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AbstractThis study focuses on the fabrication of AA7475/B4C/Al2O3 hybrid surface composites (HSCs) using multi-pass friction stir processing (FSP) to enhance their microstructure and mechanical properties. The study utilized four numbers of FSP passes with constant tool rotational speed (1300 rev/min), feed rate (45 mm/min), and reinforcement ratio (50% B4C and 50% Al2O3) and investigated the ultimate tensile stress (UTS), tensile elongation (El), and hardness distribution of HSCs. The study identified that multi-pass FSP resulted in significant grain refinement, fragmentation of reinforced particles, and uniform distribution throughout the stir region of the HSCs. For a single-pass FSP, the average grain size at the stir region was 6.5 ± 0.20 µm, whereas for the four-pass process, it decreased to 2.2 ± 0.18 µm. The XRD results revealed that as the number of passes increased, the peak intensity of B4C and Al2O3 phases was consistently reduced, which suggests fragmentation of nanoparticles. The UTS recorded for 1 pass, 2 passes, 3 passes, and 4 passes were 447 MPa, 464 MPa, 481 MPa, and 501 MPa, respectively. The hardness measurements at the SZ were recorded as 162, 174, 183, and 192 HV for 1, 2, 3, and 4 FSP passes, respectively. After reinforcing B4C and Al2O3, AA7475 showed percentage improvements in UTS, El, and hardness of 22.79%, 69.01%, and 38.12%, respectively. Field emission electron microscopy (FESEM) image of tensile fractured surfaces exhibit honeycomb-like coarser dimples, resulting in brittle-ductile mode of fracture for one-pass FSP, while HSCs produced through four-pass FSP display finer dimples, demonstrating ductile fracture mode. [ABSTRACT FROM AUTHOR]

This study examines the influence of direct drive friction welding (DDFW) on Cr-Ni-Mo steel (AISI 316) with a focus on metallurgical, mechanical, and electrochemical properties. Different friction times, ranging from 5.5 s to 12 s, were investigated while keeping other conditions constant. Temperature measurements, Macro-microstructure, microhardness, tensile tests, tensile fracture morphology, and electrochemical tests were performed. The results show that the maximum temperature (Tmax) exhibits a slight increase with an extended friction time. The temperature variation ranges from 826 °C to 879 °C for friction times of 5.5 s and 12 s, respectively, thus, the welded joint is divided into four distinct regions: highly plastically deformed zone (HPDZ), thermo-mechanically affected zone (TMAZ), heat-affected zone (HAZ), and the base metal, with grain sizes of approximately 10 μm, 100 μm, 110 μm, and 25 μm, respectively. The HPDZ is responsible for the microhardness elevation at the interface, while the TMAZ and HAZ are responsible for the microhardness attenuation in the neighboring region and weak in tension. The ultimate tensile strength (UTS) related to AISI 316 decreases from 104.50 to 94.57% for 5.5 s and 12 s, respectively, and the ductility related to AISI 316 decreases from 58.21 to 54.05% for 5.5 s and 12 s, respectively. Tensile fractures occurred at the TMAZ with a ductile fracture mode and cleavage features with irregular forms of microcavities throughout the fingerprints. The results of the electrochemical test clearly indicate that the weld zone (WZ) exhibits superior corrosion resistance compared to the base metal (BM), AISI 316. Further analysis of the results revealed that the TMAZs are more susceptible to pitting than the HAZ. Thus, only a few micro-pits are observed in the HPDZ compared to the pitting state in the TMAZs. [ABSTRACT FROM AUTHOR]

This paper estimates friction stir welded joints' ultimate tensile strength (UTS) and hardness using six supervised machine learning models (viz., linear regression, support vector regression, decision tree regression, random forest regression, K-nearest neighbour, and artificial neural network). Tool traverse speed, tool rotational speed, pin diameter, shoulder diameter, tool offset, and tool tilt are the six input parameters in the 200 datasets for training and testing the models. Deep learning artificial neural networks (ANN) exhibited the highest accuracy. Therefore, the ANN approach was used successfully to estimate the UTS and the hardness of friction stir welded joints. Additionally, the relationship of pin diameter, tool offset, and tool rotation speed over UTS and hardness were extracted over the collected data points. Furthermore, experimental results, such as UTS and hardness of steel–magnesium-based welded joints and model estimated results, were compared to cross-check model generalization capability. It was noted that ANN estimates and experimental results at desired processing conditions are consistent with sufficiently high accuracy. [ABSTRACT FROM AUTHOR]

Most published works related to gas metal arc welding processes focus on quality criteria, while energy consumed has not been considered. In this study, key parameters of the gas metal arc welding (GMAW) operation of the AISI 1045 steel, including the current (I), voltage (V), flow rate (F), and nozzle stand-off distance (D) are optimized to minimize energy consumed (EC) and enhance the ultimate tensile strength (TS) as well as elongation (EL). The radial basis function network (RBFN) is employed to propose GMAW responses. The Entropy method, modified grey wolf optimizer (MGWO), and Multi-Attributive Border Approximation Area Comparison (MABAC) were utilized to compute the weights, generate feasible solutions, and determine the best optimality. As a result, the optimal I, V, F, and D were 137 A, 23 V, 12 L/min, and 12 mm, respectively. The EC was reduced by 5.8%, while the TS and EL were improved by 3.3% and 20.7%, respectively, at the optimality. The EC and EL models were primarily affected by the I, V, F, and D, respectively. The TS model was mainly influenced by the I, D, F, and V, respectively. The RBFN-MGWO could be employed to present non-linear data and achieve better optimal results, as compared to the traditional approach. The obtained outcomes could be used to produce high-quality welds with minimizing environmental impacts. [ABSTRACT FROM AUTHOR]