AI-Driven Tool Wear Prediction Under Severe Data Scarcity with SHAP-Guided Feature Selection and Fold-Safe Augmentation : A Case Study of Titanium Microdrilling
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| Document Type: | Article (peer-reviewed) |
|---|---|
| Author: | Saman Fattahi, Bahman AzarhoushangORCiDGND, Masih Paknejad, Heike Kitzig-FrankORCiDGND |
| URN: | https://urn:nbn:de:bsz:fn1-opus4-128399 |
| DOI: | https://doi.org/10.3390/machines14020196 |
| ISSN: | 2075-1702 |
| Parent Title (English): | Machines |
| Language: | English |
| Year of Completion: | 2026 |
| Release Date: | 2026/02/11 |
| Tag: | Maximum flank wear (VBmax); Microdrilling; SHAP; Tool wear prediction; XGBoost |
| Volume: | 14.2026 |
| Issue: | 2 |
| Article Number: | 196 |
| Page Number: | 26 |
| Open-Access-Status: | Open Access |
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| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |