and therefore a laser welding macro-microscopic multi-physical field / cross-scale bidirectional coupling modeling method is provided, and precise simulation of macro-microscopic forming in the laser welding process is achieved.

The invention relates to the technical field of automatic welding control, and particularly discloses a multi-station automatic welding cooperative control method for electric iron accessories, which realizes full-closed-loop accurate control of a welding process through multi-sensor data fusion and an intelligent optimization algorithm. According to the method, an infrared thermal imager, an acoustic emission sensor and an ultrasonic thickness measuring device are adopted to collect molten pool temperature field, sound wave frequency spectrum and fusion depth data in real time, after feature extraction, the data are input into a multi-target optimization model, and a Pareto optimal solution set is generated with the fusion depth, the width of a heat affected zone and phase change latent heat as optimization targets; dynamically adjusting a segmented welding-back path and process parameters on the basis of an optimization result, and triggering model iterative correction through ultrasonic online monitoring; the central control unit adopts space voxelization monitoring and a three-level collision avoidance strategy to coordinate operation of the multiple mechanical arms, and collision emergency protection is achieved in combination with flexible pressure sensing.

The present disclosure provides a control system, method, apparatus, medium and program product for a welding process. The control system comprises a sensor module configured to collect data in the welding process; the data analysis module is configured to pre-process data in the welding process, extract key features from the pre-processed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification through a machine learning model based on the comprehensive features to identify defect features; the knowledge graph module is configured to obtain a repair strategy corresponding to the defect characteristics according to the defect characteristics and by utilizing a knowledge graph; the control module is configured to transmit the data received from the sensor module in the welding process to the data analysis module, receive the repair strategy and generate a repair instruction based on the repair strategy; and the repairing module is configured to automatically adjust welding parameters or control an executing mechanism to conduct repairing operation based on the repairing instruction.

The invention discloses a welding robot track self-adaptive correction method based on multi-modal data fusion, and particularly relates to the technical field of industrial robots, which comprises the following steps: synchronously acquiring visual three-dimensional point cloud and arc current and voltage transient waveform data in a welding process in real time; an enhanced working trajectory feature vector fusing the visual trajectory features and the arc deviation compensation amount is constructed; respectively calculating real-time confidence coefficients of the visual sense and the arc sensing mode; inputting the confidence coefficient into a weight distribution strategy model, dynamically resolving vision and arc weight coefficients, and when the confidence coefficient of one sensor is low, lifting the weight of the other sensor to dominate correction; performing weighted fusion on the two pose adjustment amounts by using a weight coefficient to generate a six-degree-of-freedom comprehensive trajectory correction amount; and finally, the robot is driven to execute online adaptive trajectory correction. Through a confidence-driven dynamic fusion strategy, the limitation of a single sensor is overcome, and the welding robot trajectory tracking precision and the adaptive capacity are improved.

The invention relates to the technical field of artificial intelligence welding, and discloses an automatic welding robot based on artificial intelligence and a welding system thereof. The system comprises a welding feature acquisition module, a process parameter generation module, a real-time regulation and control module and a motion planning module. The welding characteristic acquisition module is used for acquiring three-dimensional contour data, material component information and welding seam geometric parameters of a workpiece to be welded, and generating a welding characteristic spectrum through characteristic fusion; a process parameter generation module retrieves a matching template from a welding knowledge base according to the parameters, and outputs a reference welding process parameter combination in combination with an environment temperature and humidity compensation coefficient; the real-time regulation and control module dynamically corrects the reference parameters to generate an optimization instruction according to the molten pool form, thermal radiation distribution and electric arc voiceprint characteristics in the welding process; and the motion planning module calculates a motion track, attitude parameters and a speed curve of the welding execution mechanism according to the optimization instruction to form a robot control instruction set. The system can improve the intelligent level of welding, guarantees stable welding quality, and is suitable for welding scenes of multiple manufacturing industries.

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 relates to the technical field of welding process optimization, and discloses a welding parameter intelligent acquisition and process optimization management system. The system comprises a multi-source data acquisition module for synchronously acquiring various welding data to generate a multi-dimensional data set; the signal preprocessing module is used for carrying out noise reduction processing on the data; the dynamic parameter regulation and control module dynamically adjusts welding parameters based on a reinforcement learning model; the heterogeneous network communication module is used for designing a dual-channel protocol to guarantee data transmission; and the process optimization decision module is used for generating an optimal welding parameter scheme by using a deep neural network. And a welding quality simulation module and an abnormal process mode library are also arranged. Intelligent collection, real-time regulation and control and process optimization of welding parameters are achieved, the welding quality and the production efficiency are improved, the cost is reduced, and it is guaranteed that welding production is conducted stably.

The invention provides an inner flanging welding method of a water tank inner container and the water tank inner container, and relates to the technical field of welding manufacturing, and the inner flanging welding method comprises the following steps: obtaining three-dimensional point cloud data of an inner flanging interface of the water tank inner container in real time, and registering the point cloud data with a preset three-dimensional geometric model; based on an improved RRT algorithm, in combination with geometric features represented by the registered three-dimensional point cloud data, an initial motion track of a welding gun is planned; welding is conducted on the basis of the initial motion trail, and the weld width, the fusion depth feature and the defect feature are extracted from a molten pool image obtained in the welding process; correcting welding parameters of a welding gun in real time based on the weld width and fusion depth characteristics; and when the defect characteristics do not meet the preset conditions, path re-planning is triggered, a real-time movement track and synchronously-adjusted welding parameters are generated based on the currently-corrected welding parameters and the defect characteristics, and a welding gun is controlled to execute compensation welding. And the welding quality is improved.

The invention relates to the technical field of welding automation and robot visual perception, and discloses a robot intelligent welding method and system.The method comprises the steps that a three-dimensional point cloud image is constructed through a laser triangulation method; filtering and enhancing the point cloud image, and extracting a weld point cloud set by adopting a U-Net semantic segmentation model; carrying out space path identification and attitude regression based on the set to obtain a track element of a position-attitude pair; fitting a path by adopting a cubic B-spline curve and interpolating to generate a continuous welding track; and in the welding process, visual feedback is combined, and proportional differential control logic is introduced for closed-loop track error compensation. Compared with the technical problem that in the prior art, weld joint path recognition and accurate tracking cannot be stably achieved under complex structures such as curved surfaces and variable cross sections, a perception control closed loop based on visual servo is constructed, high-precision automatic welding of the complex weld joint structures is achieved, and the track fitting precision is improved.

The invention provides an industrial robot welding track real-time optimization method and system based on deep learning, and relates to the technical field of industrial robots, and the method comprises the steps: collecting three-dimensional point cloud data, carrying out the multi-scale processing to generate a high-precision curved surface, constructing a welding seam feature descriptor, and carrying out the real-time optimization of the welding track of an industrial robot; and the mapping relation between the technological parameters and the welding posture is optimized on the basis of a multi-target reward function, the mapping relation is converted into an initial movement track, the track is dynamically optimized through a segmented self-adaptive optimization model and a variable structure filtering algorithm, and self-adaptive adjustment of the welding process is achieved. The welding precision and quality of the complex curved surface are improved, and the adaptability and stability of robot welding are enhanced.

The invention discloses an electric arc dynamic self-adaptive control method and sensing system based on multi-modal sensing, and relates to the technical field of electric arc control. A multi-modal sensor integrated on a welding gun is used for synchronously collecting an electric arc form image, a molten pool area image, a molten pool oscillation sound signal and current and voltage multi-dimensional data; the collected data are preprocessed, and real-time parameters in the welding process are extracted; calculating and outputting an optimal welding control parameter in real time by combining a depth Q model and taking the welding quality as an optimization target; the control parameters are transmitted to a welding actuator, real-time adjustment of the welding process is completed, and real-time control over the welding arc is achieved; the control method can be used for enhancing the real-time self-adaption of a welding control system to complex welding components and the flexible manufacturing capacity of replacing a product line; the technical problems that in an existing automatic electric arc welding control method, electric arc welding information collection is single, control response is delayed, the flexible degree is low, and welding feature extraction errors are large are solved.

According to the welding process parameter recommendation method using the knowledge graph to retrieve the enhanced large model, the welding process knowledge graph is constructed, multi-source heterogeneous welding knowledge is stored in the graph, and effective management and reuse of the welding knowledge are achieved; meanwhile, through user feedback and verification, the welding process knowledge graph can be continuously updated and perfected, and continuous accumulation and iterative optimization of knowledge are achieved; the welding process knowledge graph is used as an external knowledge base to retrieve and enhance the large model, and accurate and comprehensive welding process related knowledge can be provided for the large model to serve as a reasoning basis, so that the illusion problem of the large model is effectively relieved; according to the method, intelligent recommendation of the welding process parameters is realized, personalized analysis can be carried out according to specific requirements of the user, welding process parameter suggestions meeting user expectations are quickly generated, the efficiency of welding process parameter recommendation is effectively improved, and the problems of strong expert experience dependence and relatively high cost in process parameter recommendation are solved.

The invention relates to the field of flange plate welding, in particular to an automatic flange plate welding device which comprises a fixing table, a three-jaw chuck used for clamping a flange plate is arranged at the upper end of the fixing table, and a fixing mechanism used for fixing the flange plate and a pipeline and a circumferential welding mechanism used for welding the pipeline to the flange plate are arranged above the fixing table. In the downward moving process of the fixing mechanism, the flange plate which is clamped and fixed can be limited in the circumferential direction, the upper end of a pipeline can be pressed, meanwhile, the pipeline can be clamped through the inner clamping assembly, the effect that one machine has multiple functions is achieved, it is ensured that the flange plate and the pipeline reach the optimal alignment state before welding, and the welding efficiency is improved. And meanwhile, the overall structural stability in the welding process can be further improved, the welding quality is improved, and the overall working efficiency is improved.

The invention relates to the technical field of electronic packaging, in particular to a welding process applied to a double-mesa device. The technology comprises the following steps that firstly, a heat conduction model of a double-table-board device is built, a temperature field prediction system is built in combination with measured data, secondly, an asymmetric double-pulse-current welding strategy is adopted, differential pulse waveforms are loaded to an upper table board and a lower table board respectively, independent temperature control over all welding areas is achieved, a temperature difference closed-loop feedback mechanism is introduced in the process, and the temperature field prediction system is obtained. Pulse parameters are dynamically adjusted according to real-time temperature deviation, a stress prediction and weld penetration monitoring module is used for assistance, the welding spot forming quality and heat stress balanced release are ensured, a staged annealing type cooling mode is adopted after welding, and the structural stability is improved. The method can be widely applied to MEMS modules, SiC power devices and heterogeneous chip laminated packaging double-mesa structure devices, and has the advantages of being high in welding precision, high in reliability and wide in process adaptability.