A unified prediction model of 3D surface topography in face milling considering multi-error sources

Abstract

Surface topography, which represents machined surface errors in 3D space, is a comprehensive way to estimate surface quality in face milling. This paper presents a unified simulation model for the prediction of 3D machined surface topography considering multiple error sources in face milling with multi-tooth cutter that has large diameter. In this model, the final machined surface topography is described with a height-encoded and position-maintained colorful surface image that consists of multi-scale errors. It is derived from a point cloud of residual surface height at each encoding contact location. The model includes the effects of milling parameters, different kinds of initial setup errors, and process static/dynamic characteristics of machine tool-workpiece-fixture system. The influences of inserts’ geometric structures on roughness scale are also introduced through beam elements. A numerical algorithm is proposed to obtain the resultant point cloud based on the simulation model integrating with all the effects of influence factors. Face milling experiments are conducted to validate and investigate the proposed simulation model. Comparisons between simulated and experimental results show good agreement. And the proposed model can also be applied to investigate the topography patterns induced by multiple error sources coupled and respectively. Thus provides a comprehension methodology to predict machined surface topography and the resultant topography patterns induced by multi-error sources in face milling for its industry implementation.