2001
DOI: 10.1016/s1359-6454(01)00207-5
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On misorientation distribution evolution during anisotropic grain growth

Abstract: In order to study the development of texture and boundary character during annealing, threedimensional grain crystallography and crystallographically mediated grain boundary properties were incoporated into a finite tempcrnture Monte Carlo model for grain growth. Randomly textured microstructures evolve nonnally, with growth exponent n = 0.96. While texture remains random, the steady-state boundary misorientation distribution favors low-angle boundaries. To first order, low-angle boundaries increase by lengthe… Show more

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Cited by 220 publications
(147 citation statements)
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“…It builds on the distance function-based diffusion-generated motion (DFDGM) approach [7], which was used in earlier work [4,5] to simulate long time isotropic (equal surface energy) grain growth with very large numbers of grains. The initial simulations presented demonstrate this method recovers previous results using KMC simulations [9,12,13] and front tracking [15,2]. Subsequent simulations demonstrate the importance of the surface energy-to-misorientation map in predicting the time evolution of the MDF and suggests a framework capable of explaining the different MDFs obtained in the fiber texture and random texture cases.…”
Section: Introductionsupporting
confidence: 73%
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“…It builds on the distance function-based diffusion-generated motion (DFDGM) approach [7], which was used in earlier work [4,5] to simulate long time isotropic (equal surface energy) grain growth with very large numbers of grains. The initial simulations presented demonstrate this method recovers previous results using KMC simulations [9,12,13] and front tracking [15,2]. Subsequent simulations demonstrate the importance of the surface energy-to-misorientation map in predicting the time evolution of the MDF and suggests a framework capable of explaining the different MDFs obtained in the fiber texture and random texture cases.…”
Section: Introductionsupporting
confidence: 73%
“…They observed that the MDF evolved into a steady state quite close to the initial Mackenzie distribution, characterized by a slight enhancement of the low misorientation boundaries. More recently, Gruber et al [9] carried out larger KMC simulations, both in two and three dimensions, that helped remove the statistical uncertainties in some of the results of [12,13]. Their initial conditions contained over 75,000 well-resolved grains in two dimensions and they obtained essentially the same results as found by Holm et al [12].…”
Section: Introductionmentioning
confidence: 85%
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