Bayesian optimization for inverse calibration of expensive computer models: A case study for Johnson-Cook model in machining

Jaydeep Karandikar, Anirban Chaudhuri, Timothy No, Scott Smith, Tony Schmitz

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Inverse model calibration for identifying the constitutive model parameters can be computationally demanding for expensive-to-evaluate simulation models. This paper presents a modified Bayesian optimization (BO) method, denoted as BO-bound, that incorporates theoretical bounds on the quantity of interest. A case study for the inverse calibration of the Johnson Cook (J-C) flow stress model parameters is presented using machining (cutting) force data. The results show fast calibration of the five J-C parameters within 25 simulations. In general, the BO-bound method is applicable for inverse calibration of any expensive simulation models as well as optimization problems with known bounds.

Original languageEnglish
Pages (from-to)32-38
Number of pages7
JournalManufacturing Letters
Volume32
DOIs
StatePublished - Apr 2022

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 government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. 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 ). This work has been supported in part by the DOE Office of Energy Efficiency and Renewable Energy (EERE), Manufacturing Science Division, and used resources at the Manufacturing Demonstration Facility, a DOE-EERE User Facility at Oak Ridge National Laboratory. The second author acknowledges support from Department of Energy award number DE-SC0021239.

Keywords

  • Bayesian optimization
  • Bounded objective function
  • Finite element model calibration
  • Inverse model calibration
  • Machining

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