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Multi-objective optimization of multi-pass face milling using particle swarm intelligence

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Abstract

In this paper, to facilitate manufacturing engineers have more control on the machining operations, the optimization issue of machining parameters is handled as a multi-objective optimization problem. The optimization strategy is to simultaneously minimize production time and cost and maximize profit rate meanwhile subject to satisfying the constraints on the machine power, cutting force, machining speed, feed rate, and surface roughness. An efficient fuzzy global and personal best-mechanism-based multi-objective particle swarm optimization (F-MOPSO) to optimize the machining parameters is developed to solve such a multi-objective optimization problem in optimization of multi-pass face milling. The proposed F-MOPSO algorithm is first tested on several benchmark problems taken from the literature and evaluated with standard performance metrics. It is found that the F-MOPSO does not have any difficulty in achieving well-spread Pareto optimal solutions with good convergence to true Pareto optimal front for multi-objective optimization problems. Upon achieving good results for test cases, the algorithm was employed to a case study of multi-pass face milling. Significant improvement is indeed obtained in comparison to the results reported in the literatures. The proposed scheme may be effectively employed to the optimization of machining parameters for multi-pass face milling operations to obtain efficient solutions.

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Authors and Affiliations

  1. Department of Manufacturing Engineering of Aeronautics and Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, People’s Republic of China

    Wen-an Yang, Yu Guo & Wenhe Liao

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  1. Wen-an Yang
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  2. Yu Guo
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  3. Wenhe Liao
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Correspondence to Wen-an Yang.

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Yang, Wa., Guo, Y. & Liao, W. Multi-objective optimization of multi-pass face milling using particle swarm intelligence. Int J Adv Manuf Technol 56, 429–443 (2011). https://doi.org/10.1007/s00170-011-3187-8

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  • DOI: https://doi.org/10.1007/s00170-011-3187-8

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