Milling stability identification using Bayesian machine learning

Jaydeep Karandikar; Andrew Honeycutt; Scott Smith; Tony Schmitz

doi:10.1016/j.procir.2020.04.022

Milling stability identification using Bayesian machine learning

Jaydeep Karandikar, Andrew Honeycutt, Scott Smith, Tony Schmitz

Advanced Machining and Machine Tools Res

Research output: Contribution to journal › Conference article › peer-review

15 Scopus citations

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.

Original language	English
Pages (from-to)	1423-1428
Number of pages	6
Journal	Procedia CIRP
Volume	93
DOIs	https://doi.org/10.1016/j.procir.2020.04.022
State	Published - 2020
Event	53rd CIRP Conference on Manufacturing Systems, CMS 2020 - Chicago, United States Duration: Jul 1 2020 → Jul 3 2020

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.

Funders	Funder number
DOE-EERE
Office of Energy Efficiency and Renewable Energy
Oak Ridge National Laboratory

Keywords

Artificial intelligence
Bayes' rule
Machine learning
Machining
Stability

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10.1016/j.procir.2020.04.022

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title = "Milling stability identification using Bayesian machine learning",

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.",

keywords = "Artificial intelligence, Bayes' rule, Machine learning, Machining, Stability",

author = "Jaydeep Karandikar and Andrew Honeycutt and Scott Smith and Tony Schmitz",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems; 53rd CIRP Conference on Manufacturing Systems, CMS 2020 ; Conference date: 01-07-2020 Through 03-07-2020",

year = "2020",

doi = "10.1016/j.procir.2020.04.022",

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AU - Karandikar, Jaydeep

AU - Honeycutt, Andrew

AU - Smith, Scott

AU - Schmitz, Tony

N1 - Publisher Copyright: © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Bayes' rule

KW - Machine learning

KW - Machining

KW - Stability

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M3 - Conference article

AN - SCOPUS:85098718223

SN - 2212-8271

VL - 93

SP - 1423

EP - 1428

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 53rd CIRP Conference on Manufacturing Systems, CMS 2020

Y2 - 1 July 2020 through 3 July 2020

ER -

Milling stability identification using Bayesian machine learning

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