Using Artificial Neural Networks for Real-Time Observation of the Endurance State of a Steel Specimen Under Loading
Yükleniyor...
Dosyalar
Tarih
2009
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Artificial neural network, Material fatigue, Thermal image, Image processing, Infrared thermography
Kaynak
Expert Systems With Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
36
Sayı
4
Künye
Kahramanlı, S., Şahin, Ö. S., Selek, M., (2009). Using Artificial Neural Networks for Real-Time Observation of the Endurance State of a Steel Specimen Under Loading. Expert Systems With Applications, 36(4), 7400-7408. Doi:10.1016/j.eswa.2008.09.059
