文献类型：期刊文献

英文题名：A deep learning-based digital twin system for creep behaviour of metallic alloys

作者：Zhao, Peng[1];Liu, Yang[1];Zhang, Jianrui[1];Zhang, Huyong[1];Xuan, Fu-Zhen[1]

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

年份：2026

外文期刊名：MATERIALS AT HIGH TEMPERATURES

收录：;EI(收录号：20261120284841);WOS:【SCI-EXPANDED(收录号:WOS:001713609800001)】；

基金：The work was supported by the National Key Research and Development Program of China [2022YFF0605600]; National Natural Science Foundation of China [52321002]; National Natural Science Foundation of China [52275150].

语种：英文

外文关键词：Digital twin; creep behaviour; deep learning; physics-guided; distributed architecture

摘要：Creep deformation is a dominant failure mode in metallic materials during service, governing structural stability, reliability and design life. However, conventional creep testing is time-consuming and resource-intensive, limiting the availability of high-fidelity creep data for efficient prediction. To address this issue, this study aims to develop a material-level digital twin system capable of generating high-fidelity creep strain data under limited experimental conditions. The proposed system is demonstrated using a 7-series aluminium alloy and employs a physics-guided gated recurrent unit model that integrates real-time sensor data with dynamic updating for creep strain prediction. Results show that incorporating physical constraints improves prediction accuracy by approximately 12% compared with the GRU model. Comparison with continuum damage mechanics-based creep models further demonstrated the competitive predictive capability of the proposed approach. Furthermore, a standalone application was developed to enable real-time monitoring and prediction of the creep process, facilitating efficient experimental implementation.