An in-situ predictive method for modulus degradation in composite structures with fatigue damage: Applications in digital twin technology

In recent decades, the digital twin method has garnered significant attention and been extensively researched in mechanics, materials science, engineering, and other applications, particularly in the fields of structural health monitoring and mechanical behavior prediction. A critical aspect of implementing digital twin technology is the ability to capture changes in the physical state in real time and accurately map them to the virtual space. In this study, a combination of multi-stage fatigue testing, μ-CT characterization, tensile testing, and Lamb wave detection was employed to observe and analyze the process of crack initiation and evolution in composite laminates. The influence of fatigue damage on Lamb wave velocity at different frequencies was also investigated. Based on these findings, a predictive model for modulus degradation in carbon fiber-reinforced plastic (CFRP) laminates with crack damage was developed, and its accuracy was validated through experimental verification. As a means of obtaining real-time structural state information during the digital twin process, the model was further applied to predict the modulus degradation of CFRP cylindrical structures after damage. By integrating the digital twin concept, the predicted data was mapped into the digital twin model, enabling the prediction of deformation behavior in damaged cylinders. This study offers a novel approach for in-situ crack damage detection and residual property prediction of CFRP composite structures. It highlights the unique advantages and potential of digital twin technology in advancing research and applications in this field.