Bayesian Model Updating for Chatter in Milling

The modal parameters of tooltip vibrations are crucial for determining chatter-free machining conditions. However, conventional methods often depend on measurements taken when the machine is not operating under real cutting conditions or require multiple experiments under chatter conditions, which is time-consuming and impractical for real-world manufacturing. This paper proposes a Bayesian Model Updating (BMU) approach to improve the chatter model parameters using experimental observations collected during normal, stable milling operations. Operational Modal Analysis (OMA) is adopted to extract the system dynamics from the in-process signals. These results are subsequently integrated into the BMU framework, updating the initial model parameters to reflect actual cutting conditions. The effectiveness of this approach is demonstrated through an experimental case study, highlighting its feasibility and potential for industrial applications.