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arXiv 1811.01239cond-mat.mtrl-sci
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高温合金蠕变预测的现代数据分析方法

Modern Data Analytics Approach to Predict Creep of High-Temperature Alloys

Dongwon Shin, Yukinori Yamamoto, Michael P. Brady, Sangkeun Lee, J. Allen Haynes

摘要:

合金设计的突破通常需要对复杂的多组分/多相体系有全面的了解,以产生新的材料假设。我们引入了一个现代化的数据分析工作流程,该工作流程利用从高保真模型中获得的高级功能增强的高质量实验数据。在这里,我们使用一个例子的一致测量蠕变数据集的发展高温合金结合科学合金功能填充从高通量计算热力学方法。广泛的相关性分析为最具影响力的合金抗蠕变特性提供了排名见解,这些特性是根据领域专家建议的大量候选特性进行评估的。我们还表明,我们可以准确地训练机器学习模型,通过整合从相关性分析获得的高级别的功能。所展示的方法可以扩展到结合热力学特征之外,来自领域专家的输入用于编译来自其他合金物理学的特征列表,例如扩散动力学和微观结构演变。

英文摘要:

A breakthrough in alloy design often requires comprehensive understanding in complex multi-component/multi-phase systems to generate novel material hypotheses. We introduce a modern data analytics workflow that leverages high-quality experimental data augmented with advanced features obtained from high-fidelity models. Herein, we use an example of a consistently-measured creep dataset of developmental high-temperature alloy combined with scientific alloy features populated from a high-throughput computational thermodynamic approach. Extensive correlation analyses provide ranking insights for most impactful alloy features for creep resistance, evaluated from a large set of candidate features suggested by domain experts. We also show that we can accurately train machine learning models by integrating high-ranking features obtained from correlation analyses. The demonstrated approach can be extended beyond incorporating thermodynamic features, with input from domain experts used to compile lists of features from other alloy physics, such as diffusion kinetics and microstructure evolution.

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