Abstract
Machine learning and artificial intelligence have evolved as enablers for automation in various industrial ap-plications. Barker-coded thermography is an active thermal non-destructive testing technique for examining subsurface features in industrial components. This article introduces supervised and unsupervised machine learning models for automatic defect detection in composite specimens inspected by the barker-coded stimulus. This work provides supervised and unsupervised machine learning methods to detect defects in composite specimens examined with a barker automatically coded stimulus. The suggested technology is tested using a carbon fiber-reinforced polymer sample with synthetically reproduced flat bottom hole flaws. The one-class Support vector machine is chosen for the unsupervised class of operation, whereas the supervised technique modifies the traditional Support Vector Machine (SVM). The qualitative comparison suggests that the unsu-pervised approach presents a less than 1% marginal difference in defect detection from its supervised counterpart.