Modeling of Optimizing Multi-Hole Drilling Toolpath Distance with Multiple Tool Dimensions

Abstract

Abstract Multi-hole Drilling with Multiple Tool dimensions (MDMT) is a crucial technique in today’s industry, allowing manufacturers to satisfy the increasing demand for precise and high-quality components while adopting the latest technological advancements and environmental standards. This paper introduces and validates a computational model for MDMT, offering numerous advantages over conventional drilling methods, including enhanced efficiency, accuracy, cost-effectiveness, and flexibility. The computational model was developed for the MDMT problem using the Travelling Salesman Problem (TSP) concept to measure the total toolpath distance. The Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) are applied to solving 12 cases of MDMT problems with varying numbers of holes, classified as small, medium, and large, using MATLAB software R2022b. Note that the algorithms were evaluated based on their solution quality, with lower fitness values indicating better performance. Overall, GA performed the best across most hole configurations, achieving the optimal fitness value in 5 out of 12 cases (small, medium, and large), ACO performed better in 4 out of 12 cases (small and medium) and PSO performed better in 3 out of 12 cases (medium and large). The research emphasizes the potential of multi-dimensional tools for accomplishing intricate drilling tasks. Other than that, this paper contributes to the existing literature on MDMT and highlights the importance of multi-dimensional tools in modern manufacturing. Future research could optimize the proposed computational model for various materials and drilling scenarios in MDMT.