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THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING
GEORGIA INSTITUTE OF TECHNOLOGY
Under the provisions of the regulations for the degree
DOCTOR OF PHILOSOPHY
on Thursday, May 2, 2019
1:00 PM
in MRDC 3515
will be held the
DISSERTATION PROPOSAL DEFENSE
for
Gary Whelan
"Uncertainty Informed Integrated Computational Materials Engineering for Design and Development of Fatigue Critical Alloys"
Committee Members:
Prof. David L. McDowell, Advisor, MSE/ME
Prof. Hamid Garmestani, MSE
Prof. Richard W. Neu, ME
Prof. Yan Wang, ME
Laura P. Swiler, Ph.D., Sandia National Laboratories
Abstract:
Uncertainty is intrinsically tied to decision making in design. Process-Structure-Property (PSP) relations are central to development of new and improved materials. The multitude of PSP linkages for any performance objective can be explored using the top down, inductive design exploration method (IDEM). Each PS and SP linkage has associated uncertainty, arising both from the types of models or interpretation of experimental results used to form linkages, as well as model parameters. These uncertainties can propagate and significantly affect the decision-making process in design and development of materials for specific targets. Uncertainty quantification (UQ) can be a highly computationally expensive undertaking in materials design and development. In the proposed research, computationally efficient protocols will be developed to effectively incorporate UQ in the IDEM. The uncertainty associated with PS linkages will be assigned based on existing literature results. Gaussian process (GP) surrogate models will be developed for the various SP linkages of interest as lower order approximations of computational expensive computational materials science simulations (e.g., the crystal plasticity finite element method (CPFEM)). These GP models can be used to propagate uncertainty in microstructure attributes to the quantities of interest associated with properties that are to be optimized in design. These surrogate models can then be integrated into existing python IDEM (pyDEM) protocols in the form of mapping functions. In this work, novel protocols will be developed and demonstrated for uncertainty-informed design and development of Ti-6Al-4V and Al7075-T6 microstructures for optimal combinations of fatigue resistance, elastic stiffness, and yield strength.
