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A machine learning approach for accelerated design of magnesium alloys. Part A:alloy data and property space

Journal of Magnesium and alloys 2023年第11期11卷 3620-3633页

作者： M.Ghorbani M.Boley P.N.H.Nakashima N.Birbilis Department of Materials Science and Engineering Monash UniversityVIC3800Australia Faculty of Information Technology Monash UniversityVIC3800Australia College of Engineering and Computer Science Australian National UniversityACT2601Australia Faculty of Engineering Science and the Built EnvironmentDeakin UniversityVIC3125Australia

Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this work, a new approach is proposed that utilises data science and provides a detailed understanding of the data that exists in the field of mg-alloy design to date. In this approach, first a consolidated alloy database that incorporates 916 datapoints was developed from the literature and experimental work. To analyse the characteristics of the database, alloying and thermomechanical processing effects on mechanical properties were explored via composition-process-property matrices. An unsupervised machine learning(ML) method of clustering was also implemented, using unlabelled data, with the aim of revealing potentially useful information for an alloy representation space of low dimensionality. In addition, the alloy database was correlated to thermodynamically stable secondary phases to further understand the relationships between microstructure and mechanical properties. This work not only introduces an invaluable open-source database, but it also provides, for the first-time data, insights that enable future accelerated digital mg-alloy design.

关键词： Magnesium alloy design mg-alloy database Data analysis Data visualisation Unsupervised machine learning

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