Constitutive modeling for elevated temperature flow behavior of 42CrMo steel

By Y. Lin, Ming-Song Chen, J. Zhong. 2008

609 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/11b9b5f9aab8b36b36088925c0b2a10a5c540978

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 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 high temperature flow behavior of an AZ81 magnesium alloy (2012, background): “In this regard, a considerable amount of researches have been conducted employing the analytical [10,11], phenomenological [12–15] and empirical constitutive models [16] to predict the flow behavior of materials at elevated temperatures.”
• Constitutive models for high-temperature flow behaviors of a Ni-based superalloy (2014, background): “Based on the recent researches, Lin and Chen [1] presented a critical review on constitutive descriptions for metals and alloys under hot working, and they divided the constitutive models into three categories, including the phenomenological models [16–48], physically-based models [49– 53] and artif”
• Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy (2013, background): “In this context, several empirical, phenomenolog ical, and physical-ba sed constituti ve models have been proposed to date [10–12].”