Evolutionary Computation in Combinatorial of Machining Parameters of Reinforced PEEK Composites Using NSGA-II

Issam Hanafi; Khamlichi Abdellatif; Francisco Mata Cabrera

doi:10.4028/www.scientific.net/AMR.875-877.652

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 875-877Evolutionary Computation in Combinatorial of...

Evolutionary Computation in Combinatorial of Machining Parameters of Reinforced PEEK Composites Using NSGA-II

Abstract:

The machining parameters for turning of PEEK CF30 using TiN coated tools under dry conditions have been optimized by using Non dominated Sorting Genetic Algorithm (NSGA-II), a non dominated solution set is obtained. The objectives considered are the minimisation of machining force thereby minimising specific cutting pressure as function of the main operating parameters. The results indicated that the minimal cutting parameters are preferred for reducing the machining force, and the minimal cutting speed, medium depth of cut and high feed rate are recommended for minimal specific cutting machining. As per the requirement, the manufacturing engineer should select the proper cutting parameters.

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Periodical:

Advanced Materials Research (Volumes 875-877)

Pages:

652-656

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.875-877.652

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Online since:

February 2014

Authors:

Issam Hanafi, Khamlichi Abdellatif, Francisco Mata Cabrera

Keywords:

Non-Dominated Sorting Genetic Algorithm (NSGA-II), Optimization, PEEK CF30, TiN Coated Tools, Turning

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References

[1] Bernardos, P.G. and Vosniakos, G.C. Predicting surface roughness in machining: a review. Int J Mach Tool Manuf. Vol. 43 (2003), p.833.

Google Scholar

[2] Sahin, Y., Riza and Motorcu, A. Surface roughness model for machining mild steel with coated carbide tool. Materials & Design. Vol. 26 (2005), p.321.

DOI: 10.1016/j.matdes.2004.06.015

Google Scholar

[3] Mata, F., Gaitonde, V.N., Karnik, S.R. and Davim, J.P. Influence of cutting conditions on machinability aspects of PEEK, PEEK CF 30 and PEEK GF 30 composites using PCD tools. J of Mater Proc Tech. Vol. 209 (2009), p. (1980).

DOI: 10.1016/j.jmatprotec.2008.04.060

Google Scholar

[4] Davim, J.P., Mata, F., Gaitonde, V.N. and Karnik, S.R. Machinability Evaluation in Unreinforced and Reinforced PEEK Composites using Response Surface Models. Journal of Thermoplastic Composite Materials. Vol. 23 (2010), p.15.

DOI: 10.1177/0892705709093503

Google Scholar

[5] Srinivas, N. and Deb, K. Multi-objective optimization using non dominated sorting in genetic algorithms. Evol Comput. Vol. 2 (1994), p.221.

Google Scholar

[6] Akundi, S. V. K., Simpson, T. W. and Reed, P. M. Proc. Of ASME Computers and Information in Engineering Conference, Paper No: DETC 2005/DAC-84905, California (2005), USA.

Google Scholar

[7] Kuriakose, S. and Shunmugam, M.S. Multi-objective optimization of wire-electro discharge machining process by non dominated sorting genetic algorithm. J Mater Process Tech. Vol. 170 (2005), p.133.

DOI: 10.1016/j.jmatprotec.2005.04.105

Google Scholar

[8] Latha, B. and Senthilkumar, V. S. Simulation Optimization of Process Parameters in Composite Drilling Process Using Multi-objective Evolutionary Algorithm. Advances in Recent Technologies in Communication and Computing. (2009), p.154.

DOI: 10.1109/artcom.2009.100

Google Scholar

[9] Senthilkumar, C., Ganesan, G. and Karthikeyan, R. Bi-performance optimization of electrochemical machining characteristics of Al/20%SiCp composites using NSGA-II. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. Vol. 9 (2010).

DOI: 10.1243/09544054jem1803

Google Scholar

[10] Saravanan, R., Ramabalan, S., Godwin Raja, N. and Natarajan, R. Evolutionary bi-criteria optimum design of robots based on task specifications. Int J Adv Manuf Technol . Vol. 41 (2009), p.386.

DOI: 10.1007/s00170-008-1483-8

Google Scholar

[11] Yang, S. H. and Natarajan, U. Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches. Int J Adv Manuf Technol. Vol. 49 (2010), p.773.

DOI: 10.1007/s00170-009-2404-1

Google Scholar

[12] Mata, F., Beamud, E., Hanafi, I., Khamlichi, A., Jabbouri, A. and Bezzazi, M. Multiple Regression Prediction Model for Cutting Forces in Turning Carbon-Reinforced PEEK CF30, Advances in Materials Science and Engineering, Volume 2010 (2010).

DOI: 10.1155/2010/824098

Google Scholar

[13] Palanikumar, K., Latha, B., Senthilkumar, V. S. and Karthikeyan, R. Multiple performance optimization in machining of GFRP composites by a PCD tool using non-dominated sorting genetic algorithm (NSGA-II). Metals and Materials Internat.

DOI: 10.1007/s12540-009-0249-7

Google Scholar