@inproceedings{c53a7323382b4b2c9cf90c8ce05a835a,
title = "Application of mosa algorithm in gleeble testing model updating",
abstract = "This research concerns the parametric identification of Johnson-Cook constitutive model which is frequently used to describe the mechanical behavior of metal material at high temperature. An improved multi-objective simulated annealing (MOSA) algorithm is introduced to update Johnson-Cook model based on Gleeble testing data for Steel T24. Our case study produces Pareto solutions ranked by the error corresponding to each parameter to be optimized. This algorithm improves the previous methods and yields a more suitable solution corresponding to the actual situation.",
keywords = "High temperature, Inverse identification Gleeble testing, Johnson-Cook model, T24 steel",
author = "Dong Xu and Kai Zhou and J. Tang",
note = "Publisher Copyright: Copyright {\textcopyright} 2020 ASME; 2020 International Symposium on Flexible Automation, ISFA 2020 ; Conference date: 08-07-2020 Through 09-07-2020",
year = "2020",
doi = "10.1115/ISFA2020-9646",
language = "English",
series = "2020 International Symposium on Flexible Automation, ISFA 2020",
publisher = "American Society of Mechanical Engineers(ASME)",
booktitle = "2020 International Symposium on Flexible Automation, ISFA 2020",
address = "United States",
}