An alternative to kinematic hardening in classical plasticity

By Frederic Barlat, J. Gracio, Myoung-Gyu Lee, E. Rauch, G. Vincze. 2011

414 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/f3c5651a8857c5b1281f29a360997cb88a3e5bfd

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. C. Tomé
2. Frederic Barlat
3. J. Gracio
4. E. Rauch
5. K. Kitayama
6. Ricardo A. Lebensohn
7. Irene J. Beyerlein
8. O. Cazacu

In Mechanical Engineering

1. C. Tomé
2. J. Gracio
3. Frederic Barlat
4. E. Rauch
5. Ricardo A. Lebensohn
6. O. Cazacu
7. S. Agnew
8. Jeong Whan Yoon

Citations in the corpus

• Deep learning predicts path-dependent plasticity (2019, neutral): “For example, the past decade has introduced a shift from solutions based on the Armstrong–Frederick-type hardening (3) to models based on multiple yield surfaces (11) and distortional hardening (14).”
• A crystallographic dislocation model for describing hardening of polycrystals during strain path changes. Application to low carbon steels (2013, background): “On the other hand, there are in the literature phenomeno logical attempts to mimic the material response under strain path changes (Barlat et al., 2011 ).”