Unsupervised machine learning discovers classes in aluminium alloys
In: Royal Society Open Science, Jg. 10 (2023-02-01), Heft 2
Online
academicJournal
Zugriff:
Aluminium (Al) alloys are critical to many applications. Although Al alloys have been commercially widespread for over a century, their development has predominantly taken a trial-and-error approach. Furthermore, many discrete studies regarding Al alloys, often application specific, have precluded a broader consolidation of Al alloy classification. Iterative label spreading (ILS), an unsupervised machine learning approach, was used to identify the different classes of Al alloys, drawing from a specifically curated dataset of 1154 Al alloys (including alloy composition and processing conditions). Using ILS, eight classes of Al alloys were identified based on a comprehensive feature set under two descriptors. Further, a decision tree classifier was used to validate the separation of classes.
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Unsupervised machine learning discovers classes in aluminium alloys
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Autor/in / Beteiligte Person: | Bhat, Ninad ; Barnard, Amanda S. ; Birbilis, Nick |
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Zeitschrift: | Royal Society Open Science, Jg. 10 (2023-02-01), Heft 2 |
Veröffentlichung: | The Royal Society, 2023 |
Medientyp: | academicJournal |
ISSN: | 2054-5703 (print) |
DOI: | 10.1098/rsos.220360 |
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