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Hassel and Marianne Ledbetter MatSE Colloquium - “New opportunities for inclusion of constitutive modeling in the interpretation and analysis of in-situ x-ray diffraction data”

Event Type
Seminar/Symposium
Sponsor
Materials Science and Engineering Department
Location
100 Materials Science and Engineering Building, 1304 W. Green Street, Urbana
Date
Nov 28, 2022   4:00 pm  
Speaker
Darren Pagan, Materials Science and Engineering, Penn State University
Views
23
Originating Calendar
MatSE Colloquium Calendar

“New opportunities for inclusion of constitutive modeling in the interpretation and analysis of in-situ x-ray diffraction data”

High-performance designs that utilize engineering alloys are driving a need to understand and predict deformation in-situ at the fine length scales in order to reduce weight, increase operating temperatures, and improve fatigue life. With brighter high-energy X-ray sources and more efficient detectors our ability to probe bulk microstructural and micromechanical response at these length scales in conditions mimicking processing and in-service is continuously improving. In fact, measurement capabilities have rapidly improved to the point that our ability to collect large amounts X-ray data has far outstripped our ability to effectively analyze it. A possible reason for this is that analysis techniques have grown around the paradigm of characterizing static material structure as opposed to evolving material response. Since its adoption as a material characterization technique, X-ray scattering has relied on combinations of scattering and structural material models to extract material-relevant quantities from the raw data. As X-ray techniques have become increasingly complex, the scattering half of the modeling efforts required for data interpretation have advanced continuously, while the material half of modeling efforts have progressed at much a slower pace. Here I will describe examples of how replacing static material structural models with thermomechanical constitutive modeling (both phenomenological and machine-learning driven) can build a much richer understanding of material evolution during complex thermomechanical processes and provide a means of more effective data analysis.

 

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