Title: Mathematical modelling and multi-response optimisation using response surface methodology and grey based Taguchi method: an experimental investigation

Authors: Ashok Kumar Sahoo; Bidyadhar Sahoo

Addresses: School of Mechanical Engineering, KIIT University, Bhubaneswar, Orissa, 751024, India. ' Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Orissa, 759146, India

Abstract: The present study deals with the application of full factorial design of experiment and response surface methodology in developing mathematical model considering input variables, i.e., cutting speed, feed and depth of cut for flank wear and surface roughness when hard turning AISI 4340 steel (HRC 47±1) using multilayer coated carbide insert (TiN/TiCN/Al₂O₃/ZrCN) under dry environment. Mathematical model output concluded that the RSM models proposed are statistically significant and adequate because of higher R₂ value. It shows the high correlation between the experimental and predicted values. From ANOVA table, it is evident that, feed is the significant factor affecting surface roughness followed by cutting speed. Depth of cut is found to be insignificant from the studied range. For flank wear, cutting speed is the most significant factor followed by depth of cut. Feed is found to be insignificant for flank wear. The improvement of grey relational grade from initial parameter combination (d2-f3-v4) to the optimal parameter combination (d4-f1-v3) is found to be 0.4645 and is improved through the approach of Taguchi method with grey relational analysis to optimise the process parameter for multiple performances (surface roughness and flank wear).

Keywords: hard turning; coated carbide; surface roughness; flank wear; response surface methodology; RSM; grey relational analysis; GRA; Taguchi methods; mathematical modelling; multi-response optimisation; factorial DOE; design of experiments; cutting speed; feed; depth of cut; carbide inserts.

DOI: 10.1504/IJEDPO.2011.042745

International Journal of Experimental Design and Process Optimisation, 2011 Vol.2 No.3, pp.221 - 242

Published online: 11 Oct 2014 *

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