Code and Workflows for Explainable machine learning and feature engineering applied to nanoindentation data

Codes to the publication "Explainable machine learning and feature engineering applied to nanoindentation data" (https://doi.org/10.1016/j.matdes.2025.113897) published in Materials and Design and Dataset "The High-Speed Steel S390 Microclean™ Nanoindentation Dataset" (https://doi.org/10.5281/zenodo.15639081).

The repository is structured as follows:

Explainable_Machine_Learning_Nanoindentation/

│

├── Results/

│ ├── cross-validation/

│ │ ├── *.pkl ➜ Pickled results from the cross-validation workflow

│ │ └── *.ipynb ➜ Jupyter notebooks for plotting and analyzing Cross-Validation results

│ │

│ ├── models/

│ │ ├── *.pkl ➜ Trained Machine Learning models and corresponding SHAP explainers (https://shap.readthedocs.io/en/latest/)

│ │

│ ├── plots/

│ │ └── *.ipynb ➜ Notebooks generating SHAP and other explanatory plots

│

├── Supervised Machine Learning Pipelines/

│ └── *.ipynb ➜ Cross-validation and model training pipelines

│

├── k-means/

│ └── *.ipynb ➜ Clustering analysis using k-means

C.O.W. T. gratefully acknowledges the financial support under the scope of the UFO program (SPM - PN 3022) by the Austrian State of Styria (Land Steiermark - Abteilung 12 Wirtschaft, Tourismus, Wissenschaft und Forschung).