Prediction of Creep Curves Based on Back Propagation Neural Networks for Superalloys

被引：7

作者：

Ma, Bohao ^{[1
]}

Wang, Xitao ^{[1
]}

Xu, Gang ^{[1
]}

Xu, Jinwu ^{[1
]}

He, Jinshan ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Collaborat Innovat Ctr Steel Technol, Beijing 100083, Peoples R China

来源：

MATERIALS | 2022年 / 15卷 / 19期

关键词：

metals and alloys; creep; artificial intelligence; machine learning; theta projection model; STRESS; MODEL;

D O I：

10.3390/ma15196523

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

Creep deformation is one of the main failure forms for superalloys during service and predicting their creep life and curves is important to evaluate their safety. In this paper, we proposed a back propagation neural networks (BPNN) model to predict the creep curves of MarM247LC superalloy under different conditions. It was found that the prediction errors for the creep curves were within +/- 20% after using six creep curves for training. Compared with the theta projection model, the maximum error was reduced by 30%. In addition, it is validated that this method is applicable to the prediction of creep curves for other superalloys such as DZ125 and CMSX-4, indicating that the model has a wide range of applicability.

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页数：7

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