Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm
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In this paper, a simple methodology to distribute the total stock removal in each of the rough passes and the final finish pass and a fuzzy particle swarm optimization (FPSO) algorithm based on fuzzy velocity updating strategy to optimize the machining parameters are proposed and implemented for multi-pass face milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed, and feed is obtained to achieve minimum production cost while considering technological constraints such as allowable machine power, machining force, machining speed, tool life, feed rate, and surface roughness. The proposed FPSO algorithm is first tested on few benchmark problems taken from the literature. Upon achieving good results for test cases, the algorithm was employed to two illustrative case studies of multi-pass face milling. Significant improvement is also obtained in comparison to the results reported in the literatures, which reveals that the proposed methodology for distribution of the total stock removal in each of passes is effective, and the proposed FPSO algorithm does not have any difficulty in converging towards the true optimum. From the given results, the proposed schemes may be a promising tool for the optimization of machining parameters.
KeywordsFace milling Multi-pass Machining parameters Optimization Particle swarm optimization
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This work was supported by the National Defense Foundation of China under grant D2520062. The authors would like to express their sincere appreciation to the referees for their detailed and helpful comments to improve the quality of the paper.
- 6.Wang J (1993) Constrained optimization of rough peripheral and end milling operations. PhD Thesis, University of Melbourne, AustraliaGoogle Scholar
- 9.An LB, Chen MY (2003) On optimization of machining parameters. In: Proceedings of the 4th International Conference on Control and Automation, pp 839–843Google Scholar
- 11.Saha SK (2009) Genetic algorithm based optimization and post optimality analysis of multi-pass face milling. CoRR abs/0902.0763Google Scholar
- 13.Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, 27 November/December, Perth, Australia, IV. Piscataway, NJ: IEEE Service Centre, pp 1942–1948.Google Scholar
- 16.Yang ZG, Zhou YS, Wu M (2005) Application of improved particle swarm optimization in economic dispatching. Power Syst Technol 29(2):1–4Google Scholar
- 20.Higashi N, Iba H (2003) Particle swarm optimization with Gaussian mutation. In: Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, Indiana, USA, pp 72–79Google Scholar
- 21.Nefedov N, Osipov K (1987) Typical examples and problems in metal cutting and tool design. Mir Publishers, MoscowGoogle Scholar
- 24.Deb K (1997) GeneAS: a robust optimal design technique for mechanical component design. In: Dipankar Dasgupta and Zbigniew Michalewicz (eds) Evolutionary algorithms in engineering applications, Springer, pp 497–514Google Scholar
- 26.Arora JS (1989) Introduction to Optimum Design. McGraw-Hill.Google Scholar
- 27.Hu XH, Eberhart RC, Shi YH (2003) Engineering optimization with particle swarm. In: Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, Indiana, USA, pp 53–57Google Scholar