[2A2] Characterisation and reconstruction of fibre waviness and orientation using FEM modelling and high-frequency eddy current
Q Yi1,2, R Hughes1, P Wilcox1 and O Thomsen2,3
1Department of Mechanical Engineering, University of Bristol, UK
2Department of Aerospace Engineering, Bristol Composites Institute (ACCIS), University of Bristol, UK
3National Composites Centre, UK
Carbon fibre reinforced polymer (CFRP) composite materials are widely used in many applications, such as aerospace, automotive, shipbuilding and civil engineering, due to their relatively low density, high mechanical strength, and flexibility. These characteristics are determined by the proper stacking of various layers of carbon fibres oriented at different angles and by the curing process. In these multi-layer structures, fibre misorientation (off-axis) and fibre waviness can happen during the manufacturing process, leading to reduced performance and structural failures during operation. In previous work, high-frequency ECT has demonstrated its capability for evaluating the orientation related features, including fibre orientation and waviness. However, the electromagnetic modelling of orientation related features was not fully explored in the community, which hinders the optimisation of the ECT configuration for achieving high detectability and sensitivity. This work proposes the electromagnetic modelling of both fibre waviness and different fibre orientations using a tensor-based 2D wave interpolation function. Virtual ECT test was implemented in the simulation, then, the high-frequency impedance data is then processed by Radon transform, 2D FFT, and the proposed Gabor based PCA approach for inversion of the waviness and orientations, and reconstruction of each layer, validating the simulation against experimental data. To conclude, the electromagnetic characteristics of waviness can be modelled with the proposed approach compared with experimental data. The Radon transform orientation inversion technique is capable of estimating the orientations from the impedance data. The proposed Gabor based PCA approach helps reconstruct each layer by its orientation, which provides more information related to depth when defect signature is observed.
