About this Symposium |
| Meeting |
MS&T26: Materials Science & Technology
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| Symposium
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Uncertainty Quantification in Ultra-High Temperature Materials Manufacturing
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| Sponsorship |
ACerS Basic Science Division
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| Organizer(s) |
Scott J. McCormack, University of California, Berkeley
Raymundo Arroyave, Texas A&M University
Jeremy K. Mason, University of California, Davis
Wei Xiong, University of Pittsburgh
Hessam Babaee, University of Pittsburgh
William G. Fahrenholtz, Missouri University of Science and Technology |
| Scope |
Standard ultra-high temperature material (UHTM) manufacturing processing parameters result in components with substantial differences in properties largely due to variability in the “critical flaw size” distributions within their microstructure. Critical flaws could be any irregularity in the component’s microstructure such as secondary phases inclusions, pores, etc. The resulting process and property uncertainty makes designing predictable and reproducible UHTM components difficult for high-temperature applications and makes industry partners and manufacturers reluctant to adopt new material systems.
Implementing robust design principles requires a thorough understanding of the way that property uncertainty results from uncertainty propagation through process-structure-property relationships. This makes uncertainty quantification essential to the development and design of new materials. Frameworks and methodologies that incorporate cutting edge models and statistical techniques are required to more effectively quantify both aleatoric and epistemic uncertainty, and particularly the way uncertainty originates in materials processing. This will be essential to develop surrogate models that can reliably predict material properties and the associated uncertainty from processing parameters, and to enable establishing processing parameter guidelines for manufactures.
The symposium will broadly be divided into 4 sessions on (1) Integrated computational materials engineering approaches, (2) Uncertainty quantification using CALPHAD, (3) Uncertainty quantification of microstructures, and (4) development of processing-structure-property databases for materials processing. |
| Abstracts Due |
05/19/2026 |