Thermal Parameter Reconstruction Imaging for Interlayer Defect Detection in ECPT

Abstract

Stainless steel/carbon steel double-layer structures are commonly used in industries, but they are prone to generate internal defects (such as delamination and corrosions) at the bonding interface of the carbon steel layer. Eddy current pulsed thermography (ECPT) has shown promise for subsurface defect detection due to its high excitation and concentrated heating area. However, the complex heat transfer in multilayer structures lead to poor signal-to-noise ratios and poses challenges for defect identification. Moreover, the data-driven algorithms like PCA and ICA, though widely used in postprocessing, are faced with unstable performance and poor interpretability due to the lack of attention to the specific physical mechanism. To address these issues, this article proposes a thermal parameter reconstruction (TPR) imaging method to better detect the defect regions. Specifically, TPR regards the test sample as a 3-D thermal impedance space and projects it onto a parameterized grid plane. According to the thermal diffusion mechanism in ECPT, TPR builds a physical model to calculate the spatial thermal parameters of the projected surface. Through the reconstructed visual thermal parameters grid, the shape information of internal defects can be more clearly detected. An experiment on double-layer stainless steel/carbon steel structures is conducted, which validates the enhancement effectiveness of TPR on both round and irregularly shaped interlayer defects.