Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel

By Y. Lin, Jun Zhang, J. Zhong. 2008

290 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/4091c6c330ef3eeb9f07cb12e60c87d2654596ce

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. A. Zarei‐Hanzaki
3. Hamid Reza Abedi
4. J. Deng
5. D. Wen
6. Jian Chen
7. Y. Lin
8. Guan Liu

In Mechanical Engineering

1. D. Wen
2. Y. Lin
3. J. Deng
4. N. Haghdadi
5. Hamid Reza Abedi
6. Jian Chen
7. Guan Liu
8. A. Zarei‐Hanzaki

In Mechanics of Materials

1. N. Haghdadi
2. A. Zarei‐Hanzaki
3. Hamid Reza Abedi
4. D. Wen
5. J. Deng
6. A. R. Khalesian
7. Y. Lin
8. Jian Chen

Citations in the corpus

• Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy (2013, methodology): “Taking the disadvantag es of the abovementi oned methods into consideration, artificial neural network (ANN) can provide an alternative approach to materials modeling and processing techniques [19].”
• Artificial neural network modeling to predict the high temperature flow behavior of an AZ81 magnesium alloy (2012, methodology): “To overcome the gross approximations introduced by the regression methods discussed above, ANNmodels have been successfully used to predict the hot working behavior of materials [18–22].”
• 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].”