|About this Abstract
||MS&T22: Materials Science & Technology
||Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
||Microstructure Predictions in Additive Manufacturing from Analytical Solidification Models – A Critical Assessment of Simplifying Assumptions
||Jonah Klemm-Toole, Charles Smith, Olivia DeNonno, Matthew Schreiber, Luc Hagen, Gwilym Couch, Zhenzhen Yu, Kip Findley, John Speer, Joy Gockel, Amy Clarke, Tony Petrella, Craig Brice
|On-Site Speaker (Planned)
Prediction of the as-solidified microstructure in additive manufacturing (AM) is a key capability that is needed to develop the next generation of advanced materials and processing technologies. Analytical models offer easy to use expressions that can be quickly solved enabling the rapid assessment of material and process variables. However, many analytical solidification models were developed for highly simplified conditions. In this presentation, we discuss how these models can be extended to complex alloys and higher solidification rates relevant to AM. Using austenitic stainless steels as an illustrative example, we demonstrate how common simplifying assumptions can affect predictions of solidification mode, primary and secondary dendrite arm spacing, dendrite growth morphology, and primary solidifying phase under processing conditions representative of several AM processes. The insights provided in this talk are intended to spark rich discussions on how the fundamentals of solidification can be analytically modeled to advance our understanding of AM.