A Bayesian Framework for Milling Stability Prediction and Reverse Parameter Identification

Conference · Thu Jul 01 04:00:00 EDT 2021

DOI:https://doi.org/10.1016/j.promfg.2021.06.073· OSTI ID:1808387

Cornelius, Aaron ^[1]; Karandikar, Jaydeep ^[2]; Gomez, Michael ^[2]; Schmitz, Tony ^[2]

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1. UT Knoxville
2. ORNL

This paper describes a physics-guided Bayesian framework for identifying the milling stability boundary and system parameters through iterative testing. Prior uncertainties for the parameters are identified through physical simulation and literature reviews, without physical testing of the actual milling system. Those uncertainties are then propagated to the stability map using a physics-based stability model, which is used to suggest a test point. The uncertainties are updated based on the new information acquired from the cutting test to form a new probability distribution, called the posterior. Finally, the posterior are compared to measured values for the stability boundary and system parameters to evaluate the approach. Based on experimental observations, the advantages and disadvantages of using a physics-guided model are discussed.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1808387
• Country of Publication: United States
• Language: English