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<p>Single Point Incremental Forming (SPIF) offers high flexibility for producing complex sheet metal components. However, simultaneous high formability and high surface integrity remain problematic with aluminium alloys. This study investigates the SPIF process applied to AA-2024-O sheets where the goal is to maximize forming depth (Z<sub>max</sub>) and at the same time reduce the roughness of the surface (Ra). Taguchi based mixed orthogonal array was used to test eight critical process parameters. Then eempirical models were developed using the experimental results. A constrained multi-objective optimization framework that included the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was used to find Pareto-optimal solutions. These were ranked using the MOORA decision-making method to identify a balanced and practically achievable parameter space. The optimal parameter combination resulted in surface roughness of 0.026079 and a forming depth of 48.11&#xa0;mm. Close correlation with the predicted values was shown by verification experiments with less than 5 percent deviation. The proposed combined solution provides a reliable and experimentally validated framework on which the SPIF parameters to be used on AA-2024-O can be chosen, to enhance formability and surface quality.</p>

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Investigation of forming process parameters for AA-2024-O using an integrated NSGA-II–MOORA approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Ajay Kumar,
  • Neeraj Sharma,
  • Rohit Magdum,
  • Namrata Dogra,
  • Parveen Kumar

摘要

Single Point Incremental Forming (SPIF) offers high flexibility for producing complex sheet metal components. However, simultaneous high formability and high surface integrity remain problematic with aluminium alloys. This study investigates the SPIF process applied to AA-2024-O sheets where the goal is to maximize forming depth (Zmax) and at the same time reduce the roughness of the surface (Ra). Taguchi based mixed orthogonal array was used to test eight critical process parameters. Then eempirical models were developed using the experimental results. A constrained multi-objective optimization framework that included the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was used to find Pareto-optimal solutions. These were ranked using the MOORA decision-making method to identify a balanced and practically achievable parameter space. The optimal parameter combination resulted in surface roughness of 0.026079 and a forming depth of 48.11 mm. Close correlation with the predicted values was shown by verification experiments with less than 5 percent deviation. The proposed combined solution provides a reliable and experimentally validated framework on which the SPIF parameters to be used on AA-2024-O can be chosen, to enhance formability and surface quality.