Hybrid Central Composite Design and Genetic Algorithm to Optimize Turning Parameters for Surface Roughness in Self-propelled Rotary Tools

Abstract

This paper investigates experimentally the efficiency of a self-propelled rotary tool (SPRT) using carbide inserts during turning of K110 alloy steel. The cutting conditions, namely, cutting velocity v c (m/min), feed rate f (mm/rev), and depth of cut a p (mm) were interacted at a constant tilting angle of 20°, while considering the surface roughness (R a ) as performance criteria. The exploratory strategy is based on the central composite design (CCD), which was used to investigate at the influence of cutting boundaries on a superficial harshness level to observe the optimal cutting conditions. A second-order regression model was created. The performance parameters of the turning operation were studied using analysis of variance. A genetic algorithm (GA) was applied to optimize the SPRT cutting condition. The surface roughness and corresponding cutting conditions were optimized by creating a hybrid CCD-GA. The results showed that vc has the most impact on R a , followed by f and a p . The minimum R a value was 0.58 µm obtained at 140 m/min of v c , 0.04 mm/rev off, and 0.3 mm of a p . Finally, GA and hybrid CCD-GA optimization techniques have optimal surface roughness and compared with experimental results.