Abstract In this study, dissimilar butt welding of 0.8 mm thick DP600 and 1 mm thick DP1180 advanced high-strength steels was performed using fiber laser beam welding with beam oscillation. The effects of oscillation type (linear and circular), oscillation amplitude (0.5, 1, 1.5 mm), and welding speed (50–80 mm s −1 ) on weld bead geometry, microhardness, and tensile load were investigated. A total of 12 experimental sets were conducted by keeping the laser power and frequency constant at 1.2 kW and 100 Hz, respectively. Metallographic evaluations, Vickers microhardness tests, and tensile tests were carried out in accordance with standard procedures. The results revealed that welding speed had a significant influence on weld penetration and width, with optimal parameters determined as 60 mm s −1 speed and 1 mm amplitude in both oscillation types. Circular oscillation generally led to higher microhardness values, whereas linear oscillation produced wider weld seams. While amplitude increase decreased penetration depth, it improved weld width. The tensile load of all joints was largely influenced by the DP600 base metal, where fractures were consistently observed. However, the joint at 1.5 mm amplitude in circular mode fractured in the weld zone, indicating insufficient penetration. The findings suggest that proper selection of oscillation parameters can enhance weld quality and mechanical performance when joining dissimilar high-strength steels for lightweight automotive applications.

Inconel 625 is widely employed in high-temperature and corrosive environments, where the integrity of welded joints critically influences component performance. This study systematically investigates how laser beam welding (LBW) heat input governs cooling behaviour, microstructure evolution, elemental segregation, and the mechanical performance of Inconel 625 weld joints aiming to become sustainable joints. A single-spot monochromatic non-contact type infrared pyrometer is used to monitor the thermal cycles of the molten weld pool and the cooling rate and melt pool lifetime were determined based on the thermal cycle data. The impact of cooling rate and melt pool lifetime on weld geometry, microstructure, micro-segregation, and mechanical properties were thoroughly investigated. The findings revealed that the fibre laser welding produced sound, defect-free joints across all experimental heat-input conditions and the weld quality was fairly dictated by cooling rate during solidification. Reducing heat input (by using faster laser scan speeds) increased the cooling rate (1.45 × 103 to 3.65 × 103 °C/s), resulting in a shortened melt-pool lifetime and altered weld bead geometry from hourglass to truncated-cone profiles. Eventually, the fusion-zone microstructure transitioned from coarse cellular/columnar dendrites at high heat inputs to refined dendrites at low heat inputs. The EDS analysis revealed pronounced Nb and Mo segregation in slowly cooled welds and Laves phase formation due to insufficient time for solute redistribution and γ-Ni matrixes were consistent noted with XRD-observed peaks. The presence of the brittle Laves phase adversely affects the microhardness and tensile strength of the weld joints. Mechanical testing confirmed that decreasing heat input (in faster laser scan speeds) enhanced micro-hardness and tensile strength due to grain refinement and solute entrapment in the γ matrix. The highest joint strength (989.3 ± 10.4 MPa) and elongation (40.3 ± 1.8%) approached those of the work material, and these findings establish processing parameter–structure–property relationships for the LBW of Inconel 625. The co-relation in the present manuscript can be used in the future for process monitoring and for controlling the mechanical properties of laser welding and may provide a practical guidance for optimizing weld quality in advanced industrial applications.

In this study, we systematically investigated the mechanisms enabling localized solidification crack-free welding of directionally solidified (DS) 247LC superalloy using a single-mode fiber laser, with a particular focus on the correlation with the columnar-to-equiaxed transition (CET) theory. Welding was performed on a single DS grain under twelve conditions, varying heat input (1, 1.5, 2 J/mm), welding speed (500, 750 mm/s), and energy density (7.1, 14.0 J/mm<sup>2</sup>). The weld bead geometry was found to depend on the energy density, resulting in either heat conduction mode (7.1 J/mm<sup>2</sup>) or keyhole mode (14.0 J/mm<sup>2</sup>). In the heat conduction mode, solidification cracking was completely suppressed across all conditions, while in the keyhole mode, cracking was prevented under some conditions but appeared along the bead centerline under specific high heat input conditions (C-3 and C-4). Crystallographic analysis revealed that reduced epitaxial growth rates and increased high-angle grain boundaries were associated with crack formation in these cases. CET maps were calculated using the Thermo-Calc Additive Manufacturing module to elucidate the relationship between weld morphology and crack susceptibility, reflecting rapid solidification behavior under laser welding conditions. The results showed that heat conduction mode welds remained within the columnar growth region, while keyhole mode welds under higher heat input shifted towards the CET boundary or equiaxed region, correlating strongly with crack formation. These findings demonstrate that the suppression of CET and the promotion of epitaxial growth are critical for achieving crack-free welding in DS 247LC superalloy using single-mode fiber lasers, providing practical guidelines for advanced manufacturing and repair of turbine components under rapid solidification conditions.

In multi-pass welding processes, achieving a uniform and smooth weld surface is crucial for mechanical performance and dimensional accuracy. However, the complex nonlinear relationships between welding parameters and weld bead geometry present significant challenges for traditional optimization methods. This study proposes an intelligent prediction and optimization framework that integrates a backpropagation (BP) neural network with an improved hill-climbing algorithm to enhance weld surface smoothness in automated multi-bead overlay welding. Experimental data collected under varying arc voltages, wire feed rates, and welding speeds were used to train the neural network. The improved hill-climbing algorithm adaptively adjusts weights and biases in the BP model to overcome issues of local minima and slow convergence. Comparative results demonstrate that the proposed method significantly outperforms conventional BP approaches in terms of prediction accuracy and convergence efficiency. Furthermore, optimal welding parameters identified by the model yield smoother weld surfaces, reducing the need for post-processing. This work provides a novel solution for intelligent control and real-time optimization in advanced welding systems.

While modern power sources have improved process stability, real-time monitoring and feedback control remain essential for ensuring consistent weld quality under dynamic conditions. To address this need, a vision-based closed-loop control system was developed for pulsed Metal-Active Gas (MAG) welding. The system dynamically adjusts the welding speed based on real-time visual feedback in the welding process. Otsu thresholding combined with morphological operations was applied to molten pool images for brightness-based feature extraction. These features, representing the dynamic behavior of the molten pool, were incorporated into a feedback loop for real-time control. Without relying on complex model-based prediction or sensor fusion, the proposed method reduces fluctuations in weld bead geometry and lowers the occurrence of defects. The experimental results showed that, under optimized control conditions and after a steady welding state was achieved, the weld bead’s height deviation exhibited an average standard deviation of 0.08 mm, and a process stability rate of 92%. The combination of conventional hardware and straightforward image processing makes the proposed approach practical for industrial implementation.

This study examines the effects of tungsten electrode sharpening angle, torch angle, and arc length on weld bead geometry in gas tungsten arc welding (GTAW). Low-carbon steel plates (4 mm thickness) were welded using ER70S-6 filler wires (2 mm diameter). Experimental analyses and response surface methodology (RSM) revealed that increasing the electrode sharpening angle enhances penetration depth, while reducing the torch angle widens the bead. Longer arc lengths decrease penetration but increase bead width. Validation tests confirmed the accuracy of the RSM models, showing an average deviation of 1.14%. Analysis of variance highlighted the statistical significance of linear, quadratic, and interaction terms in the models. The findings provide critical insights for optimizing GTAW parameters, improving weld quality, and advancing process control, thereby contributing both practical and academic value to welding research.

Since the quality of the weld bead geometry depends on a number of factors that are influenced by the atmospheric conditions, much effort is being made to determine the ideal welding process parameters in order to improve the welding quality. Previous studies have solely examined the impact of process variables, such as welding current, voltage, speed, and gas flow rate, on the quality of the weld; weather conditions have not been taken into account. Atmospheric conditions during a welding process must be carefully considered, as they influence the heating and cooling rates and ultimately affect the characteristics of the welded joint. The current work includes an investigation using the metal active gas welding method on mild steel at ambient temperature and at low temperature around 0 °C. With the goal of minimizing the number of experimental runs, Taguchi's experimental design method was used to identify the process parameter settings. In addition to other crucial criteria like gas flow rate and stick out staying constant, this work took into account three welding process parameters: current ( I ), voltage ( V ), and traversal speed ( S ). To choose the ideal number of experiments, an experiment design based on Taguchi's L16 orthogonal array has been introduced. With constant parameters, compared to low temperature conditions, welding at room temperature resulted in notable improvements in joint characteristics: the average weld bead penetration increased by 15.02%, heat-affected zone depth by 8.23%, and bead width by 5.06%. In contrast, the average reinforcement height decreased by 4.09%. Grain refinement was observed as grain size improved by 10.4% in the weld zone and 10.14% in the heat-affected zone. Meanwhile, hardness values showed a reduction of 4.7% in the weld zone and 4.18% in the heat-affected zone. These findings emphasize that ambient environmental conditions and process parameters critically influence the thermal cycle of the weld, thereby affecting microstructure, hardness, grain morphology, and bead geometry—factors that collectively determine the mechanical integrity and overall performance of the welded joint.

The article presents a new AM (Additive Manufacturing) process development, necessary to repair parts made from Aluminum 6061 material, with T6 treatment. The laser Directed Energy Deposition (DED) and Extreme High-Speed Directed Energy Deposition (EHLA) capabilities are evaluated for repairing Al large components. To optimize the process parameters, single-track depositions were analyzed for both laser-powder DED (feed rate of 2 m/min) and EHLA (feed rate 20 m/min) for AlSi10Mg and Al6061 powders. The cross-sections of single tracks revealed the bonding characteristics and provided laser-powder DED, a suitable parameter selection for the repair. Three damage types were identified on the Al component to define the specification of the repair process and to highlight the capabilities of laser-powder DED and EHLA in repairing intricate surface scratches and dents. Our research is based on variation of the powder mass flow and beam power, studying the influence of these parameters on the weld bead geometry and bonding quality. The evaluation criteria include bonding defects, crack formation, porosity, and dilution zone depth. The bidirectional path planning strategy was applied with a fly-in and fly-out path for the hatching adjustment and acceleration distance. Samples were etched for a qualitative microstructure analysis, and the HV hardness was tested. The novelty of the paper is the new process parameters for laser-powder DED and EHLA deposition strategies to repair large Al components (6061 T6), using AlSi10Mg and Al6061 powder. Our experimental research tested the defect-free deposition and the compatibility of AlSi10Mg on the Al6061 substrate. The readers could replicate the method presented in this article to repair by laser-powder DED/EHLA large Al parts and avoid the replacement of Al components with new ones.

ABSTRACT This study investigates autogenous diode laser welding of two differently strengthened Fe-Ni-Cr superalloys: solid-solution strengthened Incoloy 800 and precipitation-strengthened Incoloy A286. Effects of laser power, welding speed, and defocusing distance on weld bead geometry, microstructure, and mechanical properties were comprehensively analyzed. Butt weld joints produced with optimum and similar heat input conditions achieved joint efficiencies comparable to their base metals. Microstructural analysis of fusion zone revealed columnar dendrites of γ-(Ni,Fe) in both alloys, with A286 showing segregation of Ti, Cr, and Mo intermetallics at interdendritic regions, while Incoloy 800 exhibited refined columnar/equiaxed dendrites with chromium carbides and Fe-Ni-Ti intermetallics. Mechanical testing indicated enhanced yield strength but reduced impact toughness and marginal hardness gain in A286 welds, whereas Incoloy 800 welds maintained similar hardness to the base material. Overall, the study demonstrated utilization of fiber-coupled diode laser welding for producing butt-joints in both superalloys, supporting their potential adoption in high-temperature applications.

The foremost aspect in welding is to improve the mechanical and microstructural properties in the weldment. The properties depend upon the weld bead characteristics. This is in turn depend upon the welding process. Shielded Metal Arc welding (SMAW) is the welding process which is simpler, usable and robust in nature. The bead characteristics such as bead width (BW), reinforcement ( R ), penetration ( P ), weld penetration shape factor (WPSF), weld reinforcement form factor (WRPF) has been selected as output weld characteristics which are influenced by parameters such as welding current ( I ), welding speed ( S ), arc length ( A ), electrode advance angle €, and joint gap ( G ). In this paper a response surface methodology is implemented which predicts the optimum results of 0.51% error from the observed results. To find the optimum results metaheuristic algorithms that is, Jaya (JA) algorithm and Firefly (FA) algorithm was implemented. The results obtained are within the maximum and minimum permissible limits and of 0.22% and 0.31% deviation from the observed values. This shows the accuracy and refinement of results using algorithms. The real world verification is done from the regression model. The sensitivity analysis and parameter effect to show variation in parameters influence the results has been presented.

The electric current magnitude is a fundamental parameter that directly affects penetration characteristics and weld bead geometry. This study analyzes the effect of current variations on physical and mechanical properties in the cladding process using the Shielded Metal Arc Welding (SMAW) method. The materials used are low-carbon steel and HV-600 electrodes with a diameter of 3.2 mm. The SMAW method was applied with current variations of 100 A, 120 A, 140 A, and 160 A. The tests carried out were NDT liquid penetrant, macro structure, micro structure and Vickers hardness. The results indicate a positive correlation between increasing current and hardness values in the weld area, with the highest hardness recorded at 665.803 HVN at 160 A and the lowest at 515.143 HVN at 100 A. Meanwhile, in the Heat Affected Zone (HAZ), a non-linear pattern was observed, with a maximum hardness of 263.237 HVN at 120 A and a minimum of 219.110 HVN at 140 A. Microstructural analysis revealed the formation of ferrite, pearlite, bainite, and martensite phases in the weld area, providing insights into optimizing welding parameters to enhance the mechanical properties of low-carbon steel cladding.

A data-driven approach was applied in this research to determine input parameters for producing high-quality welds in mild steel sheets. By utilizing an L16 orthogonal array, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) techniques were utilized to optimize weld characteristics. The Multi-Objective Optimization based on Ratio Analysis (MOORA) method was used to rank these conflicting objectives according to their importance in different scenarios. From principal component analysis (PCA), setting the voltage at 42V, welding current at 250A, wire feed rate at 8 mm/min, and gas flow rate at 15 L/min results in ideal characteristics: penetration of 2.961 mm, reinforcement of 5.658 mm, bead width of 12.753 mm, and dilution percentage of 4.183%. Through the MOORA method, it was determined that a voltage of 40V, welding current of 175A, wire feed rate of 4 mm/min, and gas flow rate of 10 L/min would yield optimal weld bead geometry with penetration of 0.884 mm, reinforcement of 6.489 mm, bead width of 11.715 mm, and dilution percentage of 1.218%. This study effectively optimized welding parameters for superior welding in sheet metal fabrication for small and medium-sized enterprises.

Abstract Single weld beads were deposited on a steel plate using three different welding wire feed rates (slow, medium, and fast). The samples were preheated before welding at three different temperatures (100, 150, and 200°C). Fuzzy logic models were developed and integrated into the analysis for predicting weld bead geometries. The experimental results demonstrated that preheating and wire feed rates had significant impact on the geometric shape characteristics of 1020 weld beads. Higher preheating temperatures and optimal wire feed rates led to improved weld bead geometry. The integrated fuzzy logic model predicted the weld bead geometry with optimal input variables of 23 V, 150 A, 3 mm/s welding speed, and 540 J/mm heat input, with an optimal bead width, bead height, depth of penetration, heat affected zone (HAZ) width, and height (9.72, 2.02, 1.62, 12.54 and 2.73 mm). The accuracy of the fuzzy models were examined via regression plots, which yielded R 2 values of 0.9146, 0.9909, 0.9467, 0.9805, and 0.8239, for the bead width, bead height, depth of penetration as well as HAZ width and height. This implies that the fuzzy models were effective in predicting the bead height, justifying from its very high degree R 2 value of 0.9909. This showcased the viability of fuzzy logic for predicting weld bead geometry.

Directed energy deposition (DED) is an advanced 3D printing technique of adding molten metal in layers that requires precise control over weld bead geometry to enhance the quality and reliability of the final manufactured product. The present research is focused on developing a model for predicting weld bead geometry during deposition of Nickel-Aluminium Bronze on SS316L substrate. Response surface methodology (RSM) with Box-Behnken design was selected to develop a predictive model for weld bead height and width. Three significant process parameters, like current, voltage and travel speed were investigated for the prediction of the height and width of the weld bead. Different experimental trial runs were performed according to Box-Behnken’s design. The developed RSM model effectively predicted weld bead height and width with an accuracy of 94.70% and 97.20%, respectively. The model also revealed significant interaction effects between process parameters on the resulting weld bead geometry. A confirmatory test was conducted which validated the predictive capability of the model and demonstrated excellent agreement between predicted and experimental values. The outcome of this research provides a valuable tool for accurately predicting weld bead geometry in DED of dissimilar materials, which enables improvement in process control and optimization for achieving desired geometrical characteristics.

In nuclear industry, producing high-quality welds with precise weld bead dimensions is essential to ensure weld integrity and prevent radioactive leakage. The present study applies artificial neural networks to predict weld bead geometry during the fabrication of zircaloy-2 cladded fuel pins for boiling water reactors, using tungsten inert gas welding. Welding experiments were conducted with varied input parameters to develop a robust training and testing dataset. An artificial neural network model was designed with an optimized architecture, exploring different configurations of hidden layers and neurons. The model’s performance was evaluated across a range of activation functions, batch sizes, and learning rates. Once the optimal network configuration and training strategy were identified, the model was trained using a training dataset. The predictive accuracy of the trained model was assessed using an independent test dataset, and it was found that predicted values were within ± 10% of experimentally obtained values.

This study analyzes the effect of Heat Input (HI) on the austenite/ferrite ratio in Duplex Stainless Steel (DSS) 2205 welds manufactured through the Robotic Gas Tungsten Arc Welding (GTAW) process using an Ar-2%N2 shielding gas. Macrostructural and microstructural evaluations were performed to determine the effect of HI variations on weld bead geometry, phase balance and microhardness. The results indicate that, due to nitrogen desorption at higher temperatures, increasing HI leads to a reduction in austenite content. Lower HI promotes a higher austenite content, resulting in increased hardness. The study highlights the influence of nitrogen solubility and diffusion in the melt and demonstrates that shielding gas composition and welding parameters significantly affect the final microstructure and mechanical properties of DSS welds.

Abstract This work examines the behavior of using different backing plate materials on weld bead geometry, microhardness, tensile strength, and fracture of 1020 low carbon steel. Autogenous tungsten inert gas welding was performed with and without the use of backing plates. Two different backing plates were tested. It was found that the maximum depth-to-width ratio of 0.63 was obtained when welding was performed without the use of backing plate. It is recommended that backing plates with high thermal conductivity values should be used when faster cooling rates are required; however, if deeper weld penetration is required, the recommendation is not to use any backing plate. The maximum ultimate tensile stress was found to be 412 MPa when welding was performed using 2024 aluminum alloy as a backing plate. On the other hand, the minimum ultimate tensile strength of 373 MPa was obtained when the welding was performed without any backing plate. Using aluminum plates as backing material improved the welded metal strength by only 2%. In addition, the fracture surfaces of the samples were examined by scanning electron microscopy. The use of backing plates depending on their thermal behavior increases brittleness and decreases the ductility of the metals being welded.

Selecting the appropriate process parameters is vital for obtaining the required bead geometry and minimising defects or discontinuity like porosity, weld bead cracks, and pinholes in surfacing, wire arc additive manufacturing, and simple joining by basic welding. In the current work, the effects of wire flux arc melting (WFAM) process parameters on weld bead geometry have been investigated. A flux feeder was coupled with a robot arm, allowing a varying flux flow rate (FFR) for a continuous WFAM process. Weld beads have been deposited on mild steel substrate by varying voltage, current, welding speed, nozzle-to-plate distance, and self-shielded FFR as per central composite design. The relationships between welding parameters and weldment characteristics, namely bead width, reinforcement, and penetration, have been developed using multiple polynomial regression. The analysis of variance (ANOVA) test was used to confirm the model’s suitability. The main effects of individual process parameters and their interactions on response parameters were studied. Finally, the ideal welding process parameters were identified by numerical optimisation with response surface methodology. Validation experiments have revealed that the model can accurately forecast the weld bead geometry based on the percentage error for penetration, reinforcement height, and the bead width of fewer than ± 10 % , respectively. A comparison was made on optimised parameters for gas metal arc welding (GMAW) and WFAM processes. The WFAM process was found to have an increased value of bead geometry and homogeneous microstructure as compared to the GMAW process.

Adding hydrogen (H2) to shielding gas in Tungsten Inert Gas (TIG) welding has garnered attention for its potential to enhance weld quality. This study explores the effects of H2 and helium (He) content on AISI 316L stainless steel welding, focusing on their influence on weld bead geometry, microstructural properties, and mechanical properties. The H2 (1.5%, 3%, 4.5%) and He (10%, 20%, 30%) concentrations were evaluated in a shielding gas primarily composed of argon (Ar). The study underscores the need for precise gas blend control to balance enhanced performance with material safety. These findings offer insights into optimizing welding parameters for AISI 316L, with implications for broader applications in industries demanding high quality. The result shows that H2 (1.5–3.0%) improves penetration, geometry, and surface finish, while He (10–20%) enhances arc stability and smoothness; however, excessive levels of H2 (>4.5%) cause defects. Optimal mechanical properties (UTS: 714.54 MPa, YS: 449.03 MPa, hardness: 93.34 HRB, impact toughness: 34.45 J) are achieved with 3% H2, 30% He, and 150 A arc current.

Accurate prediction of weld bead geometry is critical for optimizing laser overlap welding of low-carbon galvanized steel, as it directly affects joint quality and mechanical performance. Traditional finite element method (FEM)-based models provide reliable predictions but are computationally expensive and impractical for real-time applications. This study presents an artificial neural network (ANN)-based predictive model trained on a combination of experimental data and validated FEM simulations to estimate key weld characteristics, including depth of penetration (DOP), weld bead width at the surface (WS), and weld bead width at the interface (WI). The ANN model was evaluated using various improved statistical metrics. Results demonstrated a strong correlation between ANN predictions and experimental measurements, with R2 values exceeding 95% for WS and DOP and 92% for WI, and mean errors below 7%. A comparative analysis between ANN, FEM, and experimental data confirmed the model’s reliability across different welding conditions. Additionally, ANN significantly reduced computational time compared to FEM while maintaining high accuracy, making it a practical tool for real-time process optimization. These findings highlight the potential of ANN models as efficient alternatives to conventional simulation techniques in laser overlap welding applications. Future improvements may involve integrating real-time sensor data and deep learning techniques to further enhance predictive performance.