Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy

By N. Haghdadi, A. Zarei‐Hanzaki, A. R. Khalesian, Hamid Reza Abedi. 2013

182 citations recorded by the source corpus.

Abstract Prediction of the material flow behavior is an essential step to optimize the design of any forming process. In this context, artificial neural network (ANN) may be used as a reliable modeling method for simulating and predicting the flow behavior of materials under different thermomechanical conditions. In the present study, an ANN model has been established to estimate the high temperature flow behavior of a cast A356 aluminum alloy. A series of isothermal compression tests was conducted in the temperature range of 400–540 °C and strain rates of 0.001–0.1 s −1 . A feed-forward back propagation ANN with single hidden layer composing of 20 neurons was employed to simulate the flow behavior. The neural network has been trained using an in-house database obtained from hot compression tests. Finally, in comparison with a strain-dependent Arrhenius type constitutive equation, the reliability of the proposed ANN model has been evaluated using standard statistical indices. The results indicate that the trained ANN model is a robust tool to predict the high temperature flow behavior of cast A356 aluminum alloy.

Subfields: Materials Chemistry, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/409ac193504236ea8bcc91abee0b9896be651e1e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. J. Deng
2. D. Wen
3. Jian Chen
4. Y. Lin
5. Guan Liu
6. Xiao-Min Chen

In Mechanical Engineering

1. D. Wen
2. Y. Lin
3. J. Deng
4. Jian Chen
5. Guan Liu
6. Xiao-Min Chen

In Mechanics of Materials

1. D. Wen
2. J. Deng
3. Y. Lin
4. Jian Chen
5. Guan Liu
6. Xiao-Min Chen
7. Ming-Song Chen

Citations in the corpus

• Constitutive models for high-temperature flow behaviors of a Ni-based superalloy (2014, background): “In addition, a number of researchers developed some artificial neural network constitutive models to predict the high-temperature flow behaviors of 42CrMo steel [54], as-cast Ti–6Al–2Zr– 1Mo–1V alloy [55], A356 aluminum alloy [56], as-cast Ti60 titanium alloy [57], and 12Cr3WV steel [58].”