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A novel MOALO-MODA ensemble approach for multi-objective optimization of machining parameters for metal matrix composites

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Abstract

Machining of metal matrix composites (MMCs) has now become an essential task in shaping them to their final usable forms for various industrial applications. These machining operations may range from drilling of simple through holes to cutting and shaping of the composites into complex shapes. Determination of the optimal process parameters during their machining presents a combinatorial optimization problem, which may turn out to be more complex due to involvement of various material dependent parameters of the MMCs, like particle size, reinforcement percentage etc. The importance of optimization increases manifold due to conflicting settings of different process parameters for attaining the desired quality characteristics. In this paper, two nature-inspired optimization algorithms, i.e. multi-objective antlion optimization (MOALO) and multi-objective dragonfly algorithm (MODA) are applied for optimization of various process parameters during machining of MMCs. To mitigate uncertainties in global optimality of the predicted Pareto optimal fronts arising due to stochastic nature of the metaheuristics, an ensemble approach integrating MOALO and MODA techniques is proposed here. It is demonstrated with the help of two case studies that MOALO-MODA ensemble is far superior to its individual counterparts and thus, can be employed for development of robust and reliable Pareto optimal fronts.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India

    Kanak Kalita

  2. Department of Production Engineering, Jadavpur University, Kolkata, India

    Vikas Kumar & Shankar Chakraborty

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  1. Kanak Kalita
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Correspondence to Shankar Chakraborty.

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Kalita, K., Kumar, V. & Chakraborty, S. A novel MOALO-MODA ensemble approach for multi-objective optimization of machining parameters for metal matrix composites. Multiscale and Multidiscip. Model. Exp. and Des. 6, 179–197 (2023). https://doi.org/10.1007/s41939-022-00138-5

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