Flute based analysis of ball-nose milling signals using continuous wavelet analysis features

Surface Finishing and End Milling are among the most sophisticated manufacturing processes. For the industry to improve the quality of its end-line products, it is important for improving the performance of these processes by having a descriptive reference model. Using this reference model, non-intrusive prediction of the resulting surface quality and the tool status can be accurately conducted. Many modeling techniques have been used in literature. Since there are no report of success on a general model that support all the tool specifications, cutting conditions and correlation of the tool-health, cutting signals and resulting surface roughness or tool-wear, the researches based on the several available AI techniques and different sensor signals and there features are on going. This paper investigates the existing correlation between the resulted wavelet coefficients and ball-nose tool-wear using Cascaded Feed Forward Neural Networks (CFFNN). Considering the changes in the shape of the signals during the cutting process and the similarity of the resulting signals to some mother wavelets and the lack of literature on wavelet analysis for ball-nose cutters' signals, this specific analysis is chosen. CFFNN is also selected for its capability to deal with a non-linear process and being comparatively simple. The results are satisfying with the proposed structure. More studies for the optimal structure and features are expected in the future.