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Description
This work investigates how machine learning can be used to classify mechanical phases in WC–Co hardmetals from nanoindentation data. High-speed nanoindentation provides depth-resolved mechanical responses that contain rich information about local microstructural behavior. By focusing on the full hardness–depth curves instead of single-point values, meaningful features can be extracted for phase identification.
The study combines unsupervised and supervised learning to explore different classification strategies. Data cleaning and clustering are first used to identify representative mechanical behaviors and label them as WC or binder. Convolutional neural networks trained on curve-derived images provide phase classification with confidence scores, enabling the detection of uncertain or transition regions near interfaces. A PCA–Random Forest approach offers a complementary and faster alternative with comparable performance.
The main open questions concern model generalization, overfitting, and data dependence on experimental setup.