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Machine Learning-Enabled Uncertainty Quantification for Modeling Structure–Property Linkages for Fatigue Critical Engineering Alloys Using an ICME Workflow
Integrating Materials and Manufacturing Innovation ( IF 3.3 ) Pub Date : 2020-11-18 , DOI: 10.1007/s40192-020-00192-2
Gary Whelan , David L. McDowell

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.



中文翻译:

借助机器学习的不确定性量化,可使用ICME工作流程对疲劳关键工程合金的结构-属性链接进行建模

集成计算材料工程(ICME)促进了新材料发现和设计以及现有材料优化的有效方法。计算模型提供了一种快速筛选候选材料设计的方法,以便可以针对产品设计周期中的特定应用量身定制材料。ICME的流程,结构,属性工作流程中普遍存在不确定性。它代表了有效利用建模结果为材料设计和开发提供高信心决策支持的主要障碍。这项工作解决了微观结构的统计不确定性,并展示了一种通过结构-属性链接量化,减少和传播这些不确定性的方法,可以对感兴趣的输出特性中的不确定性进行可靠的量化。进一步,

更新日期:2020-11-19
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