Project description
Focused ion beam (FIB) tomography determines the three-dimensional microstructure of materials via a series of two-dimensional scanning electron microscope (SEM) images. In nanoporous metals, FIB tomography is often facing problems with so-called shine-through effects, which can significantly reduce the accuracy with which FIB tomography data can be segmented. This project will use machine learning to develop a new segmentation method which can efficiently suppress shine-through effects and thus enable a reconstruction even of complex hierarchical multi-scale microstructures of nanoporous metals with excellent accuracy.
Project leader | |
 | Prof. Dr.-Ing. Christian J. Cyron, TUHH Contact |
 | Dr.-Ing. Martin Ritter TUHH Contact |
Keywords
nanoporous
metal
electron microscopy
network
microstructure
machine learning
Publications
1. T. Sardhara, R. C. Aydin, Y. Li, N. Piché , R. Gauvin, C. J. Cyron and M. Ritter: Training Deep Neural Networks to Reconstruct Nanoporous Structures From FIB Tomography Images Using Synthetic Training Data
. Front. Mater. 9, 837006 (2022)
https://doi.org/10.3389/fmats.2022.837006 open access
2. F.E. Bock, R.C. Aydin, C.J. Cyron, N. Huber, S.R. Kalidindi and B. Klusemann: A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics.
Frontiers in Materials 6, (2019)
https://doi.org/10.3389/fmats.2019.00110 open access
3. R.C. Aydin, F.A, Braeu and C.J. Cyron: General Multi-Fidelity Framework for Training Artificial Neural Networks With Computational Models.
Frontiers in Materials 6, (2019)
https://doi.org/10.3389/fmats.2019.00061 open access