Acoustic Signal Analysis by Teager–Huang Transform for Milling Chatter Recognition

Abstract

Many types of sensor have been utilized to monitor milling vibration, and many analysis methods are devoted to the investigation of milling vibration or milling dynamics. In this work, a noncontact sensor and a time-frequency domain analysis method were applied to identify the state of milling vibration. A microphone was employed in practical tests to record the milling dynamics. The Teager-Huang transform (THT) was adopted for the acoustic signal analysis owing to its high resolution in the time-frequency domain. The potential frequency range for the analysis of milling dynamics is reported in this work to improve the recognition accuracy of milling vibration limited by the effect of environmental noise. The THT was used to distinguish the chatter state from the normal milling dynamics. In addition, the statistical index called the coefficient of variation was applied to define the threshold of chatter occurrence. Milling experiments (including dry and wet cuttings) were performed to verify the proposed chatter detection method.

Chatter detection in milling processes—a review on signal processing and condition classification

“…Energy operators, like the squared energy and Teager-Kaiser operators, have been used in chatter detection to estimate instantaneous frequency and amplitude, along with diverse decomposition methods [179,189]. Lee et al [168] utilized the Teager-Huang transform (THT), which combines EMD with the Teager energy operator. Moreover, novel methods have been considered as an alternative to VMD.…”

“…* Two or more signal processing methods combined[91,136,152,159,165,179,192,196,220,348] Other methods[72,73,84,95,101,135,137,147,148,150,155,157,161,164,168,173,190,227,258,259] …”

Improved Estimation of End-Milling Parameters from Acoustic Emission Signals Using a Microphone Array Assisted by AI Modelling

“…In this context, techniques based on the sensing of acoustic emission (AE) phenomena have gained increasing interest during the last few years due to their simplicity of use and the ability to monitor a wide variety of features, such as teeth breakage [ 7 , 8 ], runout [ 9 , 10 ] and chattering [ 11 , 12 ]. In particular, those techniques based on the use of noncontact sensors in the range of audible sound, such as different types of microphones [ 13 , 14 ], are particularly attractive due to the reduced intrusiveness, so they have been used for detecting the chattering phenomenon [ 15 , 16 , 17 ] and tool condition monitoring in conventional machining [ 18 , 19 ], high-speed machining [ 20 ] and abrasion processes [ 21 ].…”