本构方程
流动应力
高温合金
材料科学
阿累尼乌斯方程
变形(气象学)
应变率
人工神经网络
热力学
机械
有限元法
合金
复合材料
计算机科学
物理
人工智能
经典力学
动力学
作者
Cheng Peng,Decheng Wang,Junying Zhou,Shanchao Zuo,Pengfei Zhang
出处
期刊:Metals
[MDPI AG]
日期:2022-08-29
卷期号:12 (9): 1429-1429
被引量:9
摘要
In order to realize a better description of plastic flow behavior in the warm deformation process of GH4149, the GH4169 superalloy was compressed by Gleeble-3800 at a temperature of 700–900 °C and a strain rate of 0.01–10 s−1. The constitutive model of GH4169 superalloy was established using artificial neural network (ANN) and the Arrhenius equation, and the accuracy of the model was compared. The results show that the average absolute relative error (AARE) of the ANN constitutive model is 4.34%. The AARE of the Arrhenius equation constitutive model is 29.95%. The ANN constitutive model is more accurate than the Arrhenius constitutive model, and has consistent accuracy in the whole parameter range. The stress–strain curve obtained by the model is in good agreement with the experimental curve. The process of the warm compression test is simulated by finite element software importing the ANN constitutive material model. The results verified the reliability of the model. The ANN constitutive model can effectively predict the flow stress of GH4169 superalloy during the warm deformation process.
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