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

Kouta, Hanan Kamel; Elsanabary, Samar

doi:10.21608/pserj.2022.154750.1191

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

Document Type : Original Article

Authors

¹ Department of production engineering and mechanical design, Faculty of Engineering, Port Said University, Port Said, Egypt

² Department of productionengineering and mechanical design, Faculty of Engineering, Port Said University, Port Said, Egypt

10.21608/pserj.2022.154750.1191

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 vc (m/min), feed rate f (mm/rev), and depth of cut ap (mm) were interacted at a constant tilting angle of 20°, while considering the surface roughness (Ra) 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 Ra, followed by f and ap. The minimum Ra value was 0.58 µm obtained at 140 m/min of vc, 0.04 mm/rev of f, and 0.3 mm of ap. Finally, GA and hybrid CCD-GA optimization techniques have optimal surface roughness and compared with experimental results.

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