Identifying Abnormal CFRP Holes Using Both Unsupervised and Supervised Learning Techniques on In-Process Force, Current, and Vibration Signals

This study aims to conduct abnormality detection by applying machine learning algorithms when drilling a carbon fiber reinforced plastic laminate. In-process signals including current, thrust force, and vibration were captured during the dry drilling experiments using a 6 mm physical vapor deposit d...

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Detalles Bibliográficos
Autores principales: Svinth, Christian N., Wallace, Scott, Stephenson, Daniel B., Kim, Dave, Shin, Kangwoo, Kim, Hyo-Young, Lee, Seok-Woo, Kim, Tae-Gon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society for Precision Engineering 2022
Materias:
Regular Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9062864/
http://dx.doi.org/10.1007/s12541-022-00641-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9062864/
http://dx.doi.org/10.1007/s12541-022-00641-2

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