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Optimization of production time in the multi-pass milling process via a Robust Grey Wolf Optimizer

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Abstract

In order to optimize multi-pass milling process, selection of optimal values for the parameters of the process is of great importance. The mathematical model for optimization of multi-pass milling process is a multi-constrained nonlinear programing formulation which is hard to be solved. Therefore, a novel robust meta-heuristic algorithm named Robust Grey Wolf Optimizer (RGWO) is proposed. In order to develop a RGWO, a robust design methodology named Taguchi method is utilized to tune the parameters of the algorithm. Therefore, in contradiction to previous researches, there is no need to design costly experiments to obtain the optimal values of the parameters of the GWO. In addition, an efficient constraint handling approach is implemented to handle complex constraints of the problem. A real-world problem is adopted to show the effectiveness and efficiency of the proposed RGWO in optimizing the milling process within different strategies. The results indicated that the RGWO outperforms the other solution methods in the literature as well as two novel meta-heuristic algorithms by obtaining better and feasible solutions for all cutting strategies.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Soheyl Khalilpourazari

  2. Department of Mechanical Engineering, Urmia University of Technology, Urmia, Iran

    Saman Khalilpourazary

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  1. Soheyl Khalilpourazari
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  2. Saman Khalilpourazary
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Correspondence to Saman Khalilpourazary.

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Khalilpourazari, S., Khalilpourazary, S. Optimization of production time in the multi-pass milling process via a Robust Grey Wolf Optimizer. Neural Comput & Applic 29, 1321–1336 (2018). https://doi.org/10.1007/s00521-016-2644-6

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