Welding Status Research Articles

A novel monitoring method based on the fusion of sound and image signals for laser welding penetration status

Accurately identifying penetration status is crucial for ensuring high-quality welding. However, relying on a single sensor to monitor the welding status falls short in accuracy. Thus, a novel monitoring method that fuses sound and image is proposed for assessing the laser welding penetration status. First, a microphone and a high-speed camera are employed to collect sound and image signals during welding. Second, to overcome noise challenges in sound signal, a denoising method is proposed that utilizes Grey Wolf Optimization (GWO) to find the optimal parameters for Variational Mode Decomposition (VMD). The time and frequency domain features from the denoised sound and Local Binary Pattern (LBP) features from the image are extracted. These features constitute a high-dimensional dataset, which is reduced using Principal Component Analysis (PCA). Finally, a back propagation neural network (BPNN) is constructed for fusion monitoring. The results demonstrate that fusion monitoring achieves higher accuracy (97.9%) compared to using sound (79.9%) or image (92.3%) features independently. In comparison to other methods like K-Nearest Neighbor (KNN), Naive Bayesian (NB), Support Vector Machine (SVM), and Decision Tree, BPNN fusion monitoring achieves higher accuracies, with increases of 18.09%, 12.79%, 6.53%, and 25.84% respectively. The proposed method helps to promote the development of welding intelligence.

Current Status of Laser Welding for Bipolar Plates in Proton Exchange Membrane Fuel Cells

Hydrogen fuel cells have become a hot research topic in recent years. Bipolar plates are the core components of fuel cells and the bipolar plate accounts for 70% of the total weight of the fuel cell, and laser welding is considered one of the most suitable technologies for fuel cell bipolar plate welding. This article summarizes the current research status in the field of fuel cell bipolar plate laser welding, which can clearly represent the latest achievements in the field of fuel cell bipolar plate welding. This article's research on fuel cell bipolar plates can serve as a fundamental understanding and reference, providing background guidance for future research. However, current research on bipolar plates is still too idealistic and has not been combined with engineering practice. Future researchers can study the welding of fuel cell bipolar plates from the perspective of engineering practice.

Online welding status monitoring method of T-joint double-sided double arc welding based on multi-source information fusion

In modern manufacturing, double-sided double arc (DSDA) welding brings advantages in properties and efficiency for T-shaped joints. However, the double-side forming makes it difficult to detect the imperfections and ensure weld joint quality, which few studies have covered. Most existing methods deal with single-arc welding with limited sensing sources. To fill the gap, an online welding status monitoring method for DSDA welding is proposed based on the fusion of welding current, arc voltage, arc sound, and weld pool images. In pre-processing, the automatic weld pool region of interest (ROI) detection method based on the lightweight YOLO-L model and the waveform denoising algorithm are designed. In feature engineering, waveform signals are analyzed in both the time and frequency domain. A weld pool feature extractor based on a convolutional neural network (CNN) is proposed with good effectiveness and interpretability. The output feature combination is refined by Fisher-based selection and evaluation. In model building, an ensemble learning model based on three high-fit basic learners is proposed, with an accuracy of 98.538 %. The proposed model shows significant advantages over the single basic classifier and other ensemble methods. Experiments verify high precision and robustness, laying a foundation for accurate real-time monitoring of DSDA welding production.

RESEARCH STATUS AND PROSPECTS FOR TIG-MIG HYBRID-ARC-WELDING TECHNOLOGY

TIG-MIG hybrid welding integrates the merits of tungsten insert-gas welding (TIG) and metal inert-gas welding (MIG), and achieves a new material-joining process with high quality, high efficiency and low cost. However, TIG-MIG hybrid welding also had some shortcomings, such as a complex process, unstable arc, large heat input, limiting its application. To further improve and promote TIG-MIG hybrid welding, numerous universities and research institutes have put forward a series of improvement programs that achieve relatively good results. In this study TIG-MIG hybrid welding is discussed in three aspects: the welding-process improvement, welding-parameters optimization and numerical simulation of the welding process. It was found that the stability of the hybrid arc welding process and the quality of the weld bead can be improved by improving the current polarity, wire type and arc swing. The TIG current, the distance between the wire and tungsten, and the heat input had an important impact on the weld quality and the formation of defects. A numerical simulation of the welding process analyzed the effects of torch angle, the distance between the wire and tungsten, the welding speed and the temperature fields on the arc morphology, molten-pool behavior, and droplet transfer. According to the above analysis, it summarized the current research status of TIG-MIG hybrid welding, and then proposed future research directions based on the existing shortcomings and deficiencies.

Solid-State Welding of Aluminum to Magnesium Alloys: A Review

With the continuous improvement of lightweight requirements, the preparation of Mg/Al composite structures by welding is in urgent demand and has broad prospective applications in the industrial field. However, it is easy to form a large number of brittle intermetallic compounds when welding Mg/Al dissimilar alloys, and it is difficult to obtain high-quality welded joints. The solid-state welding method has the characteristics of low energy input and high efficiency, which can inhibit the formation of brittle intermetallic compounds and help to solve the problem of the poor strength of welded joints using Mg/Al dissimilar alloys in engineering applications. Based on the literature of ultrasonic welding, friction welding, diffusion welding, explosive welding, magnetic pulse welding, and resistance spot welding of Al/Mg in recent years, this paper summarized and prospected the research status of solid-state welding using Mg/Al dissimilar alloys from three aspects: the optimization of welding parameters, the addition of interlayers, and hybrid welding process.

Higher-order modeling of a thin-walled beam with a welded multicell cross-section and its application to welding line optimization

Thin-walled multicell structures observed in vehicle frames are partially welded to lower the welding process cost while maintaining their overall structural stiffness comparable to those of fully-welded multicell structures. A fast and reliable beam-based model for analyzing such structures is required in the concept stage of the design process. In this investigation, an analysis method for a thin-walled beam of a multi-cell cross-section partially welded along its axial direction is newly established using a higher-order beam theory (HoBT). Our approach uses the Lagrange multipliers to impose the partial welding condition along the common edges of multiple thin-walled closed sections using the three-dimensional field derived from a higher-order beam theory. The developed method in this study can be an accurate and effective alternative without full finite element analysis employing shell and solid elements. To find the optimal welding locations, we introduced binary design variables parameterized through their corresponding welding constraints to identify the status of welding or non-welding along a candidate welding line. A genetic algorithm is adopted to solve optimization problems. After establishing the analysis method and optimization technique, their validity was checked using double-cell and triple-cell sectioned beams and beam frame structures, including a bus structure. The stiffnesses of the structures with optimized partial welding lines (20%) drop less than 6% from those of fully welded counterparts.

Challenges on friction stir welding of magnesium alloys in automotives

The most important advancement in metal joining during the last few decades is the Friction Stir Welding (FSW) process. When welding magnesium or light weight alloys, FSW offers several benefits. Due to its advantages in lightweight construction, friction stir welding of magnesium alloys are widely used in the aerospace, marine, transport sector, particularly in automobiles and trucks, where they may significantly increase fuel economy and reduce emissions. Research and development in the area of FSW and related technologies have recently been advancing quickly on a global scale. The fundamentals of friction stir welding and a number of characteristics of friction stir welded magnesium alloys have been discussed in this article. Furthermore, this review reports the summary of the present status of friction stir welding of magnesium alloys with automotive applications. In addition to that, the properties, structure, flammability and wear behavior of magnesium alloys have been described.

Research on welding penetration status monitoring based on Residual-Group convolution model

Welding penetration cannot be directly visible in non-penetrating laser lap welding, making it difficult to monitor the penetration status online. Aiming at this issue, scale-invariant feature transform (SIFT) and image thresholding segmentation are adopted to calculate and analyze the steady evolution of image feature information of welding images in three different statuses of the side-axis image acquisition system. According to the regularity evolution, a Residual-Group convolution model (Res-GCM) is proposed for feature extraction and analysis of welding images to predict the penetration status of non-penetrating laser welding. Compared with the VGG, ResNet, and WresNet models, Res-GCM has the best test accuracy of 90.53%. In the ablation experiments, the introduction of the Residual-Group convolution module can improve test accuracy by 6% compared with the residual module and 8% compared with the traditional convolutional module. Gradient-weighted class activation mapping (Grad-CAM) visualization results show that the train weights in each model layer are concentrated in the plasma and molten pool regions.

Online Detection of Keyhole Status in a Laser-MIG Hybrid Welding Process

During laser-metal inert gas (MIG) hybrid welding, a large amount of welding status information is generated in droplet transfer, keyhole and molten pool. In this paper, austenitic stainless steel was adopted as an experimental object, with a dual high-speed camera system used to obtain real-time images of droplet transfer, keyhole and molten pool in a laser-MIG hybrid welding process. The changing regulation of a keyhole in three different penetration states (i.e., non-penetration, partial penetration and normal penetration) was analyzed by extracting the morphological characteristics of a keyhole shape, and combining the droplet transition information and the shape of the weld pool. Experimental results show that the proposed method could effectively reflect the variation characteristics of the keyhole, and the correlation among the keyhole characteristics, the droplet transfer information, the weld pool shape and the welding status, and provide a new perspective for online detection of the laser-MIG welding quality.

Improved Image-Based Welding Status Recognition with Dimensionality Reduction and Shallow Learning

Improved Image-Based Welding Status Recognition with Dimensionality Reduction and Shallow Learning

Research status of deep penetration welding of medium-thick plate aluminum alloy

Research status of deep penetration welding of medium-thick plate aluminum alloy

Discrimination of Poor Electrode Junctions within Lithium-Ion Batteries by Ultrasonic Measurement and Deep Learning

Lithium-ion batteries, which have high energy density, are the most suitable batteries for use in high-tech electromechanical applications requiring high performance. Because one of the important components that determines the efficiency of lithium-ion batteries is the electrode, the manufacturing process for this junction plays an important role in the entire production process. In particular, the process related to the resistance spot welding of the electrode is very important, directly affecting the safety of users, and greatly affecting the performance of the batteries. However, because the electrode tab is spot-welded onto the inner surface of the case, it is impossible to verify with visual testing (using the naked eye) whether the junction is well bonded. Therefore, it is very important to perform quality evaluation of the resistance welding of electrodes after completing the manufacturing process, using non-destructive testing methods. In this paper, a non-destructive ultrasonic testing technique was applied to examine the quality of lithium-ion batteries in which the negative electrode tabs were welded to the inner surface of the cell cans. The status of resistance spot welding between the electrode and the can was verified using deep-learning techniques with the experimentally acquired ultrasonic signal database.

Monitoring of 304 austenitic stainless-steel laser-MIG hybrid welding process based on EMD-SVM

Real-time monitoring of laser-metal inert gas (MIG) hybrid welding plays a significant role in modern automatic production. In this paper, an innovative prediction model is proposed for laser-MIG hybrid welding process monitoring and weld defect detection. Specifically, the status of laser-MIG hybrid welding was monitored using a high-speed imaging system that could observe the visual information from the top and bottom of the weldment simultaneously. Then, the morphological features were extracted by image process algorithm. Furthermore, the empirical mode decomposition (EMD) was employed to analyze the time series of visual features, and the decomposed component intrinsic mode functions (IMFs) and residue were inputted to the support vector machine (SVM) classification model. Finally, the EMD-SVM model was employed to evaluate the welding status of four weld formations, including sound weld, root humping, incomplete penetration and burn through. Experimental results demonstrate the superiority of the proposed method in terms of accuracy and robustness compared with traditional methods such as the backpropagation (BP) neural network, SVM, wavelet packet decomposition (WPD)-BP, WPD-SVM, and EMD-BP. This proposed method provides a novel and accurate approach to perform laser-MIG welding process monitoring and online defects detection.

Friction welding of similar and dissimilar materials: A review

In recent decades, the requirements for higher eco-friendliness in the car segment have persuaded the expanded utilization of design in lightweight mixes of multi-material. It has unavoidably prompted difficulties in joining base materials (for example, low, medium, and ultrahigh quality) where magnesium combinations, steels, and aluminium compounds are joined. Simultaneously, every one of these materials recommends fluctuating execution and superior properties for different segments in various areas of novel design. Accordingly, these base material blends will unavoidably be welded, which presents significant difficulties because of their contradiction during friction welding techniques utilized in vehicle fabricate. This paper discussed current advancement in friction welding procedures for joining the various parent materials and explains welding parameters controlling the joint quality. Be that as it may, financially savvy and dependable joints of lightweight amalgams (for example, aluminium) and steel will need significant advancement and contemplation. The reason for the current survey paper is to evaluate the status of friction welding of different base materials of vehicle manufacturing alloys, with explicit thoughtfulness regarding the future development, vehicle manufacturing sector, impediments, and difficulties.

3DSMDA-Net: An improved 3DCNN with separable structure and multi-dimensional attention for welding status recognition

The vision-based welding status recognition (WSR) provides a basis for online welding quality control. Due to the severe arc and fume interference in the welding area and limited computational resources at the welding edge nodes, it becomes a challenge to mine the most discriminative feature contained in welding images by using a lightweight model. In this paper, we propose an improved three-dimensional convolutional neural network (3DCNN) with separable structure and multi-dimensional attention (3DSMDA-Net) for WSR. The proposed 3DSMDA-Net uses 3DCNN to adaptively extract abstract spatiotemporal features in a welding process and then leverages such time sequence information to improve the recognition accuracy of WSR. In addition, we decompose the classical 3D convolution into depthwise convolution and pointwise convolution to produce a lightweight model. A multi-dimensional attention mechanism is further proposed to compensate for the loss of accuracy caused by the separation operation. The results of experiments reveal that the proposed method reduces the model size to 1/7 of the classical 3DCNN without sacrificing accuracy. The comparison experiment results have indicated that the accuracy of the proposed method is more accurate and noise-resistant than that of the conventional model.

Investigation into keyhole-weld pool dynamic behaviors based on HDR vision sensing of real-time K-TIG welding process through a steel/glass sandwich

To obtain a deep insight into keyhole tungsten inert gas welding, it is necessary to observe the dynamic behavior of the weld pool and keyhole. In this study, based on the steel/glass sandwich and high dynamic range camera, a vision system is developed and the keyhole-weld pool profiles are captured during the real-time welding process. Then, to analyze the dynamic behavior of the weld pool and keyhole, an image processing algorithm is proposed to extract the compression depth of the weld pool and the geometric parameters of the keyhole from the captured images. After considering the variations of these parameters over time, it was found that the front and rear lengths of the keyhole were dynamically adjusted internally and had opposite trends according to the real-time welding status while the length of the keyhole was in a quasi-steady state. The proposed vision-based observation method lays a solid foundation for studying the weld forming process and improving keyhole tungsten inert gas welding.

Research Status and Prospect of Laser Impact Welding

The demands for the connection between thin dissimilar and similar materials in the fields of microelectronics and medical devices has promoted the development of laser impact welding. It is a new solid-state metallurgical bonding technology developed in recent years. This paper reviews the research progress of the laser impact welding in many aspects, including welding principle, welding process, weld interface microstructure and performance. The theoretical welding principle is the atomic force between materials. However, the metallurgical combination of two materials in the solid state by atomic force but almost no diffusion has not been confirmed by microstructure observation. The main theories used to explain the wave formation in impact welding were compared to conclude that caved mechanism and the Helmholz instability mechanism were accepted by researchers. The rebound of the flyer is still a critical problem for its application. With proper control of the welding parameters, the weld failure occurs on the base materials, indicating that the weld strength is higher than that of the base materials. Laser impact welding has been successfully applied in joining many dissimilar materials. There are issues still remained unresolved, such as surface damage of the flyer. The problems faced by laser impact welding were summaried, and its future applications were proposed. This review will provide a reference for the studies in laser impact welding, aiming process optimization and industrial application.

Research on the virtual reality technology of a pipeline welding robot

Purpose This paper aims to improve the scene sense of a virtual scene, the welding model of a virtual reality system of riser automatic equipment was constructed using Unity3D and UG software, which mainly included a welding car, welding guide rail, welding power supply, virtual camera and other equipment and the model was rendered. Design/methodology/approach The human-computer interaction page and simulation test of the system was produced using the user interface GUI system for creating a human-computer interaction scene. The operator could capture the welding status of the physical equipment accurately and in real-time so the virtual reality technology was very suitable for the remote monitoring operation integrated with the welding system. Findings Human-computer interaction design and collision detection were realized. In addition, the system simulation experiment was accomplished. With the continuous improvement and development of virtual reality technology real-time virtual simulation and monitoring, technology will become the main development trend. Research limitations/implications Based on virtual reality, the monitoring system can capture the operation status of physical welding equipment in real-time and accurately, which is very suitable for remote monitoring operation integrated with the welding system and also conducive to improving the monitoring level of the welding process. Practical implications This technology is time-saving and money-saving, for the operators do not have to be in a real welding environment and therefore they can get away from dangerous places. Consequently, it can avoid unnecessary injuries and problems. Social implications This technology can replace people to enter the dangerous and extreme environment to carry out welding operation, so it becomes the most effective means of nuclear power plant maintenance, space structure construction and marine engineering construction. In addition, it is time-saving and money-saving. Originality/value With the rapid development of virtual reality technology in recent years, it is a new research direction to apply virtual reality technology to the remote welding operation. This technology is different from the traditional way of welding for the operators can stay away from the welding scene especially some dangerous places.

Development of friction stir welding technology of low-carbon steel plates

The purpose of this study is to develop recommendations for low-carbon steel (0,18% C) products assembling technology using friction stir welding. Relevant information on the status of friction stir welding of low-carbon steels is presented and an analysis of existing articles is carried out. Experiments on joining plates of 4 mm thick by using the tool of different geometry are conducted. It was found that the optimum temperature when joining using WC tool is 800–950 °C. The sound welds is manufactured when using a conical pin at a tool rotation speed 500 rpm and a traverse feed rate 50 mm/min with a plunging depth of 0,05–0,1 mm and an axial force of 15–18 kN.

In-situ monitoring of the penetration status of keyhole laser welding by using a support vector machine with interaction time conditioned keyhole behaviors

Laser welding is a complex manufacturing process for which diagnosis and control are essential to ensuring quality control regardless of any disturbances to the welding process. In this study, the interaction time conditioned keyhole behaviors (ITCKBs) were proposed and analyzed for different penetration status, and an innovative method was developed that uses ITCKBs as the input of a support vector machines (SVM) to realize in-situ monitoring of the weld penetration status independent of process parameters. Four different ITCKBs were extracted to guarantee accurate estimation at different penetration status: the average area of the keyhole, average area of the full penetration hole, frequency of the full penetration hole, and maximum area of the full penetration hole. Penetration statuses were classified with different combinatorial inputs for comparison. Compared with using the keyhole area, full penetration hole area, or keyhole area and full penetration hole area as the input, using the four ITCKBs as the input improved the classification accuracy of the SVM by 39.1%, 33.3%, and 24.1%, respectively. This improvement in performance was because the point clusters of the ITCKBs are linearly separable. The results also indicated the presence of a positive synergistic effect because the classification accuracy of the SVM improved from 63.5% to 96.5% as the number of ITCKBs increased.