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

Authors
  • Svinth, Christian N.
  • Wallace, Scott
  • Stephenson, Daniel B.
  • Kim, Dave
  • Shin, Kangwoo
  • Kim, Hyo-Young
  • Lee, Seok-Woo
  • Kim, Tae-Gon
Type
Published Article
Journal
International Journal of Precision Engineering and Manufacturing
Publisher
Korean Society for Precision Engineering
Publication Date
May 03, 2022
Volume
23
Issue
6
Pages
609–625
Identifiers
DOI: 10.1007/s12541-022-00641-2
PMCID: PMC9062864
Source
PubMed Central
Keywords
Disciplines
  • Regular Paper
License
Unknown

Abstract

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 diamond-coated drill at the consistent spindle speed of 6500 RPM and 0.05 mm/rev. Across measurements from out-of-process variables, including hole diameter, roundness, surface roughness, entry/exit delamination, and entry/exit uncut fiber area, in-process measurements were most able to find outliers with respect to diameter. Both Principal Component Analysis, an unsupervised dimensionality reduction technique, and Linear Discriminant Analysis, a supervised dimensionality reduction technique, could separate oversize or undersize holes from average-sized holes when using fast Fourier transformation data of in-process vibration. Predictive performance with k-Nearest Neighbors shows that our machine learning pipeline can predict oversized vs. non-oversized holes with over 85% accuracy in this dataset. Peak prediction performance is obtained when in-process measurement data is viewed from the frequency domain, and predictions are weighted based on the relative distances of the nearest neighbors.

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