|About this Abstract
||2023 TMS Annual Meeting & Exhibition
||Quantifying Microstructure Heterogeneity for Qualification of Additively Manufactured Materials
||Location specific characterization of additively manufactured stainless steel to inform build data analytics
||Allyssa Bateman, Christopher Snyder, Scott Schier, Ana Stevanovic, Amanda Fernandez, Elizabeth Sooby, Brian Jaques
|On-Site Speaker (Planned)
Additive manufacturing (AM) could potentially produce nuclear reactor structural components with improved performance and decreased qualification time. However, micromechanical variability in AM parts can cause suboptimal micromechanical properties and chemical inhomogeneity. Data analytics shows the potential to predict defect probability and allow for in-situ part qualification, but requires significant amounts of training data. In this work, methods were developed to collect thousands of data points cataloguing location-specific microstructure, chemistry, mechanical properties and corrosion behavior across eighteen additively-manufactured stainless steel 316 parts. Wire EDM (electrical discharge machining) sectioned samples with minimal material loss while laser engraved scalebars and labels ensured precise location tracking. Characterization techniques included optical microscopy, scanning electron microscopy, x-rays, light element analysis, Raman spectroscopy, microhardness and steam oxidation. With this information, data analysts built methods to identify variances within data sets and connect trends in materials properties with original build parameters, improving future parts.
||Additive Manufacturing, Characterization,