Optimized sequencing of CNC milling toolpath segments using metaheuristic algorithms

Intelligent selection of a short toolpath is made possible by reducing machining cycle time. Each metal cutting layer in a workpiece is composed of several entities, such as lines and arcs, which form the different cutting segments of a cutting plan. During machining, the cutter moves at controlled feed rates along various segments at a high speed in a single cutting pass. The end of a segment is bridged to the start point of the next segment by the non-cutting movement of the tool. Any two consecutive segments can be connected in eight different ways. Finding the shortest tool path at polynomial time is impossible because toolpaths are constructed in millions of ways by sequencing the segments. This paper presents an effective method that uses heuristic optimization techniques to solve this NP-hard problem, which is known as the traveling salesman problem, for segments. The proposed method adopts particle swarm optimization (PSO) and the genetic algorithm (GA) because of their capability to generate quality solutions for optimization problems. GA and PSO are implemented in the MATLABR2016b computing environment because of the platform's flexibility and simple coding method. The optimization procedure is validated by comparing its results with those of two industry standard CAM systems, namely, Autodesk Inventor HSM and Mastercam. Using the proposed optimization method saves up to 40 % of the tool's airtime during machining.