Estimating Johnson-Cook Material Parameters using Neural Networks-Reference-Cited by-同舟云学术

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Estimating Johnson-Cook Material Parameters using Neural Networks

Published:2021 Issue: Volume:53 Page:680-689
ISSN:2351-9789
Container-title:Procedia Manufacturing
language:en
Short-container-title:Procedia Manufacturing

Author:

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

Funder

Oak Ridge National Laboratory

University of Tennessee

Publisher

Elsevier BV

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering

Reference45 articles.

1. A comparative study on johnson-cook, modified johnson-cook and arrhenius-type constitutive models to predict the high temperature flow stress in 20crmo alloy steel;He;Materials Design,1980

2. On the importance of the choice of the parameters of the johnson-cook constitutive model and their influence on the results of a ti6al4v orthogonal cutting model;Ducobu;International Journal of Mechanical Sciences,2017

3. Determination of the johnson-cook material parameters using the scs specimen;Dorogoy;Experimental Mechanics,2008

4. Quasi-static and dynamic loading responses and constitutive modeling of titanium alloys;Khan;International Journal of Plasticity,2004

5. H Kolsky. An investigation of the mechanical properties of materials at very high rates of loading. Proceedings of the Physical Society. Section B, 62(ll):676-700, nov 1949.

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2. Cyber–Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era—A Review;Sensors;2024-04-05

3. Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling;Materials;2024-01-08

4. Distribution estimation of Johnson-Cook parameters considering correlation in quasi-static state;International Journal of Mechanical Sciences;2023-04

5. Distribution Estimation of Johnson-Cook Model Parameters Considering Correlation;2022

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