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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S026412752500317X |
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| author | C.O.W. Trost S. Žák S. Schaffer L. Walch J. Zitz T. Klünsner H. Leitner L. Exl M.J. Cordill |
| author_facet | C.O.W. Trost S. Žák S. Schaffer L. Walch J. Zitz T. Klünsner H. Leitner L. Exl M.J. Cordill |
| author_sort | C.O.W. Trost |
| collection | DOAJ |
| description | 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 inverse nanoindentation problem were adopted to describe the load–displacement curves. More than 3000 indents in high-speed steels were labelled via imaging after indenting. The resulting dataset was used to train and benchmark supervised (classification) and unsupervised (clustering) machine learning models, showing that feature engineering was more impactful than model selection and hyperparameter tuning, increasing the prediction quality in all studied models. The best classifier’s predictions were explained via a game theory-based approach, allowing insights into the model’s decision-making process and connecting the fields of materials property clustering and materials mechanics. |
| format | Article |
| id | doaj-art-dea9d2e3aafa4366b2a0944c9c3c400c |
| institution | OA Journals |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-dea9d2e3aafa4366b2a0944c9c3c400c2025-08-20T02:00:47ZengElsevierMaterials & Design0264-12752025-05-0125311389710.1016/j.matdes.2025.113897Explainable machine learning and feature engineering applied to nanoindentation dataC.O.W. Trost0S. Žák1S. Schaffer2L. Walch3J. Zitz4T. Klünsner5H. Leitner6L. Exl7M.J. Cordill8Erich Schmid Institute of Materials Science, Austrian Academy of Sciences, Jahnstrasse 12, 8700 Leoben, Austria; Corresponding author.Erich Schmid Institute of Materials Science, Austrian Academy of Sciences, Jahnstrasse 12, 8700 Leoben, AustriaWolfgang Pauli Institute c/o Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria; University of Vienna Research Platform MMM Mathematics - Magnetism - Materials, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, AustriaMaterials Center, Leoben Forschung GmbH, Leoben, AustriaMaterials Center, Leoben Forschung GmbH, Leoben, AustriaMaterials Center, Leoben Forschung GmbH, Leoben, AustriaVoestalpine BÖHLER Edelstahl GmbH & Co KG, Mariazeller-Straße 25, A-8605 Kapfenberg, AustriaWolfgang Pauli Institute c/o Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria; University of Vienna Research Platform MMM Mathematics - Magnetism - Materials, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, AustriaErich Schmid Institute of Materials Science, Austrian Academy of Sciences, Jahnstrasse 12, 8700 Leoben, Austria; Department of Materials Science, Montanuniversität Leoben, Jahnstrasse 12, Leoben 8700, AustriaThe 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 inverse nanoindentation problem were adopted to describe the load–displacement curves. More than 3000 indents in high-speed steels were labelled via imaging after indenting. The resulting dataset was used to train and benchmark supervised (classification) and unsupervised (clustering) machine learning models, showing that feature engineering was more impactful than model selection and hyperparameter tuning, increasing the prediction quality in all studied models. The best classifier’s predictions were explained via a game theory-based approach, allowing insights into the model’s decision-making process and connecting the fields of materials property clustering and materials mechanics.http://www.sciencedirect.com/science/article/pii/S026412752500317XNanoindentationExplainable machine learningHigh-speed steelComposite material |
| spellingShingle | C.O.W. Trost S. Žák S. Schaffer L. Walch J. Zitz T. Klünsner H. Leitner L. Exl M.J. Cordill Explainable machine learning and feature engineering applied to nanoindentation data Materials & Design Nanoindentation Explainable machine learning High-speed steel Composite material |
| title | Explainable machine learning and feature engineering applied to nanoindentation data |
| title_full | Explainable machine learning and feature engineering applied to nanoindentation data |
| title_fullStr | Explainable machine learning and feature engineering applied to nanoindentation data |
| title_full_unstemmed | Explainable machine learning and feature engineering applied to nanoindentation data |
| title_short | Explainable machine learning and feature engineering applied to nanoindentation data |
| title_sort | explainable machine learning and feature engineering applied to nanoindentation data |
| topic | Nanoindentation Explainable machine learning High-speed steel Composite material |
| url | http://www.sciencedirect.com/science/article/pii/S026412752500317X |
| work_keys_str_mv | AT cowtrost explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata AT szak explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata AT sschaffer explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata AT lwalch explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata AT jzitz explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata AT tklunsner explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata AT hleitner explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata AT lexl explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata AT mjcordill explainablemachinelearningandfeatureengineeringappliedtonanoindentationdata |