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

被引：5

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.

引用

下载

页数：7

共 50 条

[1] Spectrum Prediction based on Improved-Back-Propagation Neural Networks
Bai, Suya
Zhou, Xin
Xu, Fanjiang
2015 11TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC), 2015, : 1006 - 1011
[2] Financial prediction using back propagation neural networks with opposition based learning
Kalaiselvi, K.
Velusamy, K.
Gomathi, C.
SECOND NATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE (NCCI 2018), 2018, 1142
[3] "Soft Decision" Spectrum Prediction based on Back-Propagation Neural Networks
Bai, Suya
Zhou, Xin
Xu, Fanjiang
2014 INTERNATIONAL CONFERENCE ON COMPUTING, MANAGEMENT AND TELECOMMUNICATIONS (COMMANTEL), 2014, : 128 - 133
[4] Research on load prediction of back propagation neural networks based on genetic algorithms
Yan L.
Mu G.
Wang Q.
He Z.
Zhu Y.
International Journal of High Performance Systems Architecture, 2023, 11 (04) : 198 - 205
[5] Prediction of defects in castings using back propagation neural networks
Karunakar, D. Benny
Datta, G. L.
INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL, 2008, 3 (02) : 140 - 147
[6] Respiratory Signals Prediction based on Particle Swarm Optimization and Back Propagation Neural Networks
Chang, Panchun
Yang, Jimin
Yang, Juan
You, Tao
2017 5TH INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN, MANUFACTURING, MODELING AND SIMULATION (CDMMS 2017), 2017, 1834
[7] Back Propagation neural networks
Buscema, M
SUBSTANCE USE & MISUSE, 1998, 33 (02) : 233 - 270
[8] Web Recommendation Based on Back Propagation Neural Networks
Zhong, Jiang
Deng, Shitao
Cheng, Yifeng
ADVANCES IN NEURAL NETWORKS - ISNN 2011, PT III, 2011, 6677 : 397 - 406
[9] Non-destructive prediction techniques for asphalt mixture based on back propagation neural networks
Meng, Yongjun
Mo, Shuyi
Li, Hui
Yan, Tianyi
Lei, Jiechao
Fan, Liupeng
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2023, 24 (01)
[10] A Fault Prediction Algorithm Based on Rough Sets and Back Propagation Neural Network for Vehicular Networks
Geng, Rong
Wang, Xiaojie
Ye, Ning
Liu, Jun
IEEE ACCESS, 2018, 6 : 74984 - 74992

← 1 2 3 4 5 →