Modelling and Characterization of Material Effect on Surface Generation in Ultra-Precision Raster Milling

Abstract

This chapter presents theoretical and experimental investigations into the effect of the workpiece material on surface roughness in ultra-precision raster milling (UPRM). The influence of material elastic recovery on chip thickness and surface generation in UPRM is studied. A holistic surface roughness prediction model is developed by considering the influences of cutting parameters, tool geometry, and the material elastic recovery on surface generation. A new method is also proposed to characterize material-induced surface roughness on the raster-milled surface by defining a new parameter to characterize the extent of surface roughness profile distortion induced by the materials being cut. Experiment is conducted to compare the proposed method with surface roughness parameters and power spectrum density analysis method by machining three different workpiece materials. The results show that the presence of elastic recovery improves the surface finish in ultra-precision raster milling and that, among the three materials being cut in the experiment, aluminum bronze has the greatest influence on surface finish due to its highest elastic recovery rate and hardness. The results also show that, in the case of faster feed rates, the proposed method more efficiently characterizes material-induced surface roughness.