Modelling the Hot Flow Behaviors of AZ80 Alloy by BP-ANN and the Applications in Accuracy Improvement of Computations

• Autor(a) principal: Quan,Guo-Zheng
• Data de Publicação: 2015
• Outros Autores: Zhang,Zhi-hua, Pan,Jia, Xia,Yu-feng
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Materials research (São Carlos. Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392015000601331
• topic: magnesium alloy; flow stress; constitutive model; BP-ANN; prediction map

Hot compressions of as-cast AZ80 magnesium alloy in a wide temperature range of 523-673 K and strain rate range of 0.01-10 s–1 with a height reduction of 60% were conducted by a Gleeble-1500 thermo-mechanical test simulator. The hot flow behaviors show highly non-linear intrinsic relationships with temperature, strain and strain rate. In order to model the complicated flow behaviors, error back-propagation algorithm, a representative method to minimize the target error, was selected to train the artificial neural network. A comparative study was made on the predictabilities of the improved Arrhenius-type and BP-ANN model by using two standard statistical parameters including correlation coefficient (R) and average absolute relative error (AARE). Comparison results show that the well-trained BP-ANN has higher prediction accuracy. Three highlight applications were presented. Firstly, the strain-stress data volume was expanded by BP-ANN predictions above experimental conditions. Secondly, the expanded data were applied in the simulations of isothermal compressions, and high simulation accuracy for the load-stroke curve was achieved. Thirdly, a three-dimensional (3D) interaction space of stress, strain, temperature and strain rate was constructed based on the intensive data, which supplies the stress data to arbitrary temperature, strain rate, and strain.