Cutting Parameters Optimization of Thin-Walled Workpiece Based on PSO and FEM

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Abstract:

An approach to optimize the cutting parameters based on Particle Swarm Algorithm(PSO) and Finite Element Method(FEA) was proposed. A cutting parameters optimization model was established whose design variables are the cutting parameters and objective function is to minimize the maximum deformation. PSO was used to optimize the cutting parameters and FEA was utilized to predict the machining deformation of the thin-walled workpiece. Finally, the entire technique was demonstrated in a case study. The simulation and experimental results show that the approach can be further employed into the practical machining situation.

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408-412

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December 2012

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] M. Tolouel-rad, I. M. Bidhendi,On the optimization of machining parameters for milling operations [J]. International Journal of Machine Tools & Manufacture, Vol. 37 (1997), No. 1, pp.1-16.

DOI: 10.1016/s0890-6955(96)00044-2

Google Scholar

[2] J. Wang, Computer-aided economic of end-milling operation [J]. International Journal of Production Economics, Vol. 54 (1998), No. 3, pp.307-320.

DOI: 10.1016/s0925-5273(98)00008-5

Google Scholar

[3] H. Oktem, T. Erzurumlu, H. Kurtaran, Application of response surface methodology in the optimization of cutting conditions for surface roughness [J]. Journal of Materials Processing Technology, Vol. 170 (2005), No. 1-2, pp.11-16.

DOI: 10.1016/j.jmatprotec.2005.04.096

Google Scholar

[4] P.E. Amiolemhen, Application of genetic algorithms-determination of optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile [J]. International Journal of Machine Tool & Manufacture, Vol. 44 (2004).

DOI: 10.1016/j.ijmachtools.2004.02.001

Google Scholar

[5] M.S. Shunmugam, S. V. Bhaskara Reddy, T. T. Narendran, Selection of optimal conditions in multi-pass face-milling using a genetic algorithm [J]. International Journal of Machine Tools & Manufacture, Vol. 40 (2000), No. 3, pp.401-414.

DOI: 10.1016/s0890-6955(99)00063-2

Google Scholar

[6] Z.G. Wang, Y.S. Wong, M. Rahman, Optimization of Multi-pass Milling Using Parallel Genetic Algorithm and Parallel Genetic Simulated Annealing [J]. International Journal Tools & Manufacture, Vol. 45 (2005), No. 15, pp.1726-1734.

DOI: 10.1016/j.ijmachtools.2005.03.009

Google Scholar

[7] Fanci Cus, Uros Zuperl, Approach to optimization of cutting conditions by using artificial neural networks [J]. Journal of Materials Processing Technology, Vol. 173 (2006), No. 3, pp.281-290.

DOI: 10.1016/j.jmatprotec.2005.04.123

Google Scholar