• OMA is used for the identification of self-exited vibration during turning. • The dominant poles of the turning system are identified from process vibrations. • This method can potentially forecast chatter when vibrations are still stable. Unstable vibrations in machining operations, known as chatter, may cause damages to the tool, workpiece, or the machine tool. Chatter is usually avoided by selecting the appropriate spindle speed and depth of cut determined by the dynamic models of the machining process. Due to modeling uncertainties or variations of the process dynamics, chatter may happen even when stable cutting conditions are used in the process planning stage. Therefore, it is critical to detect chatter during the process and take corrective actions in the shortest possible time. The existing chatter detection methods process various signals such as sound, vibration, and force to extract chatter indicators during the process, however these methods detect chatter only after vibrations become unstable. In this paper, a new chatter detection method is presented based on the identification of the dynamics of turning processes using Operational Modal Analysis (OMA). Because this new method estimates the stability margin of the process, rather than the stability/instability of vibrations, it determines the level of stability of the cut before vibrations become unstable. The performance of the presented chatter detection method is studied using numerical simulations and machining experiments.
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