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Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm

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Abstract

Grinding is one of the most commonly used operations in the industry. Optimization of the grinding process can significantly improve the product quality and minimize operational costs and production time. Due to nonlinearity and complexity, optimization of the grinding process is one of the most challenging tasks in the field of mechanical engineering. This paper aims to optimize grinding process considering a tri-objective mathematical model to simultaneous optimization of final surface quality, grinding cost and total process time. To obtain non-dominated Pareto optimal solutions, a novel meta-heuristic algorithm named multi-objective dragonfly algorithm is utilized. Besides, an efficient constraint handling technique is implemented to handle complex operational constraints. Furthermore, an experimental case study is solved using the proposed algorithm and the results are compared to NSGA-II in the literature. The results revealed that the proposed algorithm is able to find non-dominated Pareto optimal solutions and make a significant improvement over the existing approaches.

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Source: Mirjalili et al. [28]

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Source: Mirjalili et al. [28]

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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 Soheyl Khalilpourazari.

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Khalilpourazari, S., Khalilpourazary, S. Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm. Neural Comput & Applic 32, 3987–3998 (2020). https://doi.org/10.1007/s00521-018-3872-8

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