@Article{cmc.2025.060194,
AUTHOR = {Xiyu Gao, Peng Liu, Anran Zhao, Guotai Huang, Jianhai Zhang, Liming Zhou},
TITLE = {Digital Twin-Driven Modeling and Application of High-Temperature Biaxial Materials Testing Apparatus},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {82},
YEAR = {2025},
NUMBER = {3},
PAGES = {4137--4159},
URL = {http://www.techscience.com/cmc/v82n3/59920},
ISSN = {1546-2226},
ABSTRACT = {The High-Temperature Biaxial Testing Apparatus (HTBTA) is a critical tool for studying the damage and failure mechanisms of heat-resistant composite materials under extreme conditions. However, existing methods for managing and monitoring such apparatus face challenges, including limited real-time modeling capabilities, inadequate integration of multi-source data, and inefficiencies in human-machine interaction. To address these gaps, this study proposes a novel digital twin-driven framework for HTBTA, encompassing the design, validation, operation, and maintenance phases. By integrating advanced modeling techniques, such as finite element analysis and Long Short-Term Memory (LSTM) networks, the digital twin enables high-fidelity simulation, real-time predictive modeling, and robust remote monitoring of HTBTA. The research contributes to bridging the knowledge gap in applying digital twin technology to high-temperature multi-axial testing systems. Unlike existing solutions, the proposed approach achieves <2% synchronization error, real-time monitoring with <100 ms delay, and predictive accuracy for temperature distributions under extreme conditions up to 2500°C. The findings highlight the effectiveness of the digital twin in improving system reliability, enhancing interaction efficiency, and reducing maintenance costs. This study not only advances the application of digital twin technology in high-temperature material testing but also establishes a foundation for broader adoption in aerospace, automotive, and other industrial sectors. Future research directions include exploring non-proportional loading scenarios, expanding multi-environment simulations, and integrating in-situ observation techniques.},
DOI = {10.32604/cmc.2025.060194}
}