publication . Other literature type . 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;
  • Published: 16 Jul 2019
  • Publisher: SPIE-Intl Soc Optical Eng
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...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Artificial neural network, End-to-end principle, Image segmentation, Deep learning, Artificial intelligence, business.industry, business, Advanced manufacturing, Pattern recognition, Graphical model, Computer science, Bayesian network, Probabilistic logic
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

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publication . Other literature type . 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;