Statistical modelling of depth milling in Ti-6AL4V using abrasive water jet machining

Abstract

Multi-objective grey relational analysis optimization technique and multiple regression analysis were employed to determine the optimum values for depth of cut, surface roughness ( Ra), and kerf at entry and exit ([Formula: see text] and [Formula: see text]), for abrasive waterjet machining of Ti6AL4V materials. This method highlights a new process to extend the grey relational analysis technique for determining the optimum conditions for obtaining the best quality characteristics. The input parameters of the study were water pressure ( Wp), transverse speed ( Ts), abrasive mass flow rate ( Amf), abrasive orifice size ( Aos), nozzle/orifice diameter ratio ( N/Odia). The experiments were conducted as per the Taguchi-based L27 orthogonal array. The grey relational analysis technique found that Ts was the most significant parameter on the combined outputs. The regression models developed had an R2 of 81.58%, 79.79%%, 77.20%, and 74.39% for depth of cut, Ra, [Formula: see text] and [Formula: see text], respectively. Additionally, the analysis of variance showed that Wp and Aos had a significant influence on the output parameters. The predicted values were found to be reasonably close with the experimental values, and the maximum average deviation was 8.15% for [Formula: see text].