About this Abstract |
Meeting |
5th International Congress on 3D Materials Science (3DMS 2021)
|
Symposium
|
5th International Congress on 3D Materials Science (3DMS 2021)
|
Presentation Title |
Application of Machine Learning to 3D Reconstruction of SOFC Electrodes |
Author(s) |
Sicen Du, Scott A. Barnett, Katsuyo Thornton |
On-Site Speaker (Planned) |
Sicen Du |
Abstract Scope |
Solid oxide fuel cells (SOFCs) are one of the promising energy conversion devices with pollution-free and high efficiency under elevated temperature operations. Understanding the source and mechanism of microstructural degradation as well as linking microstructure to performance is vital to optimizing the performance of SOFCs. In recent years, 3D reconstruction is the state-of-the-art approach used in microstructure investigations, within which the image segmentation is the most critical step. The 2D images of SOFC electrodes obtained from FIB-SEM or XCT are grayscale images with unavoidable noises and artifacts, which makes microstructural analyses difficult. In our work, we present an advanced image processing approach to achieve high-quality microstructure reconstruction of a SOFC electrodes enhanced by machine learning techniques. We also generated an artificial dataset with similar noises compared to the experimental data to enable quantification of errors resulting from different algorithms. |
Proceedings Inclusion? |
Definite: Other |