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 managi...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 synchronization error,real-time monitoring with ms delay,and predictive accuracy for temperature<2%<100distributions under extreme conditions up to 2500℃.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.展开更多
基金supported by Natural Science Foundation of China(NSFC),Grant Number 5247052693.
文摘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 synchronization error,real-time monitoring with ms delay,and predictive accuracy for temperature<2%<100distributions under extreme conditions up to 2500℃.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.
