On the basic mechanism of grain growth: the role of texture on grain boundary migration rate

Abstract

In this paper we will review some of the many achievements of Statistical Theory of Grain Growth (which is 40 years old, this year!), based on the classic law of grain growth. For instance, we will show that grain growth in presence of texture (which is the usual feature in a real microstructure) is not exclusively depending on the curvature of the grain boundary, as in this case the effect of curvature at the vertexes (in 2-D case) contributes as well (which, depending on the texture pattern, sometimes may be more relevant than curvature along the boundary). Such phenomenon requires a reformulation of Von Neuman’s equation in 2-D by involving, beside the different boundary mobilities, the different energies of the “third” grain boundary, which is pulling the vertex and inducing an extra curvature which is not balanced along the grain perimeter (for simplicity’s sake we are referring to a 2-D case, but the analysis can also be done in 3-D). We can then observe that boundary movement is not only depending on a couple of grains in contact (with the boundary curvature acting like in a bicrystal) but also includes the network of “third” grains operating at the vertexes. The overall consequence is that the growth kinetics and the grain size distribution shape are unique and variable for each texture component which contradicts classic growth laws as well as the expected results for the whole grain size distribution.

Large-Scale Multi-Phase-Field Simulation of 2D Subgrain Growth

“…The subject of grain growth has been studied using both analytical (e.g., [21][22][23][24]) and full-field numerical approaches (e.g., [25][26][27][28]). Generally, the analytical approaches average the microstructure based on the statistical information from microstructural features, while the full-field approaches explicitly describe the microstructure based on its building blocks, such as grains and their interfaces, which allows for more sophisticated assumptions and more accurate predictions.…”