Constitutive base analysis of a 7075 aluminum alloy during hot compression testing

By M. Rokni, A. Zarei‐Hanzaki, Ali A. Roostaei, A. Abolhasani. 2011

154 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Materials Chemistry, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/85ac929038f6a740238f8a0c5cfbc8ec485f7e50

How researchers see this paper

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

In Aerospace Engineering

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

In Materials Chemistry

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

In Mechanics of Materials

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

Citations in the corpus

• Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy (2013, background): “The effect of strain rate is justified considering the role of dislocation tangles in lowering the mobility of dislocatio ns thereby increasing the flow stress, which is more pronounced at higher deformation rates [30].”
• Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy (2012, methodology): “The higher value of R however may not always indicate better performance of a model/equation because of the tendency of the model/equation to be biased towards higher or lower values [39].”