@article{GUO2025, 
author = {Chengjie GUO and Dian XU and Jinbao LI and Chaoyu CHENG and Shuochang GUO and Rui LI},
title = {Stress characterization of high-temperature digital image correlation experiments based on a data fusion-knowledge transfer method},
year = {2025},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {46},
number = {19},
keywords = {high-temperature digital image correlation, data fusion, transfer learning, stress characterization, multilayer perception neural network},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2025.31574},
doi = {10.7527/S1000-6893.2025.31574},
abstract = {The Digital Image Correlation (DIC) experimental system is widely used for mechanical performance evaluation of structures in high-temperature environments due to its ability to achieve non-contact precise measurements of full-field strain. However, experimental methods based on DIC measurement technology face challenges in directly characterizing the stress field of structures, particularly in high-temperature environments where material parameters are closely related to temperature, and the heavy reliance of the Finite Element Method (FEM) on these parameters leads to insufficient accuracy of stress outcomes. Therefore, it is of great significance to understand and evaluate the mechanical properties of structures by combining high-temperature DIC experiments and FEM to accurately characterize the full-field stress of structures. This paper proposes a data fusion-knowledge transfer method for stress characterization in high-temperature DIC experiments. A high-temperature DIC experimental system, a high-precision FEM model, and a transfer learning framework are developed for typical high-temperature titanium alloys in aerospace structures, achieving accurate characterization of stress fields based on small-sample experimental data. Firstly, a multi-layer perceptron neural network is employed to pre-train large-sample simulation data, capturing the mapping relationship between structural coordinates and stress in complex high-temperature environments. Secondly, based on small-sample high-temperature DIC test data, the pre-trained model is fine-tuned to characterize a structural stress field in high-temperature environments, and effectively transfer knowledge from the finite element model to the experimental data. Finally, the accuracy and applicability of the proposed method for predicting structural stress fields are verified using indicators such as the mean square error and the coefficient of determination, which provides a new approach for characterizing experimental stress fields in high-temperature environments.}
}