Bayesian Interference for Updating Chatter Model Parameters in Turning

dc.contributor.authorAhmadi, Keivan
dc.date.accessioned2022-04-29T15:35:17Z
dc.date.available2022-04-29T15:35:17Z
dc.date.copyright2022en_US
dc.date.issued2022-04-29
dc.descriptionThis 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.en_US
dc.description.abstractVariations 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.en_US
dc.description.reviewstatusUnrevieweden_US
dc.description.scholarlevelFacultyen_US
dc.identifier.urihttp://hdl.handle.net/1828/13904
dc.language.isoenen_US
dc.subject.departmentDepartment of Mechanical Engineering
dc.titleBayesian Interference for Updating Chatter Model Parameters in Turningen_US
dc.typePreprinten_US

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