Efficient Path Planning for Drilling Processes: The Hybrid Approach of a Genetic Algorithm and Ant Colony Optimisation - AY, MUSTAFA (My University, 2024)
Efficiency in machining time during drilling is affected by various factors, with one key element being the machining path. Solving the machining path closely resembles the Travelling Salesman Problem (TSP). In this article, drilling on a sample model is simulated using a hybrid algorithm that is developed based on TSP. This hybrid algorithm (GACO) is created by combining the strengths of the Genetic Algorithm (GA) and Ant Colony Optimisation (ACO). Codes written to verify the stability of the algorithms were executed 10 times, and results were recorded indicating the shortest path and machining sequence. Accordingly, the performance of the hybrid GACO algorithm was observed to be 3.16% better than the ACO algorithm in terms of both total path length and total machining time. In terms of computation time, the ACO algorithm lagged behind the GACO algorithm by 6.46%. Furthermore, the hybrid GACO algorithm demonstrated enhanced performance in both total path length and total machining time when compared with the literature. This study aims to contribute to the industry, professionals, and practitioners in this field by providing cost and time savings.
Keywords: Makina Mühendisliği; Konstrüksiyon ve İmalat; Talaşlı İmalat Yöntemleri; Mühendislik ve Teknoloji; Mechanical Engineering; Construction and Manufacturing; Machining Methods; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Mühendislik; MÜHENDİSLİK, MEKANİK; Engineering, Computing & Technology (ENG); ENGINEERING; ENGINEERING, MECHANICAL; Makine Mühendisliği; Hesaplamalı Mekanik; Otomotiv Mühendisliği; Fizik Bilimleri; Computational Mechanics; Automotive Engineering; Physical Sciences; ant colony; drilling; machining; tool pathing optimisation; travelling salesman person