Milling stability identification using Bayesian machine learning

Conference · Wed Jul 01 04:00:00 EDT 2020

DOI:https://doi.org/10.1016/j.procir.2020.04.022· OSTI ID:2205462

Karandikar, Jaydeep ^[1]; Honeycutt, Andrew ^[1]; Smith, Scott ^[1]; Schmitz, Tony ^[1]

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1. ORNL

This paper describes automated identification of the milling stability boundary using Bayesian machine learning and experiments. The Bayesian machine learning process begins with the user’s initial beliefs about milling stability. This “prior” is a distribution that uses all available information, which may be based only on experience or may be informed by physics-based model predictions. Experiments are then completed to update this prior by calculating the “posterior,” a modified probabilistic description of the milling stability limit based on the new information. The approach is demonstrated and results are presented for both numerical and experimental cases.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 2205462
• Country of Publication: United States
• Language: English