Abstract

The present study is focused on the multi-objective optimization of performance parameters such as specific energy (u), metal removal rate (MRR) and surface roughness(Ra) obtained in grinding of Al-SiC35P composites. The enhanced elitist non-dominated sorting genetic algorithm (NSGA -II) is used to solve this multi-objective optimization problem. Al-SiC specimens containing 8 vol. %, 10 vol. % and 12 vol. % of silicon carbide particles of mean diameter 35µm, feed and depth of cut were chosen as process variables. A mathematical predictive model for each of the performance parameters was developed using response surface methodology (RSM). Further, an enhanced NSGA-II algorithm is used to optimize the model developed by RSM. Finally, the experiments were carried out to validate the results obtained from RSM and enhanced NSGA-II. The results obtained were in close agreement, which indicates that the developed model can be effectively used for the prediction.

References

• Cronjage L. and Meister D. 1992 Machining of fibre and particle-reinforced aluminium, Annals of CIRP, 41(1), 63–66.
• Kwak J.S. and Kim Y.S. 2008 Mechanical properties and grinding performance on aluminum-based metal matrix composites, Journal of Materials Processing Technology. 201(1-3) 596-600.
• Jae-Seob Kwak 2005 Application of Taguchi and response surface methodologies for geometric error in surface grinding process, International Journal of Machine Tools & Manufacture. 45(3-4) 327–334.
• Krajnik P., Kopac J. and Sluga A. 2005 Design of grinding factors based on response surface methodology, Journal of Materials Processing Technology. 162–163 , 629–636.
• Jones K., Johnson M and Liou J.J., 1992 The Comparison of response surface and Taguchi methods for multiple-response optimization using simulation, in symposium of IEEE/CHMTI Int. Electronics Manufacturing Technology Baltimore, USA
• Wen X.M., Tay A.A.O. and Nee A-Y.C. 1992 Microcomputer based optimization of the surface grinding process, Journal of Materials Processing Technology. 29(5) 75–90
• Saravanan R. and Sachithanandam M. 2001 Genetic algorithm for multi variable surface grinding process optimization using a multi-objective function model, International Journal of Advanced Manufacturing Technology. 17(5) 330-338
• Asokan P, Bhaskar N., Babu K., Prabhaharan G. and Saravanan R. 2005 Optimization of surface grinding operations using particle swarm optimization technique, Journal of Manufacturing Science and Engineering. 127, 885-892
• Bhaskar V. Gupta S.K. and A.K. Ray 2001 Multiobjective optimization of an industrial wiped film (polyethylene terephthalate) reactor: some further insights, Computers & Chemical Engineering. 25 (2–3), 391–407
• Gopala Krishna A. 2007 Optimization of surface grinding operations using a differential evolution approach, Journal of Materials Processing Technology. 183(2-3) 202-209
• Suresh P.V.S., Rao P.V. and Deshmukh S.G. 2002 A genetic algorithmic approach for optimization of surface roughness prediction model, International Journal of Machine Tools & Manufacture. 42(6) 675–680
• Saravanan R., Asokan P. and Sachidanandam M. 2002 A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations, International Journal of Machine Tools & Manufacture. 42(12) 1327–1334
• Hsu C.M. 2004 An integrated approach to enhance the optical performance of couplers based on neural networks, desirability functions and tabu search, International Journal of Production Economics. 92(3) 241-54
• Azouzi R., and Guillot M. 1998 On-line optimization of the turning using an inverse process neuro controller. Journal of Manufacturing Science and Engineering, 120(1) 101–107
• Kilickap E., Huseyinoglu M. and A. Yardimeden 2010 Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm International Journal of Advanced Manufacturing Technology.52(1-4)
• Benardos P. G, Vosniakos G. C 2003 Predicting surface roughness in machining: a review, International Journal of Machine Tools & Manufacture 43 (8) 833–844
• Pai D., Rao S.S., Shetty R. 2011 Studies on application of response surface methodology for the analysis of specific energy during grinding of DRAC’s, In National Conference on Advances in Mechanical Engineering-2011, January 3-5, Manipal, India
• Pai D, Rao S.S, Shetty R.and Nayak R. 2010 Application of response surface methodology on surface roughness in grinding of aerospace Materials (6061Al-15vol%SiC), ARPN Journal of Engineering and Applied Sciences. 5(6),
• Shetty R., Pai R.B., Rao S.S , and Kamath V. 2008 Machinability study on discontinuously reinforced aluminium composites (DRACs) using response surface methodology and Taguchi’s design of experiments under dry cutting condition, Maejo International Journal of Science and Technology. 2(1), 227-239
• Montgomery D.C. 2001 Design and Analysis of Experiments, John Wiley and Sons, New York.
• Deb K. 2000 An efficient constraint handling method for genetic algorithms, Computer Application in Applied Mechanics and Engineering. 186 (2-4) 311-388
• Deb K, Pratap A, Agarwal S, Meyarivan T 2002 A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transaction on Evolutionary Computation. 6(2) 182-197
• D’Souza R. G. L. Chandra Sekaran K. and Kandasamy A 2010 Improved NSGA-II based on a novel ranking scheme, Journal of Computing , 2 (2), 91-95.