- You are here:
Dissertation Proposal Defense – Adrienne Muth
MSE Grad Presentation
Monday, October 22, 2018 - 1:45pm
MRDC 3515, Hightower Conference Room
Committee Members: Prof. David McDowell, Advisor, MSE Prof. Surya Kalidindi, MSE Prof. Richard Neu, ME Prof. Reji John, Air Force Research Lab Prof. Adam Pilchak, Air Force Research Lab
"Advanced Structure-Property Linkages Based on Extreme Value Statistics for Fatigue Indicator Parameters for Alloys"
Fatigue lifetime is a critical performance requirement for polycrystalline metal alloys used in aerospace applications but is a significant challenge to study, as fatigue behavior exhibits highly variable responses to microstructure attributes and loading conditions. Elucidating the role that combinations of microstructure attributes play in promoting fatigue crack formation and early growth requires a prohibitive number of experiments. Identifying the local microstructure states that favor fatigue crack formation and, separately, early crack growth will allow for materials processing to be adapted to minimize risk of crack initiation. In the proposed work, a hierarchical, multiscale, statistically-driven computational workflow is laid out to provide a template for using data science methods to enhance investigation fatigue response of polycrystalline alloys, using Ti-6Al-4V as an example.
First, microstructure data from experimental results are used to create statistically representative microstructure volumes, which are then subjected to cyclic loading using a constitutive model that captures crystallographic slip using finite element modeling. Fatigue Indicator Parameters (FIPs) are investigated as surrogate measures of driving force for fatigue crack formation within the nucleant phase or grain and characterized by the phase of the grain in which they are located. The proposed project will utilize these FIPs and methodology to provide insight for improved fatigue resistance behavior for both fatigue crack formation and growth past first barrier. Significance of the work includes:
- Investigation of effects of microstructure and loading conditions on phase of grains coincident with locations of maximal FIP values.
- Novel data-science approach to identify the combinations of microstructure attributes with highest likelihood to lead to formation of fatigue crack using extreme value distributions (EVDs), 2-point statistics, and principal component analysis (PCA).
- Utilization of extreme value marked correlation functions (EVMCFs) at locations of maximal FIP values to study joint conditional probability that a fatigue crack has sufficient driving force to both form and to grow past first barrier, which have only been studied separately before.
- First ever preliminary assessment of model form effects on EVD and EVMCF results.