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A revisit to atomistic rationale for slip in shape memory alloys

Journal

PROGRESS IN MATERIALS SCIENCE
Volume 85, Issue -, Pages 1-42

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.pmatsci.2016.10.002

Keywords

Shape memory; Plastic deformation; Dislocation; Generalized stacking fault energy; Critical resolved shear stress; Density functional theory

Funding

  1. Nyquist Chair funds
  2. NSF [CMMI-13-33884]
  3. Div Of Civil, Mechanical, & Manufact Inn
  4. Directorate For Engineering [1333884] Funding Source: National Science Foundation

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Performance degradation in shape memory alloys (SMA) arises due to a gradual loss of strain recoverability attributable to slip mediated plasticity. The slip-induced changes in SMAs can be profound creating accumulation of permanent strains, altering the critical stress and hysteresis in an adverse manner. Slip nucleation in ordered SMA lattices can often be triggered due to energetically favorable dissociation reactions. Partial slip can dominate over full slip, generating planar defects (e.g. anti-phase boundary, superlattice or complex stacking faults) as evidenced through electron microscopy. Considerable advances are made lately on physically rationalizing the observed plastic micromechanism(s) benefitting from quantum mechanical models. In-depth analyses of crystal variables (e.g. lattice ordering, atomic stacking and stable/metastable fault structures) subjected to intrinsic solid-state effects have unequivocally established the genesis of empirical slipping propensity in terms of atomic fault energetics. This article systematically revisits the empirical physical evidence of slip in important SMAs from the literature, presents the pertinent experimental findings, and then embarks on reviewing the investigations of atomistic studies as exemplified by the authors' group. In closing, we discourse on the potential use of lattice scale theories in devising other important structure property relationships such as role of precipitates, cracking resistance, constitutive modeling. (C) 2016 Elsevier Ltd. All rights reserved.

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