Explainable machine learning and feature engineering applied to nanoindentation data

Explainable machine learning and feature engineering applied to nanoindentation data

The work aims to challenge the hegemony in the literature of clustering nanoindentation data solely relying on elastic modulus and hardness as features, thereby discarding information provided by the full load–displacement curve. Features based on dimensional analysis initially aimed to solve the in...

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Bibliographic Details
Main Authors: C.O.W. Trost, S. Žák, S. Schaffer, L. Walch, J. Zitz, T. Klünsner, H. Leitner, L. Exl, M.J. Cordill
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:Materials & Design
Subjects:
Nanoindentation
Explainable machine learning
High-speed steel
Composite material
Online Access:http://www.sciencedirect.com/science/article/pii/S026412752500317X
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