Unsupervised machine learning discovers classes in aluminium alloys.

R Soc Open Sci

College of Engineering and Computer Science, The Australian National University, Acton, ACT 2601, Australia.

Published: February 2023

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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9890099PMC
http://dx.doi.org/10.1098/rsos.220360DOI Listing

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