Bulk texture evolution of Cu–Nb nanolamellar composites during accumulative roll bonding
Source abstract
Abstract
Abstract A combination of accumulative roll bonding (ARB) and rolling is used to fabricate nanolamellar Cu–Nb multilayers with individual layer thicknesses (h) of 600 μm ⩾ h ⩾ 10 nm with a total strain imposed between 0.5 and 11.6. Neutron diffraction, scanning electron microscopy and transmission electron microscopy are used to characterize the microstructures and measure orientation distribution functions of both phases as a function of layer thickness. Fiber plots are calculated from the orientation distribution functions in order to understand the texture evolution in the Cu and Nb layers with increasing strain. Results are compared with rolling studies of single phase Cu, single phase Nb, and cast Cu–20 wt.% Nb composite. Results indicate that textures develop in the Cu and Nb layers during ARB that are distinct from classical rolling textures frequently observed both in their single-phase counterparts and in rolled composites. The atypical texture that develops shows a preferential strengthening of specific β fiber components at the expense of others in Cu and a strengthening of the α fiber at the expense of the γ fiber in Nb. No dynamic recrystallization is observed in Cu, even at strains above 99.99%, further delineating the behavior from single phase and composite behavior previously observed. Viscoplastic self-consistent (VPSC) polycrystal simulations were carried out to provide an understanding of the texture evolution in accumulative roll bonding. Enforcing planar slip in Cu leads to texture evolution for VPSC consistent with observations. A reasonable fit for Nb could be produced via the selection of specific {1 1 0} and {1 1 2} slip systems.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Microstructure and mechanical properties · Aluminum Alloys Composites Properties · Metal Forming Simulation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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