Data-Augmented Manifold Learning Thermography for Defect Detection and Evaluation of Polymer Composites

Infrared thermography techniques with thermographic data analysis have been widely applied to non-destructive tests and evaluations of subsurface defects in practical composite materials. However, the performance of these methods is still restricted by limited informative images and difficulties in feature extraction caused by inhomogeneous backgrounds and noise. In this work, a novel generative manifold learning thermography (GMLT) is proposed for defect detection and the evaluation of composites. Specifically, the spectral normalized generative adversarial networks serve as an image augmentation strategy to learn the thermal image distribution, thereby generating virtual images to enrich the dataset. Subsequently, the manifold learning method is employed for the unsupervised dimensionality reduction in all images. Finally, the partial least squares regression is presented to extract the explicit mapping of manifold learning for defect visualization. Moreover, probability density maps and quantitative metrics are proposed to evaluate and explain the obtained defect detection performance. Experimental results on carbon fiber-reinforced polymers demonstrate the superiorities of GMLT, compared with other methods.

Automated summary

In this article, a novel generative manifold learning thermography (GMLT) method is proposed for defect detection and evaluation of composites. The method utilizes spectral normalized generative adversarial networks as an image augmentation strategy, to learn the thermal image distribution and generate virtual images to enrich the dataset. Manifold learning is employed for unsupervised dimensionality reduction, and partial least squares regression is used for defect visualization. Probability density maps and quantitative metrics are introduced to evaluate and explain the defect detection performance. Experimental results demonstrate the superiority of GMLT compared to other methods.