|About this Abstract
||2023 TMS Annual Meeting & Exhibition
||2023 Technical Division Student Poster Contest
||SPG-16: A Computer Vision Method of Grain Segmentation for Additive-manufactured Haynes 282 Alloys Under Various Heat Treatments
||Yu-Tsen Yi, Nicholas Lamprinakos, Junwon Seo, Anthony Rollett
|On-Site Speaker (Planned)
Metal additive manufacturing technology represents a new fabrication method that is able to produce complex geometry components with specific requirements in properties. However, the correlation between the complex geometries of the printed part and microstructural properties, such as grain size distribution, which affects overall performance, is not well understood yet. Here we proposed a computer vision method that can segment out grain and output quantitative numbers of the grain size distribution from scanning electron microscope (SEM) images within seconds, allowing researchers to save significant amounts of time and resources. In this work, we analyze and compare the grain size distribution of additive-manufactured H282 alloy under various heat treatments, hoping to gain a deeper understanding of the relationship between various process parameters and microstructure.
||Additive Manufacturing, ICME, Characterization