ArticleMaterials (Basel, Switzerland)2021
Pop-In Identification in Nanoindentation Curves with Deep Learning Algorithms.
Article in Materials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Data-driven prediction of hardness and layer behavior in YbSi-mullite-Si environmental barriers.Journal of computer-aided molecular design · 2026Article
- Insights into Engineering Super-Duplex Stainless-Steel Microstructures: Composition Alterations and Processing Strategies in LPBF.Materials (Basel, Switzerland) · 2026Article
- Tribo-Mechanical Properties of Nanomultilayer TiCN/ZrCN Coatings with Different Carbon Content.Materials (Basel, Switzerland) · 2026Article
- Machine Learning Inversion of Layer-Wise Plasticity and Interfacial Cohesive Parameters in Multilayer Thin Films.Materials (Basel, Switzerland) · 2025Article
- Review
- Multiscale Mechanical Characterization of Polyether-2-ketone (PEKK) for Biomedical Application.Bioengineering (Basel, Switzerland) · 2024Article
- The Instrumented Indentation Test: An Aiding Tool for Material Science and Industry.Materials (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
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Abstract
High-speed nanoindentation rapidly generates large datasets, opening the door for advanced data analysis methods such as the resources available in artificial intelligence. The present study addresses the problem of differentiating load-displacement curves presenting pop-in, slope changes, or instabilities from curves exhibiting a typical loading path in large nanoindentation datasets. Classification of the curves was achieved with a deep learning model, specifically, a convolutional neural network (CNN) model implemented in Python using TensorFlow and Keras libraries. Load-displacement curves (with pop-in and without pop-in) from various materials were input to train and validate the model. The curves were converted into square matrices (50 × 50) and then used as inputs for the CNN model. The model successfully differentiated between pop-in and non-pop-in curves with approximately 93% accuracy in the training and validation datasets, indicating that the risk of overfitting the model was negligible. These results confirmed that artificial intelligence and computer vision models represent a powerful tool for analyzing nanoindentation data.
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