Bayesian Interference for Updating Chatter Model Parameters in Turning
Date
2022-04-29
Authors
Ahmadi, Keivan
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Variations in the mechanics and dynamics of the machining process under operational conditions cause inaccuracies in chatter model predictions. The parameters of chatter models therefore require re-calibration based on experimental observations during the process. Focusing on turning operations, this paper takes a Bayesian model updating approach to present a new method for in-process calibration of chatter model parameters. The presented new method identifies the dominant closed-loop pole of the machining system from in-process vibrations and updates the probability distribution of the model parameters based on the identified poles. Compared to existing methods, which require experimental observations under both stable and unstable conditions, the presented method requires a limited set of vibration measurements during stable conditions only. Moreover, the updated probability distributions are used to establish credibility bounds around the Stability Lobe Diagrams (SLD). An experimental example is presented to demonstrate the efficiency and effectiveness of the presented method in enhancing the accuracy of chatter predictions in turning.
Description
This research was conducted during the author’s study leave at the Institute
for Machine Tools and Industrial Management (iwb) at the Technical University
410 of Munich (TUM). The author thanks Prof. Michael Zaeh and the iwb colleagues for their
support.