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

Bohao Ma; Xitao Wang; Gang Xu; Jinwu Xu; Jinshan He

doi:10.3390/ma15196523

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

Materials (Basel). 2022 Sep 20;15(19):6523. doi: 10.3390/ma15196523.

Authors

Bohao Ma¹, Xitao Wang¹, Gang Xu¹, Jinwu Xu¹, Jinshan He¹

Affiliation

¹ Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China.

Abstract

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 θ 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.

Keywords: artificial intelligence; creep; machine learning; metals and alloys; θ projection model.

Abstract

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