A Bayesian framework for milling stability prediction and reverse parameter identification

Aaron Cornelius, Jaydeep Karandikar, Michael Gomez, Tony Schmitz

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)760-772
Number of pages13
JournalProcedia Manufacturing
Volume53
DOIs
StatePublished - 2021
Event49th SME North American Manufacturing Research Conference, NAMRC 2021 - Cincinnati, United States
Duration: Jun 21 2021Jun 25 2021

Funding

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US 2351-9789 © 2019 The Authors, Published by Elsevier B.V. g2o3v5e1r-n9m78e9nt©re2ta0i1n9s Tanhde Athuetphuobrsl,isPhuebr,libshyeadccbeypEtilnsgevthieeraBrt.Vic.le for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, wPP3eeoee5rrr1ldrr-w9eevv7iiid8eee9wwl©iundeucne2nd0se1err9tt hehToe hperrueebAsslpopuiosthnnssoiirbibrsi,rllePiittpuyyrboofoldfisutthcheheeedtsshbcceiiyeepntnEutliibsffeliiiccvsihcceooerdmmBfmm.oViirtt.mtteeeeofooffthNNisAAmMRMaRnuII//sSScMEMripEt, or allow others to do so, for US government purposes. DOE will provide public 2351-9789 © 2019 The Authors, Published by Elsevier B.V. access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • Machine learning
  • Milling
  • Modeling
  • Stability

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