Advances in Mechanical Engineering (Sep 2022)

Response surface, neural network, and experimental approach to optimize process parameters and characterization of Al-Mg alloy by friction stir welding

  • Chandra Shekar Anjinappa,
  • Sagr Alamri,
  • Asif Afzal,
  • Abdul Razak Kaladgi,
  • ChanduVeetil Ahamed Saleel,
  • Nasim Hasan,
  • Bahaa Saleh

DOI
https://doi.org/10.1177/16878132221120460
Journal volume & issue
Vol. 14

Abstract

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In the present study, an interaction relationship has been developed by following a design matrix consisting of few combinations of tool rotational speed, traverse speed, and tool pin configurations to understand the evolution of structural and mechanical properties of friction stir welded (FSW) Al-Mg alloy. The welded Al-Mg alloy was characterized in terms of microstructure to analysis different zones of weld using optical and scanning electron microscope. The mechanical properties such as bulk & micro hardness, and tensile strength were evaluated using Brinell, Vickers hardness tester, and tensometer respectively. The weld textures and grain size of the three different zones obtained using electron beam scattered diffraction (EBSD). Further, confirmation test was conducted to ensure the reliability. Results reveal that process parameters and tool pin profile influence the properties of the friction stir welded joints. The straight square pin configuration was the optimal structure for friction stir welding of Al-Mg alloy at a rotational speed of 1000 rpm and a welding speed of 40 mm/min, produced defect free weld with highest tensile strength of 136.25 MPa, bulk-hardness of 80.41 BHN, micro-hardness of 82 VHN in comparison with rest of the combinations considered in the investigation under same conditions. A fine recrystallized equiaxed grain with a partial fiber texture was evolved in the nugget zone. The measured experimental values agreed with the predicted data well.