Automatic high depth-resolvability inspection of TBC debonding defects using nonlinear frequency modulation photothermal radar

In this article, a novel nonlinear frequency modulation (NLFM) photothermal radar whose instantaneous frequency curve is a concave quadratic function is proposed and further combines K-means clustering image segmentation algorithm to achieve automatic high depth-resolvability inspection of debonding defects in thermal barrier coating (TBC) structure. Firstly, a 3-dimensional (3D) photothermal finite element (FE) model of TBC structure is established, where the extrapolated Beer-Lambert model is introduced to describe the spatially volumetric heat source within the ceramic coating. Afterwards, the superior depth resolvability of the proposed NLFM photothermal radar is demonstrated compared to conventional waveforms using the established FE model. Meanwhile, its depth resolvability dependence on the thickness and thermal diffusivity of the ceramic coating is also investigated. Finally, the photothermal experiments of the TBC structure containing artificial debonding defects are conducted, and the influence of the coating thickness and metal substrate on the non-destructive testing (NDT) detection of TBC structure by NLFM photothermal radar combining K-means clustering is also experimentally studied. The results reveal that NLFM photothermal radar combining K-means clustering can achieve automatic high depth-resolvability inspection of debonding defects in TBC structure and the coating thickness and metal substrate have also a great influence on the photothermal NDT of the debonding defects of TBC structure.