Conductivity inversion of unidirectional CFRP laminate based on ECT using neural network

Abstract

For the electrical anisotropy of carbon fiber reinforced polymer (CFRP), conductivity of unidirectional CFRP laminate in three directions was inverted in this paper. The three-dimensional eddy current electromagnetic model of unidirectional composites was constructed by ANSYS software, and the influence of the electrical conductivity of the material on the detection signal of the probe in the longitudinal, transverse and thickness directions was studied. In order to improve the amplitude of the probe output signal induced by the change of conductivity, the optimal detection angle of the eddy current probe was determined. On this basis, the relationship between the conductivity and the detection signal was studied to estimate the initial values of the conductivity based on the experimental data obtained by the eddy current testing (ECT). According to the forward model, the theoretical probe voltage under the estimated conductivity were calculated. The database consisting of conductivity and corresponding theoretical results was built for the neural network to construct the mapping that can estimate conductivity by experimental results. Using neural network for iteration, the conductivity was inverted quickly and precisely.