10.5281/zenodo.3381930
https://zenodo.org/records/3381930
oai:zenodo.org:3381930
Sebastian Zambal
Sebastian Zambal
0000-0001-9235-0590
PROFACTOR GmbH
Christoph Heindl
Christoph Heindl
PROFACTOR GmbH
Christian Eitzinger
Christian Eitzinger
PROFACTOR GmbH
Machine Learning for CFRP Quality Control
Zenodo
2019
2019-09-18
10.5281/zenodo.3381929
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
Abstract - Automation in CFRP production poses multiple challenges. The material at hand is very un-isotropic and deformable, leading to various difficulties in handling. We believe that visual inspection and quality control are key technologies to improve automation in CFRP production. In this paper, we point out possible ways to exploit modern machine learning methods in the context of CFRP quality control. Taking the example of AFP, we show how to transform prior knowledge about the production process into a probabilistic model. By drawing samples from this model, we demonstrate how to infer hidden variables of the process efficiently. We show how to use the methodology to perform inline defect detection and to reconstruct global process parameters. We present results for artificial and selected real AFP monitoring data acquired during inline process monitoring.
European Commission
10.13039/501100000780
721362
Zero-defect manufacturing of composite parts in the aerospace industry