Evaluation of process parameters using GRA while machining low machinability material in dry and wet conditions

Abstract

The machinability rating of an engineering material is the fundamental property of material which decides increase and decrease of productivity, machining cost and optimisation of material selection in design of mechanical parts. Therefore, this work focuses on the study of surface roughness parameter (Ra), tool temperature (T) and material removal ate (MRR) while plain turning of low machinability AISI 304 stainless steel bar for various combination of machining parameters like feed (f), depth of cut (doc), and spindle speed (N) using Taguchi philosophy with non coated carbide inserts under dry and wet conditions.Surface roughness tester and thermocouple are used to measure the roughness and temperature respectively, while the MRR is calculated theoretically. Taguchi design of experiments (DOE) basing on Orthogonal Arrays (OA) and Signal-to-noise ratio (SN ratio) is used for experimental design. Responses thus generated are used to predict performance and significance of machining parameters combinations in turning operation on a CNC lathe using Analysis of Variance (ANOVA). Linear regression empirical models are modelled predicting more than 81% goodness-of-fit (R2). Individual optimality of responses is carried out using SN ratios of the responses and a multi-response optimization of responses is carried out using Grey Relational Analysis (GRA) for both the conditions of machining and better responses in wet condition while machining low machinability material