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<p>The present work aims to optimize the process parameters of friction stir welding (FSW) to improve the mechanical behavior of AISI 1018 carbon steel joints. The study explores the influence of welding speed, tool rotational speed, and shoulder diameter on ultimate tensile strength (UTS), percentage elongation (PE), percentage reduction in area (RA), and impact energy (IE). To achieve this, both single-response and multi-response optimization methods were applied. For multi-response optimization, an integrated approach combining Grey Relational Analysis (GRA) and Principal Component Analysis (PCA) was used to objectively determine response weights and identify the optimal parameter settings. The optimized settings led to enhancements in mechanical performance: 3.73% in UTS, 7.30% in PE, 23.13% in RA, and 2.35% in IE for multi response optimization. Single-response optimization showed 3.73%, 1.95%, 36.46%, and 2.35% improvements, respectively. Microstructural analysis revealed finer grain structures that contributed directly to these improvements. Confirmation tests aligned with the predicted outcomes, which demonstrates the effectiveness of the optimized parameters. The findings provide a structured framework to optimize FSW parameters and offer valuable insights for industrial applications in the automotive and structural manufacturing sectors.</p>

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Optimization of friction stir welding parameters for enhanced mechanical properties of AISI 1018 carbon steel

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月27日

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  • Anmol Bhatia,
  • Reeta Wattal,
  • Rajeev Kumar,
  • Anant Prakash Agrawal,
  • Saurabh Dewangan

摘要

The present work aims to optimize the process parameters of friction stir welding (FSW) to improve the mechanical behavior of AISI 1018 carbon steel joints. The study explores the influence of welding speed, tool rotational speed, and shoulder diameter on ultimate tensile strength (UTS), percentage elongation (PE), percentage reduction in area (RA), and impact energy (IE). To achieve this, both single-response and multi-response optimization methods were applied. For multi-response optimization, an integrated approach combining Grey Relational Analysis (GRA) and Principal Component Analysis (PCA) was used to objectively determine response weights and identify the optimal parameter settings. The optimized settings led to enhancements in mechanical performance: 3.73% in UTS, 7.30% in PE, 23.13% in RA, and 2.35% in IE for multi response optimization. Single-response optimization showed 3.73%, 1.95%, 36.46%, and 2.35% improvements, respectively. Microstructural analysis revealed finer grain structures that contributed directly to these improvements. Confirmation tests aligned with the predicted outcomes, which demonstrates the effectiveness of the optimized parameters. The findings provide a structured framework to optimize FSW parameters and offer valuable insights for industrial applications in the automotive and structural manufacturing sectors.