Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy

By O. Sabokpa, A. Zarei‐Hanzaki, Hamid Reza Abedi, N. Haghdadi. Materials & Design, 2012

164 citations recorded by the source corpus.

Abstract In the present work, the capability of artificial neural network (ANN) has been evaluated to describe and to predict the high temperature flow behavior of a cast AZ81 magnesium alloy. Toward this end, a set of isothermal hot compression tests were carried out in temperature range of 250–400 °C and strain rates of 0.0001, 0.001 and 0.01 s −1 up to a true strain of 0.6. The flow stress was primarily predicted by the hyperbolic laws in an Arrhenius-type of constitutive equation considering the effects of strain, strain rate and temperature. Then, a feed-forward back propagation artificial neural network with single hidden layer was established to investigate the flow behavior of the material. The neural network has been trained with an in-house database obtained from hot compression tests. The performance of the proposed models has been evaluated using a wide variety of statistical indices. The comparative assessment of the results indicates that the trained ANN model is more efficient and accurate in predicting the hot compressive behavior of cast AZ81 magnesium alloy than the constitutive equations.

Subfields: Biomaterials, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/6a7561fd4ed34919299648a11114ee64ad7dd4ad

How researchers see this paper

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In Biomaterials

1. N. Haghdadi
2. Hamid Reza Abedi

In Mechanical Engineering

1. N. Haghdadi
2. Hamid Reza Abedi
3. A. R. Khalesian
4. A. Zarei‐Hanzaki

In Mechanics of Materials

1. N. Haghdadi
2. A. R. Khalesian
3. Hamid Reza Abedi
4. A. Zarei‐Hanzaki

Citations in the corpus

• Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy (2013, methodology): “Such ANN models, which have been already adapted to predict the high temperature flow behavior of steels [21–23], titanium alloys [24], metal matrix composites [25], and magnesium alloys [26] are of high interest to be examined on cast Al–Si–Mg alloys.”