Multi criteria optimization in hard turning of titanium alloy ti 6al 7nb using taguchi based grey relational analysis coupled with principal component analysis

Abstract

An attempt made to optimize the turning parameters of Titanium ALLOY Ti-6Al-7Nb using Taguchi based GREY relational analysis coupled with principal component analysis. This optimization method provides reliable, fast and efficient that can provide a suitable solution to many problems faced by the manufacturing industries today. Titanium-Metal of future selected as research workpiece material in the experiments carried, as per the design of experiments approach to minimize cost, time, maximize productivity and quality. Significant process parameters such as cutting speed, feed, depth of cut, cutting environment, insert shape, insert coating material and nose radius and response variable surface roughness, tool wear, roundness, material removal rate, power consumption, and workpiece-tool temperature considered for the research work. Experiments were conducted using L18 orthogonal array. The degree of effect in each process parameter on the individual response variable analyzed from the experimental results obtained using weighted GREY relational grade. The influence of each process parameter is carried out using Analysis of Variance. From GRA the optimal conditions are obtained as cutting speed (100 m/min), feed (0.24 mm/rev), depth of cut (0.2 mm) and tool nose radius (0.8 mm), insert angle (80°), tool coating type (TiAlN) and cutting environment (wet) for optimal response variable surface roughness (0.2881 and#956;m), tool wear (0.1and#956;m), roundness (1.76 and#956;m), material removal rate (1930 mm3 /min), temperature (44.1ºC) and power consumption (0.038 Kwh). The feed identified as the most significant parameter for the tuning operationaccording to the weighted sum grade of the response variables. The weighted GRG value increase from 0.0589 to 0.136 which confirms the improvement in the performance of laborious turning process using optimal values of process parameters. Regression technique is used for modelling and analysis of the problem. The predicted and measured values are quite close which indicates that developed mo