TOOLPATH OPTIMIZATION USING ANT COLONY OPTIMIZATION AND PARTICLE SWARM OPTIMIZATION FOR COMPLEX MACHINING WITH MULTIPLE ENTRY AND EXIT POINTS

Abstract

This paper presents a solution for toolpath optimisation using a combination of Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) algorithms. The toolpath optimization problem is represented as a Traveling Salesman Problem (TSP) and specifically addresses it as a Sequential Ordering Problem (SOP). This formulation allows for a more structured approach to managing the sequence of toolpaths, ensuring optimal order and transitions.The proposed approach leverages the strengths of both algorithms to enhance the efficiency of toolpath planning in complex machining oper- ations with fixed and flexible toolpath orders. The study investigates the effectiveness of this created algorithm in handling these complex operations represented as multi- ple vectors of varying states and orientations. Extensive simulations and real-world experiments are done to demonstrate improvement gains in toolpath optimization and reduction of machining time. The results indicate a significant improvement in tool- path optimization compared to using traditional toolpath optimisation.