基于机器学习的增材制造用高温合金设计：研究现状与未来趋势

机器学习辅助材料设计是一种高效的合金设计方法，该方法结合高通量计算/实验技术能够极大推动材料领域的发展。然而，目前材料研究者尚未充分利用该方法。针对上述问题，详细介绍了基于机器学习的高温合金设计的基本方法，包括数据收集与预处理、模型建立与训练、合金性能预测等步骤，阐述了增材制造用高温合金的设计现存困难和亟待解决“权衡裂纹敏感性与高温性能”的关键问题，深入分析了机器学习在增材制造用高温合金设计中的必要性。从高温合金数据获取、性能预测、合金设计等方面梳理了机器学习辅助增材制造用高温合金设计的应用现状，指出了该领域目前存在的数据来源、建模范式、模型的应用与推广等核心问题，并对该领域后续的发展提供了建议。

Design of Nickel-based Superalloys for Additive Manufacturing Based on Machine Learning： Research Status and Future Trends

Machine learning assisted material design is an efficient alloy design method, which can greatly promote the development of the material field by combining high-throughput computing/experimental techniques. However, material researchers have not yet made full use of this method. In view of the above problems, this research introduces the basic methods of superalloy design based on machine learning in detail, including data collection and preprocessing, model establishment and training, alloy performance prediction and other steps. The existing difficulties in the design of superalloys for additive manufacturing and the key problems to be solved urgently are expounded. The necessity of machine learning in the design of superalloys for additive manufacturing is deeply analyzed. Furthermore, the application status of machine learning-assisted design of superalloys for additive manufacturing is reviewed from the aspects of data acquisition, performance prediction and alloy design of superalloys. The core issues such as data sources, modeling models, application and promotion of models in this field are pointed out, and suggestions for future development in this field are provided.