Software architecture for CALPHAD modeling of phase stability and transformations in alloy additive manufacturing processes

Open Access
Otis, Richard Albert
Graduate Program:
Materials Science and Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
June 16, 2016
Committee Members:
  • 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
  • software
  • Python
  • thermodynamics
  • alloys
  • additive manufacturing
  • uncertainty quantification
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.