论文标题
基于物理学的机器学习方法，用于建模超合金的温度依赖性屈服强度
Physics-Based Machine Learning Approach for Modeling the Temperature-Dependent Yield Strength of Superalloys
论文作者
论文摘要
为了追求具有提高特性的高温合金，以满足下一代能源和航空航天需求的性能要求，综合计算材料工程（ICME）发挥了至关重要的作用。在本文中，提出了一种机器学习（ML）方法，能够利用双线性日志模型来预测超合金的温度依赖性屈服强度。重要的是，该模型引入了参数中断温度，即$ t_ {break} $，它是操作条件的上限，确保可接受的机械性能。与传统的黑盒方法相反，我们的模型基于直接内置在模型中的基本物理学。我们提出了一种全局优化的技术，它允许在低温和高温方向上同时优化模型参数。提出的结果扩展了先前的高渗透合金（HEAS）的工作，并为双线性日志模型及其在对超合金和HEAS的温度依赖性强度行为进行建模的适用性提供了进一步的支持。
In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a crucial role. In this paper a machine learning (ML) approach is presented, capable of predicting the temperature-dependent yield strengths of superalloys, utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature, $T_{break}$, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black-box approaches, our model is based on the underlying fundamental physics, directly built into the model. We present a technique of global optimization, one allowing the concurrent optimization of model parameters over the low-temperature and high-temperature regimes. The results presented extend previous work on high-entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature-dependent strength behavior of superalloys as well as HEAs.
