Monte Carlo Simulation of Texture and Microstructure Transformation during Annealing of Steel

Hua Long Li; Jong Tae Park; Jerzy A. Szpunar

doi:10.4028/www.scientific.net/SSP.129.83

Paper Titles

Cluster Dynamics Modeling of Materials: Advantages and Limitations
p.51

Interdiffusion of Two L1₀Phases without Long-Range Order Decrease: Experiments and Molecular Dynamics Simulations
p.59

Orientation of Interstitials in Clusters in α-Fe: A Comparison between Empirical Potentials
p.67

Anisotropy of the Vacancy Migration in Ti, Zr and Hf Hexagonal Close-Packed Metals from First Principles
p.75

Monte Carlo Simulation of Texture and Microstructure Transformation during Annealing of Steel
p.83

A Phase Field Model for grain Growth and Thermal Grooving in Thin Films with Orientation Dependent Surface Energy
p.89

An Extensive Study of Charge Effects in Silicon Doped Heterofullerenes
p.95

Interface Shape Change and Shift Kinetics on the Nanoscale
p.105

Analysis of Oxygen Segregation at Metal-Oxide Interfaces Using a New Lattice Monte Carlo Method
p.111

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Monte Carlo Simulation of Texture and Microstructure Transformation during Annealing of Steel

Abstract:

Controlling texture and microstructure evolution during annealing processes is very important for optimizing properties of steels. Theories used to explain annealing processes are complicated and always case dependent. An recently developed Monte Carlo simulation based model offers an effective tool for studying annealing process and can be used to verify the arbitrarily defined theories that govern such processes. The computer model takes Orientation Image Microscope (OIM) measurements as an input. The abundant information contained in OIM measurement allows the computer model to incorporate many structural characteristics of polycrystalline materials such as, texture, grain boundary character, grain shape and size, phase composition, chemical composition, stored elastic energy, and the residual stress. The outputs include various texture functions, grain boundary and grain size statistics that can be verified by experimental results. Graphical representation allows us to perform virtual experiments to monitor each step of the structural transformation. An example of applying this simulation to Si steel is given.