Constitutive models to predict flow stress in Austenitic Stainless Steel 316 at elevated temperatures

被引:100
作者
Gupta, Amit Kumar [1 ]
Anirudh, V. K. [1 ]
Singh, Swadesh Kumar [2 ]
机构
[1] BITS Pilani, Dept Mech Engn, Hyderabad Campus 500078, AP, India
[2] GRIET, Dept Mech Engn, Hyderabad 500072, AP, India
来源
MATERIALS & DESIGN | 2013年 / 43卷
关键词
ARTIFICIAL NEURAL-NETWORK; HOT DEFORMATION; TI-6AL-4V ALLOY; STRAIN-RATE; BEHAVIOR; MICROSTRUCTURE; COMPRESSION; EQUATIONS;
D O I
10.1016/j.matdes.2012.07.008
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Strain, strain rate and temperature have a significant impact on the flow stress of a material. To study the impact of these factors on flow stress, quite a few empirical, semi-empirical constitutive models have been reported. In this work, four such models are being presented for estimation of flow stress on Austenitic Stainless Steel 316. While the Johnson Cook model, Modified Zerilli-Armstrong model are semi-empirical models, the Arrhenius type equation is a physical based equation. The Artificial Neural Network model on the other hand is trained based on the training data and employed to predict the flow stress on the testing data. The experiments for these data were conducted at various strain rates (0.1-0.0001 s(-1)) and at various temperatures (323-623 K). Values of stress were taken at strain intervals of 0.05 (from 0.05 to 0.3) to evaluate the material constants of the constitutive models. A comparative study on the reliability of the four models has also been made at the end. The correlation coefficient values observed were 0.9423 (JC model), 0.9879 (modified ZA model), 0.9852 (modified Arrhenius type equation) and 0.9930 (ANN model). (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:410 / 418
页数:9
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