Grain Boundaries, Interfaces, and Surfaces: Fundamental Structure-Property-Performance Relationships: Grain Growth
Sponsored by: ACerS Basic Science Division
Program Organizers: Shen Dillon, University of California, Irvine; Wolfgang Rheinheimer, University of Stuttgart; Catherine Bishop, University of Canterbury; Ming Tang, Rice University; John Blendell, Purdue University; Wayne Kaplan, Technion - Israel Institute Of Technology; Melissa Santala, Oregon State University
Wednesday 8:00 AM
October 12, 2022
Room: 323
Location: David L. Lawrence Convention Center
Session Chair: Shen Dillon, University of California, Irvine; Amanda Krause, Carnegie Mellon University
8:00 AM Cancelled
Understanding the Microstructure and Migration Mechanisms of Terrace-defect Interfaces Using Multiscale Characterizing Methods: Jian Song1; Yue Liu1; Jian Wang2; 1Shanghai Jiao Tong University; 2University of Nebraska-Lincoln
Terrace‑defect interface (TDI) is generally defined as coherent interface with terraced defect structures, accompanied elastic strain associated with dislocation (b) and step height (h) characters, arises in both homogeneous and heterogeneous structures. Basic knowledge of the interface structure, elastic strain and migration mechanisms of TDI should help us to develop materials with desired properties and microstructures. Here, using hexagonal packed metal as a model material and combining with multiscale characterizing methods, we first modified the peak-pair algorithm to extract the local elastic strain field associated with terrace defects in deformation twins. Then, the anisotropic propagation of deformation twin is attributed to structural differences of TDIs based on the three-dimensional interface characterization. Moreover, by using in-situ straining method inside the high-resolution transmission electron microscopy (HRTEM) we also identified the interface structure evolution associated with TDI migration at atomic scale. This work can improve our interpretation on TDI related materials development.
8:20 AM
In-situ TEM Observation on the Two Distinct Shear-coupled Migration Behaviors of One Mixed Grain Boundary: Zhengwu Fang1; Scott Mao1; Guofeng Wang1; 1University of Pittsburgh
Shear-coupled GB migration has received extensive attention in the past decades. However, the atomistic mechanisms of the shear-coupled migration of general mixed tilt-twist grain boundary have remained largely elusive to date. Here, by employing the in-situ transmission electron microscopy study, we have observed two distinct migration behaviors during the shear-coupled migration of a ⟨001⟩{200}⁄⟨01 ̅1⟩{1 ̅11} mixed tilt-twist grain boundary (GB) in the Au nano bi-crystal. The underlying atomistic mechanism is attributed to the activation of GB disconnections carrying Burgers vectors with different magnitudes and opposite directions. Two different lattice correspondence transformation relations are identified, which are attributed to the structural similarity of these atomic planes. This work provides direct experimental evidence on GB disconnections mediating the shear-coupled migration of mixed tilt-twist GB migration, and the uncovered two novel lattice transformation relations may have some implications for the development of theoretical models to predict the shear-coupled migration of general GBs.
8:40 AM Cancelled
Migration Kinetics of Twinning Disconnections in Nanotwinned Cu: An In Situ HRTEM Deformation Study: Quan Li1; Yue Liu1; XiaoQin Zeng1; 1Shanghai Jiao Tong University
Growth or annealing twins normally form rectangular shape with abundant incoherent twin boundaries (ITBs), while deformation twins exhibit lenticular shape that composed of serrated coherent twin boundaries (SCTBs). Using in situ high-resolution TEM deformation technique, here we report the formation-mechanism differences between 1-layer or 2-layer serration/facet and {112} ITBs. The atomic resolution microscopy results of nanotwinned Cu manifested the 1-layer and 2-layer serration/facet formation on CTBs by interaction of mixed partial dislocations, via migration of twinning disconnections (TDs) along shearing direction. In comparison, periodic 3-layered TDs or ITBs structure were normally formed by annealing or growth process introduced spontaneous directionless partial dislocations.
9:00 AM Invited
Elucidating the Role of Grain Boundary Networks on Grain Growth in Textured Alumina: Bryan Conry1; Joel Harley1; Michael Tonks1; Michael Kesler2; Amanda Krause3; 1University of Florida; 2Oak Ridge National Laboratory; 3Carnegie Mellon University
Experimental observations of microstructure evolution often deviate from normal grain growth predictions. These deviations are often attributed to a particular grain boundary (GB) type. However, grain growth is the collective motion of many GBs, suggesting that the GB connectivity or network plays an important role in the microstructure evolution. Thermomagnetic processing is an opportunity to create different GB networks by inducing crystallographic texture. Here, we compare the grain growth behavior of textured and untextured calcia-doped alumina to evaluate the influence of the GB network on microstructure evolution. The textured alumina is prepared by slip casting in a magnetic field (9T). After heat treatments at 1600°C, electron backscattered diffraction and atomic force microscopy were used to characterize the microstructures and grain boundary energy distributions, respectively. The implications of crystallographic texture and grain boundary energy distribution on the evolution of grain size and morphology will be discussed.
9:30 AM
Simulate Grain Growth with Machine Learning Techniques: Shaoxun Fan1; Ming Tang1; Fei Zhou2; 1Rice University; 2Lawrence Livermore National Lab
Grain growth is a type of microstructure evolution important for many engineering materials. Grain growth is traditionally simulated by phase-field, surface evolver and lattice Monte Carlo models. Here we demonstrate the ability of machine learning (ML) techniques to learn the grain growth rules and predict the phenomenon in both 2D and 3D. Two types of ML models based on convolutional recurrent and graphic neural networks are trained on short image sequences generated by phase-field simulation. Properly trained ML models can extend predictions in temporal and spatial domains by >10 times of the training dataset. In addition to very good pixelwise agreement at short times, the models accurately capture the grain growth statistics in the long term. Compared with phase-field models, ML models accelerate simulations by 2-3 orders of magnitude, and can also forecast the evolution of systems with unknown parameters.
10:00 AM Break
10:20 AM
Suppression of Abnormal Grain Growth in Alumina by Grain Boundary Engineering: Bryan Conry1; Joel Harley1; Michael Tonks1; Michael Kesler2; Amanda Krause3; 1University of Florida; 2Oak Ridge National Laboratory; 3Carnegie Mellon University
Contemporary grain growth models have yet to account for the true relationship between grain boundary character and mobility/energy anisotropy. As a result, unique grain growth phenomena in many material systems remain poorly understood – one example being anisomorphic abnormal grain growth. One method to explore this problem is grain boundary engineering (GBE) of a material system known to exhibit these phenomena. Here, we employ thermomagnetic processing to strongly texture the microstructure of asymmetric Ca-doped Al2O3 by slip casting under a 9T magnetic field. The textured alumina, along with an untextured analog, was heat treated at 1600°C for 64 hours to investigate differences in grain growth behavior. Atomic force microscopy and electron microscopy techniques are used to identify the energy and character, respectively, of normal and abnormal grain boundaries in the two systems. Implications of texture and resulting grain boundary character on abnormal grain growth behavior will be discussed.
10:40 AM Invited
Learning the Grain Boundary Solute Drag Hypersurface: Fadi Abdeljawad1; Malek Alkayyali1; 1Clemson University
Even minute amounts of dopants or impurities at grain boundaries (GB) result in profound changes to GB dynamics. GB segregation has been the subject of active research efforts; however, dynamic solute drag has not been systematically explored. The challenge here is that GB solute drag greatly depends on several thermodynamic properties of the alloy and kinetic processes, including GB solute diffusion, mobility, and migration velocity−solute drag is a hypersurface. Based on recently developed theoretical and machine learning models, we explore GB solute drag in regular solution alloys. The governing equation describing solute drag is solved numerically to generate data to train, test, and validate a neural network model, which is then used to establish the solute drag hypersurface. Our results reveal a plethora of solute drag trends that are used to explain experimental observations of sluggish grain coarsening in a wide range of alloys.
11:10 AM
Changes in the Energy of the Grain Boundary Network during Grain Growth in Polycrystals: Zipeng Xu1; Robert Suter1; Gregory Rohrer1; 1Carnegie Mellon University
In polycrystals, each grain boundary is connected to several neighboring grain boundaries within an interfacial network. Therefore, when grain boundaries migrate, there are changes in the areas and orientations of the connected boundaries. In this talk, based on high energy diffraction microscopy measurement of a large population of grain boundary faces (~ 60000) within three-dimensional polycrystals (α-Fe and Ni) evolving during grain growth, several grain boundary properties (migration velocity, curvature, energy) are described as a function of all five crystallographic parameters. Using experimentally measured grain boundary energies, together with functions interpolated from computed grain boundary energies, we examined how the grain boundary energies change as the grain boundary network evolves. We find that the areas of low energy grain boundaries tend to increase while the areas of high energy boundaries tend to decrease during grain growth. The observations imply that grain boundary energy anisotropy influences grain boundary migration.