Author
Uz, M. M., Hazar Yoruç, A. B., Çokgünlü, Okan, Aydoğan, C. S., Yapıcı, Güney Güven
Publication Date
2022-12
Publication Place
-
Elsevier
Subject
Arrhenius, Artificial neural network, Constitutive modeling, Modified Hensel-Spittel, Thermomechanical behavior, Ti6Al4V alloy
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2352-4928
Record ID
b1d44341-955b-4f77-ab72-1015963ecfe6
Library Location
Mechanical Engineering
Date
2022-12
Notes
Türk Havacılık ve Uzay Sanayi ; Ozyegin University ; TÜBİTAK
Sample Text
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
