Info

Title:

Applications of Machine Learning in Lightweight Alloy Research

Author(s):

Siyu Zhang1；Ri Li

School of Materials Science and Engineering, Hebei University of Technology, Tianjin 300130, China

Keywords:

Machine Learning;  Lightweight Alloys;  Performance Prediction;  Alloy Design;  Process Optimization

CLC:

PACS:

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DOI:

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DocumentCode:

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 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 of lightweight alloys, alloy design, and process optimization. Finally, the challenges faced by current machine learning research in the field of lightweight alloys are summarized, and the development prospects are discussed.