A comparative study on Arrhenius-type constitutive model and artificial neural network model to predict high-temperature deformation behaviour in Aermet100 steel

By G. Ji, Fuguo Li, Qing-hua Li, Huiqu Li, Zhi Li. 2011

191 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/0430947e2ac4b12f0dcaac1a851c5cb1757e98c3

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. N. Haghdadi
2. Hamid Reza Abedi
3. A. Zarei‐Hanzaki
4. O. Sabokpa

In Mechanical Engineering

1. N. Haghdadi
2. Hamid Reza Abedi
3. O. Sabokpa
4. A. Zarei‐Hanzaki

In Mechanics of Materials

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

Citations in the corpus

• Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy (2012, background): “Analytical models are based on kinematics and dynamics of dislocations thereby requiring very clear understanding of the mechanisms that control the deformation of the materials and are hardly possible to apply in practical use [17].”