Constitutive flow behaviour of austenitic stainless steels under hot deformation: artificial neural network modelling to understand, evaluate and predict-Reference-Cited by-同舟云学术

Constitutive flow behaviour of austenitic stainless steels under hot deformation: artificial neural network modelling to understand, evaluate and predict

Published:2006-07-31 Issue:6 Volume:14 Page:1053-1070
ISSN:0965-0393
Container-title:Modelling and Simulation in Materials Science and Engineering
language:
Short-container-title:Modelling Simul. Mater. Sci. Eng.

Author:

Mandal Sumantra,Sivaprasad P V,Venugopal S,Murthy K P N

Publisher

IOP Publishing

Subject

Computer Science Applications,Mechanics of Materials,Condensed Matter Physics,General Materials Science,Modelling and Simulation

Reference50 articles.

1. Dynamic recrystallization behavior of AISI 304 stainless steel

2. Kinetics of flow and strain-hardening

3. A unified phenomenological description of work hardening and creep based on one-parameter models

4. A dislocation model for the stress-strain behaviour of polycrystalline α-Fe with special emphasis on the variation of the densities of mobile and immobile dislocations

5. The high temperature and high strain-rate behaviour of a plain carbon and an HSLA steel

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