机器学习在轻质合金研究中的应用-中国材料进展

文章信息/Info

Author(s):: Siyu Zhang1；Ri Li; School of Materials Science and Engineering, Hebei University of Technology, Tianjin 300401, China

Keywords:: machine learning; lightweight alloys; performance prediction; composition design; process optimization

摘要:: 轻质合金以其低密度和高强度特性在航空航天、汽车、电子和建筑等领域具有重要应用。然而，传统的基于经验的“试错法”和基于理论的模拟计算方法，需要进行大量实验，周期长、成本高，难以满足现代轻质合金的发展需求。随着人工智能和数据驱动技术的迅猛发展，机器学习作为目前人工智能领域应用最广泛、发展最快的分支之一，已广泛应用于材料科学，显著加速了新材料的发现和优化。对机器学习计算在轻质合金研究中的应用进展进行了综述，介绍了机器学习在材料研究中的工作流程，阐述了机器学习在轻质合金性能预测、成分设计以及工艺优化方面的研究进展及应用实例。最后，对当前机器学习在轻质合金领域的研究中面临的挑战进行了总结，并对其发展前景进行了展望。

Abstract:: Lightweight alloys, with their low density and high strength characteristics, hold significant applications in aerospace, automotive, electronics and construction fields. However, traditional experience-based “trial and error” methods and theoretical simulation methods require a large number of experiments, which are time-consuming and costly, making it difficult to meet the demands of modern lightweight alloy development. With the rapid development of artificial intelligence and data-driven technologies, machine learning, as one of the most widely used and fastest-growing branch of artificial intelligence, has been extensively applied in materials science, significantly accelerating the discovery and optimization of new materials. This paper reviews the progress of machine learning calculations in the research of lightweight alloys, introduces the workflow of machine learning in materials research, and elucidates the research progress and application examples of machine learning in predicting the properties, composition design and process optimization of lightweight alloys. Finally, the challenges faced by current machine learning research in the field of lightweight alloys are summarized, and the development prospects are discussed.