Anomaly Detection in Automated Fibre Placement: Learning with Data
Limitations
- URL: http://arxiv.org/abs/2307.07893v2
- Date: Tue, 15 Aug 2023 02:21:20 GMT
- Title: Anomaly Detection in Automated Fibre Placement: Learning with Data
Limitations
- Authors: Assef Ghamisi, Todd Charter, Li Ji, Maxime Rivard, Gil Lund, Homayoun
Najjaran
- Abstract summary: We present a comprehensive framework for defect detection and localization in Automated Fibre Placement.
Our approach combines unsupervised deep learning and classical computer vision algorithms.
It efficiently detects various surface issues while requiring fewer images of composite parts for training.
- Score: 3.103778949672542
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. To overcome this limitation,
we present a comprehensive framework for defect detection and localization in
Automated Fibre Placement. Our approach combines unsupervised deep learning and
classical computer vision algorithms, eliminating the need for labelled data or
manufacturing defect samples. It efficiently detects various surface issues
while requiring fewer images of composite parts for training. Our framework
employs an innovative sample extraction method leveraging AFP's inherent
symmetry to expand the dataset. By inputting a depth map of the fibre layup
surface, we extract local samples aligned with each composite strip (tow).
These samples are processed through an autoencoder, trained on normal samples
for precise reconstructions, highlighting anomalies through reconstruction
errors. Aggregated values form an anomaly map for insightful visualization. The
framework employs blob detection on this map to locate manufacturing defects.
The experimental findings reveal that despite training the autoencoder with a
limited number of images, our proposed method exhibits satisfactory detection
accuracy and accurately identifies defect locations. Our framework demonstrates
comparable performance to existing methods, while also offering the advantage
of detecting all types of anomalies without relying on an extensive labelled
dataset of defects.
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