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Evaluating the hot deformation behavior of a super-austenitic steel through microstructural and neural network analysis

Mirzaei, A, Zarei-Hanzaki, A, Pishbin, MH, Imandoust, A and Khoddam, Shahin 2015, Evaluating the hot deformation behavior of a super-austenitic steel through microstructural and neural network analysis, Journal of Materials Engineering and Performance, vol. 24, no. 6, pp. 2412-2421, doi: 10.1007/s11665-015-1518-x.

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Title Evaluating the hot deformation behavior of a super-austenitic steel through microstructural and neural network analysis
Author(s) Mirzaei, A
Zarei-Hanzaki, A
Pishbin, MH
Imandoust, A
Khoddam, ShahinORCID iD for Khoddam, Shahin orcid.org/0000-0002-5205-2086
Journal name Journal of Materials Engineering and Performance
Volume number 24
Issue number 6
Start page 2412
End page 2421
Total pages 10
Publisher Springer Verlag
Place of publication United States
Publication date 2015-06
ISSN 1059-9495
1544-1024
Keyword(s) austenitic stainless steel
flow behavior
microstructure
thermo-mechanical processing
Science & Technology
Technology
Materials Science, Multidisciplinary
Materials Science
TEMPERATURE FLOW BEHAVIOR
INDUCED PLASTICITY STEEL
AZ81 MAGNESIUM ALLOY
STAINLESS-STEEL
DYNAMIC RECRYSTALLIZATION
STRAIN-RATE
PREDICT
STRESS
TRANSFORMATION
MODELS
Summary A series of hot compression tests were conducted in the temperature range of 800-1100 °C under the strain rates of 0.001, 0.01, and 0.1 s−1 to assess the flow behavior and microstructure evolution of a super-austenitic stainless steel. The occurrence of dynamic recrystallization has been characterized as the dominant restoration mechanism operating in the investigated range of temperature. This is considered as the main factor affecting the related flow characteristics of the material. To better analyzing the obtained results, an artificial neural network (ANN) model with single hidden layer composed of 20 neurons has been established to simulate the flow behavior of the material. To train the model, a feed-forward back propagation algorithm has been employed. The reliability of the proposed model has been evaluated using standard statistical indices. In addition, the capability of the model has been assessed under the conditions at which the related data were not incorporated in the model. It was found that the developed ANN model employing this algorithm could efficiently track the work hardening and dynamic softening regions of the deforming material.
Language eng
DOI 10.1007/s11665-015-1518-x
Field of Research 099999 Engineering not elsewhere classified
0912 Materials Engineering
Socio Economic Objective 0 Not Applicable
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2015 ASM International
Persistent URL http://hdl.handle.net/10536/DRO/DU:30096392

Document type: Journal Article
Collections: Institute for Frontier Materials
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