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Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA
Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7663048/ https://www.ncbi.nlm.nih.gov/pubmed/33158099 http://dx.doi.org/10.3390/ma13214952 |