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Publication . Article . Conference object . Preprint . 2019

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

Sebastian Zambal; Christoph Heindl; Christian Eitzinger; Josef Scharinger;
Open Access
Abstract
Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts raises. Existing inspection systems make use of handcrafted filter chains and feature detectors, tuned for a specific measurement methods 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 based on recent architectures 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.
Comment: Presented at Quality Control by Artificial Vision (QCAV), 2019
Subjects by Vocabulary

Microsoft Academic Graph classification: Artificial neural network Image segmentation Domain (software engineering) Computer science Graphical model Probabilistic logic Artificial intelligence business.industry business Deep learning Computer vision Bayesian network Filter (signal processing)

Subjects

Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning

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Funded by
EC| ZAero
Project
ZAero
Zero-defect manufacturing of composite parts in the aerospace industry
  • Funder: European Commission (EC)
  • Project Code: 721362
  • Funding stream: H2020 | IA
Validated by funder
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Conference object . 2019
Providers: ZENODO
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