Publication Type
Conference Paper
Journal Name
Procedia CIRP
Publication Date
Page Numbers
1423 to 1428
Volume
91
Issue
Special Is
Conference Name
53rd CIRP Conference on Manufacturing Systems
Conference Location
Chicago, Illinois, United States of America
Conference Sponsor
CIRP
Conference Date
-
Abstract
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.
