Journal
COMPUTATIONAL MATERIALS SCIENCE
Volume 67, Issue -, Pages 93-103Publisher
ELSEVIER
DOI: 10.1016/j.commatsci.2012.07.028
Keywords
Austenitic stainless steel; Hot deformation; Constitutive relationship; Artificial neural network; Arrhenius-type
Categories
Funding
- scientific research program
- State Key Laboratory for Mechanical Behavior of Materials [20111212]
- Education Department of Shaanxi Province [2011JG14]
- Industry-University-Research cooperation
- Yulin City [K04102]
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Constitutive relationship of as-cast 904L austenitic stainless steel is comparatively investigated by the Arrhenius-type constitutive model incorporating the strain effect and back-propagation (BP) neural network. The experimental true stress-true strain data were obtained from hot compression tests on the Gleeble-1500D thermo-mechanical simulator in the temperature range of 1000-1150 degrees C and strain rate range of 0.01-10 s(-1). The corrected data with the friction and the temperature compensations were employed to develop the Arrhenius-type model and BP neural network respectively. The accuracy and reliability of the models were quantified by employing statistical parameters such as the correlation coefficient and absolute average error. The results show that the proposed models have excellent predictabilities of flow stresses for the present steel in the specified deformation conditions. Compared with the Arrhenius-type model, the optimized BP neural network model has more accuracy and capability in describing the compressive deformation behavior at elevated temperature for as-cast 904L austenitic stainless steel. (C) 2012 Elsevier B.V. All rights reserved.
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