文献类型：期刊文献

英文题名：Microstructural feature-based physics-informed neural network for creep residual life prediction of P91 steel

作者：Liu, Zhi[1];Zheng, Zhou[1];Zhao, Peng[1];Gong, Jian-Guo[1];Zhang, Xiao-Cheng[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety, MOE, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2025

卷号：319

外文期刊名：ENGINEERING FRACTURE MECHANICS

收录：;EI(收录号：20251018009922);WOS:【SCI-EXPANDED(收录号:WOS:001443730300001)】；

基金：Supports from National Key Research and Development Program of China (Grant No.: 2023YFF0614903) and National Natural Science Foundation of China (Grant No.: 52175139) are greatly acknowledged.

语种：英文

外文关键词：Machine learning; physics-informed neural network (PINN); Creep residual life; Microstructural feature; Evolution mechanism

摘要：Creep residual life prediction of materials at elevated temperature is an important topic in the field of structural integrity. Traditional creep residual life prediction methods only consider mechanical parameters (e.g. stress, strain, temperature), while the microstructural features are rarely mentioned, reducing the prediction accuracy. In this work, taking the P91 steel as an example, a microstructural feature-based physics-informed neural network (PINN) for predicting creep residual life was developed by integrating the microstructural characteristics and mechanical parameters. The influence of microstructural features on the prediction results was discussed, and the prediction results of the proposed model and some conventional machine learning methods were compared. The effect of the strain data on creep residual life prediction results was included. The results indicated that the introduction of the microstructural evolution mechanisms (i.e. coarsening of precipitations and subgrain growth) could enhance the creep residual life prediction capacity of the proposed PINN model. The proposed PINN model outperforms the aforementioned traditional machine learning methods in predicting the creep residual life of materials. This model also exhibits excellent prediction performance without incorporating the creep strain data as an input feature, demonstrating the generalization ability and robustness of the proposed model.