OPTIMIZATION OF DRILLING PROCESS ON CARBON-FIBER REINFORCED PLASTICS USING GENETIC ALGORITHM

Abstract

The usages of carbon-fiber reinforced polymer (CFRP) in aerospace, defense, and structural fields are increasing due to their excellent properties. However, the materials design, forming of material, machine tool and processing conditions are major tasks in manufacturing industries. Particularly, the micro feature making on macro-components using vertical machining center is a challenge nowadays. In this work, two different drill bits, such as high-speed steel (HSS) and solid carbide (SC) micro-drill, were used to make drilling on CFRP material. The performance of drills was evaluated by obtaining minimum delamination and stress in drilling by varying cutting velocity (CV), feed rate (FR), and air pressure (AP). Regression equations were formed according to the measured quality performance characteristics. The linear weighted method-based combined objective function algorithm and Genetic Algorithm was followed to multi-objective optimization. Besides, the most influencing factors were also identified and discussed using analysis of variance. The results explained that the SC micro-drill performance was better than HSS micro-drill. Also, the CV has the most eminent parameters followed by FR.