|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Additive Manufacturing: Building the Pathway towards Process and Material Qualification
||Toward a New Generation of Thermodynamic Models for Alloy Additive Manufacturing
||Richard Otis, Lourdes D. Bobbio, Allison M. Beese, Zi-Kui Liu
|On-Site Speaker (Planned)
Thermodynamic modeling is now routinely used to predict and understand the observed properties of alloy samples produced by additive manufacturing. In this work the properties of compositionally graded Ti-6Al-4V-to-Invar and 304L stainless steel-to-Inconel 625 alloys are studied by modeling and experiment. While the thermodynamic predictions are shown to be in good agreement with experiments, we show that there are significant limitations in the commercially available thermodynamic databases. These limitations are due to the cumbersome task of maintaining complex multi-component databases. This work describes new software architecture, with a particular focus on uncertainty quantification, for improving these databases. This approach requires little input from the user, making it possible to be integrated into automated, high-throughput modeling infrastructure. With a more robust and efficient method for generating multi-component thermodynamic databases based on the latest available data, thermodynamic studies of non-conventional materials systems such as compositionally graded alloys will become more predictive and accurate.
||Planned: Supplemental Proceedings volume