7057 results about "Welding process" patented technology

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

The invention relates to the technical field of industrial automatic welding and discloses an intelligent control method and system for industrial robot welding, and the method comprises the steps that after a welding task is started, an initial welding process parameter model is called based on the current workpiece specification, and a first welding control instruction sequence is generated; according to the first welding control instruction sequence, a welding gun is controlled to execute welding operation, and multi-source sensing data flow in the welding process is synchronously collected; carrying out fusion feature extraction on the real-time sensing data stream to generate a current welding situation feature vector, when situation deviation exists between the current welding situation feature vector and the reference welding situation feature vector, obtaining a self-adaptive welding process parameter model through online incremental learning, and obtaining a self-adaptive welding process parameter model on the basis of the self-adaptive welding process parameter model. And generating an optimized welding control instruction sequence and issuing the optimized welding control instruction sequence to a robot controller and a welding gun so as to execute self-adaptive welding operation. The welding quality stability and the production efficiency can be improved, and the equipment maintenance cost is reduced.

The invention belongs to the field of robot control, and particularly relates to a robot self-adaptive impeller welding control method and system fused with multi-modal data, and the method comprises the steps: obtaining an impeller workpiece image and three-dimensional point cloud data through an industrial vision subsystem, and extracting a three-dimensional welding seam position point set representing a welding seam track; generating a candidate welding path set based on welding seam geometric feature analysis; in combination with welding seam morphology and process requirements, optimal welding process parameters are predicted through a regression model, and a welding gun attitude angle is calculated through quaternion conversion according to a curved surface normal vector; in the welding process, the relative position deviation of a robot and a workpiece and the parameter deviation of welding current / voltage and a planned value are monitored in real time, when the deviation exceeds a preset tolerance interval, a PID control algorithm is adopted to dynamically adjust robot motion parameters or welding process parameters, and closed-loop control over the welding process is achieved; according to the method, precise planning of the welding path and self-adaptive regulation and control of the process are achieved, and the welding quality and the forming consistency are remarkably improved.

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

The invention discloses an intelligent welding control system and method based on multiple sensors and electronic equipment, and belongs to the technical field of automatic welding, the system comprises a sensing layer, a decision-making layer and an execution layer, through cooperative work of a front laser vision sensor, a rear laser vision sensor and a molten pool image sensor, a groove three-dimensional model can be accurately established before welding, and the welding precision is improved. And dynamic information of the molten pool is continuously collected in the welding process, a dual monitoring mechanism for the geometric characteristics of the welding line and the state of the molten pool is formed, and multi-source data collection and fusion in the whole welding process are achieved. The control method comprises the steps of pre-scanning modeling, feedforward parameter planning, real-time feedback adjustment of a molten pool, data fusion optimization and the like, and high-precision self-adaptive control over welding parameters is achieved by combining deep learning and the Kalman filtering technology. According to the method, the welding adaptability, the control precision and the intelligent level can be remarkably improved, and the method is suitable for high-precision welding scenes such as pipelines, pressure containers and steel structures and has remarkable engineering application value and popularization prospects.

The invention relates to the technical field of welding, and discloses an intelligent control method and system for the welding process of a thin-wall metal component, and the intelligent control method for the welding process of the thin-wall metal component comprises the steps that an initial welding parameter set is determined, and time synchronization is conducted; obtaining multi-source original data with timestamps in the welding process; time alignment is carried out; respectively calculating a current stability index, a heat accumulation index and a molten pool width measured value to obtain a key deviation index; based on the key deviation index, current adjustment amount, pulse parameter adjustment amount and position compensation amount are obtained through calculation; obtaining an adjustment amount set; the current welding parameters are subjected to amplitude limiting updating, and an updated current welding parameter set is obtained through a step-by-step transition method and a high-priority fast instruction; recording a welding process data log, and judging a welding state; carrying out comprehensive evaluation and data storage on the welding process; according to the method, various dynamic changes in the welding process are comprehensively coped with, and the stability and consistency of the welding quality are improved.

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.

The invention discloses a welding robot self-adaptive wire feeding control method, a platform and a medium, relates to the technical field of welding automation process control, and is suitable for welding process management under complex working conditions. According to the method, an arc voltage sequence and a molten pool characteristic parameter sequence in the welding process are collected, a welding state characteristic value is obtained through calculation, a reference database containing welding process parameters, welding working condition parameters and the welding state characteristic value is constructed based on historical experimental data, and corresponding preset reference indexes are generated through a mapping relation function; and in real-time welding, the welding state characteristic value of the current wire feeding control period is obtained and compared with a preset reference index, the deviation value is calculated, the welding state is judged according to the grading threshold value interval, the wire feeding speed is dynamically adjusted, and self-adaptive matching of the wire feeding speed and the welding state is achieved. According to the method, instability caused by traditional adjustment depending on experience can be overcome, dynamic control over the welding process is achieved, and the stability of a welding pool and the welding seam forming quality are improved.

The invention provides an intelligent pipeline welding method and system based on data decision, and relates to the technical field of intelligent pipeline welding.The intelligent pipeline welding method comprises the steps that a welding request is responded, a groove image of a to-be-welded pipeline is obtained through an active visual sensor, groove morphology characteristic parameters of the to-be-welded pipeline are determined based on the groove image, and the to-be-welded pipeline is welded according to the groove morphology characteristic parameters. Based on the groove morphology characteristic parameters, corresponding basic layer channel arrangement rules and reference process parameters are determined from a preset layer channel arrangement database, a welding process scheme matched with the current groove is generated in combination with the groove morphology characteristic parameters, and the current groove is welded on the basis of the welding process scheme. And the welding operation is executed in the groove of the to-be-welded pipeline, the technical problems that in the prior art, the global planning capacity is deficient, the deviation correction strategy is locally lagged, and the whole-process accurate control is insufficient are solved, and the effect of improving the stability and reliability of the welding quality of the pipeline is achieved.

The invention provides a multi-sensor fusion welding temperature monitoring method and a related device, and the method comprises the steps: dividing a welding region into a plurality of sub-regions according to the spatial distribution of the welding region, and obtaining the initial temperature data corresponding to each sub-region according to various temperature sensors; performing time alignment on the initial temperature data through difference resampling of different sampling points to generate a time sequence temperature matrix; performing real-time quality detection on the time sequence temperature matrix to generate a real-time quality score, and performing weighted combination on the real-time quality score and a preset confidence coefficient parameter table to obtain a dynamic confidence coefficient result set; fusing the effective temperature data exceeding a preset confidence threshold in the dynamic confidence result set according to a fusion rule corresponding to each sub-region to obtain a fused temperature result; and the fusion temperature results of the sub-regions are subjected to time sequence splicing to obtain a global temperature curve of the welding process, so that the accuracy of temperature detection in the welding process can be effectively improved.

The invention relates to the technical field of process optimization, in particular to a collaborative optimization analysis method for multi-station tower drum welding. In order to solve the problem that cooperative control of tower drum multi-station welding is difficult, firstly, multi-mode sensing data of welding seam vision, arc sensing, thermal field distribution and welding voiceprint are loaded, a fusion evaluation model is constructed to obtain a welding seam deviation value and a sensor confidence score, and the health state of a sensor is judged according to a threshold value; and a corresponding fault-tolerant reconstruction control mode is triggered to obtain welding tracking control parameters, then a multi-station welding heat source model is loaded, thermal interference is decoupled in combination with the control parameters and thermal field data, multiple stations are cooperatively regulated and controlled, a cooperative control instruction is output and executed, and welding process optimization is achieved.

The invention provides a welding method and device, electronic equipment and a storage medium, and belongs to the technical field of intelligent manufacturing. A target wire feeding speed corresponding to the actual average current value is determined according to the target compensation relation; the actual wire feeding speed is adjusted to the target wire feeding speed; the target compensation relation is determined from the compensation relation set based on the target average current value, and the target compensation relation comprises the corresponding relation between the actual average current value and the target wire feeding speed. Through the pre-established compensation relation, the wire feeding speed can be actively adjusted in the welding process according to the deviation of the monitored actual average current value so as to compensate the deviation in real time, and therefore it is guaranteed that the heat input amount in the whole welding process is constant, and the welding quality is improved. The technical problems of welding current fluctuation and inconsistent welding seam forming caused by dry elongation change due to factors such as out-of-roundness of workpieces are solved, and the quality and reliability of welding seams are remarkably improved.

Disclosed in the present application are a dual-sided laser synchronous welding heat source model establishment method, a dual-sided laser synchronous welding heat source simulation method, a dual-sided laser synchronous welding system, and a medium. Firstly, combined heat sources each comprising a Gaussian cylindrical heat source attenuation model and a Gaussian surface heat source correction model are established, and a dual-sided laser synchronous welding heat source model is established by means of a rotating coordinate system; a three-dimensional geometric model and a three-dimensional mesh model of a T-joint are established; and the consistency between simulated molten metal morphology and an actual weld shape is used as a criterion for determining whether parameter settings of the dual-sided laser synchronous welding heat source model are rational, so as to obtain an optimal finite element simulation heat source model. By establishing two heat sources each obtained by combining a Gaussian cylindrical heat source attenuation model and a Gaussian surface heat source correction model, and symmetrically applying the two heat sources to two sides of a T-joint to act on the interior and surface of a weld, the present invention realizes the accurate simulation of a weld penetration zone in a T-shaped thin-plate weldment, and thus enables the prediction of residual stress and welding deformation during a design phase, thereby optimizing the welding process, improving the welding quality and efficiency, and reducing material waste and production costs.

The invention discloses an obstacle avoidance motion planning method and system for redundant drive of an intelligent process of a welding robot and computer equipment. According to the method, firstly, a motion path of a robot is planned based on welding process redundancy, then a sampling strategy that the position and posture of a welding gun are separated is adopted, then the sampled posture is mapped to a planar two-dimensional space, and the relevance between an obstacle collision boundary and the obstacle avoidance posture of the welding gun is constructed; an incremental extreme gradient lifting model is adopted to carry out probability modeling on the collision boundary and obstacle avoidance attitude relevance, nearest neighbor search and new planning node expansion are carried out based on target cost to obtain new planning nodes, collision detection is carried out on the new planning nodes, and the new planning nodes passing the collision detection are used for incremental learning of an obstacle avoidance model; and finally, an optimization strategy of spherical linear interpolation and cosine slow motion time mapping is adopted to ensure continuity and smoothness of a motion planning path trace. The problem that in the prior art, it is difficult to quickly calculate and generate a collision-free welding track is solved.

The invention discloses a pre-welding deviation rectifying device for a submerged-arc welding straight seam steel pipe, and relates to the technical field of steel pipe submerged-arc welding. The submerged-arc welding device comprises a machining table, a first linear sliding rail module and a second linear sliding rail module are installed at the top of the machining table, a submerged-arc welding assembly is installed on the second linear sliding rail module, a fixed plate is fixedly installed at the left end of the top of the machining table, a movable plate is installed on the first linear sliding rail module, and three-jaw chucks are installed on the movable plate and the fixed plate correspondingly. A three-jaw chuck drives an inner correction rod to expand outwards to conduct center positioning on a steel pipe, an inner circle can be corrected, then a guide disc is rotated to enable a connecting rod to enter an arc groove part of an inner correction guide groove to lock the position of the inner correction rod, and a swing rod drives an outer correction rod to swing through a telescopic rod. The outer correcting rods correct the outer circle of the steel pipe, the two sets of outer correcting rods are pressed on the two sides of a welding seam to clamp the welding seam, and therefore welding deformation caused by thermal expansion and contraction in the welding process is reduced.

The invention relates to the technical field of computer-aided engineering, in particular to a nonlinear efficient iterative solving method for thermal strain load, which is used for solving an obtained finite element equation by using a modified Newton-Raphson method and comprises the following steps of: firstly, establishing a geometric model to be analyzed and performing grid division; the method comprises the following steps: firstly, setting a model to be analyzed, then setting boundary conditions and temperature field change conditions of the model to be analyzed, finally, inputting the conditions into an algorithm solver, solving a nonlinear equation set, iteratively inputting a load continuously according to a modified Newton-Raphson method in the solving process, calculating node displacement until convergence, and solving stress-strain conditions in the welding process according to a material constitutive relation. In the solving process, by judging the size of the two norms of the displacement increment obtained in the adjacent increment step heuristic stage, the convergence of nonlinear solving of the structure is enhanced through the calculation mode, and the solving speed of the nonlinear problem in the thermal elastic-plastic constitutive structure is increased.

The invention relates to a welding gun self-adaptive swing adjusting system and method. The system comprises a detection module, a control module, an execution module, a man-machine interaction module, a power module and a storage module. The detection module detects welding seam unevenness information, and after receiving the information, the control module judges whether to drive the execution module to adjust a welding gun to swing or not according to a preset threshold value determined on the basis of the vertical distance between a replaced plate welding initial point and the detection module and the process fluctuation range; meanwhile, a welding seam is tracked through a point-by-point comparison method based on the linear interpolation principle, the moving direction is judged through a deviation function, the feeding direction is determined through a coordinate absolute value and a symbol in a full quadrant, interpolation is stopped when the total step length is gradually decreased to zero, and the method comprises the steps of module initialization, parameter setting, communication detection, judgment and adjustment and welding seam tracking. The invention aims to solve the problems of poor welding quality, low efficiency and more manual intervention caused by poor welding seam flatness, large gap and high-low misalignment in the welding process.

The invention provides a swing control method based on molten pool oscillation multi-feature fusion and related equipment, and relates to the technical field of welding, the method comprises the steps that in the argon arc welding process, an electric arc signal and a molten pool oscillation signal of a variable wall thickness welding area are collected in real time; oscillation amplitude, main frequency characteristics, current fluctuation coefficients and voltage-current phase difference characteristics are extracted from the molten pool oscillation signals and the electric arc signals and combined, and an oscillation comprehensive characteristic matrix is obtained; inputting the oscillation comprehensive characteristic matrix into a pre-trained classification model, and identifying a current molten pool state type; a corresponding swing compensation strategy is matched according to the state type of the molten pool, and asymmetric swing parameters are generated; and the asymmetric swing parameters and a preset basic welding track are subjected to superposition operation, and a composite control instruction is generated to control a welding gun to execute asymmetric swing welding. By implementing the method, different abnormal states of different molten pools can be subjected to differential control, and the stability of the variable-wall-thickness welding process and the forming quality of a final weld joint are improved.

The invention provides an ultrasonic welding quality control system and method, and relates to the technical field of ultrasonic welding, and the system comprises an acquisition and recognition module which is used for collecting sensor data of welding head displacement, weldment interface temperature, welding head amplitude and welding power in real time in the ultrasonic welding process, identifying a welding head displacement peak value, an interface temperature inflection point and a power fluctuation critical characteristic value; and the parameter extraction module is used for extracting a displacement peak value characteristic value, an interface temperature inflection point characteristic value, a power fluctuation critical characteristic value, a welding head amplitude characteristic value and a welding time characteristic value in the welding process based on the sensor data to form a characteristic parameter set. By means of multi-parameter feature extraction, correlation analysis, dynamic adjustment and closed-loop feedback, accurate control over the welding process is achieved, the welding quality stability is improved, and the rate of unqualified products is reduced.

The invention discloses a marine seat framework welding track real-time correction system based on machine vision and mechanical feedback. The marine seat framework welding track real-time correction system comprises a robot control unit, a welding unit, a vision sensing unit, a force sensing unit and a track correction module running on the robot control unit. The visual sensing unit is installed on an end effector of the robot and used for collecting image data including a to-be-welded weld joint and a peripheral area of the to-be-welded weld joint in real time. The force sensing unit is installed between a robot end effector and a welding gun and used for detecting six-dimensional force and torque signals between the welding gun and a workpiece in the welding process in real time. The core of the track correction module is a real-time correction algorithm based on multi-sensor information fusion, and the track correction module receives image data from the visual sensing unit and mechanical data from the force sensing unit. The sensing limitation of a single sensor is overcome, the vision provides macroscopic position deviation, the force sensing provides microcosmic contact and attitude information, and more comprehensive environment sensing is realized through complementation of the vision and the force sensing.

The invention relates to the technical field of welding devices, and discloses a low-alloy structural steel welding device capable of cleaning surface welding slag, which comprises a welding frame, a rotating frame is rotatably mounted on the welding frame, a driving gear ring is rotatably mounted on the rotating frame, and a feeding and discharging station, a preheating station, a welding station and a slag cleaning station are sequentially arranged on the welding frame in the anticlockwise direction. A guide shaft is rotationally mounted at the position, corresponding to the slag removal station, of the welding frame, a guide gear meshed with the driving gear ring is mounted on the guide shaft, and four welding systems are mounted on the welding frame. Through differential meshing design of the fan gear and the driven gear and in combination with an elastic reset mechanism of the torsional spring, the cleaning frame generates a circulating switching mode of'high-frequency small-amplitude vibration 'and'low-frequency large-amplitude vibration' at the slag cleaning station, the high-frequency vibration can accurately crush loose welding slag, the low-frequency vibration can effectively shake off the firmly attached welding slag, and therefore the welding slag cleaning efficiency is improved. And the two modes alternately act to cover welding slag characteristics generated by different welding processes.

The invention provides a demand-based adaptive welding process parameter generation system, and relates to the technical field of welding processes, the system comprises: a demand information acquisition module for extracting N initial welding parameters; the population construction module is used for constructing a plurality of initial welding process parameter populations; the welding result obtaining module is used for determining a plurality of preset environment conditions and obtaining a plurality of welding results; the optimization module is used for obtaining an optimal welding result; the backtracking adjustment module carries out backtracking adjustment, gradient compensation is carried out according to a backtracking adjustment result, and a welding process parameter optimization population is obtained; and the welding optimization module is used for performing intelligent welding optimization. By means of the method, the problems that in the prior art, welding parameters cannot be flexibly adjusted, the welding parameters need to be frequently replaced or adjusted when workpieces of different materials or different thicknesses are welded, so that the welding efficiency is reduced, and the welding process parameters in the welding process are adaptively adjusted based on the welding requirements; therefore, more accurate welding process parameters are generated, and the welding efficiency is improved.

The invention relates to a cabinet body structure processing device for high-voltage cabinet production, which comprises a processing rack, a displacement box mounted at the upper end of the processing rack, a sliding seat arranged on the surface of the displacement box, a welding assembly arranged below the sliding seat and displacement racks arranged at the two ends of the processing rack, and a displacement frame is arranged between the two displacement racks; the displacement frame is used for clamping and turning over a high-voltage cabinet body, an outer moving frame and an inner positioning frame are arranged on the displacement frame, an outer clamping assembly is arranged on the outer moving frame, and an inner clamping assembly is arranged at the upper end of the inner positioning frame; by arranging a multi-point internal and external cooperative clamping and supporting structure, bidirectional mechanical constraint on a thin plate cabinet body in the whole welding process is achieved, the limiting effect on a plate body is improved, meanwhile, a pressing assembly is driven in the working process of a welding gun, angular deformation and wave deformation caused by thermal expansion and cold contraction are effectively restrained, and the welding quality is improved. The diagonal deviation control quantity of the cabinet body is small, and the assembly precision and the sealing performance between the door body and the cabinet body are improved.

The invention belongs to the technical field of bridge maintenance, and discloses a machine vision and digital twinning combined remote repair welding repair method and system for a cracked steel bridge structure. A transmission steel bridge maintenance method mainly depends on manual detection and field repair, and has many limitations in practical application and is difficult to meet bridge maintenance requirements. The method comprises the following steps: acquiring steel bridge data through a detection robot to identify a steel bridge crack, generating a three-dimensional model of the steel bridge crack based on the steel bridge crack data, performing finite element stress analysis, generating a crack welding strategy, and issuing the crack welding strategy to a welding robot to execute a welding action; further dynamically mapping the welding information to the three-dimensional model of the crack, fusing the three-dimensional dynamic model of crack repair into a VR operation interface, and monitoring the welding process of the welding robot through the VR operation interface; and finally, the welding robot detects the repair integrity of the steel bridge crack, and the repair integrity information is updated to the three-dimensional dynamic model of steel bridge crack repair.

The invention discloses a fuzzy neural network PID photovoltaic series welding temperature control method based on improved PSO optimization. The method comprises the steps that a target photovoltaic series welding equipment heating transfer function model is acquired; setting an initial PID parameter; adjusting a PID increment parameter of the PID control module in real time according to the temperature error, the temperature error change rate and a preset fuzzy rule base; the neural network is combined with fuzzy control, and parameters and rules of fuzzy control are automatically modified through training data; according to the photovoltaic series welding temperature condition, the fitness function of the PSO algorithm is improved, the adjustment mode of the inertia weight is improved, and the improved PSO algorithm is used for optimizing the initial PID parameters of fuzzy neural network PID photovoltaic series welding temperature control. Self-adaptive precise control and robust control of the welding temperature control system are achieved, the temperature fluctuation phenomenon in the photovoltaic series welding process is effectively improved, and the robustness and control reliability of the system are enhanced.

The invention provides a flexible welding deformation control method and an obtained bogie framework, specifically, a deformation monitoring sensor used for collecting workpiece deformation and a force sensor used for collecting clamping force are installed on a workpiece clamp; the deformation monitoring sensor and the force sensor are connected with the data acquisition control module; the method comprises the following steps: pre-installing a workpiece on a clamp, and establishing a mapping relation among the workpiece, the clamp and each sensor in the clamp; according to historical welding data of the same type of structure, presetting reversible deformation parameters, collecting pre-welding reference data, verifying data of each sensor, and if reversible deformation is effective, entering a welding process monitoring state; welding machine signals and sensor data are collected in real time, the deformation state is processed and analyzed in real time, and adjustment suggestions are output; and after welding is completed, the deformation trend of the workpiece after loosening and clamping is judged in combination with sensor data, deformation data of the workpiece after loosening and clamping are collected, whether the deformation data are qualified or not is judged, and if not, adjustment and repair are conducted till the deformation data are qualified.

The invention discloses a welding quality on-line detection method based on machine vision, and particularly relates to the field of welding quality detection.The welding quality on-line detection method comprises the steps that multi-dimensional basic data collection is conducted, the two-dimensional form, heat distribution, micro texture, three-dimensional topological data and welding process parameters of a welding seam are obtained, and a full-dimensional original data set with time-space synchronization is constructed; performing feature decoupling on the data, respectively constructing geometric morphology, thermal, texture and three-dimensional topological feature functions, and converting unstructured data into structured feature values; integrating multi-dimensional features through a nonlinear deep fusion model, introducing thermal probability mapping, texture attention weighting and a three-dimensional residual compensation mechanism, and outputting a weld comprehensive quality value; and finally, positioning dominant defect dimensions and identifying defect types in combination with a dynamic threshold model to finish quality grading judgment. Multi-dimensional accurate detection and dynamic grading of the welding quality are achieved, different scene requirements are met, and the detection efficiency and the judgment accuracy are improved.