Application of MOSA Algorithm in Gleeble Testing Model Updating

Dong Xu, K. Zhou, J. Tang
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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.
MOSA算法在Gleeble测试模型更新中的应用
本文研究了用于描述金属材料高温力学行为的Johnson-Cook本构模型的参数识别问题。提出了一种改进的多目标模拟退火(MOSA)算法,对基于Gleeble测试数据的T24钢的Johnson-Cook模型进行更新。我们的案例研究产生了帕累托解,根据每个要优化的参数对应的误差进行排名。该算法对以往的方法进行了改进,得到了更适合实际情况的解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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