Simulation approach of cutting tool movement using artificial intelligence method

In recent years, the productivity of machine tools has been significantly improved by using computer-based CAD/CAM systems for Computer Numerical Control (CNC). Various types of CAM software in the market that provide tool path programming and can be applied for different types of the machining process such as drilling, milling, and turning. However, sometimes the default tool path generated in the CAD/CAM system is not the optimal tool path which produces longer distance and increase the machining time. In this paper, we present cutting tools movement by Genetic Algorithm (GA) and Ant Colony Optimization (ACO) method in generation of shortest tool path. For observation of the performance of both methods, comparisons with conventional method have been carried out. The shortest path of drilling tool path adapts Travelling Salesman Problem (TSP) in determining the distance during machining. The simulation result shows that ACO and GA based tool path optimization is useful to find a lower distance of tool path generation for holes drilling process.