Yazar
Uz, M. M., Hazar Yoruç, A. B., Çokgünlü, Okan, Aydoğan, C. S., Yapıcı, Güney Güven
Basım Tarihi
2022-12
Basım Yeri
-
Elsevier
Konu
Arrhenius, Artificial neural network, Constitutive modeling, Modified Hensel-Spittel, Thermomechanical behavior, Ti6Al4V alloy
Tür
Süreli Yayın
Dil
İngilizce
Dijital
Evet
Yazma
Hayır
Kütüphane
Özyeğin Üniversitesi
Demirbaş Numarası
2352-4928
Kayıt Numarası
b1d44341-955b-4f77-ab72-1015963ecfe6
Lokasyon
Mechanical Engineering
Tarih
2022-12
Notlar
Türk Havacılık ve Uzay Sanayi ; Ozyegin University ; TÜBİTAK
Örnek Metin
Due to its critical use in lightweight components requiring elevated temperature operation, it is very important to determine and model the high temperature thermomechanical flow behavior of Ti6Al4V. In this study, uniaxial tensile tests were performed at quasi-static strain rates and at temperatures ranging from 500 °C to 800 °C. The ductile behavior provided at a temperature of 800 °C and at a strain rate of 0.001 s−1 can be preferred for forming operations due to the steady state flow behavior. However, stress peaks during deformation at the strain rates of 0.1 s−1 and 0.01 s−1 are indicative of an unsafe zone. For modeling the flow stress behavior, three models including the Artificial Neural Network, Modified Hensel-Spittel and Arrhenius are employed with varying prediction performance as shown by the correlation coefficient (R) and average absolute relative error (AARE) values. Accordingly, the Artificial Neural Network model is claimed to be a more suitable approach for capturing the mechanical behavior of Ti6Al4V within the forming temperature range utilized in this study.
DOI
10.1016/j.mtcomm.2022.104933
Cilt
33
