Microstructure-based classification and electronmicroscopy analysis of nanoporous metals by machine learning (B09)

点击翻译按钮获取中文摘要

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.