Defect characterization in infrared non-destructive testing with learning machines

It is well known that thermal contrast-based quantification methods are strongly affected by the non-uniform heating, the sample shape and the chosen sound area. In this work we propose a reference-free thermal contrast by using the thermal quadrupoles theory and evaluate the limits of defect detection in composite samples by using dynamic principal components analysis (DPCA) and k-nearest neighbor algorithm. Additionally, we propose and validate the radial basis functions (RBF) networks and support vector machines (SVM) for the detection and quantification of defect depth in composite material samples affected by non-uniform heating and with complex shapes.