Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

Tony Schmitz; Aaron Cornelius; Jaydeep Karandikar; Christopher Tyler; Scott Smith

doi:10.1016/j.cirp.2022.03.020

Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

Tony Schmitz, Aaron Cornelius, Jaydeep Karandikar, Christopher Tyler, Scott Smith

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. The result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.

Original language	English
Pages (from-to)	321-324
Number of pages	4
Journal	CIRP Annals - Manufacturing Technology
Volume	71
Issue number	1
DOIs	https://doi.org/10.1016/j.cirp.2022.03.020
State	Published - Jan 2022

Funding

This research was supported by the DOE Office of Energy Efficiency and Renewable Energy (EERE), Energy and Transportation Science Division, and used resources at the Manufacturing Demonstration Facility, a DOE EERE User Facility at Oak Ridge National Laboratory. This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05–00OR22725 with the DOE. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Funders	Funder number
U.S. Department of Energy
Office of Energy Efficiency and Renewable Energy
Oak Ridge National Laboratory

Keywords

Machine learning
Milling
Stability

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10.1016/j.cirp.2022.03.020

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abstract = "This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. The result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.",

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AB - This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. The result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.

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KW - Stability

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Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

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Keywords

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