Software architecture for CALPHAD modeling of phase stability and transformations in alloy additive manufacturing processes
Otis, Richard Albert
Materials Science and Engineering
Doctor of Philosophy
Date of Defense:
June 16, 2016
Zi-Kui Liu, Dissertation Advisor/Co-Advisor Zi-Kui Liu, Committee Chair/Co-Chair Allison Michelle Beese, Committee Member Ismaila Dabo, Committee Member Qiang Du, Outside Member Bryan McEnerney, Special Member
This work is motivated by a desire to study the thermodynamics of compositionally-graded alloy systems built by additive manufacturing. Because of the peculiar behavior of graded alloy systems, we need to be able to rapidly construct new thermodynamic databases. However, the existing software infrastructure makes doing that very difficult. This dissertation describes both new software and new methods for thermodynamic modeling within the CALPHAD method, with particular focus on improved miscibility gap detection, automated parameter selection, and uncertainty quantification. The advantages of this approach are that the software implementation is freely available and that using this approach to model thermodynamic systems requires substantially less input from the user than conventional methods, making it possible to be integrated into automated, high-throughput modeling infrastructure. Moreover, the described method leads to a more sophisticated treatment of the CALPHAD prediction uncertainty. With a more robust and efficient method for generating multi-component thermodynamic databases based on the latest available experimental and first-principles data, thermodynamic studies of non-conventional materials systems such as compositionally-graded alloys will become more predictive and accurate.