On the Relation of Microstructure and Texture Evolution in an Austenitic Fe-28Mn-0.28C TWIP Steel During Cold Rolling

Haase, Christian; Chowdhury, Sandip Ghosh; Barrales‐Mora, Luis A.; Molodov, Dmitri A.; Gottstein, Günter

doi:10.1007/s11661-012-1543-4

Cited by 77 publications

(55 citation statements)

References 49 publications

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“…The development of technological routes involving cold working for producing TWIP-steels with the beneficial combination of strength and ductility requires detailed investigation of the mechanisms of microstructure evolution during deformation and careful analysis of the strain-hardening mechanisms. Recent studies on TWIP steels with various manganese contents have revealed the common sequence of structural changes during cold rolling [5,25,27,28]. Following a rapid increase in the dislocation density at an early deformation, the deformation twinning progressively develops throughout the deformation microstructures at low to medium strains, whereas shear banding occurs at rather high strains.…”

Section: Introductionmentioning

confidence: 99%

Microstructure evolution and strengthening mechanisms of Fe–23Mn–0.3C–1.5Al TWIP steel during cold rolling

Kusakin

Belyakov

Haase

et al. 2014

Materials Science and Engineering: A

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118

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a b s t r a c tThe effect of cold rolling on the microstructure evolution and mechanical properties of Fe-23Mn-0.3C-1.5Al twinning-induced plasticity (TWIP) steel was studied. The extensive mechanical twinning subdivides the initial grains into nanoscale twin lamellas. In addition, the formation of deformation micro bands at ε440% induces the formation of nanostructured bands of localized shear. It is demonstrated that the mechanical twinning is notably important for dislocation storage within the matrix, as the twin boundaries act as equally effective obstacles to dislocation glide as conventional high-angle grain boundaries. However, the contribution of the grain size strengthening to the overall yield stress (YS) is much smaller than that of the deformation strengthening, which plays a major role in the superior work-hardening behavior of TWIP steels. A very high dislocation density of $ 2 Â 10 15 m À 2 is achieved after plastic deformation with moderate strains. The superposition of deformation strengthening and grain boundary strengthening leads to an increase in the YS from 235 MPa in the initial state to 1400 MPa after 80% rolling.

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Section: Introductionmentioning

confidence: 99%

Microstructure evolution and strengthening mechanisms of Fe–23Mn–0.3C–1.5Al TWIP steel during cold rolling

Kusakin

Belyakov

Haase

et al. 2014

Materials Science and Engineering: A

Self Cite

118

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“…The high yield strength comes from the large increase of dislocation activity forming a large number of subgrains together with the interaction with mechanical twins [32,33]. Despite the large SFE values at 300°C [5,9], mechanical twinning has been repeatedly observed in a large number of austenite grains for the present TWIP steel, in the same way than in the other TWIP steels processed by ECAP [16,17].…”

Section: Discussionmentioning

confidence: 61%

“…This texture component is usually found in hot rolled FCC metals [29] and it has also been described for low SFE metals in the as-rolled condition [30]. For the specific case of TWIP steels, the evolution of Brass-type rolling texture has also been intensively studied [31][32][33]. In this case, after the last annealing step and the increase of annealing twins, the presence of Goss and β-fiber texture components have practically disappeared and the brass texture components have been reinforced.…”

Section: Texture Analysismentioning

confidence: 92%

Equal channel angular pressing of a TWIP steel: microstructure and mechanical response

et al. 2017

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A Fe-20.1Mn-1.23Si-1.72Al-0.5C TWIP steel with ultra-fine grain structure was successfully processed through Equal Channel Angular Pressing (ECAP) at warm temperature up to four passes following the BC route. The microstructure evolution was characterized by Electron Back Scattered Diffraction (EBSD) to obtain the grain maps, which revealed an obvious reduction of grain size, as well as a decrease of the twin fraction, with increasing number of ECAP passes. The texture evolution during ECAP was analyzed by Orientation Distribution Function (ODF). The results show that the annealed material presents Brass (B) as dominant component. After ECAP, the one pass sample presents A1 * and A2 * as the strongest components, while the two passes and four passes samples change gradually towardcomponents. TEM analysis shows that all samples present twins. The twin thickness is reduced with increasing the number of ECAP passes. Nano-twins, as a result of secondary twinning, are also observed in the one and two passes samples. In the four passes sample the microstructure is extensively refined by the joint action of ultra-fine subgrains, grains and twins. The mechanical behavior was studied by tensile samples and it was found that the yield strength and the ultimate tensile strength are significantly enhanced at increasing number of ECAP passes. Although the ductility and strain hardening capability are reduced with ECAP process, the present TWIP steel shows significant uniform deformation periods with positive work-hardening rates.

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“…The corresponding legend of the ideal texture components is given elsewhere. 22 With increasing rolling degree, the texture transformed gradually from Cu-type to Brass-type, as indicated by the decreasing f112gh111i Cu texture component and the more pronounced a-fiber (h110i k ND) with a spread towards the f552gh115i CuT texture component. After recrystallization, the rolling texture was retained but significantly weakened in intensity as a result of the oriented nucleation and the formation of recrystallization twins.…”

Section: Integrated Computational Materials Engineering Procedures Expmentioning

confidence: 97%

Identifying Structure–Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach

Diehl

Groeber

Haase

et al. 2017

JOM

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Predicting, understanding, and controlling the mechanical behavior is the most important task when designing structural materials. Modern alloy systems-in which multiple deformation mechanisms, phases, and defects are introduced to overcome the inverse strength-ductility relationship-give raise to multiple possibilities for modifying the deformation behavior, rendering traditional, exclusively experimentally-based alloy development workflows inappropriate. For fast and efficient alloy design, it is therefore desirable to predict the mechanical performance of candidate alloys by simulation studies to replace time-and resource-consuming mechanical tests. Simulation tools suitable for this task need to correctly predict the mechanical behavior in dependence of alloy composition, microstructure, texture, phase fractions, and processing history. Here, an integrated computational materials engineering approach based on the open source software packages DREAM.3D and DA-MASK (Dü sseldorf Advanced Materials Simulation Kit) that enables such virtual material development is presented. More specific, our approach consists of the following three steps: (1) acquire statistical quantities that describe a microstructure, (2) build a representative volume element based on these quantities employing DREAM.3D, and (3) evaluate the representative volume using a predictive crystal plasticity material model provided by DAMASK. Exemplarily, these steps are here conducted for a high-manganese steel.

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On the Relation of Microstructure and Texture Evolution in an Austenitic Fe-28Mn-0.28C TWIP Steel During Cold Rolling

Cited by 77 publications

References 49 publications

Microstructure evolution and strengthening mechanisms of Fe–23Mn–0.3C–1.5Al TWIP steel during cold rolling

Microstructure evolution and strengthening mechanisms of Fe–23Mn–0.3C–1.5Al TWIP steel during cold rolling

Equal channel angular pressing of a TWIP steel: microstructure and mechanical response

Identifying Structure–Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach

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