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 (R_a), and kerf at entry and exit ( $θ_{1}$ and $θ_{2}$ ), 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 L₂₇ orthogonal array. The grey relational analysis technique found that T_s 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, R_a, $θ_{1}$ and $θ_{2}$ , respectively. Additionally, the analysis of variance showed that W_p and A_os 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 $θ_{2}$ .

Keywords

Abrasive water jet machining Ti6AL4V grey relational analysis regression analysis

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