Adaptive milling chatter detection in variable tool-workpiece systems: A novel approach using signal reconstruction...

Chatter remains a significant challenge in milling operations, resulting in deteriorated surface quality, accelerated tool wear, and excessive noise-induced environmental impacts. Timely chatter detection is therefore essential for maintaining sustainable manufacturing processes. The primary contribution of this work is the introduction of a smart chatter identification approach designed for different tool-workpiece systems in milling. First, chatter and stable frequency were automatically identified based on the frequency distribution characteristics of milling chatter using frequency search methods. Then, an adaptive signal reconstruction approach integrating Wavelet Packet Decomposition (WPD) was developed to automatically extract chatter and stable signal components. Based on these developments, a novel energy indicator, Chatter to Stable Energy Ratio after Signal Reconstruction (CSERSR), was constructed through signal energy calculations to realize different degrees of chatter detection in real-time. A simulation study was conducted to preliminarily validate the effectiveness of the proposed method, and experimental validation further demonstrates its capability to accurately identify different degrees of chatter across varying tool-workpiece systems. Comparative analyses with existing methodologies further substantiate the reliability and adaptability of the presented approach.