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<p>This study investigates the parametric optimization of friction stir welding (FSW) for joining AA1100 alloys using the (MOGOA) multi-objective grasshopper optimization algorithm the non-dominated sorting genetic algorithm II (NSGA-II). Regression equations was employed to determine to forecast hardness and tensile strength of frictional stir welding joints, whereas tensile testing and hardness measurements were conducted to acquire the empirical evidence. The SECA–COCOSO framework, together with the empirical model, was utilized to structure experimental methodology, while empirical results and the adequacy of the predicted were evaluated through a systematic examination of difference. Five distinct instrument categories was evaluated for various amounts of input parametric rates. Optimum input parameters included a tool rotation speed of 1300&#xa0;rpm, a welding speed of 60&#xa0;mm/min, an axial force of 5.5 kN, and a cylindrical threaded tool pin, which demonstrated the maximum. The axial force emerged as the predominant input parameter affecting microhardness and the output tensile strength, succeeded by the tool pin shape and welding speed. NSGA-II demonstrated superior optimization relative to MOGOA. Fractography study revealed a ductile fracture in sample ‘2’, which had the highest UTS of 173.36&#xa0;MPa and an improved Vickers hardness of 99.13 HV, whereas maximum hardness recorded were 93.75 HV in samples 30 throughout the empirical experiments.</p>

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Multi-objective optimization of friction stir welding parameters for AA1100 alloy using NSGA-II and MOGOA coupled with SECA–COCOSO framework

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Ennamuri Subbarao,
  • G. Meena Devi,
  • T. R. Vijaya Lakshmi,
  • A. Elaiyaraja,
  • Rubaid Ashfaq,
  • M. Gokilhashree

摘要

This study investigates the parametric optimization of friction stir welding (FSW) for joining AA1100 alloys using the (MOGOA) multi-objective grasshopper optimization algorithm the non-dominated sorting genetic algorithm II (NSGA-II). Regression equations was employed to determine to forecast hardness and tensile strength of frictional stir welding joints, whereas tensile testing and hardness measurements were conducted to acquire the empirical evidence. The SECA–COCOSO framework, together with the empirical model, was utilized to structure experimental methodology, while empirical results and the adequacy of the predicted were evaluated through a systematic examination of difference. Five distinct instrument categories was evaluated for various amounts of input parametric rates. Optimum input parameters included a tool rotation speed of 1300 rpm, a welding speed of 60 mm/min, an axial force of 5.5 kN, and a cylindrical threaded tool pin, which demonstrated the maximum. The axial force emerged as the predominant input parameter affecting microhardness and the output tensile strength, succeeded by the tool pin shape and welding speed. NSGA-II demonstrated superior optimization relative to MOGOA. Fractography study revealed a ductile fracture in sample ‘2’, which had the highest UTS of 173.36 MPa and an improved Vickers hardness of 99.13 HV, whereas maximum hardness recorded were 93.75 HV in samples 30 throughout the empirical experiments.