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Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model
In the industrial sector, tool health monitoring has taken on significant importance due to its ability to save labor costs, time, and waste. The approach used in this research uses spectrograms of airborne acoustic emission data and a convolutional neural network variation called the Residual Netwo...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10051468/ https://www.ncbi.nlm.nih.gov/pubmed/36991794 http://dx.doi.org/10.3390/s23063084 |