Journal of Materials Science

, Volume 49, Issue 22, pp 7697–7706 | Cite as

Texture evolution and probability distribution of Goss orientation in magnetostrictive Fe–Ga alloy sheets

  • Suok-Min Na
  • Alison B. Flatau


Texture evolution and the distribution of Goss orientation in polycrystalline Fe–Ga alloy were investigated as a series of rolling and subsequent annealing processes were used to develop highly textured rolled sheet. A dramatic change from the random nature of the as-rolled and primary recrystallized texture is observed when careful control of atmosphere and temperature during anneal leads to development of a sharp Goss orientation over up to 98 % of the surface of a sample during secondary recrystallization. In this work, grain boundary properties in local areas surrounding Goss grains are investigated and the evolution of Goss orientation is traced through the different stages of alloy processing using electron backscatter diffraction analysis. To evaluate the evolution of grains with Goss orientation, {011} grains are selected and separated from other texture components at each processing step and statistical analysis used to correlate the structural inheritance chain of Goss-oriented grains. The four processing stages considered are the alloy after hot rolling, the as-rolled alloy (i.e., after subsequent warm and cold rolling), the alloy after an initial anneal during which primary recrystallization occurs, and the alloy after final anneals in which secondary recrystallization with abnormal grain growth occurs. Analysis of Goss grain orientation probability distribution functions after primary and secondary recrystallization convincingly demonstrates that the orientation of the abnormally grown Goss texture that develops during secondary recrystallization is determined by the orientation of Goss components that develop during the primary recrystallization stage of alloy processing.


Grain Boundary Misorientation Angle Coincidence Site Lattice Secondary Recrystallization Primary Recrystallization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by ONR MURI Grant No. N000140610530.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Aerospace EngineeringUniversity of MarylandCollege ParkUSA

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