8703 results about "Welding joint" patented technology

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

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

The 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 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 discloses a weld joint key point recognition method, device and equipment, a medium and a program product, and the method comprises the steps: generating a data set based on training image data and annotation data, constructing an original recognition model, improving the original recognition model, and training and optimizing the improved original recognition model based on the data set. Obtaining a target recognition model; acquiring a depth image and an RGB image of the welding part through image acquisition equipment on the welding robot; the RGB image is input into the target recognition model for weld joint key point recognition, weld joint key point coordinates of the welding part are determined based on the recognition result and the depth image, accurate recognition of the weld joint key points is achieved, a welding robot is guided to accurately reach the weld joint key point coordinate positions for welding, key point recognition autonomy and adaptability are improved, and the welding quality is improved. And welding tasks with different requirements are effectively adapted, the welding efficiency is greatly improved while the welding quality is ensured, and the time cost is reduced.

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

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

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

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

The invention relates to the technical field of welding control, in particular to a welding control method and device for a signboard. The method comprises the following steps: acquiring a high-definition monitoring image and a welding work log of a signboard to be welded; performing signboard texture visual identification on the to-be-welded signboard high-definition monitoring image, and performing welding visual effect simulation to obtain a signboard welding visual effect; welding points are accurately recognized based on the signboard welding visual effect, and a dynamic welding path is planned, so that the dynamic welding path is constructed; performing real-time welding control on the signboard to be welded based on the dynamic welding path, and then performing smooth optimization on the motion track of the welding gun so as to construct an optimal smooth motion track of the welding gun; current real-time working parameters of the welding gun are obtained; and self-adaptive welding gun parameter adjustment is conducted on the current welding gun real-time working parameters based on the optimal welding gun movement smooth track, and therefore self-adaptive welding gun adjustment parameters are obtained. The signboard welding quality is improved, and welding errors are reduced.

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

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

The invention relates to the technical field of pipeline welding seam identification and measurement, aims to solve the problem of uncontrollable data acquisition quality and the problems of robustness and precision of single-feature welding seam extraction, and provides a nuclear power station pipeline welding seam automatic identification and measurement method which comprises the following steps: accurately acquiring pipeline data under a self-adaptive front view posture; carrying out point cloud data processing on the collected data, and carrying out fitting of a pipeline model after segmenting out part of point cloud of the pipeline; after pipeline modeling is completed, a multi-dimensional data fusion method is adopted for weld joint recognition; and after the welding seam area is back projected back to the three-dimensional point cloud space, three-dimensional point cloud of the welding seam area is obtained, modeling is conducted on the welding seam area in combination with the pipeline model, and finally measurement data of the welding seam are obtained. According to the invention, automatic hand-eye calibration and multi-camera calibration of the guiding system can be realized without manual intervention.

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

The invention relates to the technical field of building steel pipe welding machining, and discloses a steel pipe welding device suitable for constructional engineering, which comprises a mounting table, a mounting groove is formed in the mounting table, and two first bidirectional threaded rods are rotatably connected in the mounting groove; according to the steel pipe welding device suitable for the constructional engineering, two steel pipes are placed in the mounting ring, then the steel pipes are clamped and fixed through a clamping mechanism in the mounting ring, and then the steel pipes are driven to move, so that the steel pipes are welded to the mounting ring, and the steel pipes are welded to the mounting ring. Then the steel pipe groove grinding mechanism is used for automatically grinding grooves of the two steel pipes, the welding mechanism is used for automatically welding the steel pipes after grinding is completed, and meanwhile, the welded weld joints are automatically ground, so that groove grinding and welding integrated full-automatic machining of the steel pipes of different sizes is achieved; and the welding efficiency and the welding effect of the steel pipe are greatly improved.

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.

Provided is a friction stir spot welded joint having high cross tensile strength even in the case where a steel sheet having a soft surface layer is used as a material to be welded. An annular groove is formed on an upper surface of an upper sheet out of overlapping steel sheets, and a shape and hardness of a welded portion are appropriately controlled simultaneously.

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 present invention relates to a large-span venue steel structure, which is characterized in that: the large-span venue steel structure comprises standard exhibition halls and an entrance hall; there are a pair of standard exhibition halls; the entrance hall presents a T-shaped structure, and the standard exhibition halls are arranged on both sides of the entrance hall respectively; and the standard exhibition halls are symmetrically arranged with respect to the entrance hall. A latticed shell structure is optimized to form hub joints, and a machining process is simple, so as to achieve batch production in a factory; compared with a latticed shell structure formed by weld joints, this method greatly reduces the appearance treatment difficulty of the latticed shell; in addition, latticed shell rod members and the hub joints are welded in an intersecting mode, such that positioning is simple, thus better facilitating control of the overall structure quality and linear control; a special hoisting structure is designed, and proper hoisting points are selected, thereby providing hoisting data support theoretically; using a specially designed jig reduces hoisting installation risks, and also solves the hoisting precision control problem; said improvement points ensure the hoisting installation performance, and an assembly profile contour of the structure meets design requirements, thereby reducing the rework frequency.

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

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

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

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

The invention discloses a laser weld joint welding system based on diffuse reflection material preheating, and belongs to the technical field of laser welding. The system comprises a preheating welding system and a welding workbench, the preheating welding system is composed of a welding laser generator and a preheating device, the welding laser generator is provided with a first laser welding head and a second laser welding head which are used for main welding and auxiliary preheating respectively, and the focusing and working distance can be accurately adjusted; proper sizes of welding light spots and preheating light spots are ensured; the preheating device effectively reflects laser beams and evenly distributes preheating light spots to a welding area through a mirror reflection plate and a U-shaped diffuse reflection light reflection baffle. The system further has an intelligent control function, the laser light source power, the welding parameters and the angle and position of the preheating device can be automatically adjusted according to feedback of the temperature sensor, the laser power and the preheating parameters are automatically adjusted, and the safety of the welding process and the consistency of welding seams are guaranteed.

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

The invention discloses a method for homogenizing a microstructure of a low-temperature high manganese steel welding heat affected zone by using pulse current, and belongs to the technical field of welding and postweld heat treatment of high manganese steel for liquefied natural gas (LNG) storage and transportation. The treatment method comprises the following steps: (1) sampling a heat affected zone of the low-temperature high-manganese steel welded joint with microstructure gradient distribution; (2) the low-temperature high manganese steel welding heat affected zone sample is subjected to pulse current treatment at the room temperature, the pulse frequency ranges from 0.6 kHz to 6 kHz, the pulse width ranges from 0.2 ms to 6 ms, the current density ranges from 20 A / mm < 2 > to 200 A / mm < 2 >, and the treatment time ranges from 5 min to 120 min; and (3) grinding and polishing the sample, observing and analyzing the element distribution state in the heat affected zone, and quantitatively representing the grain boundary structure characteristics. According to the method, atomic diffusion, dislocation slippage and grain boundary migration of the low-temperature high-manganese steel welding heat affected zone can be promoted by applying the pulse current, redistribution of segregated atoms and dislocations in grains and at grain boundaries is rapidly achieved, and then the purposes of improving the microstructure uniformity of the low-temperature high-manganese steel welding heat affected zone and improving the impact toughness of the low-temperature high-manganese steel welding heat affected zone are achieved. The method is easy to operate, short in treatment time, environmentally friendly and particularly suitable for postweld treatment of low-temperature high manganese steel parts.