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. Pich├ę , 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