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用于316L不锈钢热变形建模的本构方程、神经网络和支持向量回归的比较研究

A Comparison Study of Constitutive Equation, Neural Networks, and Support Vector Regression for Modeling Hot Deformation of 316L Stainless Steel.

作者信息

Song Shin-Hyung

机构信息

Department of Smart Automobile, Soonchunhyang University, 22 Soonchunhyang-ro, Sinchang-myeon, Asan-si, Chungcheongnam-do 31538, Korea.

出版信息

Materials (Basel). 2020 Aug 26;13(17):3766. doi: 10.3390/ma13173766.

DOI:10.3390/ma13173766
PMID:32858909
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7503741/
Abstract

In this research, hot deformation experiments of 316L stainless steel were carried out at a temperature range of 800-1000 °C and strain rate of 2 × 10-2 × 10. The flow stress behavior of 316L stainless steel was found to be highly dependent on the strain rate and temperature. After the experimental study, the flow stress was modeled using the Arrhenius-type constitutive equation, a neural network approach, and the support vector regression algorithm. The present research mainly focused on a comparative study of three algorithms for modeling the characteristics of hot deformation. The results indicated that the neural network approach and the support vector regression algorithm could be used to model the flow stress better than the approach of the Arrhenius-type equation. The modeling efficiency of the support vector regression algorithm was also found to be more efficient than the algorithm for neural networks.

摘要

在本研究中，对316L不锈钢进行了热变形实验，实验温度范围为800 - 1000°C，应变速率为2×10 - 2×10。发现316L不锈钢的流变应力行为高度依赖于应变速率和温度。经过实验研究后，使用阿累尼乌斯型本构方程、神经网络方法和支持向量回归算法对流变应力进行了建模。本研究主要集中于对三种用于模拟热变形特性的算法进行比较研究。结果表明，神经网络方法和支持向量回归算法在模拟流变应力方面比阿累尼乌斯型方程方法表现更好。还发现支持向量回归算法的建模效率比神经网络算法更高。

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