3752 results about "Molten pool" 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 provides an electromagnetic-assisted nickel-based alloy laser melting deposition crack inhibition method and system, relates to the technical field of automatic control, constructs an intelligent control system, carries out deep fusion and real-time prediction on multi-source heterogeneous sensing data, generates a prediction result, sets a reward function and an action-value network, and improves the performance of the nickel-based alloy laser melting deposition crack inhibition system. Solving to obtain an optimization control strategy and analyzing and outputting the optimization control strategy; and constructing a three-dimensional electromagnetic field distribution matrix. In combination with real-time feedback of the shape of the molten pool, the molten pool is stimulated to flow directionally and oscillate non-linearly according to the resonance effect, oscillation parameters are obtained, thermodynamic behaviors corresponding to the molten pool are predicted according to the oscillation parameters, and a thermodynamic prediction result is fed back to the central control unit; the method comprises the following steps: detecting the surface appearance of a part, extracting defect characteristic parameter vectors, inverting defect influence factors, generating a dynamic regulation and control curve, dispersing the dynamic regulation and control curve into multi-dimensional control instruction vectors, updating a preset regulation and control model, outputting optimized regulation and control model parameters, and carrying out crack suppression operation again.

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 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 provides an electromagnetic field composite powder feeding type plasma arc additive manufacturing formability regulation and control method and system, and relates to the technical field of plasma arc additive manufacturing. The flowing direction and speed of liquid metal in a molten pool in the deposition process are regulated and controlled through the target composite electromagnetic field, and the morphology and the internal organization structure of a deposition layer are controlled; in the process of regulating and controlling the liquid metal in the molten pool, process parameters and molten pool dynamic image data in the deposition process are collected in real time, and a pre-trained state recognition model is used for conducting self-adaptive adjustment on the process parameters of a subsequent deposition layer; and after deposition of each layer is completed, a laser scanning system is adopted for measuring the actual geometric size and surface appearance of the deposition layer, deposition manufacturing of the next layer is executed based on the optimized composite electromagnetic field control parameters, the corrected layered slicing data and the process path, and the execution is executed circularly till manufacturing of the whole workpiece is completed.

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 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 discloses a metal 3D printing forming precision monitoring method and device and electronic equipment thereof, and relates to the technical field of metal 3D printing, and the method comprises the following steps: obtaining multi-modal sensor data, collecting multi-dimensional data in the metal 3D printing process based on the multi-modal sensor data, determining an optical sensor to record the molten pool form and powder distribution, and determining the metal 3D printing forming precision. The thermal infrared camera is determined to monitor temperature field distribution, the acoustic sensor is determined to collect and print medium-high frequency sound wave signals, and defect signals are identified; extracting boundary and geometric features from the optical image based on a convolutional neural network according to local preprocessing data of an edge computing device, and extracting a key temperature change area in response to thermal field gradient analysis; spectral features are extracted from the acoustic data based on time-frequency analysis. According to the invention, through synchronous acquisition of optical, thermal and acoustic multi-modal data, data fusion and defect detection are realized by using an advanced deep learning technology.

The invention discloses a distributed reinforcement learning control method and system for multi-robot cooperative welding. A heterogeneous multi-robot task allocation algorithm, a multi-agent reinforcement learning model and welding parameters are combined. The method comprises the steps that a state space containing welding current, voltage, speed, welding gun posture, welding seam position and environment obstacle distribution is constructed, and an initial feasible operation area of the robot is determined; the overall welding task is decomposed into sub-tasks, and candidate robots are distributed; configuring a local decision module for each robot, and outputting an action decision containing a welding gun track adjustment amount and a parameter correction value; setting a global coordination module to generate coordination factors; each robot updates the decision and converts the decision into a driving instruction to execute operation, and molten pool temperature and weld size parameters are collected; the parameters are fed back to the model to adjust the state space characterization and decision weight. According to the method, decision collaborative precision is improved in combination with process parameters, self-adaptive optimization is achieved through real-time feedback, and collaborative welding efficiency and quality are improved.

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 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 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 belongs to the technical field of intelligent manufacturing and laser arc hybrid welding, and discloses a double-robot laser arc hybrid welding real-time regulation and control method based on digital twinning. According to the method, double robots are used, a laser-electric arc composite heat source digital twinborn model is constructed, a Gaussian distribution laser heat source and a double-ellipsoid electric arc heat source are integrated, and the dynamic behaviors (molten pool flowing and keyhole stability) of a molten pool and the generation probability of defects (air holes, cracks and the like) are predicted. And a visual sensor and an acoustic emission sensor on the two sides of the welding robot are used for capturing molten pool oscillation frequency and internal defect signals. And in combination with a reinforcement learning algorithm, welding path offset self-adaptive compensation and laser arc energy ratio adjustment are achieved, and closed-loop real-time regulation and control are formed by controlling the position of a welding gun of a welding robot and technological parameters. Based on a digital twinborn model, robot welding guns on the two sides can conduct synchronous welding on the two sides and can be independently adjusted while coupling is guaranteed according to the characteristics of specific materials, and high-quality welding is achieved.

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.

The invention discloses a gradient welding strength control method and system for a welded galvanized steel pipe for fire fighting, particularly relates to the technical field of welding automation control, and aims to solve the problem of abnormal welding seam strength gradient caused by instantaneous unbalance of an electric field and a thermal field during dynamic parameter switching in the prior art. Electromagnetic distortion characteristic quantity is extracted through frequency domain energy analysis, and thermodynamic offset state quantity is deduced in combination with a thermal diffusion trend; on the basis of frequency band correlation mapping of electromagnetic and thermodynamic parameters, thermoelectric cooperative imbalance levels are divided through mutual information entropy evolution, and a self-adaptive harmonic attenuation weight tuning instruction is generated; analyzing a mismatching relation between transient disturbance of an environment magnetic field and arc voltage modulation distortion in real time, dynamically generating a magnetic field compensation factor and reversely superposing the magnetic field compensation factor to a control instruction; and in combination with the matching degree of molten pool oscillation energy and a harmonic frequency spectrum, correcting wire feeding rate calibration, reconstructing a multi-band harmonic energy proportion spectrum, outputting an anti-interference welding control signal, and realizing dynamic balance between arc stability and molten pool heat input.

The invention discloses a method and system for adjusting the wire feeding speed of a wire feeder, and relates to the related technical field of welding machining.The method comprises the steps that a workpiece data set is read; feature extraction is conducted on workpiece material data and workpiece geometrical shape data in the workpiece data set, workpiece features are established, wire feeding speed control optimization is conducted through the workpiece features, and a wire feeding speed optimization space is established; wire feeding control of the wire feeder is conducted through the wire feeding speed optimizing space, and molten pool monitoring feedback and electric arc monitoring feedback are established; joint optimization analysis of the wire feeding speed is carried out through molten pool monitoring feedback and electric arc monitoring feedback, and optimized wire feeding speed control parameters are established; and carrying out wire feeding control on the wire feeder according to the optimized wire feeding speed control parameters. The technical problem that in the prior art, dynamic optimization control over the wire feeding speed is difficult to conduct according to workpiece materials and geometrical shapes, and consequently the machining quality is unstable is solved, and the technical effect of improving the wire feeding speed control precision and the machining quality is achieved.

The invention discloses a side-blown furnace digital intelligent prediction system based on deep learning, and the system comprises a data collection and preprocessing module which is used for collecting the working data of a real-time sensor through the real-time sensor, and carrying out the preprocessing of the real-time sensor; the numerical calculation module is used for carrying out numerical simulation on a multi-physics field smelting environment in the side-blown furnace based on the preprocessed working data; the deep learning module is used for establishing and training a graph neural network GNN model based on the numerical simulation result, and obtaining a dynamic behavior rule of the molten pool smelting process through the GNN model; and the digital twinning module is used for constructing a digital twinning model consistent with the actual operation state of the side-blown furnace based on the dynamic behavior rule. According to the invention, accurate monitoring and prediction of the side-blown furnace can be realized, so that the smelting process is optimized and the operation efficiency is improved.

The invention belongs to the technical field of laser processing, and particularly relates to a multi-mode laser coupling high-efficiency and high-quality additive manufacturing method for a metal-based composite material, which comprises the steps of pretreatment, substrate pretreatment, multi-mode coupling laser construction, additive manufacturing and the like. High-power pulse laser is adopted as energy supply main laser to melt the metal matrix composite wire, meanwhile, flowing of a molten pool is accelerated, the nucleation rate is increased, and metal matrix composite parts are machined through the additive manufacturing method. The continuous laser is adopted to effectively slow down the cooling speed of the molten pool, and the residual tensile stress generated in the component due to rapid solidification of the molten pool is greatly reduced. Oscillating laser is adopted to improve the shape of a molten pool and reduce the number of times of transverse lap joint. According to the method, multi-mode laser coupling and cooperation with the flat-topped laser light source are utilized, and the metal-based composite material machining part with the uniform and flat surface appearance macroscopically and the stable and uniform organization structure microcosmically is obtained.

The invention belongs to the technical field of robot welding, and discloses a welding robot motion path optimization planning method based on multi-sensor fusion. Comprising the steps that sensor data are obtained, and the sensor data comprise a welding seam three-dimensional point cloud, a molten pool image, acceleration and angular velocity of the tail end of a welding gun in three axial directions, welding current and welding voltage; converting the sensor data into a unified coordinate system to obtain converted sensor data; performing filtering processing on the converted three-dimensional point cloud of the welding seam to obtain a filtering point cloud, and performing feature extraction on the filtering point cloud based on an RANSAC (Random Sample Consensus) algorithm to obtain a center line feature of the welding seam; in combination with the converted three-dimensional point cloud of the welding seam and the characteristics of the center line of the welding seam, an initial welding path is obtained through planning based on an improved A * algorithm; intelligent and precise adjustment of welding parameters is achieved, the stability of welding quality is guaranteed, and the performance of motion path planning and welding operation of the welding robot is improved in an all-around mode.

The invention discloses a whole welding process quality regulation and control method of a welding robot, and relates to the technical field related to welding. The method at least comprises the steps that S1, a pre-welding parameter drawing technology is designed, welding numerical simulation analysis is conducted to predict optimal welding parameters, the optimal welding parameters are compared with a thermal cycle curve of a test, the accuracy of a model is verified, the predicted parameters are input into a welding robot, and the welding prediction effect is achieved; and S2, designing a welding molten pool regulation and control technology, collecting a molten pool image through an industrial camera, processing the collected molten pool image, then building a molten pool monitoring platform, and regulating and controlling the quality of the molten pool during welding through the platform. According to the method, a proper welding quality guarantee strategy of the welding robot is provided, and the welding quality of the welding robot is guaranteed in the whole process of pre-welding, during-welding and after-welding through the cooperative design of pre-welding parameter drawing-up based on the numerical simulation technology, during-welding molten pool monitoring based on camera recognition and after-welding quality study and judgment based on deep learning.

The invention is suitable for the technical field of laser welding, and particularly relates to a laser welding control method which comprises the steps that an initial polarization image and an initial thermal imaging image of a to-be-welded workpiece are obtained; according to the initial polarization image and the initial thermal imaging graph, the welding path and initial laser parameters of the to-be-welded workpiece are determined; based on the welding path and the initial laser parameters, a laser welding device is controlled to weld the to-be-welded workpiece, and meanwhile a real-time polarization image and a real-time thermal image of the to-be-welded workpiece are obtained; according to the real-time polarization image, the real-time thermal image and the welding path, real-time state information of a molten pool of the to-be-welded workpiece is determined; and real-time laser parameters are determined according to the real-time state information of the molten pool, and the laser welding device is controlled to weld the workpiece to be welded according to the real-time laser parameters. According to the method, accurate welding process control is achieved, and the defects of a traditional visual system are overcome.

The invention discloses a welding quality detection method and system, and relates to the technical field of welding quality detection.The method comprises the steps that a plurality of to-be-detected welding parts are determined, the temperature of a molten pool is continuously detected through a thermal imaging module in the welding process, and a molten pool temperature field distribution time sequence is generated. Based on the time series data, heat conduction analysis and cooling rate identification are performed to generate a heat conduction path and a cooling curve distribution. By analyzing the data, whether a heat accumulation or loss abnormal area exists or not is judged, and if the heat accumulation or loss abnormal area is abnormal, a quality detection signal is sent out. The technical problem that an existing welding quality detection method cannot accurately recognize welding defects caused by uneven heat conduction and abnormal cooling rate in the welding process in real time is solved, and the technical effect of improving the welding quality detection precision and timeliness is achieved.

The invention discloses a vacuum cup laser welding device, and belongs to the technical field of laser welding, the vacuum cup laser welding device comprises a processing table, the top of the processing table is connected with a base box, and the top of the base box is provided with a synchronous clamping assembly. According to the device, the air curtain assembly is arranged, a continuous and uniform annular laminar flow air curtain is formed by means of the nozzle array evenly distributed in the circumferential direction, a stable inert gas protection environment is formed in a welding area, the oxidation probability of a molten pool is remarkably reduced, meanwhile, the air curtain structure optimally designed through fluid dynamics can effectively disperse welding smoke dust, and the welding quality is improved. By means of a mechanical structure, the angle of the direction adjusting pipe can be accurately adjusted, the coverage range of the air curtain can be flexibly adjusted, accordingly, the vacuum cup body with different specifications can be adapted, possible protection blind areas are eliminated, and the service life of the vacuum cup body is prolonged. And the optimal action angle between the welding protection airflow and the workpiece surface is always kept.

The invention discloses a laser-TIG (Tungsten Inert Gas) hybrid welding and method based on ultrasonic-assisted wire powder coaxial conveying, after entering, a laser beam forms a semicircular Bessel beam through a lens, a metal wire passes through an ultrasonic-assisted device and then introduces vibration into a welding pool, and six powder conveying guide pipes are uniformly distributed around the metal wire; according to the wire core six-way powder channel coaxial device, a semi-circular light beam passes through the wire core six-way powder channel coaxial device and enters a laser head, and finally the effect of coaxially melting a wire powder material through laser is achieved. A TIG welding auxiliary device is added to one side of a molten pool, metallurgical reaction is promoted, and compactness of a welding seam is improved. The device mainly comprises an annular laser beam wire core six-way powder channel simultaneous melting system, an automatic wire feeding system, a powder feeding system, an ultrasonic vibration system, a TIG auxiliary welding system and the like. The welding efficiency can be improved through the wire core six-way powder channel coaxial melting technology, the flowing stability of a molten pool can be promoted through the ultrasonic-assisted technology, and the welding seam forming quality can be improved through laser-TIG composite welding.

The invention discloses a laser power optimization method based on a deep reinforcement learning strategy, belongs to the technical field of simulation and artificial intelligence, and is suitable for a laser powder bed melting (LPBF) process. The method comprises the steps that firstly, an LPBF simulation model is built, a molten pool three-dimensional temperature field distribution model is built through a moving heat source, and the forming process under a laser scanning path is simulated in combination with a part STL file; a state space and a reward function are constructed by extracting state features of a molten pool in real time, and a laser power adjustment strategy is optimized by using a deep reinforcement learning DQN network model. An optimal laser adjustment action is generated by adopting an epsilon-greedy algorithm, and intelligent agent interaction data is stored and updated based on an experience pool, so that network model parameters are optimized, and the stability and uniformity of the depth of a molten pool are ensured. According to the method, internal defects are effectively reduced, the quality and intelligent control level of the LPBF process are improved, and a new thought is provided for efficient and accurate process parameter optimization.

The invention provides a molten pool level online monitoring system and method, and the system comprises a data collection module, a data preprocessing module, a model training and optimizing module, an angle detection and compensation module, a nonlinear data processing module, a system testing and optimizing module, a display module, a communication module, and a control module. The online real-time monitoring of the molten pool level can be realized, the production automation level is improved, when the data acquisition module reaches a detection position, the control system triggers a signal through the coil, the camera is started to capture the images of the liquid level of the slag surface and the liquid level of the copper solution surface of the current molten pool, the online real-time monitoring function of the molten pool level is realized, and the production efficiency is improved. Compared with a traditional manual measurement or intermittent monitoring method, the method has the advantages that the detection efficiency can be greatly improved, the dependence on manual operation is reduced, and the automation level of the production process is improved.

The invention discloses a thick plate welding residual stress numerical prediction method based on heat-fluid-solid multi-field coupling, which combines computational fluid dynamics with a finite element method, and comprehensively considers the flow characteristic and thermodynamic effect of liquid metal. In the CFD, the influence of metal melting, molten pool flowing, surface tension, buoyancy, heat transfer and fluid flowing on the geometric shape and temperature distribution of the weld joint is simulated, in the FEM module, the geometric shape and temperature difference of the weld joint are calculated based on the CFD, residual stress and deformation are further simulated and analyzed in combination with a life-death unit technology, and through the FEM module, the residual stress and deformation of the weld joint are obtained. The forming mechanism of the convex welding seam, the evolution rule of the thick plate welding residual stress and the influence of the geometrical shape on residual stress concentration are systematically researched.