The uniaxial warm tensile experiments were carried out in deformation temperatures (50-250 °C) and strain rates (0.005 to 0.0167 s-1) to investigate the material workability and to predict flow stress of AZ31B magnesium alloy. The back-propagation artificial neural network (BP-ANN) model, a hybrid models with a genetic algorithm (GABP-ANN), and a constrained nonlinear function (CFBP-ANN) were investigated. In order to train the exploited machine learning models, the process parameters such as strain, strain rate, and temperature were accounted as inputs and flow stress was considered as output; moreover, the experimental flow stress values were also normalized to constructively run the neural networks and to achieve better generalization and stabilization in the trained network. Additionally, the proposed model's closeness and validness were quantified by coefficient of determination (R2), relative mean square error (RMSE), and average absolute relative error (AARE) metrics. The computed statistical outcomes disclose that the flow stress predicted by both GABP-ANN and CFBP-ANN models exhibited better closeness with the experimental data. Moreover, compared with the GABP-ANN model outcomes, the CFBP-ANN model has a relatively higher predictability. Thus, the outcomes confirm that the proposed CFBP-ANN model can result in the accurate description of AZ31 magnesium alloy deformation behavior, showing potential for the purpose of practicing finite element analysis.
Keywords: AZ31 magnesium alloy; artificial neural network; constrained nonlinear function; finite element analysis; flow stress; genetic algorithm; warm tensile experiments.