Defect detection for large-series automated fibre placement using a neural network-assisted machine vision approach
Author:
Funder
Deutsche Forschungsgemeinschaft
Institute of Physics
Publisher
Elsevier BV
Reference30 articles.
1. Heinecke, F., Willberg, C., 2019. Manufacturing-Induced Imperfections in Composite Parts Manufactured via Automated Fiber Placement 3, p. 56.
2. Gholizadeh, S., 2016. A review of non-destructive testing methods of composite materials 1, p. 50.
3. Effect of manufacturing defects and their uncertainties on strength and stability of stiffened panels;Marrouzé,2013
4. Li, X., Hallett, S.R., Wisnom, M.R., 2015. Modelling the effect of gaps and overlaps in automated fibre placement (AFP)-manufactured laminates 22, p. 115.
5. Nguyen, M.H., Davidson, P., Waas, A.M., 2021. Experimental and numerical study on the tensile failure behavior of toughened-interlayer composite laminates with automated fiber placement (AFP) induced gap and overlap defects 14, p. 105.
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