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Meeting Materials Science & Technology 2020
Symposium Materials Design through AI Composition and Process Optimization
Presentation Title Statistics-based Microstructural Digital Image Correlation Method for Estimating Ex-situ Strain from Dissimilar Micrographs
Author(s) Patxi Fernandez-Zelai, Quinn Campbell, Yousub Lee, Michael M Kirka, Sebastien N Dryepondt
On-Site Speaker (Planned) Patxi Fernandez-Zelai
Abstract Scope Digital image correlation (DIC) methods are ubiquitously used throughout engineering for estimating in-situ strain. Ex-situ DIC estimation of strain from deformed micrographs is not possible as there are no persistent trackable features. Two point spatial statistics enable the quantification of spatial patterns in heterogeneous media. Similar to particle tracking methods, computation of two point statistics rely on the use of convolutions suggesting there is a connection between the two. In this talk we present a novel method for estimating strain directly from dissimilar micrographs using a continuum mechanics approach. The proposed method can be interpreted as a statistics-based microstructural digital image correlation method as it operates on image statistics rather than directly on images. A Bayesian bootstrapping framework is proposed for quantifying prediction uncertainty. This method is broadly applicable in a number of settings: materials processing, dynamically impacted materials, and failure analysis, to name a few.
Proceedings Inclusion? Undecided


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Statistics-based Microstructural Digital Image Correlation Method for Estimating Ex-situ Strain from Dissimilar Micrographs
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