Rapid prediction of multi-directionality of polished surface topography based on angular spectrum

Abstract

The multi-directionality of polished surface topography (PST) is a key index to characterize the polished surface quality and surface integrity, and its prediction before actual machining is of great significance to the toolpath planning, verification, and optimization. However, most related works reported thus far focus mainly on the material removal modeling but few on the prediction of multi-directionality. Hence, this paper proposes a rapid method to precisely predict the multi-directionality of the PST for the pad-polishing process of freeform surfaces. With this method, a pressure distribution model and a material removal profile (MRP) model are first established, in which the MRP is founded subject to the quadratic function distribution. Then, to avoid time-consuming integral operation in the MRP model, an artificial neural network is developed to fit the quadratic function of MRP. With this model, a multi-directionality prediction algorithm is further proposed based on the angular spectrum of the PST. Simulation and experimental studies have shown that the proposed method can predict the multi-directionality of the PST with very high accuracy and efficiency for freeform surface polishing, showing great application potential in promoting the efficiency of toolpath planning and optimization.