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<p>This study employs an integrated, data-driven approach to optimize and rank optimal solutions for unbiased cutting parameter selection in turning AA6063 alloy. The objectives are to achieve an optimal trade-off between surface roughness (Ra) and material removal rate (MRR) using entropy and Gini weighting methods. Accordingly, dry turning experiments were conducted, and data were collected. Four machine learning (ML) models, including Kolmogorov–Arnold network (KAN), Artificial Neural Network (ANN), XGBoost, and CatBoost, are used to evaluate the predictive performance. Through the model performance metrics RMSE, MAE, and R<sup>2</sup>, the best models are embedded in the NSGA-III algorithm to find Pareto solutions. Then, two multi-criteria decision-making (MCDM) methods (MABAC and EDAS) are combined with different weighting schemes to rank the optimal solutions. The results show that KAN outperforms the other models. Thirteen Pareto solutions are generated and ranked roughly according to the prior of MRR. Overall, the proposed method demonstrates the benefits of integrating interpretable ML with evolutionary optimization and rational decision-making, providing a robust solution for machining process planning under conflicting objectives.&#xa0;</p>

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Multi-objective optimization and decision-making in AA6063 turning using Kolmogorov–Arnold network and NSGA-III

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Van-Hai Nguyen,
  • Tri-Hung Ha,
  • Tien-Dung Nguyen,
  • Van-Luc Ngo,
  • Tien-Thinh Le,
  • Van-Phong Le

摘要

This study employs an integrated, data-driven approach to optimize and rank optimal solutions for unbiased cutting parameter selection in turning AA6063 alloy. The objectives are to achieve an optimal trade-off between surface roughness (Ra) and material removal rate (MRR) using entropy and Gini weighting methods. Accordingly, dry turning experiments were conducted, and data were collected. Four machine learning (ML) models, including Kolmogorov–Arnold network (KAN), Artificial Neural Network (ANN), XGBoost, and CatBoost, are used to evaluate the predictive performance. Through the model performance metrics RMSE, MAE, and R2, the best models are embedded in the NSGA-III algorithm to find Pareto solutions. Then, two multi-criteria decision-making (MCDM) methods (MABAC and EDAS) are combined with different weighting schemes to rank the optimal solutions. The results show that KAN outperforms the other models. Thirteen Pareto solutions are generated and ranked roughly according to the prior of MRR. Overall, the proposed method demonstrates the benefits of integrating interpretable ML with evolutionary optimization and rational decision-making, providing a robust solution for machining process planning under conflicting objectives.