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.

Eddy current pulsed thermography (ECPT) has attracted much attention in nondestructive testing for its noncontact and large field of view. However, the ECPT images usually suffer from the thermal diffusion blurs. The fusion of temperature spatial and temporal features is the hotspot in the new research of ECPT enhancing methods, but these two features are contradictory on the speed and the accuracy of algorithms. Specifically, spatial features process fast but perform poorly in accuracy and antinoise ability, while temporal features are usually calculated from the whole ECPT video sequence, which inevitably increases the demand for data storage and the time cost, especially in the detection of large workpieces, such as engine blades or pressure pipelines. In this article, an enhanced diffusion-based method (EDBM) is proposed to solve this issue, which maps the temporal features through the spatial features of a single ECPT image, significantly reduces the input data volume and shows great potential in ECPT online detection. Experiments on multiple artificial and natural samples verify that, compared with raw ECPT image, the proposed EDBM can reduce root-mean-square error by 51.7%–86.5% (73.2% on average) and improve signal-to-noise ratio by 3.70–16.8 times (6.82 times on average), which performs better than the commonly spatial-based and temporal-based ECPT enhancement algorithms, such as enhanced Canny and independent component analysis, close to the latest sparse-model decomposition methods, but with two orders of magnitude less time cost.

Accurately evaluating the 3-D profile of nonferromagnetic metal surface defects is essential for nondestructive evaluation. Eddy current pulsed thermography (ECPT) has been used for reconstructing surface defects, which utilizes multiphysics coupling of electromagnetic and thermal fields. Previous investigations mainly employ the thermal field for reconstruction. However, the thermal field varies slowly over time and has weak correlation, which causes serious blur and noise. In this article, a surface defect 3-D profile reconstruction method based on eddy current field distribution is proposed. The proposed method takes advantage of the strong global correlation of eddy current fields and proposes an inversion technique to reconstruct defects. Experimental results demonstrate that the proposed method can effectively reconstruct the 3-D profile of defects with depths within the skin depth, including complex-shaped defects and natural cracks.

The recently proposed induced current thermo-electrical impedance tomography (ICTEIT) is a nondestructive evaluation method for nonferromagnetic metal materials. The method reconstructs the defect profile by solving conductivity distribution from eddy currents, which has shown good performance in simple defects, but worse performance in complex defects (such as multiple adjacent defects and defects with complex contours). The main reason is that the unidirectional excitation produces low eddy current regions near the defect, which lacks reliable reconstruction information. For the problem, multidirectional excitation is able to make up for the low eddy current regions. However, previous methods simply superimpose the information of all eddy currents, and cannot integrate them for reconstruction. To solve the problem, we present a multidirectional information fusion method for defect reconstruction. The proposed method uses least squares optimization and transforms the information of multidirectional currents into a system of linear equations associated with conductivity, which constrains the solution and makes the solution satisfy each current simultaneously. Furthermore, to solve the ill-conditioning in the inverse problem, the total variation regularization method is introduced. Experiments are conducted on multiple neighboring defects and defects with complex shapes to validate the proposed method's performance.

Nonferromagnetic metal materials are widely used in industry. Defects generated during manufacture and use may lead to serious accidents. The defect reconstruction is important for nondestructive evaluation. Eddy current pulsed thermography (ECPT) is a well-known nondestructive testing and evaluation method, but hardly reconstruct fully defect profile. Electrical impedance tomography (EIT) shows promising potential in defect profile reconstruction, but suffers from electrode number limitation. In this article, EIT is introduced to ECPT image sequences processing, and a new method, induced current thermo-electrical impedance tomography, is developed. The proposed method takes each infrared camera pixel as a virtual electrode, captures the electric current distribution with spatial resolution as high as camera, and reconstructs conductivity distribution at pixel level from which the defect profile can be identified. Experiments with different defects are carried out to verify the performance of the proposed method.