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Series: ASM Handbook
Volume: 22A
Publisher: ASM International
Published: 01 December 2009
DOI: 10.31399/asm.hb.v22a.a0005428
EISBN: 978-1-62708-196-2
...Abstract Abstract The misorientation of a boundary of a growing grain is defined not only by its crystallography but also by the crystallography of the grain into which it is growing. This article focuses on the Monte Carlo Potts model that is typically used to model grain growth, Zener-Smith...
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Published: 01 December 1998
Fig. 2 Monte Carlo simulations of the interaction volume of a 20 keV primary electron beam in an iron sample. (a) Electron trajectories. (b) Sites of K-shell ionizations and production of characteristic x-rays. Source: Ref 1 More
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Published: 31 October 2011
Fig. 2 Monte Carlo simulations of primary (blue, or gray in grayscale image) and backscattered (red, or black in grayscale image) electron beam paths for 30 keV (top) and 10 keV (bottom) beams of 100 nm (left) and 1 µm (right) diameter in pure nickel. Five hundred electron trajectories have been More
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Published: 01 January 2005
Fig. 5 Monte Carlo (three-dimensional) model predictions of (a, b, and c) grain structure (two-dimensional) sections after 1000 Monte-Carlo Steps and (d) grain-growth behavior for materials with various starting textures and assumed grain-boundary properties. (a) Case A, isotropic starting More
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Published: 01 January 2005
Fig. 12 Monte Carlo (3D) model predictions of (a, b, c) grain structure (2D sections after 1000 MC steps) and (d) grain-growth behavior for materials with various starting textures and assumed grain-boundary properties. (a) Case A, isotropic starting texture and isotropic boundary properties More
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Published: 01 December 2009
Fig. 5 Monte Carlo model simulation of texture-controlled grain growth for a material with two texture components. (a) Comparison of predicted grain-growth kinetics (solid line) and normal grain-growth kinetics (broken line). MU, model lattice units; MCS, Monte Carlo steps. (b) Simulated (100 More
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Published: 01 December 2009
Fig. 6 Monte Carlo model predictions of the grain-size distributions after 15 Monte Carlo steps for the simulation of texture-controlled grain growth in a material with two texture components. (a) For the entire material. (b) For the grains belonging to texture component “A.” MU, model lattice More
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Published: 01 December 2009
Fig. 9 Monte Carlo simulation predictions of the effect of initial stored-energy distribution on recrystallization, assuming randomly oriented nuclei. (a) Recrystallized fraction X . (b) Corresponding Avrami plots ( Ref 34 ). The letters “A,” “B,” and “C” refer to the stored-energy distributions More
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Published: 01 December 2009
Fig. 10 Monte Carlo-predicted dependence of microstructure evolution. (a) Initially wrought material. (b) After 100 Monte Carlo steps (MCS), assuming identical nuclei orientations and a mobility of the special boundaries which was the same as that for nonspecial boundaries. (c) After 100 MCS More
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Published: 01 December 2009
Fig. 14 Monte Carlo predictions of the dependence on recrystallized fraction X of the average velocity ( V CH ) and total length per unit area ( L A ) of the recrystallization front and the rate of recrystallization (Δ X /ΔMCS). (a) Classical Johnson-Mehl-Avrami-Kolmogorov (JMAK) condition More
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Published: 30 August 2021
Fig. 17 (a) The probabilistic approach and (b) the Monte Carlo flow diagram used for a turbine blade More
Series: ASM Handbook
Volume: 4B
Publisher: ASM International
Published: 30 September 2014
DOI: 10.31399/asm.hb.v04b.a0005967
EISBN: 978-1-62708-166-5
Series: ASM Handbook Archive
Volume: 10
Publisher: ASM International
Published: 01 January 1986
DOI: 10.31399/asm.hb.v10.a0001774
EISBN: 978-1-62708-178-8
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Published: 01 December 2009
Fig. 15 Comparison of microstructure evolution during recrystallization of commercially pure titanium cold rolled to a 60% thickness reduction and then annealed at 600 °C (1110 °F). (a) Experimental observations. (b) Monte Carlo predictions. MCS, Monte Carlo steps. Source: Ref 42 More
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Published: 01 December 2009
Fig. 7 Predictions of the volume fraction (%) of the texture components and the grain size as a function of annealing time for a two-component initial texture. (a) A Monte Carlo method ( Ref 25 ) is used. MCS, Monte Carlo steps. (b) An analytical approach ( Ref 27 ). The labels for components More
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Published: 01 December 2009
Fig. 13 Static recrystallization behavior of commercially pure titanium rolled to a thickness reduction of 60% and annealed at 600 °C (1100 °F). (a) Measured recrystallization kinetics. (b) Corresponding experimental Avrami plot. (c) Predicted kinetics from a Monte Carlo simulation; see text More
Series: ASM Handbook
Volume: 9
Publisher: ASM International
Published: 01 December 2004
DOI: 10.31399/asm.hb.v09.a0003729
EISBN: 978-1-62708-177-1
... account of the general capabilities of the various models that can generate microstructure maps and thus transform the computer into a dynamic microscope. These include standard transport models, phase-field models, Monte Carlo models, and cellular automaton models. cellular automaton models...
Series: ASM Handbook
Volume: 11A
Publisher: ASM International
Published: 30 August 2021
DOI: 10.31399/asm.hb.v11A.a0006803
EISBN: 978-1-62708-329-4
... method, and the most frequently used simulation methods, standard Monte Carlo sampling, Latin hypercube sampling, and discrete probability distribution sampling. Further, the article discusses methods developed to analyze the results of probabilistic methods and covers the use of epistemic and aleatory...
Series: ASM Handbook Archive
Volume: 11
Publisher: ASM International
Published: 01 January 2002
DOI: 10.31399/asm.hb.v11.a0003514
EISBN: 978-1-62708-180-1
... method, the response surface method, and Monte Carlo sampling. A brief introduction to importance sampling, time-variant reliability, system reliability, and risk analysis and target reliabilities is also provided. The article examines the various application problems for which probabilistic analysis...
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Published: 01 January 2005
Fig. 10 Microstructural evolution during recrystallization simulated using a hybrid Monte Carlo-Potts cellular automaton model; the white grains are recrystallized. Source: Ref 23 More