Thermographical Investigation of Crack Initiation Using Artificial Neural Networks

In this study, a thermographic infrared imaging system was used to detect the temperature rise of AISI37 steel specimen under reverse bending fatigue. Fatigue behavior of metals shows temperature profiles with three stages: an initial increase of the specimen mean temperature region, a constant (equilibrium) temperature region, an abrupt temperature increase region at end of which the specimen fails and its temperature falls instantly. In order to recognize critical third region, it is necessary to observe endurance state of the specimen being tested. In this study, the temperature profiles of the specimen under testing are recorded by thermal camera and transferred to the image processing program. The artificial neural networks obtain spot temperatures of the inspected specimen by using its temperature profiles. By analyzing the values of obtained data, we detect spots of highest temperatures as ones that are exposed to most intensive deformation. These regions considered to be probable crack initiation sites.