Hybrid Artificial Neural Network-Based Models to Investigate Deformation Behavior of AZ31B Magnesium Alloy atWarm Tensile Deformation

The uniaxial warm tensile experiments were carried out in deformation temperatures (50-250 degrees 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 (R-2), 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.

Automated summary

The workability and flow stress of AZ31B magnesium alloy were investigated through uniaxial warm tensile experiments at various deformation temperatures (50-250 degrees C) and strain rates (0.005 to 0.0167 s(-1)). Three models, namely BP-ANN, GABP-ANN, and CFBP-ANN, were explored for predicting flow stress. Machine learning models were trained using process parameters as inputs and flow stress as the output, with normalized experimental flow stress values to enhance network performance. Statistical analysis revealed that both GABP-ANN and CFBP-ANN models exhibited better accuracy in predicting flow stress, with the CFBP-ANN model showing higher predictability compared to GABP-ANN. This confirms the potential of the proposed CFBP-ANN model for accurately describing the deformation behavior of AZ31 magnesium alloy and its applicability in finite element analysis.