材料科学
微观结构
微晶
随机性
粒度
概率逻辑
流动应力
压力(语言学)
背景(考古学)
机器学习
人工智能
冶金
计算机科学
统计
数学
古生物学
语言学
哲学
生物
作者
Yejun Gu,Christopher D. Stiles,Jaafar A. El‐Awady
出处
期刊:Acta Materialia
[Elsevier]
日期:2024-03-01
卷期号:266: 119631-119631
被引量:1
标识
DOI:10.1016/j.actamat.2023.119631
摘要
The mechanical properties of a material are intimately related to its microstructure. This is particularly important for predicting mechanical behavior of polycrystalline metals, where microstructural variations dictate the expected material strength. Until now, the lack of microstructural variability in available datasets precluded the development of robust physics-based theoretical models that account for randomness of microstructures. To address this, we have developed a probabilistic machine learning framework to predict the flow stress as a function of variations in the microstructural features. In this framework, we first generated an extensive database of flow stress for a set of over a million randomly sampled microstructural features, and then applied a combination of mixture models and neural networks on the generated database to quantify the flow stress distribution and the relative importance of microstructural features. The results show excellent agreement with experiments and demonstrate that across a wide range of grain size, the conventional Hall–Petch relationship is statistically valid for correlating the strength to the average grain size and its comparative importance versus other microstructural features. This work demonstrates the power of the machine-learning based probabilistic approach for predicting polycrystalline strength, directly accounting for microstructural variations, resulting in a tool to guide the design of polycrystalline metallic materials with superior strength, and a method for overcoming sparse data limitations.
科研通智能强力驱动
Strongly Powered by AbleSci AI