Characterization of Microstructure in Additively Manufactured 316L using Automated Serial Sectioning

A review is presented of recent advancements in characterization and automation and how this has led to a new generation of automated serial-sectioning facilities, which has allowed for the analysis of larger 3D datasets with thousands of objects of interest at high resolution. A case study is presented in which the Robotic Serial Sectioning System for 3D (RS3D) is used to analyze the correlation of the initialization of new grain orientations and defects within additively manufactured 316L steel. Finally, we provide a brief discussion of new technologies including new microscopy methods, machine learning, and sparse sampling that could potentially reduce the effort and cost of collecting and analyzing 3D microstructural data in the near future.