Quantification of local boundary migration in 2D/3D

Abstract

With the development of advanced electron and X-ray microscopy techniques, the local boundary migration during recrystallization and grain growth can be followed in 2D at a sample surface and/or in 3D inside bulk samples during in/ex situ annealing. The results show that locally boundaries migrate in a much more complex way than commonly imagined, for example by the development of local protrusions and retrusions and by migrating in a stop-go type of fashion. A quantitative analysis of the local boundary migration is essential for understanding this heterogeneous process. In this paper, methods for quantifying local boundary migration are summarized, including methods for quantification of local boundary migration velocities, misorientation (angle/axis pair) and plane normal of migrating/non-migrating boundaries, driving force (from both the energy stored in the deformation matrix and local boundary curvature), boundary mobility and activation energy. Through a quantitative analysis of both experimental and simulation data, local boundary migration is rationalized in several material systems during recrystallization and grain growth.

Selective Growth of Recrystallizing Grains into Well-Characterized Deformed Aluminum at Hardness Indentation

“…In recent years, efforts have been devoted to following in/ex situ the local recrystallization process using either EM on the sample free surface [6][7][8][9] or non-destructive 3D X-ray microscopy in sub-surface/bulk regions of the material [10][11][12][13][14]. The results show evidently the complexity of the local heterogeneous boundary migration patterns, including the formation of protrusions/retrusions at the recrystallizing boundary front, a stop-go type of migration, and different growth along different sample macroscopic directions [10,15,16]. Quantitative analysis based on such 3D(x,y,t) and 4D(x,y,z,t) datasets indicates that locally the driving/dragging force from the curvature of the recrystallizing boundary can be comparable in magnitude to that from the stored energy in the deformed matrix, and that the local heterogeneities in the deformed microstructure (caused by the alignment of dislocation boundaries) are at least partly responsible for the growth heterogeneity of recrystallizing grains [13,17].…”

Unsupervised Deep Learning for Laboratory-Based Diffraction Contrast Tomography

“…Non-destructive 3D characterization of materials microstructures using methods at large international synchrotron facilities offers substantial advantages compared to conventional 2D methods. For metals and alloys in particular, the possibility to follow the microstructural evolution in the bulk has led to several major scientific breakthroughs [1][2][3][4][5]. However, to broaden the use of this novel and outstanding characterization possibility, there is a need for techniques that can operate in the home laboratories.…”