Statistical characterization of twin transmission across grain boundaries in magnesium

Kumar, Mariyappan Arul; McCabe, Rodney James; Taupin, Vincent; Tomé, Carlos N.; Capolungo, Laurent

doi:10.1016/j.matchar.2022.112457

Title: Statistical characterization of twin transmission across grain boundaries in magnesium

Journal Article · Fri Nov 04 00:00:00 EDT 2022 · Materials Characterization

DOI:https://doi.org/10.1016/j.matchar.2022.112457· OSTI ID:1898358

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Universite de Lorraine (France); Centre National de la Recherche Scientifique (CNRS) (France); Arts et Métiers ParisTech (France)

Here, a detailed statistical analysis of twin transmission (TT) across grain boundaries is performed in rolled, commercial purity magnesium compressed along the rolling direction. EBSD images are acquired from two different cuts: a section containing the rolling (RD) and normal (ND) directions; and a section at 35° to the ND which contains transverse direction (TD). An automated twinning analysis software, METIS, is used to obtain the statistical correlations between deformation twins and other microstructural features by analyzing EBSD microstructures comprising thousands of grains and twins. This detailed statistical analysis reveals that the TT propensity is sensitive to the grain boundary (GB) misorientation angle but not to the GB misorientation axis. Specifically, TT propensity decreases with increasing GB misorientation angle; however, the decreasing trend is not monotonic. Further, the detailed analysis of TT events combining macroscopic Schmid factor and the geometric measures (m’: accounts for the alignment of both twin plane normal and shear directions; m”: accounts for the alignment of only twin shear directions; and m”’: accounts for the alignment of only twin plane normal) helps in understanding and identifying the process of twin-pair formation, i.e., co-nucleation versus transmission, also to identify the role of local stresses at GBs induced by inter-grain interaction versus twinning shear transformation processes. In addition, Bayesian inference is used to draw statistically meaningful conclusions as to the likelihood of transmission given misorientation angles. Lastly, twin chain frequency as a function of chain length is predicted using the probability distribution obtained from the Bayesian inference and compared with the actual data from the EBSD microstructures.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: 89233218CNA000001; FWP 06SCPE401

OSTI ID:: 1898358

Alternate ID(s):: OSTI ID: 1962125

Report Number(s):: LA-UR-22-20355

Journal Information:: Materials Characterization, Vol. 194; ISSN 1044-5803

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
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