Automated detection of yarn orientation in 3D-draped carbon fiber fabrics and preforms from eddy current data

Abstract

Abstract Ensuring the correct fiber orientation in draped textiles and 3D preforms is one of the current challenges in the production of carbon-fiber reinforced plastics (CFRP), especially in resin transfer molding (RTM). Small deviations in fiber angle during preforming have a considerable effect on the mechanical properties of the final composite. Therefore, this paper presents an automated method for determining local yarn orientation in three-dimensionally draped, multi-layered fabrics. The draped fabric is scanned with a robot-guided high-frequency eddy current sensor to obtain an image of the sample's local conductivity and permittivity. From this image, the fiber orientation not only of the upper, but also of the lower, optically non-visible layers can be analyzed. A 2D Fast Fourier Transform is applied to local segments of the eddy current image to determine the local yarn orientation. Guidelines for processing the eddy current data, including phase rotation, filtering and evaluation segment size, are derived. For an intuitive visualization and analysis of the determined yarn orientation, reference yarn paths are reconstructed from the determined yarn angles. The developed process can be applied to quality inspection, process development and the validation of forming simulation results.