Challenges in the phase identification of steels using unsupervised clustering of nanoindentation data

Conference Dates

October 2 – 7, 2022


Cluster analysis tools are used in data interpretation to separate information without the bias of a user. In the current study we investigate two techniques, the elbow method and K-means clustering to achieve a phase classification for a dual phase (DP) and a high strength low alloy (HSLA) steel by using hardness and reduced Young’s modulus from nanoindentation tests as input variables. For the DP steel the contrast in hardness of the two phases ferrite and martensite is high, while for the HSLA steel the hardness contrast between ferrite and bainite is small, as seen from the corresponding load-displacement curves (Fig. 1).

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