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...

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Detalles Bibliográficos
Autores principales: Ahmed, Mustajab, Kamal, Khurram, Ratlamwala, Tahir Abdul Hussain, Hussain, Ghulam, Alqahtani, Mejdal, Alkahtani, Mohammed, Alatefi, Moath, Alzabidi, Ayoub
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10051468/
https://www.ncbi.nlm.nih.gov/pubmed/36991794
http://dx.doi.org/10.3390/s23063084

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