Multi-objective optimization using Taguchi based grey relational analysis in turning of Rock dust reinforced Aluminum MMC. (June 2020)

• Record Type: Journal Article
• Authors: Soorya Prakash, K.; Gopal, P.M.; Karthik, S.
• Is Part Of: Measurement. Volume 157(2020)
• Publication Date: 2020-06
• Subjects: Aluminium -- Rock Dust -- Stir casting -- Machining -- Optimization -- GRA; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107664
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: Rock dust, a naturally available cheap material is used as the reinforcement. Effect of materials and machining factors on Ra & MRR were analysed during turning. Multi criteria optimization was carried over through Taguchi based GRA. Developed mathematical models effectively predict Ra and MRR with minimal error. Abstract: The present work deals with the optimization of material and machining parameters for surface finish and Material Removal Rate (MRR) enhancements while turning Aluminium/Rock dust composite through Taguchi and Grey Relational Analysis (GRA). Rock dust particles weight percent (5, 10 & 15%) and particle size (10, 20 & 30 μm) are varied accordingly to investigate the effect of reinforcement parameters on composite properties. Along with reinforcement weight % and particle size, also the turning parameters viz. speed, feed and depth of cut are chosen as input parameters with surface roughness (Ra ) and MRR as responses. CNC turning is performed based on the L27 orthogonal array designed by Taguchi approach. Results expose that feed has remarkable effect on Ra and MRR rather than any of the other parameters examined. The optimum parameter combination identified through Multi criteria optimization technique GRA is S2W2N3F1D3 and the improvement in GRG approximates to 0.194.