About this Abstract |
Meeting |
6th International Congress on 3D Materials Science (3DMS 2022)
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Symposium
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6th International Congress on 3D Materials Science (3DMS 2022)
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Presentation Title |
A Novel Approach for High-resolution Phase Analysis using Hybrid Machine Learning Segmentation of Multi-modal FIB-SEM Datasets and its Application for Analysis of Next Generation Wear-resistant Coatings |
Author(s) |
Jiri Dluhos, Hana Tesařová, Vendulka Bertschová, Frédéric Voisard, Alexandre Migneault, Nadi Braidy |
On-Site Speaker (Planned) |
Jiri Dluhos |
Abstract Scope |
Acquisition of complete analytical information about material microstructure for understanding its relation to mechanical properties often requires a multi-scale or multi-modal characterization approach involving multiple analytical techniques.
The scanning electron microscope (SEM) is a technology that allows materials characterization from few nanometers up to millimeter range. Besides high-resolution imaging the analytical potential of the system also allows corelative analysis by multiple micro/nano analytical techniques like EDS, EBSD or TOF-SIMS. With addition of focused ion beam (FIB) this extends also the capability into serial-sectiong 3D volume analysis.
However, the segmentation of the large multi-modal FIB-SEM datasets is still challenging. The approach presented in this work tries to overcome some of the limitations of traditional techniques by hybrid machine learning segmentation on the multi-modal dataset followed by phase identification from spectral data.
A high-resolution 3D analysis used in this work shows the application for development of next generation WC-Fe3Al wear resistant coatings. |
Proceedings Inclusion? |
Definite: Other |