Flow curve prediction of Al-Mg alloys under warm forming conditions at various strain rates by ANN

Küçük Resim Yok

Tarih

2011

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

Künye