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A novel parametric level set eddy current defect tomography method for defect imaging
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AI
DOI：10.1080/10589759.2026.2718486.png)
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Abstract
En 中文
Non-destructive testing technology prevents catastrophic accidents by detecting hidden hazards without damaging the inspected object, ensuring product reliability and reducing total lifecycle costs. Compared to defect detection, defect imaging enables precise quantification of defect morphology, dimensions, and spatial distribution through visual characterisation, thereby providing a critical tool for structural integrity assessment. In this paper, a parametric level set (PLS) eddy current defect tomography method based on gaussian radial basis functions is investigated. Specifically, the conductivity distribution can be sparse represented by the coefficients of gaussian radial basis functions. The PLS representation substantially decreases the number of unknowns, thereby enhancing the solvability of the underdetermined problem in the imaging process. Moreover, the inverse problem for reconstructing conductivity parameters is solved iteratively with the Gauss-Newton method, wherein the Jacobian matrix is calculated at each step. By continuously updating the coefficients of the basis functions, high-resolution defect imaging in metal components is achieved. Finally, experiment and numerical simulation are carried out to evaluate the correlation coefficients and reconstruction error of the proposed method. Results demonstrate that the proposed approach significantly outperforms conventional algorithms, achieving at least 20% improvement in evaluation metrics.
Keywords:
Defect imaging
eddy current testing
parametric level set
tomography
conductivity reconstruction
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