Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model

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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Bibliographic Details
Main Authors: Mustajab Ahmed, Khurram Kamal, Tahir Abdul Hussain Ratlamwala, Ghulam Hussain, Mejdal Alqahtani, Mohammed Alkahtani, Moath Alatefi, Ayoub Alzabidi
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Sensors
Subjects:
acoustic emission
spectrograms
convolutional neural network
signal processing
feature extraction
tool health monitoring
Online Access:https://www.mdpi.com/1424-8220/23/6/3084

Internet

https://www.mdpi.com/1424-8220/23/6/3084

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