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Erschienen in: Journal of Materials Engineering and Performance 11/2017

05.10.2017

A Simplified Micromechanical Modeling Approach to Predict the Tensile Flow Curve Behavior of Dual-Phase Steels

verfasst von: Tarun Nanda, B. Ravi Kumar, Vishal Singh

Erschienen in: Journal of Materials Engineering and Performance | Ausgabe 11/2017

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Abstract

Micromechanical modeling is used to predict material’s tensile flow curve behavior based on microstructural characteristics. This research develops a simplified micromechanical modeling approach for predicting flow curve behavior of dual-phase steels. The existing literature reports on two broad approaches for determining tensile flow curve of these steels. The modeling approach developed in this work attempts to overcome specific limitations of the existing two approaches. This approach combines dislocation-based strain-hardening method with rule of mixtures. In the first step of modeling, ‘dislocation-based strain-hardening method’ was employed to predict tensile behavior of individual phases of ferrite and martensite. In the second step, the individual flow curves were combined using ‘rule of mixtures,’ to obtain the composite dual-phase flow behavior. To check accuracy of proposed model, four distinct dual-phase microstructures comprising of different ferrite grain size, martensite fraction, and carbon content in martensite were processed by annealing experiments. The true stress–strain curves for various microstructures were predicted with the newly developed micromechanical model. The results of micromechanical model matched closely with those of actual tensile tests. Thus, this micromechanical modeling approach can be used to predict and optimize the tensile flow behavior of dual-phase steels.

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Metadaten
Titel
A Simplified Micromechanical Modeling Approach to Predict the Tensile Flow Curve Behavior of Dual-Phase Steels
verfasst von
Tarun Nanda
B. Ravi Kumar
Vishal Singh
Publikationsdatum
05.10.2017
Verlag
Springer US
Erschienen in
Journal of Materials Engineering and Performance / Ausgabe 11/2017
Print ISSN: 1059-9495
Elektronische ISSN: 1544-1024
DOI
https://doi.org/10.1007/s11665-017-2979-x

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