A repeatability study of artificial neural network predictions in flow stress model development for a magnesium alloy

A repeatability study of artificial neural network predictions in flow stress model development for a magnesium alloy

Hubert Siewior, Lukasz Madej

AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland.




This work is devoted to an evaluation of the capabilities of artificial neural networks (ANN) in terms of developing a flow stress model for magnesium ZE20. The learning procedure is based on experimental flow-stress data following inverse analysis. Two types of artificial neural networks are investigated: a simple feedforward version and a recursive one. Issues related to the quality of input data and the size of the training dataset are presented and discussed. The work confirms the general ability of feedforward neural networks in flow stress data predictions. It also highlights that slightly better quality predictions are obtained using recursive neural networks.

Cite as:

Siewiór, H., & Madej, L. (2021). A repeatability study of artificial neural network predictions in flow stress model development for a magnesium alloy. Computer Methods in Materials Science, 21(4), 209-218. https://doi.org/10.7494/cmms.2021.4.0769

Article (PDF):


Flow stress, Artificial neural networks, Feedforward, Recursive


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