Automated Operational Modal Analysis of self-excited vibrations in turning

Regenerative chatter is a prevalent issue in machining, stemming from the instability of self-excited vibrations within the tool or workpiece. Operational Modal Analysis (OMA) of the tool or workpiece vibrations during turning operations is an effective method to predict and mitigate chatter. However, it requires substantial input from an expert user, undermining its application in process monitoring. This paper presents an Automated Operational Modal Analysis (AOMA) approach to eliminate user intervention from the online chatter prediction process. The proposed approach combines clustering algorithms with knowledge about the system’s underlying physics to eliminate spurious poles as well as those representing the undamped harmonic oscillations. As a result, the dominant pole of self-excited dynamics is identified automatically, quantifying the stability of process vibrations. The accuracy and effectiveness of the proposed method are validated through experiments and numerical simulations.