Efficient tool path planning for 5-axis flank milling of ruled surfaces using ant colony system algorithms

Abstract

Five-axis CNC flank milling has recently received much attention in industry. The flank milling operation is efficient in shaping ruled geometry, but introduces a challenging task in machining error control. Previous work proposed a dynamic-programming based scheme for generating optimal tool path by minimising the machining error. However, to compute the tool path takes a considerable amount of time. This paper presents a new scheme using meta-heuristics, ant colony system (ACS), for tool path planning in 5-axis flank milling, with a focus on improving the computation efficiency. The path planning problem is first formulated as mapping two boundary curves of a ruled surface. An ACS-based optimisation algorithm is then applied to search for the mapping that minimises the machining error. The solution is nearly as good as using dynamic programming but takes only half of the computational time. The results from machining experiment and 3D measurement validate the effectiveness of the proposed scheme.