Applications of genetic algorithms in process planning: tool sequence selection for 2.5-axis pocket machining

摘要

Tool sequence selection is an important activity in process-planning for milling and has great bearing on the cost of machining. Currently, it is accomplished manually without consideration of cost factors a priori. Typically, a large tool is selected to quickly generate the rough shape and a smaller clearing tool is used to generate the net-shape. In this paper, we present a new systematic method to select the optimal sequence of tool(s), to machine a 2.5-axis pocket given pocket geometry, a database of cutting tools, cutting parameters, and tool holder geometry. Algorithms have been developed to calculate the geometric constructs such as accessible areas, and pocket decomposition, while considering tool holders. A Genetic Algorithm (GA) formulation is used to find the optimal tool sequence. Two types of selection mechanisms namely “Elitist selection” and “Roulette method” are tested. It is found that the Elitist method converges much faster than the Roulette method. The proposed method is compared to a shortest-path graph formulation that was developed previously by the authors. It is found that the GA formulation generates near optimal solutions while reducing computation by up to 30% as compared to the graph formulation.