Machine Learning-Enabled Uncertainty Quantification for Modeling Structure–Property Linkages for Fatigue Critical Engineering Alloys Using an ICME Workflow


Integrated computational materials engineering (ICME) facilitates efficient approaches to new material discovery and design, as well as optimization of existing materials. Computational models provide a way to rapidly screen candidate material designs such that materials can be tailored for specific applications in the product design cycle. Uncertainty is ubiquitous in ICME process–structure–property workflows; it represents a major barrier to the effective use of modeling results for high-confidence decision support in materials design and development. This work addresses microstructure statistical uncertainties, and demonstrates an approach to quantify, reduce, and propagate these uncertainties through structure–property linkages to provide robust quantification of uncertainties in output properties of interest. Further, this work demonstrates the use of Gaussian process machine learning models to significantly decrease the computational cost of the aforementioned robust uncertainty quantification.

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This work was sponsored in part by the Office of Naval Research (ONR), under Grant Number N00014-17-1-2036. The views and conclusions contained herein are those of the authors only and should not be interpreted as representing those of ONR, the U.S. Navy or the U.S. Government. In addition, the authors are grateful for the support of the Carter N. Paden, Jr. Distinguished Chair in Metals Processing.

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Correspondence to Gary Whelan.

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Whelan, G., McDowell, D.L. Machine Learning-Enabled Uncertainty Quantification for Modeling Structure–Property Linkages for Fatigue Critical Engineering Alloys Using an ICME Workflow. Integr Mater Manuf Innov (2020).

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  • Uncertainty quantification
  • Machine learning
  • ICME
  • Fatigue
  • Ti64
  • Alloy design