journal article·2013-04

Crystallographic classification of kink bands in an extruded Mg–Zn–Y alloy using intragranular misorientation axis analysis

Michiaki Yamasaki, Koji Hagihara, Shin-ichi Inoue, Jason Paul Hadorn, Yoshihito Kawamura

Acta Materialia· Volume 61· Issue 6
Publisher page

Abstract

Abstract Within extruded Mg89Zn4Y7 (at.%) alloys containing a long period stacking ordered (LPSO) phase a unique deformation mode referred to as “kink deformation” is found to occur within the LPSO phase grains, forming kink bands that are each represented by an array of geometrically necessary dislocations. Intragranular misorientation axis (IGMA) analysis suggests that this kink deformation results in lattice rotation predominately about the 〈 1 1 ¯ 0 0 〉 , 〈 0 1 ¯ 1 0 〉 , 〈 0001 〉 , and 〈 1 2 ¯ 1 0 〉 axes of the 18R-type LPSO structure. IGMA analysis is based on slip-induced lattice rotation and determination of its rotation axis (Taylor axis). Therefore, it is possible to determine the dominant slip mode in a grain that is deformed by matching the Taylor axis for a given slip system to its experimentally obtained IGMA distribution. In this study the strong preference of 〈 1 1 ¯ 0 0 〉 and 〈 0 1 ¯ 1 0 〉 Taylor axes suggests that kinking occurs through a basal 〈a〉 slip mode. The 〈0 0 0 1〉 Taylor axes suggest that kink bands form through a prismatic 〈a〉 slip mode. These axes are categorized as “principal rotation” axes, which result from one Burgers vector of dislocations. Kink bands with a 〈 1 2 ¯ 1 0 〉 Taylor axis are produced through combination of basal 〈a〉 slip variants. This axis is categorized as a “combination-type rotation” axis, which results from more than one Burgers vector of dislocations. It is concluded that IGMA analysis can be effectively used to study and geometrically classify kink bands.
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Research context

OpenAlex topics: Magnesium Alloys: Properties and Applications · Corrosion Behavior and Inhibition · Advanced Welding Techniques Analysis

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

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