Deep learning neural network approach to thermal-wave imaging of damage in solids with application to diffusivity measurements of a green (unsintered) metal powder compact slab

Abstract

A method is proposed to measure the thermal diffusivity of a green (unsintered) metal powder compact slab by combining numerical solution and deep learning. The thermal diffusion and thermal-wave equation is first discretized in space and time domains. Square wave excitation signals at different frequencies are considered, and the corresponding thermal wave responses are obtained. The space and frequency dependencies of the thermal-wave amplitude and phase are obtained by using lock-in thermography. Then, deep learning candidate neural network sets composed of spatial coordinates, excitation frequency, amplitude, and phase values are quickly and massively obtained for training the deep learning network. The validity of the deep learning network is verified by predicting the known thermal diffusivity, and the parameters and robustness of the deep learning network are discussed. Experimentally, a square wave modulated laser beam is used to illuminate one side of a green metal powder compact slab and the spatial distribution of amplitude and phase at three excitation frequencies is obtained. The predicted slab thermal diffusivity is extracted through the formed deep learning network, and the validity of the predicted value is verified by comparing with independent measurements.