Flow curve prediction of Al-Mg alloys under warm forming conditions at various strain rates by ANN
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER SCIENCE BV
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper describes an approach based on artificial neural network (ANN) to identify the material flow curves of strain hardened 5083-H111 and 5754-O Al-Mg alloys at the temperature ranges from room temperature (RT) to 300 degrees C and a strain rate of 0.0016-0.16 s(-1). The tensile tests were performed to determine the material responses at various temperatures and strain rates. An ANN model was developed to predict the flow curves of the materials in terms of experimental data. The input parameters of the model are strain rate, temperature, and strain while tensile flow stress is the output. A three layer feed-forward network was trained with BFGS (Broyden, Fletcher, Goldfarb, and Shanno) algorithm. The amount of the neurons in the hidden layer was determined by determining of the root mean square error (RMSE) values for each material. Results reveal that the predicted values in the ANN model are in very good agreement with the experimental data. The ANN model, described in this paper, is an efficient quantitative tool to evaluate and predict the deformation behavior of 5083-H111 and 5754-O Al-Mg alloys for tensile test at prescribed deformation conditions. (C) 2010 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Al-Mg alloys, 5083, 5754, Flow curves, Modeling, Artificial neural network, ANN
Kaynak
APPLIED SOFT COMPUTING
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
11
Sayı
2
