End-to-end defect detection in automated fiber placement based on artificially generated data

Sebastian Zambal; Christoph Heindl; Christian Eitzinger; Josef Scharinger

doi:10.1117/12.2521739

16 July 2019 End-to-end defect detection in automated fiber placement based on artificially generated data

Sebastian Zambal, Christoph Heindl, Christian Eitzinger, Josef Scharinger

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111721G (2019) https://doi.org/10.1117/12.2521739
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

Abstract

Automated fiber placement (AFP) is an advanced manufacturing technology that has led to an increased rate of production in composite materials. At the same time, the need for adaptable and fast inline control methods increases. Existing inspection systems make use of handcrafted filter chains and feature detectors tuned for a specific measurement method by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using artificially generated training data. We employ a probabilistic graphical model to generate training images and annotations. We then train a deep neural network using a recent architecture designed for image segmentation. This leads to an appealing method that scales well with new defect types and measurement devices and requires little real world data for training.

Citation Download Citation

Sebastian Zambal, Christoph Heindl, Christian Eitzinger, and Josef Scharinger "End-to-end defect detection in automated fiber placement based on artificially generated data", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111721G (16 July 2019); https://doi.org/10.1117/12.2521739

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