Measurement and multi-response optimization of turning parameters for magnesium alloy using hybrid combination of Taguchi-GRA-PCA technique. (15th July 2020)

• Record Type: Journal Article
• Authors: Viswanathan, R.; Ramesh, S.; Maniraj, S.; Subburam, V.
• Is Part Of: Measurement. Volume 159(2020)
• Publication Date: 2020-07-15
• Subjects: Cutting force -- Magnesium -- Optimization -- Turning -- GRA -- PCA; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107800
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: The machining condition of Magnesium alloy requires attention due to chip ignition risk. Turning of magnesium alloy is carried out under dry with PVD coated cutting inserts. Optimal cutting parameters are obtained using Hybrid Combination of Taguchi-GRA-PCA Technique. Feed rate is the most dominant factor for this multi-objective characteristics process. Short chips were observed under low cutting speed, higher feed and depth of cut conditions. Abstract: The requirement for magnesium alloys has been rising constantly over the years because of the thrust towards weight reduction in various fields that demand fuel-efficient automobile, low-priced electronics, hand-held compact devices and biodegradable medical implants. Hence machinability studies of magnesium alloys contribute significantly to use them in appropriate applications. The present work is aimed to investigate the cutting force (Fz), material removal rate (MRR), tool flank wear (VB ) and surface roughness (Ra) in turning of magnesium alloy with physical vapour deposition (PVD) coated carbide insert in dry conditions. The tests were carried out on the basis of the orthogonal array of Taguchi's L27 . To identify the optimal parameters setting, a combination of principal component analysis (PCA) and grey relational analysis (GRA) has been conducted. From the analysis of variance, it was revealed that depth of cut is the significant contributing parameter on this multiple performance characteristics process.