Anomaly detection in automated fibre placement: learning with data limitations

Anomaly detection in automated fibre placement: learning with data limitations

Introduction: Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective samples for effective training. However, the scarcity of such labelled data poses a challenge.Methods: To...

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Bibliographic Details
Main Authors: Assef Ghamisi, Todd Charter, Li Ji, Maxime Rivard, Gil Lund, Homayoun Najjaran
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-02-01
Series:Frontiers in Manufacturing Technology
Subjects:
automated fibre placement
anomaly detection
computer vision
unsupervised learning
convolutional autoencoder
Online Access:https://www.frontiersin.org/articles/10.3389/fmtec.2024.1277152/full

Internet

https://www.frontiersin.org/articles/10.3389/fmtec.2024.1277152/full

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