Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels

Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grin...

Descripción completa

Detalles Bibliográficos
Autores principales: González, D., Alvarez, J., Sánchez, J. A., Godino, L., Pombo, I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503273/
https://www.ncbi.nlm.nih.gov/pubmed/36146262
http://dx.doi.org/10.3390/s22186911

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503273/
https://www.ncbi.nlm.nih.gov/pubmed/36146262
http://dx.doi.org/10.3390/s22186911

Ejemplares similares