A perceptive comparison of the cellular automata and Monte Carlo techniques in application to static recrystallization modeling in polycrystalline materials

• Published in: Computational materials science Vol. 67; pp. 156 - 173
• Main Authors: Sieradzki, Lukasz, Madej, Lukasz
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2013; Elsevier
• Subjects: Cellular automata; Cold working, work hardening; annealing, quenching, tempering, recovery, and recrystallization; textures; Comparison; Cross-disciplinary physics: materials science; rheology; Exact sciences and technology; Materials science; Monte Carlo; Physics; Polycrystalline materials; Static recrystallization; Treatment of materials and its effects on microstructure and properties; Cellular automata; Monte Carlo; Comparison; Polycrystalline materials; Static recrystallization; Annealing; Grain growth; Polycrystals; Two dimensional model; Numerical method; Monte Carlo methods; Recrystallization; Microstructure
• ISSN: 0927-0256; 1879-0801
• DOI: 10.1016/j.commatsci.2012.08.047

► Both models CA and MC are capable of reproducing the SRX kinetic correctly. ► The most important parameter of the Monte Carlo model is the H/J ratio. ► H/J is related to state of the material but does not have physical meaning. ► Contrary to MC method the CA model is based on parameters with physical meaning. ► Contrary to MC method in the CA approach time step is related to real time. A perceptive comparison of two numerical methods that are commonly used in explicit modeling of microstructure evolution under thermo-mechanical conditions is the subject of the present work. Discrete cellular automata and Monte Carlo models of the static recrystallization were developed and compared within the paper. The knowledge regarding the strong and weak sides of these numerical techniques is highlighted. Particular attention is put on models accuracy in appropriate prediction of micro scale phenomena associated with physical basis of the static recrystallization. Technical details of those models as well as their predictive capabilities are discussed.