@Article{icces.2023.09713,
AUTHOR = {Liang Zhang, Zigang He},
TITLE = {A Data-Fusion Method for Uncertainty Quantification of Mechanical  Property of Bi-Modulus Materials: An Example of Graphite},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {27},
YEAR = {2023},
NUMBER = {2},
PAGES = {1--1},
URL = {http://www.techscience.com/icces/v27n2/54170},
ISSN = {1933-2815},
ABSTRACT = {The different elastic properties of tension and compression are obvious in many engineering materials, 
especially new materials. Materials with this characteristic, such as graphite, ceramics, and composite 
materials, are called bi-modulus materials. Their mechanical properties such as Young’s modulus have 
randomness in tension and compression due to different porosity, microstructure, etc. To calibrate the 
mechanical properties of bi-modulus materials by bridging FEM simulation results and scarce experimental 
data, the paper presents a data-fusion computational method. The FEM simulation is implemented based on 
Parametric Variational Principle (PVP), while the experimental result is obtained by Digital Image 
Correlation (DIC) technology. To deal with scarce experimental data, Maximum Entropy Principle (MEP) is 
employed for the uncertainty quantification (UQ) and calibration of material parameters and responses, 
which can retain the original probabilistic property of a priori data. The non-parametric p-box is used as a 
constraint for data fusion. The method presented in this paper can quantify the mechanical properties of 
materials with high uncertainty, which is verified by a typical example of bi-modulus graphite. It is possible 
to find applications in the real-time estimation of structural reliability by combining with digital twin 
technology in the future.},
DOI = {10.32604/icces.2023.09713}
}