Multi-Objective Pareto Optimization of Double-Station Milling Process with the Use of PSO Algorithm

Abstract:

In the paper,a new hybrid optimization technique for multi-objective optimization of surface milling is proposed. The developed approach is based on enhanced Pareto particle swarm optimization algorithm. The optimization of double-station milling is investigated basing on the available model in terms of two objectives: spindle power and production time. The final result is not a single solution but a whole set. In order to obtain satisfied Pareto set, multi-objective evolutionary algorithm for rail milling problem (MOEA-RMP) is used. An illustrative example was used to demonstrate effectiveness and applicability of the proposed approach.

• Periodical: Advanced Materials Research (Volume 668)
• Pages: 943-948
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.668.943
• Online since: March 2013
• Authors: Xian Kun Lin, Qian Qian Wu, Jian Xu
• Keywords: Double-Station Milling, Machining Optimization, Pareto

References

[1] Tolouei-Rad M, Bidhendi IM: On the optimization of machining parameters for milling operations. Int J Mach Tools and Manuf, Vol. 37 (1997) No. 1, pp.1-16.

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

[2] Wang J, Armarego EJA: Computer-aided optimization of multiple constraint single pass face milling operations. Mach Sci Technol, Vol. 5 (2001) No. 1, pp.77-99.

DOI: 10.1081/mst-100103179

[3] Wang J: Computer-aided economic optimization of endmilling operations. Int J Prod Econ, Vol. 54 (1998) No. 3, pp.307-320.

[4] Wang J, Kuriyagawa T, Wei XP, Guo DM: Optimization of cutting conditions for single pass turning operations using a deterministic approach. Int J Mach Tools Manuf, Vol. 42 (2002) No. 9, pp.12303-1033.

DOI: 10.1016/s0890-6955(02)00037-8

[5] Armarego EJA, Smith AJR, Wang J: Constrained optimization strategies and CAM software for single-pass peripheral milling. Int J Prod Res, Vol. 31 (1993) No. 9, pp.2139-2160.

DOI: 10.1080/00207549308956849

[6] Kee KP: Development of constrained optimization analyses and strategies for multi-pass rough turning operations. Int J Mach Tools Manuf, Vol. 36 (1996) No. 1, pp.115-127.

DOI: 10.1016/0890-6955(95)00009-m

[7] Wang J: Multiple-objective optimization of machining operations based on neural net-works. Int J Adv Manuf Technol, Vol. 8 (1993) No. 4, pp.235-243.

DOI: 10.1007/bf01748633

[8] Saravanan R, Asokan P, Sachidanandam M: A multiobjective genetic algorithm (GA) approach for optimization of surface grinding operations. Int J Mach Tools Manuf, Vol. 42 (2002), pp.1327-1334.

DOI: 10.1016/s0890-6955(02)00074-3

[9] Baskar N, Saravanan R, Asokan P, Prabhaharan G. Ants colony algorithm approach for multi-objective optimisation of surface grinding operations. Int J Adv Manuf Technol, Vol. 23 (2004), pp.311-317.

DOI: 10.1007/s00170-002-1533-6

[10] Xing Ai and Sigang Xiao: Concise handbook of machining parameters (Mechanical industry press, China 1994).

[11] Nawaz, Ripon K. S, Kwong S, et al: A real-coding jumping gene genetic algorithm (RJGGA) for multi-objective optimization. Information Sciences, Vol. 177 (2007) No. 2, pp.632-654.

DOI: 10.1016/j.ins.2006.07.019

[12] Fedoroff N, Botstein D. The Dynamic Genome: Barbara McClintock's Ideas in the Century of Genetics, Cold Spring Harbor Laboratory Press, New York (1992).

DOI: 10.1126/science.259.5098.1206