Speaker
Description
The presence of internal defects poses a serious challenge to the structural integrity and performance of composite materials such as polymers and cultural heritage. Active infrared thermography (AIRT) is becoming increasingly attractive among many non-destructive testing techniques due to its low-cost and wide-area coverage advantages. However, thermograms often involve non-uniform backgrounds and measurement noise caused by uneven heating and environmental reflections, necessitating post-processing procedures. Unsupervised machine learning methods have shown promising success in AIRT for defect detection. This presentation aims to provide recent advances on unsupervised machine learning-aided thermography for defect detection. In particular, deep learning methods for thermographic data analysis are reviewed and emphasized. Finally, an outlook on the prospects and potential of these methods is provided.
