Weld Quality Research Articles

Arc stability and ablation characteristics in O2, N2, and air environments

Arcing is widely used in the processing and manufacturing of electrical materials. Arc duration and stability play a key role in the quality and efficiency of arc welding and cutting. Therefore, a platform for arc experiment and test analysis in a multi-atmosphere environment is built in this paper. The influence of the gas environments and pressures on arc breaking distance, arc stability, and ablation characteristics is explored. The experimental results show that the arc stability and arc breaking distance in the oxygen environment are significantly different from those in the air and nitrogen environment. This phenomenon is mainly caused by the strong electronegativity of oxygen and the Rayleigh–Taylor instability between the arc and the air. In addition, the variation of arc ablation characteristics under different gas environments and pressures is found. The influence mechanism of gas environments and pressures on arc breaking distance, arc stability, and ablation characteristics are cleared. The correlation mechanism between arc breaking distance and arc ablation characteristics is revealed. The research results provide theoretical support for improving the quality of electrical material processing and manufacturing and the service life of electric contact systems.

Optimization of Weld Qualities in Various Welding Operations Using Advanced Computational Techniques: A Comprehensive Review

Optimization of Weld Qualities in Various Welding Operations Using Advanced Computational Techniques: A Comprehensive Review

Cyclic behavior of improved low-yield point steel plate shear walls with T-shaped stiffeners

Based on the demand for high-performance lateral force-resistance systems for high-rise buildings, a new stiffened low-yield point (LY) steel plate shear wall (SPSW) structure is proposed. The infill plate is made of LY steel, and the stiffeners adopt a T-shaped section with greater flexural stiffness. Quasi-static tests are conducted on one unstiffened low-yield point steel plate shear wall (L-SPSW) and two stiffened L-SPSWs (using T-shaped cross and diagonal stiffeners). The cyclic behaviors of the L-SPSWs are compared through failure mode, damage development, hysteresis performance, and deformation performance. Numerical models are established and validated through experimental results. Finally, the performance of L-SPSWs with T-shaped and plate-type stiffeners is analyzed. The results show that the T-shaped cross stiffeners have the greatest enhancement in energy dissipation capacity (EDC) and stiffness, as well as the strongest suppression of the out-of-plane deformation (OPD) of the whole plate. T-shaped diagonal stiffeners have the greatest impact on enhancing the bearing capacity. It is suggested to strengthen the weld quality or select a steel material with LY for the diagonal stiffeners to ensure ductility. The use of T-shaped stiffeners can maximally improve the restraining effect on the OPD of the infill plate by 38.1 % compared with plate-type stiffeners.

Expulsion prevention in resistance spot welding of dissimilar joints with ultra-high strength steel: An analysis of the mechanism and effect of preheating current

The widespread adoption of ultra-high strength steels, due to their high bulk resistivity, intensifies expulsion issues in resistance spot welding (RSW), deteriorating both the spot weld and surface quality. This study presents a novel approach to prevent expulsion by employing a preheating current. Through characteristic analysis of joint formation under critical welding current, the importance of plastic material encapsulation around the weld nugget (plastic shell) at high temperatures in preventing expulsion is highlighted. To evaluate the effect of preheating on the plastic shell and understand its mechanism in expulsion prevention, a two-dimensional welding simulation model for dissimilar ultra-high strength steel joints was established. The results showed that optimal preheating enhances the thickness of the plastic shell, improving its ability to encapsulate the weld nugget during the primary welding phase, thereby diminishing expulsion risks. Experimental validation confirmed that by employing the optimal preheating current, the maximum nugget diameter was enhanced to 9.42 mm, marking an increase of 13.4 % and extending the weldable current range by 27.5 %. Under quasi-static cross-tensile loading, joints with preheating demonstrated a 7.9 % enhancement in maximum load-bearing capacity compared to joints without preheating, showing a reproducible and complete pull-out failure mode within the heat-affected zone. This study offers a prevention method based on underlying mechanisms, providing a new perspective for future research on welding parameter optimization with the aim of expulsion prevention.

Environmental Impact, Mechanical Properties, and Productivity: Considerations on Filler Wire and Scanning Strategy in Laser Welding

Abstract Sustainability, as well as high-quality outcomes, pose significant challenges within the context of current manufacturing cycles, in alignment with European strategies aimed at decarbonization. This framework encourages a systematic evaluation of manufacturing processes in terms of their performance and carbon footprint. One sector where this is particularly relevant is the production of batteries for electric mobility, thanks to its exponential growth. Out of all the processes involved, laser welding stands out as being a critical step since it offers potential energy savings through optimization. With the dual goals of achieving mechanical strength and environmental sustainability, this study investigates alternative solutions for laser welding of aluminum sheets. Different laser welding configurations are tested to evaluate the effect of process setups on weld quality and carbon emissions across different productivity scenarios. The key findings can be summarized as follows: (1) the selection of welding setup significantly influences both quality and sustainability requirements; (2) the optimal conditions for meeting strength requirements may diverge from those aimed at minimizing environmental impact; (3) the choice of the final solution is influenced by the specific industrial scenario. The study specifically demonstrated that aluminum alloys can be welded with higher quality (porosity below 1% and equivalent ultimate strength up to 204 MPa) when filler wire is introduced alongside an active wobbling scanning strategy. Conversely, filler wire can be omitted in scenarios prioritizing high-productivity and low-carbon emissions, such as when employing a linear scanning strategy, resulting in a reduction of equivalent carbon emissions by up to 140%.

The Effect of High-Temperature Deformation on the Mechanical Properties and Corrosion Resistance of the 2024 Aluminum Alloy Joint after Friction Stir Welding.

The 2024 aluminum alloy is one of the high-quality lightweight materials. Friction stir welding (FSW) has shown advantages in reducing welding defects and improving welding quality in 2024 aluminum alloys. Currently, the research regarding FSW joint corrosion performance is mainly about the joint without plastic deformation. However, FSW joints often need to be formed into complex shapes by plastic deformation. The influence of plastic deformation on the corrosion performance of FSW joints is the focus of scientific research. To address this problem, the effect of high-temperature deformation on the mechanical properties and corrosion behavior of 2024 aluminum alloy joints was researched. The exfoliation corrosion test, scanning electron microscopy, energy-dispersive spectroscopy, and transmission electron microscopy were employed to analyze the corrosion mechanism and microstructure. The results show that high-temperature deformation of the weld nugget zone greatly affects the mechanical properties and corrosion behavior of the FSW joint. Compared with the 0% deformation specimen, the hardness and tensile strength of the 20% deformation FSW joint increased by 32% and 21%, respectively. The FSW joint with 20% deformation shows the best mechanical properties and corrosion resistance. The number of precipitated S' phases of the FSW joint increases when the deformation increases to 20%, and the shape of the S' phase is a regular round particle shape. The dislocation density of the FSW joint increases continuously during deformation, which provides a favorable nucleation location for the S' phase.

Hysteretic performance of novel grouted steel beam-column corner-connections for modular integrated construction

In this study, three types of socket steel beam-column corner-connections with grouting for modular integrated construction are proposed and experimentally investigated. Six grouted and two ungrouted specimens were fabricated and divided into two groups: one group underwent monotonic loading and the other was subjected to quasi-static cyclic loading. A comprehensive comparison was conducted across all these specimens. The test results show that grouted bolted socket corner-connection with diagonal stiffeners can enhance its lateral stiffness, bearing capacity, structural integrity, and energy dissipation capacity. Conversely, the absence of diagonal stiffeners and bolts connecting the flanges of the floor and the ceiling channel beams led to a significant decrease to the bearing capacity, lateral stiffness, and energy dissipation capacity of the grouted bolted socket corner-connection. This underscores the critical role of diagonal stiffeners and bolts in these connections. Notably, weld cracking or tearing was a common failure, especially at the welds connecting the beam to the column and those linking the diagonal stiffeners. This failure highlights the importance of ensuring weld quality in practical engineering applications. According to Eurocode 3, all the specimens tested could be classified as semi-rigid connection, however, only the grouted bolted socket corner-connection with diagonal stiffeners met the criteria for full-strength connections.

Interpretable multi-task neural network modeling and particle swarm optimization of process parameters in laser welding

The process parameters directly affect the quality and performance of laser welding (LW) by shaping the molten pool appearance. However, with the increase in the dimensions of process parameters and the number of samples, the relationship between process parameters and molten pool often exhibits high levels of uncertainty and nonlinearity. Machine learning (ML) techniques have been used for complex LW systems modeling, but they may be limited and unable to fully capture the complex relationships when faced with complex systems. Additionally, traditional ML techniques may lack interpretability within the model, making it difficult to understand the input features and their impact on the output. To address these issues, a method that combines an interpretable multi-task neural network (MTNN) and particle swarm optimization (PSO) is proposed. Firstly, an LW experimental platform is constructed, and the orthogonal design is conducted for data collection. In the data preparation stage, the curriculum learning strategy is employed to reorganize the raw experimental data. Subsequently, the MTNN is constructed to automatically learn feature representations among input process parameters, capture nonlinear relationships between data, and share weights across multiple related tasks to improve efficiency and performance. Furthermore, the PSO is designed to determine the constraints and objective functions. In addition, the shapley additive explanation (SHAP) method is utilized to calculate the importance of process parameters and enhance the interpretability. Finally, the optimization results are validated, and the results demonstrate that the optimized results of the proposed method have achieved satisfactory performance compared to the traditional ML methods.

Deep reinforcement learning for optimal control of induction welding process

Optimizing induction welding (IW) process parameters for the application of joining thermoplastic composites is challenging as it requires achieving complex spatiotemporal thermal characteristics along the weld-line to obtain desired weld quality. We formulate an optimal control problem which captures these requirements and seeks to optimize the IW coil speed using a fast-acting dynamic IW process model. We develop a novel Deep Reinforcement Learning (DRL) framework to solve this computationally challenging control problem and demonstrate via simulation study that the learned DRL feedback control policy results in better spatiotemporal thermal characteristics as compared to the current state-of-the-art.

Machine learning-based weld porosity detection using frequency analysis of arc sound in the pulsed gas tungsten arc welding process

Automatic welding equipment has replaced human welders in the nuclear industry for safety issues and uniform and high welding quality. However, automatic welding equipment cannot predict porosity defects. So, the weldment must be inspected by non-destructive testing. This inspection was a costly and time-consuming process, and it applies to each weldment even if it welded same material. To improve the welding efficiency, a weld porosity detection system of the same weld material with different material thicknesses was needed. This paper proposed a machine-learned porosity detection system for 3.0 mm plates with welding arc sound data from the pulsed gas tungsten arc welding (P-GTAW) process of 1.6 mm plates. Ensemble-Empirical Mode Decomposition (EEMD) was used to divide the arc sound signal according to the pulse period of P-GTAW. Fast Fourier transform (FFT) was used to convert the arc sound into frequencies for features extraction according to porosity. The validity of these weld frequency features was confirmed through k-fold cross-validation across various machine learning techniques, with evaluation of F-1 scores against experimental weld sounds.

"The impact of shielding gas composition and welding speed on weld quality in MAG welding: An experimental study "

"The impact of shielding gas composition and welding speed on weld quality in MAG welding: An experimental study "

Application of Convolutional Neural Networks for Classifying Penetration Conditions in GMAW Processes Using STFT of Welding Data

For manufacturing components with thick plates, such as in the heavy equipment and shipbuilding industries, the gas metal arc welding (GMAW) process is applied. Among the components that apply the thick plate GMAW process, there are groove butt joints, which are fabricated through multi-pass welding. Various welding qualities are managed in multi-pass welding, and the root-pass weld is controlled to ensure complete joint penetration (CJP). Currently, the state of complete joint penetration during root-pass welding is managed visually, making it difficult to confirm the penetration condition in real time. Therefore, there is a need to predict the penetration condition in real time. In this study, we propose a convolutional neural network (CNN)-based prediction model that can classify penetration conditions using welding current and voltage data from the root pass of V-groove butt joints. The root gap of the joints was varied between 1.0 and 2.0 mm, and the wire feed rate was adjusted. During welding, the current and voltage were measured. The welding current and voltage are transformed into a short-time Fourier transform (STFT) representation depicting the arc and wire extension lengths. The transformed dynamic resistance STFT information serves as the input variable for the CNN model. Preprocessing steps, including thresholding, are applied to optimize the input variables. The CNN architecture comprises three convolutional layers and two pooling layers. The model classifies penetration conditions as partial joint penetration (PJP), CJP, and burn-through, achieving a high accuracy of 97.8%. The proposed method facilitates the non-destructive evaluation of the root-pass welding quality without expensive monitoring equipment, such as vision cameras. It is expected to be immediately applied to the thick plate welding process using readily available welding data.

Multi-Objective Optimization of Novel Aluminum Welding Fillers Reinforced with Niobium Diboride Nanoparticles

New and innovative technologies have expanded the quality and applications of aluminum welding in the maritime, aerospace, and automotive industries. One such technology is the addition of nanoparticles to aluminum matrices, resulting in improved strength, operating temperature, and stiffness. Furthermore, researchers continue to assess pertinent factors that improve the microstructure and mechanical characteristics of aluminum welding by enabling the optimization of the manufacturing process. Hence, this research explores alternatives, namely cost-effective aluminum welding fillers reinforced with niobium diboride nanoparticles. The goal has been to improve weld quality by employing multi-objective optimization, attained through a central composite design with a response surface model. The model considered three factors: the amount (weight percent) of nanoparticles, melt stirring speed, and melt stirring time. Filler hardness and porosity percentage served as response variables. The optimal parameters for manufacturing this novel filler for the processing conditions studied are 2% nanoparticles present in a melt stirred at 750 rpm for 35.2 s. The resulting filler possessed a 687.4 MPA Brinell hardness and low porosity, i.e., 3.9%. Overall, the results prove that the proposed experimental design successfully identified the optimal processing factors for manufacturing novel nanoparticle-reinforced fillers with improved mechanical properties for potential innovative applications across diverse industries.

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.

Vibration-Assisted Welding of 42CrMo4 Steel: Optimizing Parameters for Improved Properties and Weldability.

This study advances the vibration-assisted welding (VAW) technique for joining medium-carbon, low-alloy steels, which are typically challenging to weld. Traditional welding methods suggest low linear energy and mandatory pre- and post-heating due to these steels' poor weldability. However, VAW employs a vibrating table to maintain part vibration throughout the automatic MIG/MAG welding process. This study tested the VAW technique on 42CrMo4 steel samples, achieving satisfactory weld quality without the need for pre- and post-heating treatments. This research revealed that while vibration frequencies between 550 Hz and 9.5 kHz minimally affect the appearance of the weld joint, the oscillation acceleration has a significant impact. The acceleration along the weld axis (ax), combined with the welding speed and vibration frequency, affects the weld surface's appearance, particularly its scaly texture and size. Lateral acceleration (ay) alters the seam width, whereas vertical acceleration (az) affects penetration depth at the root. Notably, if the effective acceleration (aef) surpasses 40 m/s2, there is a risk of molten metal expulsion from the weld pool or piercing at the joint's base. The quality of the joints was assessed through macroscopic and microscopic structural analyses, micro-hardness tests in the weld zone, and bending trials. The mechanical properties of the VAW samples were found to be acceptable, with hardness slightly exceeding that of the samples subjected to pre- and post-heating. Moreover, the VAW process significantly reduced energy consumption and operational time. The employed vibration system, with a power rating of 100 W, operates for just a few minutes, resulting in substantially lower energy usage compared to the traditional pre- and post-heating method, which typically requires a 5 kW electric furnace.

Real-time tracking method for motion spatter in high-power laser welding of stainless steel plate based on a lightweight deep learning model

The high-power laser welding of stainless steel is prone to spatter defects. The dynamic behavior of motion spatter is closely related to the welding state, but the relevant features are difficult to obtain. This paper extensively describes a motion spatter tracking method based on a lightweight deep learning model and a multi-target tracking algorithm. Firstly, an in-situ observation platform with a high-speed camera-based high spatiotemporal resolution for motion spatters is established. This platform can record the generation and motion process of different spatters during welding. To reduce the interferences and highlight the spatter characteristics, a linear point operation-based image low-brightness metal vapor plume removal method is also proposed. Then, a deep learning-based lightweight spatter detection model is established, and an automatic spatter label generation method is proposed to quickly obtain a sufficient amount of highly discriminative training data to train the established model comprehensively. Compared with the basic model, the validation results show that the lightweight spatter detection model runs 5.11 times faster, with a spatter detection accuracy of 96.71%. Finally, a motion spatter tracking method based on the DeepSORT algorithm is proposed to distinguish different spatters and associate the same spatters. The spatter size, velocity, quantity, and other features are also able to be accurately extracted via this method, achieving spatter defect monitoring. In summary, this study describes a novel and robust method for motion spatter real-time tracking during laser welding and this method can provide reliable data support for accurate and fast process condition monitoring as well as weld quality assessment.

Influences of preheating parameters on the quality of weld by thermite rail welding

The major goal of this study is to enhance the mechanical and metallurgical characteristics of rail steel grade R260 joined by thermite welding under various preheating conditions, including preheating time and gas pressure. Mainly two conditions, referred to as the Normal Condition and Improved Condition, are carried out for experiments. Prior to welding, the Normal Condition was preheated using liquefied petroleum gas (LPG) and oxygen gas pressures of 1 bar and 4.5 bar for 3 min, and the Improved Condition was preheated using liquefied petroleum gas and oxygen gas pressures of 1.2 bar and 4.5 bar for 6 min and 30 s. To investigate the mechanical and physical properties, micro-Vickers hardness tests, tensile tests and slow bending tests were also carried out. Welded metal in Normal Condition has many defects, including gas holes and shrinkage cavities. When comparing the Normal Condition to the Improved Condition, the Improved Condition demonstrates significantly more bending load and deflection. Specifically, the thermite welded rail sample of Improved Condition demonstrated a remarkable ability to endure bending loads of 108 tonnes and a deflection of 16 mm, and this sample remained unbroken until it exceeded 50% of the standardized deflection limit (10 mm). In addition, the average hardness values for the Improved Condition of the weld metal zone and the heat-affected zone were 331 HV and 289 HV, respectively. The Normal Condition produced an unsatisfactory fracture surface after slow bending test. This was caused by weld defects at the thermite weld due to inappropriate preheating.

Arrangement of Nondestructive Tests for Technical Inspection Process of Electrofusion Weld in Gas Polyethylene Pipe

Due to the reduction of operational costs, Non-corrosiveness, and ease of implementation, polyethylene pipes are a suitable replacement for steel pipes in gas distribution networks. Considering the irreversible events of gas leakage, effective and feasible non-destructive testing of electrofusion welding is an essential factor to avoid the hazards of gas leakage. To control the quality of welds, the nature of the electrofusion structure leads us to manufacture vacuum boxes, internal pressure devices, and Tapping Saddle Connector for testing polyethylene joint leakages. This article focuses on the sequence of these non-destructive tests to monitor the health of polyethylene electrofusion joint structures under gas network project implementation conditions. To examine the testing chain and the proposed sequence of non-destructive tests, the data gathering process was performed, and with respect to the sensitivity of the electrofusion weld to the operator's skill, welding process, and the condition of the pipe and fittings, in each case the influential factors to find the key parameters were evaluated. The results of laboratory tests in cases of short tests and long-life tests using artificial defects such as slow crack growth with defective samples of on-site joints and defects reported in projects were compared. To validate leak detection, leak location, and leak measurement, weld simulation with finite element and CFD software analysis conforming to real conditions was considered. The test sequence, acceptance criteria, and calibration method of the tools according to EN 1555 standard were argued for performing the final inspection steps. In this perspective, for each leak testing technique, the average type, pressure level, test duration, and finally the acceptance criteria were proposed. The main outcome is the technical specifications for electrofusion weld assessment and baseline risk- based inspection scheme in gas supply projects. In this regard, a database software based on the proposed ITP (Inspection and Testing Plan) has been developed, which is fundamental for monitoring the whole project from the first stage of polyethylene granule control, pipe and fitting manufacture, and gas injection to the final stage in network and commissioning. Operations to determine failure causes and suitability for performance assessment. Another proposed idea is standardizing the recent technical specifications internationally. This idea will be implemented when management realizes that non-destructive testing can lead to cost savings, protection of humans and the environment, and when codes and standards encompass such requirements.

Exploration and application of welding technology for misalignment of pressure pipe orifices in a pumped storage power station

Abstract Pumped storage power stations are currently the most economical and clean large-scale energy storage method. Pumped storage power stations have flexible start and stop, rapid response, and have six major functions, including peak shaving, valley filling, frequency regulation, phase modulation, emergency backup, and black start. With the transformation of the energy system towards clean, low-carbon, safe and efficient, the operational characteristics of the power system will undergo significant changes, requiring sufficient flexible regulation of power sources and energy storage facilities. It is more urgent to accelerate the development and construction of pumped storage energy. Due to the characteristics of pumped storage installation, high-strength pressure steel pipes are required on the upstream side of the power station units as the water diversion pipeline. This article takes a pumped storage power station as an example to analyze and handle the problem of pipe misalignment encountered during the welding process of pressure steel pipes in the power station. Satisfactory results have been achieved, ensuring the welding quality and providing reference for other engineering projects.

Detection of electrode misalignment and its effect on joint quality in resistance spot welding: a low-cost computer vision-based approach

Electrode misalignment in resistance spot welding can be caused by poor fitting or deformation of electrode with continuous usage. This leads to asymmetric weld nugget formation, porosity and expulsion. This paper presents a novel low-cost real-time inspection system for angular misalignment using an image processing approach. The proposed solution can effectively segment the electrode tips even from the image captured at noisy industrial background such as automotive assembly line, by using a regional convolutional neural network based object identification method. The trained model has a mean average precision and recall of 99.01% and 96.6%, respectively. A series of image processing tools and mathematical operations were used to identify the edge line contours of electrode tips accurately from the detection mask, and determine the angular misalignment with a maximum deviation of less than 0.06°. Experimental results showed that the weld nuggets exhibited porosity, shrinkage voids, and cracks when performed under angular misalignment conditions.