Application of Grey Relational Analysis Along with Principal Component Analysis for Multi-Response Optimization of Hard Turning
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2016 by IJETT Journal|
|Year of Publication : 2016|
|Authors : Suha K. Shihab, Zahid A. Khan, Arshad Noor Siddiquee
|DOI : 10.14445/22315381/IJETT-V38P243|
Suha K. Shihab, Zahid A. Khan, Arshad Noor Siddiquee"Application of Grey Relational Analysis Along with Principal Component Analysis for Multi-Response Optimization of Hard Turning", International Journal of Engineering Trends and Technology (IJETT), V38(5),238-245 August 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Investigation on the effect of cutting speed, feed rate, depth of cut and different cutting conditions on the machining force components and surface roughness during hard turning of AISI 52100 has been presented in this paper. Nine turning experiments as per Taguchi’s standard L9 orthogonal array were performed on AISI 52100 hard alloy steel using a CNC lathe machine and cutting force component as well as surface roughness were measured. Subsequently, multi-response optimization was performed by employing grey relational and principal component analyses. The results revealed that grey relational analysis along with the principal component analysis is a simple as well as effective method for solving the multi-response optimization problem and it provides the optimal combination of hard turning parameters. Further, the analysis of variance (ANOVA) was also employed to identify the most significant parameter based on percentage of contribution of each machining parameter.
 Y. Huang, Y.K. Chou, and S.Y. Liang. “CBN tool wear in
hard turning: a survey on research progresses,” Int J Adv
Manuf Technol. Vol.35, 443-53, 2007.
 H. Aouici, M.A. Yallese, K. Chaoui, T. Mabrouki, and Rigal J-F. “Analysis of surface roughness and cutting force components in hard turning with CBN tool: prediction model and cutting conditions optimization,” Measurement. Vol.45, 344-53, 2012.
 Manna, A., and Salodkar, S. “Optimization of machining conditions for effective turning of E0300 alloy steel,” journal of materials processing technology. 203, 147-153, 2008.
 P. Chinnaiyan, and A. K. Jeevanantham. “Multi-Objective Optimization of Single Point Incremental Sheet Forming of AA5052 using Taguchi based Grey Relational Analysis Coupled with Principal Component Analysis,” International Journal of Precision Engineering and Manufacturing. Vol.15, 2309-2316, 2014
 K. Yang. “Improving automotive dimensional quality by using principal component analysis,” Qual Reliab Eng Int. vol.12, 1401-409, 1996.
 C-T. Su, L-I. Tong. “Multi-response robust design by principal component analysis,” Total Qual Manage. Vol.8, no.6, 409-416, 1997
 L-I. Tong, and C.H. Wang. “Multi-response optimization using principal component analysis and grey relational analysis,” Int J Ind Eng, vol. 9, no.4, 343-350, 2002.
 C.-H. Wang. “Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model,” Int J Adv Manuf Technol. Vol. 32, 617-624 , 2007.
 D. Philip Selvaraj, P Chandramohan., and M. Mohanraj. “Optimization of surface roughness, cutting force and tool wear of nitrogen alloyed duplex stainless steel in a dry turning process using Taguchi method,” Measurement. vol. 49, 205-215, 2014.
 E. Kuram, and B. Ozcelik. “Multi-objective optimization using Taguchi based grey relational analysis for micromilling of Al 7075 material with ball nose end mill,” Measurement. Vol.46, 1849-1864, 2013.
 T. Kivak. “Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts,” Measurement.vol.50, 19-28, 2014.
 S. Karabulut. “Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method,” Measurement. Vol.66, 139-149, 2015.
 N. Masmiati, and A. A.D. Sarhan. “Optimizing cutting parameters in inclined end milling for minimum surface residual stress -Taguchi approach,” Measurement. Vol.60, 267-275,2015.
 R. K. Pandey, and S.S. Panda, “Multi-performance optimization of bone drilling using Taguchi method based on membership function,” Measurement. Vol.59, 9-13, 2015.
 I. Asilturk, and H. Akkus. “Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method,” Measurement. vol.44, 1697-1704, 2011.
 Jagadish, and A. Ray. “Optimization of process parameters of green electrical discharge machining using principal component analysis (PCA),” Int J Adv Manuf Technol. Vol. 1-13. [DOI 10.1007/s00170-014-6372-8], 2015.
 S.K. Shihab, Z. A. Khan, A. Mohammad, and A. N. Siddiquee. “Investigation of Surface Integrity during Wet Turning of Hard Alloy Steel,” Int. J. Machining and Machinability of Materials. Vol. 16, no.1, 22-37, 2014.
 A. N. Siddiquee, Z. A. Khan, and Z. Mallick. “Grey relational analysis coupled with principal component analysis for optimisation design of the process parameters in in-feed centreless cylindrical grinding,” Int J Adv Manuf Technol. Vol.46, no.9, 983-992, 2010.
 Z.C. Lin, and C.Y. Ho. “Analysis and application of grey relation and ANOVA in chemical-mechanical polishing process parameters,” Int J Adv Manuf Technol. Vol.21: 10- 14, 2003.
 K. Pearson. “On lines and planes of closest fit to systems of points in space,” Phil Manag Ser. Vol.62, 559-572, 1901.  H. Hotelling. “Analysis of a complex of statistical variables into principal components,” J Educ Psychol. Vol.24, 417- 441, 1993.
 F-C. Chen, Y-F. Tzeng, M-H. Hsu, and W-R. Chen. “Combining Taguchi Method, Principal Component Analysis and Fuzzy Logic to the Tolerance Design of A Dual-Purpose Six-Bar Mechanism,” Transactions of the Canadian Society for Mechanical Engineering. Vol. 34, no.2, 277-293, 2010.
Hard turning, Optimization, Taguchi method, Grey relational analysis, Principal component analysis.