Ni-Cr-B-Si alloys are used to hardface stainless steel components in industry, including fast breeder reactors, to prevent galling. Segregation-induced microscopic strain in Ni-Cr-B-Si coatings can compromise microstructural integrity, yet the effect of deposition parameters on this strain remains underexplored. In this study, coatings were deposited on 316 L (N) using Plasma Transferred Arc Welding (PTAW) at two currents: 120 A and 150 A. The coating with 14 weight % of Fe (made at 120 A) exhibited predominantly coarse, rod-shaped precipitates dominant in CrB, pronounced elemental segregation (Si, Fe, and P) at the precipitate-matrix interfaces, and higher microscopic strain, with a maximum Kernel Average Misorientation (KAM) of similar to 4 degrees. In contrast, the coating with Fe weight % of 20 (made at 150 A), exhibited a reduction in coarse precipitates and the development of a finer, near-eutectic fish-bone Cr5B3 phase dominant structure, resulting in a lower maximum KAM of similar to 1.8 degrees. Characterization techniques employed included SEM, EDS, OES, EBSD, XRD, and Thermo-Calc simulation. The results indicate that dilution of the coating, influenced by deposition current, affects strain distribution by reducing primary boride precipitation and the associated local elemental concentration gradient developed during solidification. Hence, adjusting deposition parameters alters the segregation induced strain in Ni-Cr-B-Si coatings.
This study explores the influence of preset angle, alpha (0 and 3 degrees), on the interface microstructure of aluminium (A6061-T6)-Copper Deoxidized High Residual Phosphorus (Cu-DHP) explosive weld joints. The weld joints obtained for a parallel configuration exhibited a characteristic wavy interface along with trapped jet formation, reflecting enhanced collision velocity and jetting behaviour. However, the introduction of preset angle (3 degrees), resulted in a straight interface, indicating limited plastic deformation. Numerical simulation performed in ANSYS AUTODYN employing Smoothed Particle Hydrodynamics (SPH) determined a peak pressure of 3.9 X 10(4) MPa and 2.5 X 10(4) MPa in parallel (0 degrees) and inclined (3 degrees) configurations respectively. Similarly, the maximum temperature across the interface (1000 K and 780 K) and strain developed (1.1 and 1.4) during the process was observed for parallel and inclined configurations respectively. In addition, the experimental conditions attempted prevail within the successful regimes of weldability window comprising upper, lower, left and right boundaries, determined analytically.
This study experimentally investigates the combined influence of welding method and joint geometry on the tensile performance of 4 mm-thick AA5052 aluminum alloy joints. Six joint configurations, including conventional and structurally modified geometries, were fabricated using Gas Metal Arc Welding, Gas Tungsten Arc Welding, Shielded Metal Arc Welding, and Oxy-Acetylene Welding. Tensile tests were performed to evaluate strength, ductility, deformation response, and fracture behavior. The results demonstrated that both welding method and joint geometry significantly affected mechanical performance. The highest tensile strength, 199.88 MPa, was achieved in the GMAW-produced lap joint, corresponding to approximately 88% of the base material strength. The effect of joint geometry was non-uniform and strongly dependent on the welding method. GMAW and GTAW generally provided higher tensile performance due to stable arc characteristics, controlled heat input, and reduced defect formation. In contrast, SMAW and OAW showed lower performance in several configurations. PR-LJ and SJ provided more consistent performance across welding methods. Fracture analysis showed that high-strength joints predominantly failed in the heat-affected zone, whereas lower-performance conditions exhibited weld metal failure. Overall, tensile performance was governed by the interaction between welding method and joint geometry.
To solve the problems of difficulty in multi-source data fusion and insufficient mining of timing dependencies in high-strength steel laser welding quality prediction, this study proposes a high-strength steel laser welding quality prediction method that jointly improves the back propagation neural network and K-means clustering. This study conducts pattern analysis through K-means clustering to divide historical parameters into representative process categories, providing a basis for parameter optimization. It uses empirical mode decomposition to adaptively decompose and denoise monitoring signals during the welding process, and extract multi-scale time-frequency features. This research uses long short-term memory to capture the timing dependencies in monitoring signals. Finally, it integrates static and dynamic features through a back-propagation neural network with improved structure (introducing ReLU and Sigmoid activation functions) to achieve high-precision prediction of molten pool geometric dimensions (bead width, penetration depth, and reinforcement height). Experimental results showed that this method performed well in multiple dimensions: In terms of convergence, the loss value stabilized at 0.010 after 500 rounds of training. In terms of prediction accuracy, the average absolute error under 500 groups of samples was 0.03.
Magnesium alloys are used to manufacture lightweight vehicles to improve fuel economy and reduce carbon emissions. Implementing the idea of friction stir welding for joining increases the utility of these alloys in the automotive sector. In this study, dissimilar Mg alloys AZ31 and AZ91 were welded using the Box-Behnken experimental design criterion. The modeling of corrosion rate has been done using Response Surface Methodology (RSM), while optimization has been done using the Teaching-Learning-Based Optimization (TLBO) algorithm. The microstructural investigation suggest variation in grain size with process parameters and the affinity of magnesium towards oxygen form MgO in welded region which generates microcracks. Microstructure of corroded samples included some corroded circular rings, pits and MgO as white layers. The minimum corrosion rate 0.220 mm/year is obtained at 700 rpm of Rotational Speed (RotS), 30 mm/min of Welding Speed (WeldS) and 18 mm of Shoulder Diameter (ShD) suggesting improved homogeneity due to dynamic recrystallization. The most influencing factor for corrosion is tool rotational speed. The TLBO algorithm suggests a 5.90% improvement at 1000 rpm of RotS, 50 mm/min of WeldS and 19 mm of ShD.
