Estimating Johnson-Cook material parameters using neural networks

Nesar Ahmed Titu, Matt Baucum, Timothy No, Mitchell Trotsky, Jaydeep Karandikar, Tony L. Schmitz, Anahita Khojandi

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

Abstract

The five-parameter Johnson-Cook (J-C) material model represents the behavior of a material under extreme mechanical loading, including high temperatures, strains, and strain rates. The goal of this study is to estimate five J-C material parameters and chip thickness jointly for a given set of force components, power, and temperature. The approach uses two neural network models on a dataset simulated by finite element analysis for orthogonal cutting of aluminum 6061-T6. The first model develops a function approximator to predict the force components, power, and temperature using a given set of J-C parameters and chip thickness for aluminum 6061-T6. The second model searches the input space of the first model to estimate the J-C parameter values and chip thickness, given a set of targeted force components, power, and temperature of interest. The performance of both neural network models is evaluated using mean absolute percentage error. The results suggest that the developed neural networks-based approach is capable of estimating multiple J-C parameters and chip thickness that will result in a targeted force components, power, and temperatures of interest, given starting ‘educated guesses' about these values.

Original languageEnglish
Pages (from-to)680-689
Number of pages10
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

∗∗ Corresponding author. Tel.: +1-865-974-0234 Corresponding author. Tel.: +1-865-974-0234 ∗ E-mail address: [email protected] (Anahita Khojandi). E-mail address: [email protected] (Anahita Khojandi). E-mail address: [email protected] (Anahita Khojandi). This manuscript has been authored in part by UT-Battelle, LLC, under con-This manuscript has been authored in part by UT-Battelle, LLC, under con-This manuscript has been authored in part by UT-Battelle, LLC, under conUS government retains and the publisher, by accepting the article for publi-tract DE-AC05-00OR22725 with the US Department of Energy (DOE). The cation, acknowledges that the US government retains a nonexclusive, paid-up, cation, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE irrevocable, worldwide license to publish or reproduce the published form of will provide public access to these results of federally sponsored research in ac-will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-cordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). The authors acknowledge support from the Oak Ridge In-public-access-plan). The authors acknowledge support from the Oak Ridge Institute at the University of Tennessee (ORI@UT) Seed funding program and stitute at the University of Tennessee (ORI@UT) Seed funding program and Sciencestitute atAlliance,the UnivTheersityUniofversityTenneofsseTee(ORI@UT)nnessee. Seed funding program and Science Alliance, The University of Tennessee. Science Alliance, The University of Tennessee.

Keywords

  • Finite element analysis
  • Johnson-Cook
  • Machine learning
  • Milling force
  • Neural Networks

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