Multi-Objective Optimization of Drill-Bit Assisted Abrasive Flow Machining Process through Taguchi Based Grey Relational Analysis

Abstract

The drill-bit assisted abrasive flow machining (AFM) process is usually chosen for finishing operation in manufacturing industries where the fine surface finish of the component is an important criterion and considered as primary response in the present work. A number of experiments have been conducted according to Box-behnken design considering EN 24 as work piece material. Experiments were performed under different machining conditions by varying the parameters such as abrasive particle size, media viscosity, drill bit diameter and number of process cycle. The material removal rate (MRR) has been considered as secondary response of this process. In the present paper, a multi-objective optimization technique using Taguchi based Grey relational analysis has been applied to optimize the process performance of the drill-bit assisted AFM. How could a complicated multiple performance characteristics simplified to a single objective optimization problem has been presented here by this approach. The specific targets are minimum surface roughness and maximum material removal rate. According to importance of quality characteristics there are three criteria for optimization in grey relational analysis, which are „Larger-the-Better‟, „Smaller-the-Better‟ and „Nominal-the-Best‟. In the present analysis the lower value of surface roughness represents smooth surface i.e. better finishing performance, therefore „Lower-the-Better‟ criteria is chosen for surface roughness. On the other hand the higher value of MRR indicates more economical as compared to other finishing processes, therefore „Higher-the-Better‟ is chosen for MRR. In the present work smaller surface roughness and larger MRR are desirable. The optimal parametric setting obtained from grey relational analysis has been validated by a confirmation test.