Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations

TL;DR

This paper introduces an unsupervised deep learning framework for defect detection in Automated Fibre Placement that requires minimal labelled data, effectively identifying various surface anomalies through reconstruction errors and symmetry-based data augmentation.

Contribution

The novel framework combines unsupervised autoencoders with classical vision techniques, leveraging AFP symmetry to enhance defect detection without extensive labelled defect data.

Findings

Effective defect detection with limited training data

Comparable performance to supervised methods

Accurate localization of manufacturing defects

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.