Classification tree for material defect detection using active thermography

Abstract

Active thermography is a highly efficient and powerful technique that enables us to detect the subsurface defects by heating the investigated material sample and recording the thermal response using an infrared camera. In this work a simple variant of the time-resolved infrared radiometry method was used. The study was conducted for a sample made of the low thermal diffusivity material with artificially produced aerial defects. As a result of experiment, the sequence of thermograms was obtained. Heating and cooling curves for each thermogram pixel were determined and treated as patterns describing local features of the material. These patterns are recognized by classification tree and classified into two categories: “defect” or “non-defect”. Advantages of classification tree is an automatic feature selection and strong reduction of the pattern dimensionality. On the basis of simulation study, it can be concluded that classification tree is a useful tool for the characterisation and detection of material defects.