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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. [ABSTRACT FROM AUTHOR]

Abstract Tool path planning is a key to ensure high machining quality and productivity in 5-axis flank milling of ruled surfaces. Previous studies have shown that optimization-driven tool path planning can effectively reduce the geometrical errors on the finished surface. However, to solve the corresponding optimization problem is a challenging task involving a large number of decision variables. This paper proposes a novel approach to generating optimized tool path for 5-axis flank finishing cut based on a geometric decomposition strategy and multi-population harmony search algorithm. The proposed approach geometrically divides the surface to be machined into a number of segments. The tool paths on those sub-surfaces are independently optimized by the multi-population harmony search algorithm. Individual tool paths are then combined together to form a complete one. The test results of representative surfaces show that the proposed approach produces higher machining precision with less computational time than compared previous methods. And the computational time is further reduced by Message passing interface based parallel computing techniques. A detailed analysis is conducted to characterize how the number of divisions affects the optimization results. And the proposed approach also shows good scalability with the increasing number of cutter locations. Graphical abstract Highlights • A new tool path planning method for 5-axis flank milling is proposed. • It geometrically divides the surface into a number of segments. • The multi-population harmony search is proposed to optimize tool path on each sub-surface independently. • The effectiveness of the proposed approach is demonstrated on eight representative surfaces with different characteristics. [ABSTRACT FROM AUTHOR]

Multi-axis machining offers higher machining efficiency and superior shaping capability compared to 3-axis machining. Machining error control is a critical issue in 5-axis flank milling of complex geometries and there is still a lack of solutions. Previous studies have shown that optimisation-based tool path planning is a feasible approach to reduction of machining error. However, the error estimation is time-consuming in the optimisation process, thus limiting the practicality of this approach. In this work, we apply graphics processing unit (GPU) computing technology to solve this problem. A particle swarm optimisation (PSO)-based optimisation scheme is developed to generate a series of cutter locations (CLs) that produce a minimised error on the machined surface. The error amount induced by each CL is simultaneously calculated by the parallel processing units of GPU. The PSO search process driven by the aggregated result is effectively accelerated. Test results show that our approach outperforms previous optimisation methods in both solution quality and computation efficiency. This work demonstrates a novel application of GPU on Computer Aided Manufacturing/Computer Numerical Control. [ABSTRACT FROM AUTHOR]