A new perspective on the normal grain growth exponent obtained in two-dimensional Monte Carlo simulations

Conventional Monte Carlo Potts models were reported to produce a significantly lower normal grain growth exponent of n ≈ 0.41 in small grain size regimes. In this paper, it is shown that a three-parameter nonlinear regression analysis should be employed in order to obtain more accurate values for the grain growth exponents from the simulation data. To some degree, the lower grain growth exponent previously reported can also be attributed to the strong random nature of the simulation algorithm.