New fast micro-topography estimation algortihms for 5 axis milling

Abstract

Accurately predicting the topography of machined surfaces is vital in industries like aerospace, automotive, and biomedical manufacturing. This paper introduces a novel methodology for fast and precise areal surface micro-topography prediction in 5-axis CNC milling, particularly for finishing processes using ball-end milling tools. Although commercial CAM software can predict the macro-topography of a machined surface, incorporating the cutter edge geometry and its rotational movement is essential for accurately predicting the surface's micro-topography. In this work, three alternative algorithms based on the Z-Map method are proposed: (1) a parallelized version of Z-Map (MOD1) for increased computational efficiency, (2) a swept surface technique (MOD2) for more accurate simulations, and (3) a rounding technique (MOD3) for rapid, precise topography prediction by aligning cutter edge points. The user can choose one of the three algorithms based on computational resources and preferences. These are validated through experimental tests on a 5-axis milling machine under different machining conditions. The results demonstrate that the developed algorithms enhance the prediction of the machined surface's micro-topography, significantly reducing computation time and improving accuracy compared to the traditional Z-map method.