2427 results about "Molten pool" patented technology

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

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

The invention discloses a 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 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 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 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, 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 discloses a laser energy threshold millisecond-level dynamic matching method for multi-metal fuse manufacturing, and the method comprises the steps: pre-storing a multi-metal thermophysical parameter model, calibrating a thermal signal in real time through Kalman filtering, and carrying out the online correction of a melting threshold key parameter during the switching of a material; millisecond-level thermal feedback closed-loop control is achieved through the high-frame-rate thermal imaging technology, and the laser power accurately follows the corrected melting threshold value; meanwhile, a three-dimensional imaging and deep learning segmentation algorithm is adopted, geometric information of a welding wire and a molten pool is obtained, the position and the diameter of a light spot are adjusted in real time through an adjustable optical system after the offset is calculated, and millimeter-level space registration closed-loop control is achieved; the two closed-loop systems cooperatively operate according to respective control periods, and precise dynamic matching of laser heat input in a time domain and a space domain is achieved. The problem that the adaptability of a traditional process is insufficient during multi-metal switching and complex geometric change is solved, and the forming quality, the process stability and the efficiency of manufacturing are remarkably improved.

The invention relates to the technical field of welding parameter correction, and provides a welding parameter correction method for real-time molten pool morphology feedback, which comprises the following steps of: capturing a molten pool image, an arc spectrum and an infrared radiation signal in real time, accurately extracting geometrical characteristics and contour vibration frequency of a molten pool, and dynamically evaluating arc stability and a thermal attenuation state. And when defects are detected, welding parameter correction is generated based on a process knowledge graph and Bayesian reasoning, collaborative correction of parameters such as welding current, welding voltage and welding speed is achieved through reinforcement learning optimization weight distribution, and the change trend of morphology characteristics of a molten pool is continuously monitored after correction. According to the method, the depth belief network is judged and optimized by combining the descending rate of the morphology defect probability, and the corrected successful case is supplemented to the process knowledge graph, so that the welding quality stability is remarkably improved, the method is particularly suitable for automatic production of a high-precision welding scene, the defect rate can be effectively reduced, and the production efficiency can be effectively improved.

The invention discloses a closed-loop self-adjusting method for online defect identification and real-time feedback in the additive manufacturing process of a titanium alloy component based on intelligent sensing, belongs to the field of laser additive manufacturing, and solves the key problems of insufficient defect identification capability, quality control lag and the like in the current additive manufacturing process. The method is based on a multi-mode sensor architecture, integrates an infrared thermal imager, a molten pool camera and a spectrum sensor, and synchronously monitors a molten pool thermal field, radiation characteristics and surface topography. Traditional image processing and signal analysis are adopted to extract defect features such as air holes and cracks, and calibration is implemented in combination with off-line detection such as industrial CT. The recognition result is analyzed in real time through the central control platform, causes are automatically diagnosed based on defect types and distribution characteristics, parameters such as laser power and scanning paths are dynamically adjusted by matching a preset control strategy, and defect suppression and online repair are achieved. And a closed-loop self-adaptive dynamic adjustment mechanism improves the forming quality and reliability. The method is suitable for aerospace and other scenes with extremely high requirements for the quality of large complex titanium alloy components.

A multi-scale coupling analysis method for a high-temperature liquid metal and jet water interaction system mainly comprises the following steps as shown in an attached drawing: 1, carrying out geometric modeling and grid division on a liquid metal pool type space, jet water and other pipeline systems, and setting a valve action sequence; 2, setting a coupling boundary, and establishing an IPC communication pipeline; 3, performing steady-state calculation, and mutually providing initial boundaries for overall initialization; 4, a system program is imported into the boundary of the two-dimensional fluid domain, the fluid state in the pipeline under the decompression heating condition is calculated, and boundary information in a result is written into the IPC pipeline; 5, the two-dimensional fluid program reads source items from the system program, thermodynamic parameter distribution of the molten pool is calculated, and boundary information is provided; and 6, judging the action condition of the component at the current moment and repeating the step 4 and the step 5 until the calculation time reaches the end moment. The method provided by the invention can simulate the whole interaction process of liquid metal and jet water and embody the influence of flash evaporation in a critical flow jet boundary and an injection pipeline on jet.

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 a reduction melting separation furnace and a reduction melting separation method for preparing DRI from low-grade ore, and belongs to the technical field of metallurgy. The reduction melt separation furnace comprises a melt separation furnace body, a material bearing pore plate, a gas injection device and an electrode, the melt separation furnace body is hollow and forms a furnace cavity, the material bearing pore plate is arranged in the furnace cavity and divides the furnace cavity into a reduction chamber and a molten pool which are arranged up and down, the material bearing pore plate is formed by splicing at least two pore plate bodies, and the at least two pore plate bodies can be opened and closed; when the at least two pore plate bodies are closed, the material bearing pore plate bears the DRI, when the at least two pore plates are opened, the DRI falls into the molten pool, the gas injection device is arranged in the furnace cavity and injects reducing gas to the DRI on the material bearing pore plate, and the electrode is inserted into the molten pool and used for being electrified to generate an electric arc so as to melt the DRI. According to the method, further efficient reduction of FeO in the low-grade DRI is achieved, the metallization ratio and the yield of the DRI are effectively increased, and low-grade ore resources are utilized to the maximum extent.

The invention relates to the technical field of thin-strip cast rolling, and provides a thin-strip cast rolling tundish continuous casting device and method.The thin-strip cast rolling tundish continuous casting device comprises a base frame horizontally installed on the surface of a foundation, and the base frame is provided with a flow distribution ladle, a tundish and a large ladle from bottom to top; an adjusting mechanism is arranged below the position, where the casting position is arranged, of the base frame, the adjusting mechanism comprises two limiting plates which are symmetrically arranged, and one side of the middle of each limiting plate is fixedly connected with a second motor for driving a crystallizer roller; and a baking oven fixed on the placing rack is arranged above the middle parts of the two motors II. According to the method, molten steel qualified in smelting is directly poured into the molten pool defined by the two crystallization rollers rotating oppositely and the side sealing plates through the large ladle, the tundish and the flow distribution system, the molten steel is cooled through the two crystallization rollers rotating oppositely to form the steel belt with the thickness of 1-5 mm, the production cost can be saved, the casting continuity is improved, the production efficiency is improved, and the yield is increased.

The invention relates to the technical field of welding automation, and discloses an intelligent welding system for an electric pole steel reinforcement framework. The system comprises a welding execution module, a multi-mode sensing module, an edge calculation module, a real-time regulation and control module and a system control center. Synchronous collection and real-time analysis of images, temperature and sound signals of a welding pool area are achieved, so that defects such as air holes, slag inclusion and incomplete fusion can be recognized online in a key time window when the welding pool is not solidified, and time delay caused by a traditional off-line detection mode is thoroughly eliminated. Through a real-time regulation and control module, on the basis of a multi-target optimization decision and parameter self-adaptive adjustment mechanism, the system can dynamically adjust key process parameters such as welding current, arc voltage and welding speed at the moment of identifying defect germination or process deviation, and real-time intervention and closed-loop control on welding quality are achieved; therefore, the final formation of defect welding seams is avoided fundamentally, and the rework rate and the material waste are remarkably reduced.

The invention relates to the field of welding quality detection, and discloses a steel structure welding quality detection method and system based on machine vision, and the method comprises the steps: obtaining an original welding seam image sequence through high-frequency sampling, and carrying out the preprocessing to generate a clear sequence; extracting features from the sequence to obtain a welding seam static defect index; time sequence data are constructed according to the time arrangement characteristics, and a dynamic time sequence data set is obtained by calculating the welding seam symmetry fluctuation amplitude and the molten pool shape change trend; inputting the data set into a neural network model, analyzing a surface profile curve time sequence evolution mode, and determining potential defect points; extracting a crack period amplitude sequence from the potential defect points, and performing Fourier transform to obtain a defect frequency distribution diagram; extracting a frequency peak value position from the graph, judging process parameter deviation, generating an alarm and updating a database; and determining a sampling frequency deviation value according to the alarm, adjusting the high-frequency sampling frequency through feedback circulation, and optimizing dynamic detection. According to the invention, the detection real-time performance, the accuracy and the parameter adjustment accuracy are improved.

The invention relates to the technical field of laser deposition control, in particular to an unmanned aerial vehicle metal part ultrasonic field auxiliary laser deposition system which comprises the following modules: a data acquisition module for acquiring a molten pool thermal field signal, a sound field signal and a laser scattering signal, and performing sampling, filtering and synchronous processing to obtain molten pool original data; the micro-flow digital twinning module is used for establishing a molten pool micro-flow digital twinning model according to the molten pool original data and simulating molten pool micro-flow behavior to obtain micro-flow prediction data; and the microflow ultrasonic regulation and control module is used for generating an ultrasonic pulse sequence according to the microflow prediction data and applying the ultrasonic pulse sequence to the molten pool to regulate and control the flow field. According to the invention, through the molten pool microflow digital twinning module and the microflow ultrasonic regulation and control module, real-time modeling and regulation and control of the molten pool microflow structure in the complex geometric part deposition process are realized, the problem that local vortexes, shear layer differences and flow field anomalies cannot be identified in real time in the traditional technology is solved, and the molten pool microflow evolution can be actively reconstructed.

The invention discloses a heat exchanger welding seam image enhancement processing method, and belongs to the technical field of image processing, and the method comprises the steps: S1, collecting a heat exchanger welding seam original image through a preset collection module, and converting the original image into a grayscale image; s2, a processing module is preset, the gray level image is segmented based on a preset dynamic threshold value, and a welding seam area image and a base metal area image are generated; s3, in the welding seam area image, generating a first enhanced image, a second enhanced image and a third enhanced image through a preset generation module; and S4, extracting a reference edge contour of the weld joint area image from the base metal area image, performing pixel-level fusion on the first enhanced image, the second enhanced image and the third enhanced image, and outputting a weld joint image. According to the method, the weld joint area is divided into the highlight area, the dark area and the middle area to be processed respectively, weld pool texture loss caused by strong reflection is effectively restrained, and the problem that details are lost in a highlight scene in an existing method is solved.

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 relates to the technical field of metal smelting, and discloses a control system and method for a multi-gun top-blown immersion bath smelting furnace, and the smelting furnace comprises a furnace wall surface, a bath solution, a metal sulfur discharge port, a slag outlet, swirl vanes, a feeding port, spray guns and a flue outlet. The system comprises a data acquisition module which is used for monitoring the temperature, the liquid level, the components and the material state in the multi-gun top-blown immersion molten pool smelting furnace in real time to obtain monitoring data in the multi-gun top-blown immersion molten pool smelting furnace; the data analysis module is used for cleaning the monitoring data to obtain the cleaned monitoring data, performing dimension reduction on the cleaned monitoring data, and identifying key features influencing the smelting quality and efficiency; and the prediction feedback module is used for constructing a smelting process theoretical model and an interaction model, and realizing dynamic monitoring data prediction and adaptive multi-gun top-blown immersion bath smelting furnace parameter adjustment in the smelting process. The output of the spray gun is automatically adjusted, system abnormity is prevented, and early warning is given out in time when potential risks occur.

The invention discloses a nickel-based alloy additive manufacturing method for high-frequency ultrasonic dynamic forging printing. The method comprises the steps that nickel-based alloy powder and a base plate are pretreated; scanning the nickel-based alloy powder layer by layer through laser to form a molten pool; in the machining process of each layer, when the temperature of the molten pool ranges from 1000 DEG C to 1400 DEG C, ultrasonic waves are activated to generate compound vibration; the composite vibration comprises axial vibration and transverse vibration; when the ultrasonic vibration phase difference reaches a preset first interval, short pulse laser is triggered; the ultrasonic vibration phase difference is a phase deviation between an ultrasonic vibration waveform and a pulse laser shock triggering moment; and until all layers are machined, a formed part is obtained. According to the scheme, the dislocation density is improved, secondary grain refinement and bubble escape are achieved, and interlayer stress redistribution is made uniform.