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Asian Journal of Research in Social Sciences and Humanities
Year : 2016, Volume : 6, Issue : 10
First page : ( 179) Last page : ( 195)
Online ISSN : 2249-7315.
Article DOI : 10.5958/2249-7315.2016.01006.6

Experimental Investigation and Optimization of Machining Parameters in CNC Turning Operation of Duplex Stainless Steel

Dinesh S, Antony A Godwin, Karuppusamy S, Kumar B Suresh, Vijayan V

Department of Mechanical Engineering, K. Ramakrishnan College of Technology, Tiruchirapalli, Tamilnadu, India

Online published on 14 October, 2016.

Abstract

Turning is the one of the material removal process that finds vast application in manufacturing vital components such as axles, shafts, etc, in large numbers. In that, every manufacturing unit focuses on high quality with increased production rate, which decides the profit level of the firm. So, its important to determine the optimal machining parameters. This work deals with the selecting the optimal parameters that plays a critical role in increasing the productivity with desired product quality at minimum cost and reduced lead time. The problem is related to a single quality characteristic is called single objective optimization. It is difficult to select the optimal parameters, if more than one quality characteristics are considered. The experiments were conducted for both dry and wet conditions with Taguchi's L18 mixed orthogonal array. Cemented carbide tool was used to machine the Duplex alloy steel work pieces on Computer Numerical Controlled (CNC) lathe. The optimal parameters were found using Taguchi's approach individually for minimum surface roughness and maximum material removal rate (MRR) and were compared with the results obtained from Grey Relational Analysis by combing the objective functions into an equivalent objective. The influence of the machining parameters cutting speed, feed rate, depth of cut and tool nose radii were studied using Analysis of Variance technique (ANOVA).

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Keywords

MRR, Surface roughness, Duplex alloy steel, Cemented carbide tool, GRA, ANOVA.

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