Author/Authors :
ÖZKAN, Murat Tolga Gazi Üniversitesi - Teknik Eğitim Fakültesi - Makine Eğitimi Bölümü, Turkey , ELDEM, Cengiz Gazi Üniversitesi - Teknik Eğitim Fakültesi - Makine Eğitimi Bölümü, Turkey , KÖKSAL, Erdal Şehit Sertaç Uzun ATL ve METEM, Turkey
Title Of Article :
NOTCH SENSITIVITY FACTOR DETERMINATION WITH ARTIFICIAL NEURAL NETWORK FOR SHAFTS UNDER THE BENDING STRESS
Abstract :
Notch, hole, tap and a variety of geometric shapes such as curves or discontinuities can be found with various reasons in the design of Machine Element. Stress is caused by sudden changes in section aggregating. Stress concentration can occur with the reason of material features of size or direction of forces application. This type of stress concentration in the material brings out the effect of notch. Notch impact can lead to distortions and breakage of materials. In this study, the notch sensitivity factor values have been modelled Artificial Neural Networks (ANN) for shafts that is under the influence of bending stress, and the accuracy of the model has been verified by using Statistica software. The model has been developed using Pythia. With this software, the user can be obtained the accurate value by inputing shaft dimension and the applied force without the need for notch sensitivity factor tables and any calculations.
NaturalLanguageKeyword :
Notch sensitivity factor , Machine design , Artificial neural network
JournalTitle :
Pamukkale University Journal Of Engineering Sciences
