IIM  Vol.2 No.1 , January 2010
Simultaneous Optimization of Correlated Multiple Surface Quality Characteristics of Mild Steel Turned Product
ABSTRACT
Present work highlights application of utility theory combined with Principal Component Analysis (PCA) and Taguchi’s robust design for simultaneous optimization of correlated multiple surface quality characteristics of mild steel machined product prepared by straight turning operation. The study aims at evaluating the most favorable process environment followed by an optimal parametric combination for achieving high surface quality. Traditional Taguchi based hybrid optimization approaches rely on the assumption that quality indices are uncorrelated or independent. But it is felt that, in practice, there may be some correlation among various quality indices (responses) under consideration. To overcome this limitation of Taguchi approach, the present study proposes application of PCA to convert correlated responses into uncorrelated quality indices called principal components. Finally based on utility theory, Taguchi method has been applied to solve this optimization problem. The study demonstrates detailed methodology and concludes robustness and flexibility of the proposed optimization technique and validates its effectiveness through a case study in which correlated multiple response characteristics of turning operation have been optimized.

Cite this paper
nullS. DATTA and S. MAHAPATRA, "Simultaneous Optimization of Correlated Multiple Surface Quality Characteristics of Mild Steel Turned Product," Intelligent Information Management, Vol. 2 No. 1, 2010, pp. 26-39. doi: 10.4236/iim.2010.21004.
References
[1]   W. S. Lin, B. Y. Lee, and C. L. Wu, “Modeling the surface roughness and cutting force for turning,” Journal of Materials Processing Technology, Vol. 108, pp. 286–293, 2001.

[2]   C. X. Feng (Jack) and X. Wang, “Development of empirical models for surface roughness prediction in finish turning,” International Journal of Advanced Manufacturing Technology, Vol. 20, pp. 348–356, 2002.

[3]   P. V. S. Suresh, P. V. Rao and S. G. Deshmukh, “A genetic algorithmic approach for optimization of surface roughness prediction model,” International Journal of Machine Tools and Manufacture, Vol. 42, pp. 675–680, 2002.

[4]   E. D. Kirby, Z. Zhang and J. C. Chen, “Development of an accelerometer based surface roughness prediction system in turning operation using multiple regression techniques”, Journal of Industrial Technology, Vol. 20, No. 4, pp. 1–8, 2004.

[5]   T. ?zel and Y. Karpat, “Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks,” International Journal of Machine Tools and Manufacture, Vol. 45, pp. 467–479, 2005.

[6]   A. Kohli and U. S. Dixit, “A neural-network-based methodology for the prediction of surface roughness in a turning process,” International Journal of Advanced Manufacturing Technology, Vol. 25, pp. 118–129, 2005.

[7]   S. K. Pal and D. Chakraborty, “Surface roughness prediction in turning using artificial neural network,” Neural Computing and Application, Vol. 14, pp. 319–324, 2005.

[8]   S. G. Ahmed, “Development of a prediction model for surface roughness in finish turning of aluminium,” Sudan Engineering Society Journal, Vol. 52, No. 45, pp. 1–5. 2006

[9]   N. R. Abburi and U. S. Dixit, “A knowledge-based system for the prediction of surface roughness in turning process” Robotics and Computer-Integrated Manufacturing, Vol. 22, pp. 363–372, 2006.

[10]   Z. W. Zhong, L. P. Khoo and S. T. Han, “Prediction of surface roughness of turned surfaces using neural networks,” International Journal of Advance Manufacturing Technology, Vol. 28, pp. 688–693, 2006.

[11]   A. Doniavi, M. Eskanderzade and M. Tahmsebian “Empirical modeling of surface roughness in turning process of 1060 steel using factorial design methodology,” Journal of Applied Sciences, Vol. 7, No. 17, pp. 2509–2513. 2007.

[12]   S. Y. Kassab and Y. K. Khoshnaw, “The effect of cutting tool vibration on surface roughness of work piece in dry turning operation,” Engineering and Technology, Vol. 25, No. 7, pp. 879–889, 2007.

[13]   A. M. A. Al-Ahmari,”Predictive machinability models for a selected hard material in turning operations,” Journal of Materials Processing Technology, Vol. 190, pp. 305–311, 2007.

[14]   S. Thamizhmanii, S. Saparudin and S. Hasan, “Analysis of surface roughness by using Taguchi method,” Achievements in Materials and Manufacturing Engineering, Vol. 20, No. 1–2, pp. 503–505, 2007.

[15]   M. Y. Wang and T. S. Lan, “Parametric optimization on multi-objective precision turning using grey relational analysis,” Information Technology Journal, Vol. 7, pp. 1072–1076, 2008.

[16]   P. Sahoo, T. K. Barman and B. C. Routara, “Taguchi based practical dimension modeling and optimization in CNC turning,” Advance in Production Engineering and Management, Vol. 3, No. 4, pp. 205–217, 2008.

[17]   B. S. Reddy, G. Padmanabhan and K. V. K. Reddy, “Surface roughness prediction techniques for CNC turning,” Asian Journal of Scientific Research, Vol. 1, No. 3, pp. 256–264, 2008.

[18]   T. S. Lan, C. Y. Lo, M. Y. Wang and A. Y. Yen, “Multi quality prediction model of cnc turning using back propagation network,” Information Technology Journal, Vol. 7, No. 6, pp. 911–917, 2008.

[19]   R. Thamma, “Comparison between multiple regression models to study effect of turning parameters on the surface roughness,” Proceedings of the 2008 IAJC-IJME International Conference, ISBN 978-1-606 43-379-9, Paper 133, ENG 103, pp. 1–12, 2008.

[20]   B. Fnides, H. Aouici, M. A. Yallese, “Cutting forces and surface roughness in hard turning of hot work steel X38CrMoV5-1 using mixed ceramic,” Mechanika, Vol. 2, No. 70, pp. 73–78, 2008.

[21]   C. K. Biswas, B. S. Chawla, N. S. Das, E. R. K. N. K. Srinivas, “Tool wear prediction using neuro-fuzzy system”, Institution of Engineers (India) Journal (PR), Vol. 89, pp. 42–46, 2008.

[22]   R. Shetty, R. Pai, V. Kamath and S. S. Rao, “Study on surface roughness minimization in turning of DRACs using surface roughness methodology and Taguchi under pressured steam jet approach,” ARPN Journal of Engineering and Applied Sciences, Vol. 3, No. 1, pp. 59–67, 2008.

[23]   S. Datta, G. Nandi, A. Bandyopadhyay and P. K. Pal, “Application of PCA based hybrid Taguchi method for multi-criteria optimization of submerged arc weld: A case study,” For International Journal of Advanced Manufacturing Technology, (Article in press) DOI: 10.1007/ s00170-009-1976-0, 2009.

[24]   J. Antony, “Multi-response optimization in industrial experiments using Taguchi’s quality loss function and Principal Component Analysis,” Quality and Reliability Engineering International, Vol. 16, pp. 3–8, 2000.

[25]   R. S. Walia, H. S. Shan, and P. Kumar, “Multi-response optimization of CFAAFM process through Taguchi method and utility concept,” Materials and Manufacturing Processes, Vol. 21, pp. 907–914, 2006.

[26]   S. Datta, A. Bandyopadhyay, and P. K. Pal, “Application of Taguchi philosophy for parametric optimization of bead geometry and HAZ width in submerged arc welding using mixture of fresh flux and fused slag”, for International Journal of Advanced Manufacturing Technology, Vol. 36, pp. 689–698, 2008.

 
 
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