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

Multi-objective grey relational analysis optimization technique and multiple regression analysis were employed to determine the optimum values for depth of cut, surface roughness (R-a), and kerf at entry and exit (theta(1) and theta(2)), 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 (W-p), transverse speed (T-s), abrasive mass flow rate (A(mf)), abrasive orifice size (A(os)), nozzle/orifice diameter ratio (N/O-dia). The experiments were conducted as per the Taguchi-based L-27 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 R-2 of 81.58%, 79.79%%, 77.20%, and 74.39% for depth of cut, R-a, theta(1) and theta(2), 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 theta(2).