The Evaluation of Machining Time in Drilling Process using Modified Ant Colony Optimization and Conventional Method

Abstract

Machining time is one of the aspects of drilling process which affects productivity and cost efficiency. To minimize the machining time, optimization approaches which are based on Artificial Intelligence (AI) methods have been implemented to determine the optimum rapid tool path length in the drilling process.  Ant Colony Optimization (ACO) was used in this study to optimize the tool path in the drilling process. However, ACO had to be modified due to facing convergence issues, leading to suboptimal solutions or enhancing the length of tool path. The best tool path length was achieved by implementing Modified ACO in Matlab R2024A software, which was then evaluated and simulated in Mastercam. There were two workpieces designed in SolidWorks software which contain 36 holes and 135 holes respectively in order to analyze the effectiveness of the Modified ACO. By using a ranking-based pheromone update mechanism, the Modified ACO has enhanced convergence and minimized rapid tool movement. The Modified ACO has reduced the machining time for Sample 1 by 0.17% when compared to the conventional ACO and by 0.55% when compared to the Mastercam technique. Improvements for Sample 2 were 0.12% and 0.84%, respectively. These results have demonstrated that the Modified ACO effectively reduces rapid tool movement, machining time and increases machining efficiency, fulfilling the main goals of this study.