Research
Connecting microstructure to fatigue performance.
My research combines computational mechanics, advanced experiments, and data-driven methods to understand how processing, defects, and microstructure govern fatigue. The broader aim is to build predictive frameworks that reduce testing burdens and accelerate the qualification of new materials and manufacturing routes.
Microstructure-sensitive fatigue and fracture
Traditional fatigue models often divide life into disconnected initiation and propagation stages. My work develops crystal-plasticity-based frameworks that follow damage from localized cyclic deformation through microstructurally small-crack growth and into continuum-scale fracture mechanics. Statistically equivalent virtual microstructures make it possible to represent natural variability and predict not only mean behavior, but fatigue-life scatter.
Topics: crystal plasticity · fatigue indicator parameters · small cracks · probabilistic life prediction · multiscale modeling
Defect-aware qualification of advanced manufacturing
Additive manufacturing enables complex, high-performance components, but pores, rough surfaces, and process-induced residual stresses complicate qualification. I use controlled processing studies, three-dimensional defect characterization, and mechanistic simulations to determine which defect features are truly fatigue critical. This work supports a transition from conservative defect limits toward performance-based qualification.
Topics: additive manufacturing · porosity · surface roughness · residual stress · rapid qualification
In situ experiments and experiment-informed modeling
High-energy X-ray computed tomography and diffraction provide a window into deformation and damage inside structural alloys. I integrate laboratory and synchrotron measurements with microstructure-sensitive simulations to interrogate grain-level mechanics, track defect evolution, and test model assumptions using observations that conventional surface measurements cannot provide.
Topics: synchrotron X-rays · computed tomography · diffraction · micromechanics · model validation
Scientific machine learning for materials decisions
High-fidelity simulations reveal mechanisms but can be too expensive for design and qualification decisions. I use physically informed statistical learning, graph-based representations, and reduced-order models to preserve important microstructural information while enabling fast prediction. The goal is interpretable acceleration: models that are efficient because they exploit mechanics, not because they ignore it.
Topics: graph neural networks · scientific machine learning · reduced-order modeling · uncertainty quantification
Complete record
Explore the publications behind this research.
Peer-reviewed articles span additive manufacturing, fatigue and fracture, crystal plasticity, high-energy X-ray experiments, and machine learning.
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