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Inspection of aircraft and aerospace components has always been a challenge. Lock-in thermography is an active technique which is popularly being used for the non-destructive evaluation of subsurface defects in composite materials such as carbon-fiber-reinforced-polymer (CFRP) in aircraft structures. CFRP-foam layered structure composite is often used for anti-heat and adiabatic material in aerospace components. In this paper, the finite element analysis (FEA) of 3D heat conduction is developed in order to investigate the behaviour of thermal waves in the honeycomb structure, CFRP and CFRP-foam layered structure plates with finite thickness under convective and radiate conditions. The FEA simulation is then utilized to predict the magnitude and phase differences produced by the subsurface defects and obtain the optimum inspection parameters. The simulation results are compared with the experimental data. The optimum inspection parameters of lock-in thermography are used to detect subsurface modeling defects is also presented in this paper. The defect estimation of artificial neural network model is proposed and simulation and experimental results are used to train the ANN model. The results show that the ANN model can be applied to detect the subsurface defect and estimate the defect depth.

Defect estimation of aircraft and aerospace components based on Neural Network using Lock-in thermography