预测金属强度的统计学视角：利用机器学习重新审视 Hall-Petch 关系
A Statistical Perspective for Predicting the Strength of Metals: Revisiting the Hall-Petch Relationship using Machine Learning
AI总结：
本文提出概率机器学习框架，利用逾百万随机微观结构样本与混合模型、神经网络预测流动应力分布，验证 Hall-Petch 关系并支持高强多晶金属设计。
摘要：
材料的力学性能与其微观结构密切相关。这对于预测多晶金属的力学行为尤为重要，因为微观结构变化决定了材料的预期强度。迄今为止，现有数据集缺乏微观结构的多样性，这阻碍了能够解释微观结构随机性的、可靠的基于物理的理论模型的发展。为解决这一问题，我们开发了一个概率机器学习框架，用于预测流动应力随微观结构特征变化的函数。在该框架中，我们首先针对超过一百万个随机采样的微观结构特征生成了一个大规模流动应力数据库，随后在该数据库上结合使用混合模型与神经网络，以量化流动应力分布以及各微观结构特征的相对重要性。结果表明，预测与实验高度吻合，并证明在很宽的晶粒尺寸范围内，传统 Hall-Petch 关系在统计上仍然有效，可用于关联强度与平均晶粒尺寸，以及平均晶粒尺寸相对于其他微观结构特征的比较重要性。本工作展示了基于机器学习的概率方法在预测多晶强度方面的能力，该方法直接考虑微观结构变化，从而形成一种指导设计具有优异强度的多晶金属材料的工具，并提供了一种克服稀疏数据限制的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
