Search Results (238)

The Finite Element Method is an indispensable tool for analyzing the transient thermal phenomena in welding processes. This study aims to simulate the temperature field during Gas Metal Arc Welding of an AISI 304L V-groove butt joint, employing a volumetric heat source model. The numerical simulations were conducted using ABAQUS SIMULIA^® (version 6.11-3) on a plate measuring 200 mm × 50 mm × 9.5 mm. For validation, the numerical results were compared against experimental data obtained at the Welding Engineering Research Laboratory of Federal University of Rio Grande. A parametric study was performed by varying the geometric parameter b (controlling the volumetric heat distribution depth) to enhance the model’s accuracy and achieve the closest approximation to experimental observations. The calibrated volumetric source demonstrated high accuracy, yielding low percentage differences between predicted and experimental peak temperatures: 1.02%, 2.50%, and 4.44% at the 4 mm, 8 mm, and 12 mm thermocouple positions, respectively. Full article

This research presents a novel, low-cost optical acquisition system based on infrared imaging for real-time weld bead geometry monitoring in Gas Metal Arc Welding (GMAW). The system uniquely employs a commercial CCD camera (1000–1150 nm) with tailored filters and lenses to isolate molten pool thermal radiation while mitigating arc interference. A single camera and a mirror-based setup simultaneously capture weld bead width and reinforcement. Acquired images are processed in real time (10 ms intervals) using MATLAB R2016b algorithms for edge segmentation and geometric parameter extraction. Dimensional accuracy under different welding parameters was ensured through camera calibration modeling. Validation across 35 experimental trials (over 6000 datapoints) using laser profilometry and manual measurements showed errors below 1%. The resulting dataset successfully trained a Support Vector Machine, highlighting the system’s potential for smart manufacturing and predictive modeling. This study demonstrates the viability of high-precision, low-cost weld monitoring for enhanced real-time control and automation in welding applications. Full article

In this work, the GMAW-based wire arc additive manufacturing technique was used to additively manufacture a wall of SS316L. Consequently, the PROMETHEE II MCDM was used to optimize the process variables in the WAAM of SS316L. The input parameters used were travel speed (TS), feed speed (FS), and voltage (V), and the output parameters were set as depth of penetration (DOP), bead width (BW), and bead height (BH). Bead-on-plate trials were conducted using the response surface analysis-based BBD technique. The PROMETHEE II method and the MEREC weighting approach successfully found the optimal process variables for the WAAM of SS316L. PROMETHEE II analysis revealed the optimal parameter settings to be a WFS of 11 m/min, a TS of 450 mm/min, and a voltage of 27 V. The results highlight the effectiveness of PROMETHEE II in ranking and selecting the most suitable process parameters for SS316L deposition, offering valuable insights for improving the quality and efficiency of WAAM-based stainless steel manufacturing. Full article

This study aims to optimize the variables of the gas metal arc welding (GMAW)-based wire arc additive manufacturing (WAAM) process namely, wire feed speed (WFS), voltage (V), and travel speed (TS), to achieve the desired bead geometries, specifically bead width (BW), and bead height (BH) on a mild steel substrate. The selection of WAAM parameters significantly influences the characteristics of multi-layer structures in terms of bead geometry. By optimizing these variables, the research seeks to enhance bead geometry properties, thereby improving the overall performance of the WAAM process. Single-layer depositions were performed using TM-B6 metallic wire using Box–Behnken design methodology. Multivariable regression equations were formulated to establish relationships between design variables and their corresponding responses, with their validity assessed through ANOVA. For both BW and BH responses, the R² and adjusted R² values were found to be close to unity, indicating excellent model fitness. The results demonstrate the high accuracy of the models, enabling effective analysis of the influence of process parameters on weld bead geometry and accurate prediction of bead dimensions across the design space. The main effects plot illustrates how WFS, V, and TS affect bead width and bead height. Atomic Search Optimization (ASO) was employed to determine optimal parameter combinations. An objective function with equal weightage (0.5) for BH and BW was formulated, resulting in optimized values of BW and BH (4.01 mm and 5.86 mm, respectively) at WFS: 13 m/min, TS: 10 mm/s, and V: 20 V. The obtained findings confirm the high accuracy of the models and their effectiveness in analyzing and optimizing WAAM process parameters. Full article

The aim of the current study is to optimize the bead geometries of 80B2, namely, the bead height (BH) and bead width (BW), utilizing a mild steel substrate and a wire-arc additive manufacturing (WAAM) technique based on gas metal arc welding (GMAW). Single-layer depositions with different wire feed speed (WFS), voltage (V), and travel speed (TS) were accomplished by applying the Box–Behnken design methodology. Multivariable nonlinear regression models were developed and validated through ANOVA, revealing WFS as the most significant parameter influencing both BW and BH. The minimal influence of the error factor on each response proved the accuracy of the ANOVA findings. The favorable assessment of residual plots confirmed the appropriateness and reliability of the developed regression equations and ANOVA results. A metaheuristic Passing Vehicle Search (PVS) algorithm was applied for single-objective and multi-objective optimization, yielding a minimum BW of 5.874 mm and a maximum BH of 14.153 mm. Main effect and residual plots confirmed the accuracy and reliability of the predictive models. The parametric settings of WFS: 18 mm/min, TS: 7 mm/s, V: 19 V were obtained for simultaneous optimization of BW with 7.78 mm and BH with 10.87 mm. Pareto points were also generated, which provide non-dominated unique solutions. The study emphasizes the critical role of precise process parameter control in improving WAAM build quality and offers a robust framework for optimizing bead morphology, ultimately enhancing the efficiency and applicability of WAAM for structural component fabrication. These optimized parameters will be used in the future to manufacture a thin-walled, multi-layered structure. Full article

In the present study, the gas metal arc welding (GMAW) process was used to investigate the effect of bead morphologies of SS316L. Single-layer deposition forms the base for manufacturing metal additive structures using a GMAW-based wire-arc additive manufacturing (WAAM) process. Thus, the current work focused on the analysis of bead morphologies of SS316L through single-layer depositions. Experimental trials were conducted using Taguchi’s L9 approach with travel speed (TS), gas mixture ratio (GMR), and voltage as input WAAM variables, and bead width (BW) and bead height (BH) as output responses. The effect of WAAM variables on output measures was studied using main effect plots. The relevance and reliability of the derived regressions were verified using ANOVA analysis. For the BH response, TS was found to be the most significant factor, followed by voltage, while GMR did not have any contributing impact on the output response variable. For the BW response, voltage was found to be the largest contributing factor, followed by GMR and TS. The R² values were measured at 0.9276 and 0.9962 for BH and BW, respectively. The outcomes proved that the model fits the data well and can successfully predict new observations, as the R² values for all responses were near one. Lastly, the best input set of WAAM variables was determined for individual output variables. Full article

During the Gas Metal Arc Welding (GMAW) process, intense arc light and dense fumes cause local overexposure in RGB images and data loss in point clouds, which severely compromises the extraction accuracy of circular closed-curve weld seams. To address this challenge, this paper proposes a multimodal fusion method for weld seam extraction under arc light and fume interference. The method begins by constructing a weld seam edge feature extraction (WSEF) module based on a synergistic fusion network, which achieves precise localization of the weld contour by coupling image arc light-removal and semantic segmentation tasks. Subsequently, an image-to-point cloud mapping-guided Local Point Cloud Feature extraction (LPCF) module was designed, incorporating the Shuffle Attention mechanism to enhance robustness against noise and occlusion. Building upon this, a cross-modal attention-driven multimodal feature fusion (MFF) module integrates 2D edge features with 3D structural information to generate a spatially consistent and detail-rich fused point cloud. Finally, a hierarchical trajectory reconstruction and smoothing method is employed to achieve high-precision reconstruction of the closed weld seam path. The experimental results demonstrate that under severe arc light and fume interference, the proposed method achieves a Root Mean Square Error below 0.6 mm, a maximum error not exceeding 1.2 mm, and a processing time under 5 s. Its performance significantly surpasses that of existing methods, showcasing excellent accuracy and robustness. Full article

Welding-based manufacturing and joining processes are extensively used in various areas of industrial production. While welding has been used as a primary method of joining in many applications, its capability to fabricate metal components such as the Wire Arc Additive Manufacturing (WAAM) method should not be undermined. WAAM is a promising method for producing large metal parts, but it is still prone to defects such as porosity that can reduce structural reliability. To ensure these defects are found and measured in a consistent way, inspection methods must be tied directly to code-based acceptance limits. In this work, a three-pass WAAM joint specimen was made in a welded-joint configuration using robotic GMAW-based deposition. This setup provided a stable surface for Phased Array Ultrasonic Testing (PAUT) while still preserving WAAM process conditions. The specimen, which was intentionally seeded with porosity, was divided into five zones and inspected using the 6 dB drop method for defect length and amplitude-based classification, with AWS D1.5 serving as the reference code. The results showed that porosity was not uniform across the bead. Zones 1 and 3 contained the longest clusters (15 mm and 16.5 mm in length) and exceeded AWS length thresholds, while amplitude-based classification suggested they were less critical than other regions. This difference shows the risk of relying on only one criterion. By embedding these results in a DMAIC (Define–Measure–Analyze–Improve–Control) workflow, the inspection outcomes were linked to likely causes such as unstable shielding and cooling effects. Overall, the study demonstrates a code-referenced, dual-criteria approach that can strengthen quality control for WAAM. Full article

The Gas Metal Arc Welding (GMAW) process is widely utilized in industrial production, requiring careful selection of appropriate procedures to ensure the highest quality. A key area of study closely related to GMAW quality is the control of process stability. This research presents a methodology for analyzing welding data to identify instability, thus enabling the development of a stability indicator. Our approach focuses on sensory fusion by integrating multiple sources of information, including sound signals, images, and current signals captured during the welding process. This work explores various configurations of variables to analyze the three primary transfer modes. Additionally, a comprehensive statistical analysis of the results obtained is conducted. Image processing techniques, sound analysis, and artificial intelligence methodologies are employed to enhance the analysis process. Full article

Structural applications that use High-Strength Low-Alloy (HSLA) steels require detailed microstructural analysis to manufacture welded components that combine strength and weldability. The balance of these properties depends on both the chemical composition and the welding parameters. Moreover, in multi-pass welds, thermal cycling results in a complex Heat-Affected Zone (HAZ), characterized by sub-regions with a multitude of microstructural constituents, including brittle phases. This study investigates the influence of Vanadium addition on the microstructure and performance of the HAZ. Multi-pass welded joints were manufactured on 15 mm thick S355 steels with different Vanadium contents using a robotic GMAW process. A steel variant containing both Vanadium and Niobium was also considered, and the results were compared to those of standard S355 steel. Moving through the different sub-regions of the welded joints, the results show a heterogeneous microstructure characterized by ferrite, bainite and martensite/austenite (M/A) islands. The presence of Vanadium reduces carbon solubility during the phase transformations involved in the welding process. This results in the formation of very fine (average size 11 ± 4 nm) and dispersed precipitates, as well as a lower percentage of the brittle M/A phase, in the variant with a high Vanadium content (0.1 wt.%), compared to the standard S355 steel. Despite the presence of the brittle phase, the micro-alloyed variants exhibit strengthening without loss of ductility. The combined presence of both hard and soft phases in the HAZ provides stress-damping behavior, which, together with the very fine precipitates, promises improved resistance to crack propagation under different loading conditions. Full article

Friction stir welding (FSW) has emerged as a solid-state joining technique offering notable advantages over traditional welding methods. Gas metal arc welding (GMAW), a fusion-based process, remains widely used due to its high efficiency, productivity, weld quality, and ease of automation. To combine the benefits of both techniques, a hybrid welding approach integrating GMAW and FSW has been developed. This study investigates the impact of this hybrid technique on the joint quality and properties of AA5083-H111 and AA6082-T6 aluminum alloys. Butt joints were produced on 6 mm thick plates, with variations in friction process parameters. Characterization included macro- and microstructural analyses, mechanical testing (hardness and tensile strength), and corrosion resistance evaluation through stress corrosion cracking tests. Results showed that FSW significantly refined and homogenized the microstructure in both alloys. AA5083-H111 welds achieved a joint efficiency of 99%, while AA6082-T6 reached 66.7%, differences attributed to their distinct strengthening mechanisms and the thermal–mechanical effects of FSW. To assess hydrogen-related behavior, slow strain rate tensile (SSRT) tests were conducted in both inert and hydrogen-rich environments. Hydrogen content was measured in arc, friction, and overlap zones, revealing variations depending on the alloy and microstructure. Despite these differences, both alloys exhibited negligible hydrogen embrittlement. In conclusion, the GMAW–FSW hybrid process successfully produced sound joints with good mechanical and corrosion resistance performance in both aluminum alloys. The findings demonstrate the potential of hybrid welding as a viable method for enhancing weld quality and performance in applications involving dissimilar aluminum alloys. Full article

The study investigates the influence of post-weld heat treatment (PWHT) on the microstructure and hardness of hardfacing layers applied to hot forging tools. The research focuses on three tool steels (55NiCrMoV7, X37CrMoV5-1, and a modified X38CrMoV5-3) and uses robotized gas metal arc welding (GMAW) with DO015 filler material. It examines the structural and mechanical differences in the hardfaced layers before and after heat treatment involving quenching and tempering. The findings reveal that PWHT significantly improves microstructural homogeneity and hardness distribution, especially in the heat-affected zone (HAZ), mitigating the risk of crack initiation and tool failure. The study shows that untempered as-welded layers exhibit microstructural inhomogeneity and extreme hardness gradients, which negatively impact tool durability. PWHT leads to tempered martensite formation, grain refinement, and a more stable hardness profile across the joint. These improvements are critical for extending the service life of forging tools. The results underscore the importance of customizing PWHT parameters according to the specific material and application to optimize tool performance. Full article

Metal additive manufacturing techniques have seen technological advancements in recent years, fueled by their ability to provide industrial use parts with excellent mechanical properties. Wire Arc Additive Manufacturing is a technology that is being widely used in critical industries, and much research is conducted in this field due to the multiple factors involved in the overall process. Within WAAM, gas metal arc welding stands out for its low cost, high production volume, high quality and capability for automation. In this study, a CNC router was retrofitted with a gas metal arc welding setup to facilitate precise metal printing. The flexibility in this process allows for rapid repairs on site without the need to replace the entire part. The literature predominantly focuses on the macro-mechanical properties of GMAW parts, and very few studies try to study the interaction and influence of different process parameters on the mechanical properties. Thus, this study focused on the GMAW WAAM of stainless-steel parts by studying the influence of the wire feed rate, arc voltage and strain rate on the UTS, yield strength, toughness and percentage elongation. ANOVA and interaction plots were analyzed to study the interaction between the input parameters on each output parameter. Results showed that printing stainless steel through the gas metal arc welding process with an arc voltage of 18.7 V and a wire feed rate of 6 m/min resulted in poor mechanical properties. The input parameter that influenced the mechanical properties the highest was the wire feed rate, followed by the arc voltage and strain rate. Printing with an arc voltage of 18.7 V and a wire feed rate of 5 m/min, tested at a crosshead speed of 1 mm/min, gave the best mechanical properties. Full article

Establishing precise welding parameters is essential to achieving the desired bead geometry and ensuring consistent quality in manufacturing processes. However, determining the optimal configuration of parameters remains a challenge, particularly when relying on limited experimental data. This study proposes the use of artificial neural networks (ANNs), with their architecture optimized via differential evolution (DE), to predict key MAG welding parameters based on target bead geometry. To address data limitations, cross-validation and data augmentation techniques were employed to enhance model generalization. In addition to the ANN model, machine learning algorithms commonly recommended for small datasets, such as K-nearest neighbors (KNNs) and support vector machines (SVMs), were implemented for comparative evaluation. The results demonstrate that all models achieved good predictive performance, with SVM showing the highest accuracy among the techniques tested, reinforcing the value of integrating traditional ML models for benchmarking purposes in low-data scenarios. Full article

This study systematically evaluates the influence of copper (Cu) addition in gas-shielded solid wires on the microstructure and cryogenic toughness of X80 pipeline steel welds. Welds were fabricated using solid wires with varying Cu contents (0.13–0.34 wt.%) under identical gas metal arc welding (GMAW) parameters. The mechanical capacities were assessed via tensile testing, Charpy V-notch impact tests at −20 °C and Vickers hardness measurements. Microstructural evolution was characterized through optical microscopy (OM), scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). Key findings reveal that increasing the Cu content from 0.13 wt.% to 0.34 wt.% reduces the volume percentage of acicular ferrite (AF) in the weld metal by approximately 20%, accompanied by a significant decline in cryogenic toughness, with the average impact energy decreasing from 221.08 J to 151.59 J. Mechanistic analysis demonstrates that the trace increase in the Cu element. The phase transition temperature and inclusions is not significant but can refine the prior austenite grain size of the weld, so that the total surface area of the grain boundary increases, and the surface area of the inclusions within the grain is relatively small, resulting in the nucleation of acicular ferrite within the grain being weak. This microstructural transition lowers the critical crack size and diminishes the density for high-angle grain boundaries (HAGBs > 45°), which weakens crack deflection capability. Consequently, the crack propagation angle decreases from 54.73° to 45°, substantially reducing the energy required for stable crack growth and deteriorating low-temperature toughness. Full article