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<p>The selection of optimal machining parameters in milling operations is a complex multi-objective optimization problem involving conflicting criteria such as unit cost, machining time, and profit rate. Traditional optimization techniques often struggle to efficiently explore the nonlinear and constrained search space, leading to suboptimal solutions. To address this gap, this study proposes the application of a Multi-Objective Grey Wolf Optimizer (MOGWO) for the simultaneous optimization of cutting speed and feed rate in multi-tool, multi-operation milling environments. Unlike conventional single-objective approaches, the proposed method effectively balances multiple performance measures to obtain economically efficient solutions. The proposed algorithm is evaluated through computational validation using a benchmark milling optimization model reported in the literature. The results indicate that MOGWO achieves a 2.57% reduction in unit cost and a 2.14% improvement in profit rate compared with the best-performing existing method (Cuckoo Search), while maintaining comparable machining time. These improvements represent computational performance on the benchmark problem and have not been verified through experimental machining trials. Therefore, the findings demonstrate the potential effectiveness of MOGWO as a computational optimization approach for machining parameter selection, while further validation through laboratory experiments and industrial case studies is required to establish its practical applicability in real manufacturing environments.</p>

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Computational evaluation of multiobjective grey wolf optimization for machining parameter selection in multioperation milling using a benchmark model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Sudeep Kumar Singh,
  • Madan Mohanrao Jagtap

摘要

The selection of optimal machining parameters in milling operations is a complex multi-objective optimization problem involving conflicting criteria such as unit cost, machining time, and profit rate. Traditional optimization techniques often struggle to efficiently explore the nonlinear and constrained search space, leading to suboptimal solutions. To address this gap, this study proposes the application of a Multi-Objective Grey Wolf Optimizer (MOGWO) for the simultaneous optimization of cutting speed and feed rate in multi-tool, multi-operation milling environments. Unlike conventional single-objective approaches, the proposed method effectively balances multiple performance measures to obtain economically efficient solutions. The proposed algorithm is evaluated through computational validation using a benchmark milling optimization model reported in the literature. The results indicate that MOGWO achieves a 2.57% reduction in unit cost and a 2.14% improvement in profit rate compared with the best-performing existing method (Cuckoo Search), while maintaining comparable machining time. These improvements represent computational performance on the benchmark problem and have not been verified through experimental machining trials. Therefore, the findings demonstrate the potential effectiveness of MOGWO as a computational optimization approach for machining parameter selection, while further validation through laboratory experiments and industrial case studies is required to establish its practical applicability in real manufacturing environments.