Multi-objective Optimization of Turning Titanium Alloy by Grey Rational Analysis

Abstract

In order to optimize the turning operation on titanium alloy, one needs to understand how different parameter combinations serve under varied conditions and needs. It is critical to compare single and multiple objectives in optimization. This study focused on single and multi-objective optimization evaluations for the primary performance responses of surface roughness (SR) and tool flank wear (TW), aiming for optimal turning process parameters. To examine the effects of feed rate (f), cutting speed (R), depth of cut (d), cutting angle (X), and mist inlet pressure (P) on the responses of both SR and TW, Taguchi L27 orthogonal arrays were used in this study. Moreover, analysis of variances (ANOVA) and grey relational analysis (GRA) have been incorporated in this study, where the former was used to study the influence of each parameter on SR and TW while the latter was used to determine the best combinations for the multi-objective optimization process. The results revealed that for single-objective optimization of SR, the optimal values for f, R, d, X, and P were found to be 0.3 mm/rev, 250 rpm, 1.5 mm, 100 degrees, and 1 bar mist inlet pressure, respectively. For single-objective optimization of TW, the optimal values for f, R, d, X, and P were found to be 0.20 mm/rev, 250 rpm, 0.5 mm, 50 degrees, and 3 bar mist inlet pressure, respectively. Meanwhile, for multi-objective optimization, the optimal values for f, R, d, X, and P were found to be 0.30 mm/rev, 500 rpm, 2.0 mm, 75 degrees, and 1 bar mist inlet pressure, respectively. These optimal combinations of turning parameters resulted in the lowest SR and TW simultaneously.