Long-term creep life prediction of P91 steel using domain knowledge and back propagation artificial neural network

被引：0

作者：

Song, Chaolu ^{[1
]}

Liu, Xinbao ^{[1
]}

Zhu, Lin ^{[1
]}

Fan, Ping ^{[2
]}

Ren, Siyu ^{[1
]}

Zhang, Kai ^{[1
]}

Chen, Jie ^{[1
]}

Chen, Hongtao ^{[1
]}

机构：

[1] Northwest Univ, Sch Chem Engn, Xian, Peoples R China

[2] Northwest Univ, Sch Informat Sci & Technol, Xian, Peoples R China

来源：

MATERIALS AT HIGH TEMPERATURES | 2025年 / 42卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Creep life prediction; small samples; domain knowledge; BP artificial neural networks; FCM clustering algorithm; TEMPERATURE; BEHAVIOR;

D O I：

10.1080/09603409.2025.2453321

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this work, the long-term creep life of P91 steels with the limited experiment data was predicted using the domain knowledge and back propagation artificial neural networks (BP-ANN). Based on the traditional creep life models of Larson-Miller (L-M) parameter and theta projection, the domain knowledge was incorporated to expand the creep dataset. Then, the fuzzy C-means (FCM) clustering algorithm was introduced for training data collection. Consequently, the creep life prediction was conducted with the corresponding dataset and BP-ANN. The obtained results demonstrated that in contrast to the conventional prediction models of creep life, the prediction accuracy for long-term creep life of P91 steels can be improved significantly by the present model. For instance, the predicted creep lives 200,000 h exhibit the average errors of 6.0%. In addition, the present study offers a convenient tool to solve the issue of limited experiment data, particularly the long-term creep of P91 steels.

引用

页码：14 / 24

页数：11

共 36 条

[1] Sustainable development of renewable energy integrated power sector: Trends, environmental impacts, and recent challenges [J].