Using artificial neural networks for real-time observation of the endurance state of a steel specimen under loading

The surface temperature behavior of a steel specimen under bending fatigue is exactly divided into three stages: an initial temperature increase stage, a constant temperature stage and an abrupt temperature increase stage at the end of which the specimen fails. To obtain the endurance state of the specimen we use its thermal images (TIs). By applying artificial neural networks (ANNs) and other operations to these TIs we obtain spots with maximal, approximately medium and minimal temperatures. Then by using these temperatures we analytically obtain the temperatures all of spots of the specimen and localize the regions consisting of spots of relatively high temperatures. We consider such a region as one to be cracked firstly. This approach allows us to handle only those spots that are of interest and to work in real-time even by using an infrared (IR) camera and a computer with average technical features. We are using the result obtained in this study for fatigue testing the steel materials and for sensing the pre-fatigue state of a specific part of a machine being worked in order to take preventive measures before it breaks down.