Genetic Algorithms: A Solution to Fiber Reinforced Composite Drilling Challenges

Abstract

Natural fiber composites are a group of materials that have gained increasing attention in recent years due to their potential to replace traditional materials in various applications. However composite materials are made up of layers of fibers and resin that can separate from each other during drilling, leading to delamination. This paper proposes a multi-objective optimization approach for drilling natural fiber composites, considering three key drilling parameters: cutting speed, feed rate and tool geometry. The objective is to minimize delamination and thrust force. Multiple linear regression analysis is employed to develop the regression equations for each objective function, which are then optimized simultaneously using a multi-objective genetic algorithm (MOGA). The results demonstrate that the proposed approach can effectively identify the optimal drilling parameters that balance the trade-offs between the competing objectives. The proposed approach can be useful for improving the efficiency and quality of drilling natural fiber composites, which are increasingly used in various industrial applications.