journal article·2018-10Open access

Influence of deformation induced nanoscale twinning and FCC-HCP transformation on hardening and texture development in medium-entropy CrCoNi alloy

C.E. Slone, S. Chakraborty, J. Miao, E.P. George, M.J. Mills, S.R. Niezgoda

Acta Materialia· Volume 158

Abstract

Abstract Texture evolution during room-temperature tensile testing of recrystallized equimolar CrCoNi was studied using electron backscatter diffraction and electron channeling contrast imaging on specimens from interrupted tests. Dominant deformation mechanisms included slip at low strains and deformation twinning at larger strains, which were accompanied by the development of a strong texture parallel to the tensile axis. Highly deformed material also contained nanotwin/hcp lamellae, which have previously been hypothesized to act as potent barriers for non-coplanar dislocations. To examine this hypothesis, mean-field modeling was performed using the viscoplastic self-consistent framework with varying ratios for hardening by slip and twinning. In the optimal model, twinning produced approximately three times as much non-coplanar hardening as slip, which is larger than previous observations in other twinning-induced plasticity materials that do not form twin/hcp lamellae. Additional full-field elasto-viscoplastic simulations were performed using the fast Fourier transform (EVP-FFT) method to examine intragranular rotation and the effect of initial grain orientation on the deformation mode. Grains with initial orientations near had the greatest propensity for deformation twinning while grains near were more likely to deform by slip even at large strains. Excellent quantitative agreement was obtained between the experiments and EVP-FFT model.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: High Entropy Alloys Studies · Titanium Alloys Microstructure and Properties · Additive Manufacturing Materials and Processes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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