Artificial neural network prediction to the hot compressive deformation behavior of Al–Cu–Mg–Ag heat-resistant aluminum alloy

Lu, Zhilun; Pan, Qinglin; Liu, Xiaoyan; Qin, Yinjiang; He, Yunbin; Cao, Sufang

Abstract

The behavior of the flow stress of Al–Cu–Mg–Ag heat-resistant aluminum alloys during hot compression deformation was studied by thermal simulation test. The temperature and the strain rate during hot compression were 340–500 °C, 0.001 s−1 to 10 s−1, respectively. Constitutive equations and an artificial neural network (ANN) model were developed for the analysis and simulation of the flow behavior of the Al–Cu–Mg–Ag alloys. The inputs of the model are temperature, strain rate and strain. The output of the model is the flow stress. Comparison between constitutive equations and ANN results shows that ANN model has a better prediction power than the constitutive equations.

• Journal Article Type: Article
• Online Publication Date: Mar 3, 2011
• Publication Date: 2011-04
• Deposit Date: Oct 23, 2021
• Journal: Mechanics Research Communications
• Print ISSN: 0093-6413
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 38
• Issue: 3
• Pages: 192-197
• DOI: https://doi.org/10.1016/j.mechrescom.2011.02.015
• Keywords: Al–Cu–Mg–Ag alloys, Constitutive equations, Artificial neural network, Hot compression deformation, Flow stress
• Public URL: http://researchrepository.napier.ac.uk/Output/2815540