2903 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 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 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 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 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 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 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 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 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 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.

The invention relates to the technical field of alloy coatings, and discloses a laser cladding high-entropy alloy coating and a preparation method thereof.The laser cladding high-entropy alloy coating is prepared from, by mass, 25%-45% of Fe, 15%-25% of Cr, 10%-20% of Ni, 5%-15% of Mn, 3%-10% of Al, 0%-5% of Ti, 1%-5% of a nanometer reinforcing phase and 0.1%-0.5% of a flow promoter. Lorentz force generated by an alternating electromagnetic field drives melt convection, so that the diffusion coefficient of segregation-prone elements such as Cr and Ni is increased, the ultrasonic cavitation effect is matched to crush dendritic crystals and promote dispersion of nanoparticles, the segregation index of the elements is reduced, a uniform nanocrystalline-amorphous composite structure is formed through the synergistic effect, and uniform distribution of elements in a molten pool is achieved.

The invention provides a multi-sensor weld penetration state recognition method for improving an attention mechanism, relates to the technical field of weld recognition, and solves the technical problem of inaccurate weld quality judgment caused by insufficient recognition precision of a single signal on a penetration state in the prior art. According to the technical scheme, a sound signal in the welding process and a molten pool image in the welding process are collected; effective sound signals are extracted, and then the sound signals are visualized by using a Gramer angle field method; performing de-exposure processing on the overexposure area through a decoupling polymerization exposure correction model; a channel attention and space attention mechanism is introduced into sound and image feature extraction, deformable convolution self-adaptive sampling is utilized, a double-cross attention fusion strategy is combined to fuse multi-source information, fusion penetration state classification is completed, and therefore the quality of a welding seam is judged. According to the method, the penetration state can be accurately classified through the sound signal extracted in the welding process and the molten pool image, and a certain thought is provided for weld quality detection.

The invention discloses a dynamic monitoring and analyzing method for a thermal management component molten pool based on image recognition, and relates to the technical field of laser powder bed melting additive manufacturing. The method comprises the following steps: continuously capturing an original thermal management component molten pool image and temperature field distribution data during laser powder bed melting additive manufacturing; and optimizing the original thermal management component molten pool image to generate an optimized thermal management component molten pool image. Molten pool images and temperature field data are captured in real time through a high-frame-rate synchronous camera and an infrared sensor, defect types are recognized online in combination with a lightweight classification model, classification results are fed back to a control system to dynamically adjust laser power or scanning speed, interlayer defect accumulation can be inhibited in time, and the defect quality is improved. The defect diffusion problem caused by response lag of a traditional off-line analysis method is avoided, and the forming precision and the process stability of the heat management component are remarkably improved.

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 discloses a pipeline all-position multi-layer multi-channel TIG welding bead morphology measuring device and method, and relates to the technical field of intelligent welding. A molten pool image collecting module is used for collecting morphology information of a molten pool, and the light emitting end of a laser transmitter is used for facing a welding bead; the active and passive vision module is used for collecting an image of light emitted by the laser emitter projected on a weld pool groove and identifying a weld bead corner opening position and a weld bead width, and the active and passive vision module and the weld pool image collecting module are both electrically connected with the control module; the control module can fuse image features collected by the active and passive vision module and the molten pool image collection module, the axes of the molten pool image collection module, the welding module and the active and passive vision module are located in the same plane, and the molten pool image collection module and the active and passive vision module are located on the two sides of the welding module respectively. According to the method, manual participation in the welding process can be reduced, the welding automation degree is improved, and meanwhile, the welding bead morphology measurement accuracy is improved.

The invention relates to the technical field of welding, in particular to a double-laser double-swing auxiliary MIG electric arc thick plate narrow gap welding method and device, and the welding method specifically comprises the following steps that S1, initial positioning is conducted; s2, performing double-laser cross scanning preheating; s3, MIG electric arc auxiliary fusion is conducted, specifically, MIG electric arc is introduced on the basis of a molten pool formed by laser, so that the molten pool is stably combusted under laser induction, the common molten pool effect is achieved, and complete fusion of the side wall and the root of the groove is promoted; s4, real-time monitoring and adjustment in the welding process are conducted, specifically, in the welding process, the state of a molten pool and the welding quality are monitored in real time, and the laser power, the electric arc parameters and the welding speed are dynamically adjusted; and S5, welding seam quality detection. The double-laser-assisted MIG electric arc is used, the stability of the MIG electric arc is remarkably enhanced, the advantage of MIG filling efficiency is brought into play, and the welding efficiency and the welding quality are effectively improved.

The invention provides a molten pool temperature field in-situ monitoring method and system based on multi-view light field imaging, and the method employs a light field camera multi-view system, obtains molten pool images of R, G and B channels through building a light field sub-aperture Bayer model, and calibrates the relative spectral responsivity of a light field camera and the proportion of dual-wavelength intensity under a coaxial temperature measurement system. Molten pool images of two channels are selected from the molten pool images of the R channel, the G channel and the B channel, calibration parameters are combined, and a molten pool temperature field is derived through a dual-wavelength temperature measurement method. A blackbody furnace is adopted to calibrate a multi-view light field temperature measurement system, and the error of a monitored temperature field is kept below 3%. The method simplifies the monitoring process, improves the temperature measurement precision, has a wide temperature range and low complexity, can perform in-situ monitoring on the high-temperature metal molten pool, and has a wide application prospect in the field of additive manufacturing.

The invention discloses a method and equipment for double-ultrasonic cooperative in-situ auxiliary electric arc additive manufacturing of a large metal component, an electric arc additive manufacturing system and an ultrasonic vibration auxiliary electric arc additive manufacturing system. The ultrasonic vibration auxiliary electric arc additive manufacturing system comprises two ultrasonic impact guns, an optical sensor, a temperature sensor and a calculation control system. The optical sensor collects optical characteristics of the deposition channel and the molten pool and converts the optical characteristics into electric signals to be transmitted to the calculation control system, and the temperature sensor is used for monitoring and collecting temperature information of the deposition channel and transmitting the temperature information to the calculation control system to draw an isotherm; the calculation control system adjusts the distance between the ultrasonic impact gun and the center of the molten pool according to the isotherm and the optical characteristics of the optical sensor mobile phone, and the ultrasonic power is adjusted; one of the ultrasonic peening guns applies ultrasound from the component side edge as materials are added to the component edge. By means of the multi-axis freedom degree and accuracy of the mechanical arm, double-ultrasonic vibration is applied to all positions of a large metal component in the material adding process, and the mechanical property is improved.

The invention discloses a laser cladding temperature field analysis method based on a finite element simulation method. A metal flat plate is used as a research object. ANSYS software is adopted for simulation. A laser cladding process is simulated by constructing a finite element model and a refined mesh in combination with a moving Gaussian heat source and birth-death unit technology, and simulated laser heating is applied to the surface of a part as a thermal load. The ANSYS processing unit calculates a simulation result, and the postprocessor displays temperature field distribution. Simulated and actual coating cross section molten pool shapes are compared, it is found that the size of a simulation model is highly consistent with the size of an actual molten pool, the maximum error is only 9%, and the reliability of numerical simulation is verified. According to the method, a solid theoretical basis and an effective simulation means are provided for the metal laser cladding process, the process research level is improved, and the development cost is reduced. The method has an important value for practical engineering application, and provides a scientific basis for optimizing the laser cladding technology.