Multi-Milling-Insert Wear Assessment Using Nonlinear Virtual Sensor, Time-Frequency Distribution and Neural Networks

Abstract

Abstract The objective of this study is to establish a signal processing methodology that can infer the state of milling insert wear from translational vibration measured on the spindle housing of a milling machine. First, the tool wear signature in a translational vibration is accentuated by mapping the translational vibration into a torsional vibration using a previously identified nonlinear relationship between the two, i.e., a virtual sensor. Second, a time-frequency distribution, i.e., a Choi-Williams distribution, is calculated from the torsional vibration. Third, scattering matrices and orthogonalization are employed to identify the time-frequency components that are best correlated to the state of wear. Fourth, a neural network is trained to estimate the extent of wear from these critical time-frequency components. The combination of the virtual sensor, time frequency analysis and neural network is then validated with data obtained from real cutting tests.