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 ([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 ( 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 T
s
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
, [Formula: see text] and [Formula: see text], 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 [Formula: see text].