Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
A Comparison of Optimization Methods in Cutting Parameters Using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Micro Genetic Algorithm (MGA)
Abolfazl Golshan, Mostafa Rezazadeh Shidar
Pages - 62 - 73     |    Revised - 01-09-2011     |    Published - 05-10-2011
Volume - 2   Issue - 2    |    Publication Date - September / October 2011  Table of Contents
Cutting Parameters, Surface Roughness, Tool life Criteria, Optimizing, NSGA-II, MGA
Since cutting conditions have an influence on reducing the production cost and time and deciding the quality of a final product the determination of optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool geometry is one of vital modules in process planning of metal parts. With use of experimental results and subsequently, with exploitation of main effects plot, importance of each parameter is studied. In this investigation these parameters was considered as input in order to optimized the surface finish and tool life criteria, two conflicting objectives, as the process performance simultaneously. In this study, micro genetic algorithm (MGA) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were compared with each other proving the superiority of Non-dominated Sorting Genetic Algorithm over micro genetic since Non-dominated Sorting Genetic Algorithm results were more satisfactory than micro genetic algorithm in terms of optimizing machining parameters.
CITED BY (6)  
1 Radhika, n., vijaykarthik, k., & shivaram, p. (2015). adhesive wear behaviour of aluminium hybrid metal matrix composites using genetic algorithm. Journal of Engineering Science and Technology, 10(3), 258-268.
2 Tamizi, M., Izman, S., Shirdar, M. R., Al-Mayyahi, N. N., & Parhizkar, S. (2015). Predicting Micro-Hardness of Post-Treated Hydroxyapatite Layer Using Surface Response Methodology. Journal of Soft Computing and Decision Support Systems, 2(1), 1-7.
3 Dhandapani, K., Vasanthkumar, P., & Nagarajan, S. (2014). A Meta-Heuristic Evolutionary Algorithm to Optimize Machining Parameters in Turning AISI 4340 Steel. Journal of Advanced Engineering Research, 1(2).
4 Margonis, S. (2014). Preliminary design of an autonomous underwater vehicle using multi-objective optimization (Doctoral dissertation, Monterey, California: Naval Postgraduate School).
5 Nayak, B. E. D. A. M. A. T. I. (2014). Multi-response optimization in machining: exploration of TOPSIS and Deng’s similarity based approach (Doctoral dissertation).
6 Ojo, O. O., Ogedengbe, T. I., & Kareem, B. (2012). Effect of Cutting Conditions on Release Time of Jobs Using Lathe Machine. International Journal of Engineering Innovation and Management, 2(3), 59-66.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 Dereli, D., Filiz, I.H., Bayakosoglu, A., Optimizing cutting parameters in process planning of prismatic parts by using genetic algorithms. International Journal of Production Research 39 (15), 3303–3328, 2001
2 Pandey PPC, Pal S. In: Proceedings of the Third International Conference in Computer Integrated Machining Singapore, vol. 1, pp. 812–9, 1995
3 Hsu VN, Daskin M, Jones PC, Lowe TJ. Tool selection for optimal part production: a Lagrangian relaxation approach. IIE Trans; 27:417–26, 1995.
4 N. Srinivas and D. Kalyanmoy, Jl. Evol. Comput. 2, 221, 1994.
5 D. Kanagarajan, R. Karthikeyan, K. Palanikumar, J. P. Davim, Int. J. Adv. Manuf Tech. 36, 1124, 2008.
6 M.Rozenek.M,J.Kozak,L.Dabrovwki,K.Lubkovwki, Electrical discharge machining characteristics of metal matrix composites,J.Mater.Process.Technol.109, pp.367-370, 2001.
7 N.Tosun, C.Cogun , H.Pihtili , "The effect of cutting parameters on wire crater sizes in WEDM", int. J . Adv. Manuf. Techonl., Vol. 21, pp. 857-865, 2003.
8 N.Tosun,C.Cogun, An investigation on wire wear in WEDM, j.Mater.Process.Technol.1349 (3) , pp. 273-278, 2003.
9 Wang K., Gelgele H.L., Wang Y., Yuan Q., Fang M., "A hybrid intelligent method for modelling the EDM process", Int. J. Machine Tools Manuf. Vol.43,pp.995–999,2003
10 Su J.C. , Kao J.Y., Tarng Y.S. , "Optimization of the electrical discharge machining process using a GA-based neural network", Int. J. Adv. Manuf. Technol. Vol.24,pp.81–90,2004
11 M. Sivakumar and S. M. Kannan, Int. J. Adv. Manuf Tech. 32, 591, 2007.
12 Debabrata Mandal., Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm- II, Journal of Materials Processing Technology 186, pp.154–162, 2007.
13 Krishnakumar, K., “Micro-Genetic Algorithms for Stationary and Non-Stationary Function Optimization,” SPIE 1196, Intelligent Control and Adaptive Systems, 1989.
14 Senecal P. K., “Development of a Methodology for Internal Combustion Engine Design Using Multi-Dimensional Modeling with Validation Through Experiments,” Ph.D. Dissertation, University of Wisconsin-Madison, 2000.
15 K. Palanikumar, B. Latha , V.S.Senthilkumar ,R.Karthikeyan, Multiple Performance Optimization in Machining of GFRP Composites by a PCD Tool using Non-dominated Sorting Genetic Algorithm (NSGA-II),Met. Mater. Int.,Vol.15, No. 2, pp. 249-258, 2009.
Mr. Abolfazl Golshan
- Malaysia
Mr. Mostafa Rezazadeh Shidar
- Malaysia