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

Ahmed, Mustajab; Kamal, Khurram; Ratlamwala, Tahir Abdul Hussain; Hussain, Ghulam; Alqahtani, Mejdal; Alkahtani, Mohammed; Alatefi, Moath; Alzabidi, Ayoub

Ahmed, Mustajab; Kamal, Khurram; Ratlamwala, Tahir Abdul Hussain; Hussain, Ghulam; Alqahtani, Mejdal; Alkahtani, Mohammed; Alatefi, Moath; Alzabidi, Ayoub.

Afiliação

Ahmed M; Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan.
Kamal K; Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan.
Ratlamwala TAH; Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan.
Hussain G; Mechanical Engineering Department, Faculty of Engineering, University of Bahrain, Isa Town 32038, Bahrain.
Alqahtani M; Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia.
Alkahtani M; Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia.
Alatefi M; Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia.
Alzabidi A; Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia.

Sensors (Basel) ; 23(6)2023 Mar 13.

Article em En | MEDLINE | ID: mdl-36991794

Palavras-chave

acoustic emission; convolutional neural network; feature extraction; signal processing; spectrograms; tool health monitoring

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Paquistão

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