机器学习和数据分析用于设计和制造具有感兴趣的机械或疲劳特性的高熵材料
Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest
摘要：
本章提出了一个创新的框架，用于机器学习和数据分析的应用，以识别表现出某些所需特性的合金或复合材料。主要关注结构材料中具有大成分空间的合金和复合材料。这样的合金或复合材料被称为高熵材料（HEMs），在这里主要是在结构应用的背景下。对于每个感兴趣的输出属性，识别相应的驱动（输入）因素。这些输入因素可能包括材料成分、热处理、制造工艺、微观结构、温度、应变速率、环境或测试模式。该框架假设选择适合手头的应用程序和可用数据的优化技术。提出了基于物理的模型，例如用于预测极限拉伸强度（UTS）或抗疲劳性。我们设计的模型能够占基于物理的依赖关系。我们将这种依赖性作为先验信息纳入模型。如果人工神经网络（ANN）被认为适合手头的应用程序，建议采用与底层物理相一致的自定义核函数，以实现更紧密的耦合，更好的预测，并从通常有限的输入数据中提取最多的可用数据。
英文摘要：
This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and composites with large composition spaces for structural materials. Such alloys or composites are referred to as high-entropy materials (HEMs) and are here presented primarily in context of structural applications. For each output property of interest, the corresponding driving (input) factors are identified. These input factors may include the material composition, heat treatment, manufacturing process, microstructure, temperature, strain rate, environment, or testing mode. The framework assumes the selection of an optimization technique suitable for the application at hand and the data available. Physics-based models are presented, such as for predicting the ultimate tensile strength (UTS) or fatigue resistance. We devise models capable of accounting for physics-based dependencies. We factor such dependencies into the models as a priori information. In case that an artificial neural network (ANN) is deemed suitable for the applications at hand, it is suggested to employ custom kernel functions consistent with the underlying physics, for the purpose of attaining tighter coupling, better prediction, and for extracting the most out of the - usually limited - input data available.
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。
