Additively manufactured metals exhibit heterogeneous microstructure which dictates their
material and failure properties. Experimental microstructural characterization techniques …

Laser powder bed fusion (LPBF)-based additive manufacturing (AM) has the flexibility in
fabricating parts with complex geometries. However, using non-optimized processing …

An outstanding problem of additive manufacturing is the variability in part quality caused by
process-induced defects such as porosity. Image-based porosity detection represents a …

In this research, a novel approach for the automated quantification of grain size in single-
phase materials using scanning electron microscopy (SEM) based back scattering electron …

Metallography is crucial for a proper assessment of material properties. It mainly involves
investigating the spatial distribution of grains and the occurrence and characteristics of …

The shape and size of grains significantly impact the properties of polycrystalline materials.
In particular, high temperature and radiation exposure in nuclear reactors can lead to …

Traditional grain size determination in materials characterization involves microscopy
images and a laborious process requiring significant manual input and human expertise. In …

Microstructural features are one of the most important factors that determine performance
and failure of engineering structures. In metal parts produced through Additive …

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In
this paper, deep learning-based image analysis is performed for defect (cracks and pores) …

We report a significantly improved accuracy in grain boundary segmentation using
Convolutional Neural Networks (CNN) trained on a combination of real and generated data …