Machine learning based models for defect detection in composites inspected by Barker coded thermography: A qualitative analysis

Abstract

Machine learning and artificial intelligence have evolved as enablers for automation in various industrial applications. 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 unsupervised approach presents a less than 1% marginal difference in defect detection from its supervised counterpart.