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Research Papers

Effects of Mn Content on the Deformation Behavior of Fe–Mn–Al–C TWIP Steels—A Computational Study

[+] Author and Article Information
Y. Y. Wang

Key Laboratory for Anisotropy
and Texture of Materials,
Northeastern University,
Shenyang 110819, China;
Computational Science
and Mathematics Division,
Pacific Northwest National Laboratory,
Richland, WA 99352

X. Sun

Computational Science
and Mathematics Division,
Pacific Northwest National Laboratory,
Richland, WA 99352
e-mail: xin.sun@pnnl.gov

Y. D. Wang

Key Laboratory for Anisotropy
and Texture of Materials,
Northeastern University,
Shenyang 110819, China;
State Key Laboratory for Advanced Metals
and Materials and the Collaborative
Innovation Center of Steel Technology,
University of Science and Technology Beijing,
Beijing 100083, China

H. M. Zbib

School of Mechanical and Materials Engineering,
Washington State University,
Pullman, WA 99164

1Corresponding author.

Contributed by the Materials Division of ASME for publication in the JOURNAL OF ENGINEERING MATERIALS AND TECHNOLOGY. Manuscript received August 20, 2014; final manuscript received October 21, 2014; published online December 3, 2014. Assoc. Editor: Curt Bronkhorst.

J. Eng. Mater. Technol 137(2), 021001 (Apr 01, 2015) (9 pages) Paper No: MATS-14-1171; doi: 10.1115/1.4029041 History: Received August 20, 2014; Revised October 21, 2014; Online December 03, 2014

This paper presents a double-slip/double-twin polycrystal plasticity model using finite element solution to investigate the kinetics of deformation twinning of medium manganese (Mn) twinning-induced plasticity (TWIP) steels. Empirical equations are employed to estimate the stacking fault energy (SFE) of TWIP steels and the critical resolved shear stress (CRSS) for dislocation slip and deformation twinning, respectively. The results suggest that the evolution of twinning in Fe–xMn–1.4Al–0.6 C (x = 11.5, 13.5, 15.5, 17.5, and 19.5 mass%) TWIP steels, and its relation to the Mn content, can explain the effect of Mn on mechanical properties. By comparing the double-slip/double-twin model to a double-slip model, the predicted results essentially reveal that the interaction behavior between dislocation slip and deformation twinning can lead to an additional work hardening. Also, numerical simulations are carried out to study the influence of boundary conditions on deformation behavior and twin formation. The nucleation and growth of twinning are found to depend on internal properties (e.g., mismatch orientation of grains and stress redistribution) as well as on external constraints (e.g., the applied boundary conditions) of the material.

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Figures

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Fig. 1

Schematic illustration of computational domain at each scale

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Fig. 2

Predicted stress–strain response with/without deformation twinning

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Fig. 3

(a) The accumulated shear strain field, (b) twinning volume faction distribution, and (c) von Mises stress after deformation to 35% strain. The red color represents twinned part and the blue color represents untwinned part.

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Fig. 4

Influence of CRSSs of twin and slip (CRSS1-220/135 MPa, CRSS2-230/140 MPa, CRSS3-240/145 MPa, CRSS4-250/150 MPa, and CRSS5-260/155 MPa) related to different Mn contents in low alloys TWIP steels on: (a) stress–strain responses and (b) twinning volume fraction

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Fig. 5

Plot of predicted stress (left y-axis) and twinning volume fraction (right y-axis) as a function of strain (x-axis) for Fe–17.5Mn–1.4Al–0.6 C TWIP steel

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Fig. 6

Comparison of predicted results under three different boundary conditions (PBCs, MPCs, and FREE) and measured experimental data of Fe–17.5Mn–1.4Al–0.6 C TWIP steel: (a) tensile stress–strain responses and (b) twinning volume fraction

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Fig. 7

Evolution contour of twinning volume fraction as a function of true strain under PBCs, MPCs, and FREE boundary conditions, respectively, of Fe–17.5Mn–1.4Al–0.6 C TWIP steel: (a) ε = 10%, (b) ε = 15%, (c) ε = 20%, (d) ε = 30%, and (e) ε = 40%. The blue color stands for untwinned part and the red color stands for twinned part.

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