Tooth-wise monitoring of the asymmetrical tool wear in micro-milling based on the chip thickness reconstruction and cutting force signal

Abstract

Tooth-wise monitoring of the tool wear condition is of great importance for improving the machining quality and efficiency of micro-milling. However, the uncut chip thickness, cutting force, and tool wear condition of the multi-tooth evolve asymmetrically coupled due to the tool runout, and it is difficult to monitor the tool wear of each cutting tooth solely based on the cutting force signal. The reconstruction of chip thickness is the key to overcoming this challenge. With such consideration, this study proposes a tooth-wise monitoring method for estimating the asymmetrical micro-milling tool wear based on the chip thickness reconstruction and cutting force signal. The instantaneous uncut chip thickness of each tooth is reconstructed from the floor surface topography of the machined material with the guide of the proposed surface topography model considering the asymmetrical tool wear. The wear condition of each tooth is estimated by integrating the reconstructed chip thickness and the measured cutting force signal into the proposed asymmetrical micro-milling force model. Experimental results show that the asymmetrical models outperform the traditional symmetrical models in terms of modeling flexibility and accuracy. With the consideration of the asymmetrical tool wear, the prediction accuracy of the proposed topography and cutting force models increases by 11.4% and 5.3%. The relative monitoring error within 9% demonstrates that the proposed method can accurately estimate the tool wear condition of each cutting tooth in micro-milling.