Unsupervised machine learning discovers classes in aluminium alloys

Abstract

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.

Inverse Design of Aluminium Alloys Using Genetic Algorithm: A Class-Based Workflow

“…In this study, we use a publicly accessible dataset of Al alloys, which was curated by the authors [26]. This dataset encompasses cast and wrought alloys and includes age-hardened and strain-hardened alloys.…”

“…In a previous study, iterative label spreading was used [27] to identify eight distinct clusters in the data determined by feature similarity [28]. Further, a decision tree classifier showed that these clusters are separable classes, and information on these classes is also included in the dataset [28]. Class 1 is characterised by "as cast" or "solution heat-treated" alloys.…”

“…A random forest is constructed by generating multiple decision trees, each trained using bootstrap aggregation and random feature selection. This approach ensures that each tree in the forest uses a different subset of features, minimising the influence of any single feature on the overall partitioning process and reducing correlation amongst individual In a previous study, iterative label spreading was used [27] to identify eight distinct clusters in the data determined by feature similarity [28]. Further, a decision tree classifier showed that these clusters are separable classes, and information on these classes is also included in the dataset [28].…”