Bohao Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Bohao Ma has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Gang Xu
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China
1 shared publication - Jinshan He
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
1 shared publication - Jinwu Xu
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
1 shared publication - Xitao Wang
Shandong Key Laboratory of Aluminum Alloy Materials Preparation and Forming, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
1 shared publication
Latest Publications
- Open Access
Prediction of Creep Curves Based on Back Propagation Neural Networks for Superalloys
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.
Open AccessPrediction of Creep Curves Based on Back Propagation Neural Networks for Superalloys
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.