Integrated crystal plasticity and phase field model for prediction of recrystallization texture and anisotropic mechanical properties of cold-rolled ultra-low carbon steels

Min, Kyung Mun; Jeong, Wonseok; Hong, Seung‐Hyun; Lee, C.A.; Cha, Pil-Ryung; Han, Heung Nam; Lee, M.-G.

doi:10.1016/j.ijplas.2019.102644

Cited by 47 publications

(23 citation statements)

References 73 publications

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“…Models based on the Taylor-Bishop-Hill theory are usually chosen due to its extreme simplicity (and computational performance) [27,28]. When further accuracy is necessary, or in order to include further microstructural aspects in the simulation, more advanced crystal plasticity models are employed [29][30][31].…”

Section: Modeling Of Formability Propertiesmentioning

confidence: 99%

Advanced Crystal Plasticity Modeling of Multi-Phase Steels: Work-Hardening, Strain Rate Sensitivity and Formability

et al. 2021

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This work presents an advanced crystal plasticity model for the simulation of the mechanical behavior of multiphase advanced high-strength steels. The model is based on the Visco-Plastic Self-Consistent (VPSC) model and uses information about the material’s crystallographic texture and grain morphology together with a grain constitutive law. The law used here, based on the work of Pantleon, considers how dislocations are created and annihilated, as well as how they interact with obstacles such as grain boundaries and inclusions (carbides). Additionally, strain rate sensitivity is implemented using a phenomenological expression derived from literature data that does not require any fitting parameter. The model is applied to the study of two bainitic steels obtained by applying different heat treatments. After fitting the required parameters using tensile experiments in different directions at quasi-static and high strain rates, formability properties are determined using the model for the performance of virtual experiments: uniaxial tests are used to determine r-values and stress levels and biaxial tests are used for the calculation of yield surfaces and forming limit curves.

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Section: Modeling Of Formability Propertiesmentioning

confidence: 99%

Advanced Crystal Plasticity Modeling of Multi-Phase Steels: Work-Hardening, Strain Rate Sensitivity and Formability

et al. 2021

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“…These data describe significant morphological heterogeneities of the microstructure, but their incorporation into the numerical simulation requires substantial computing spaces and entails further limitations related to the simulation time. Numerical approaches capable of handling such 3D data during the modeling stage are called full-field models and are, most of the time, based on phase field [ 12 ], vertex [ 13 ], level set [ 3 ], Monte Carlo [ 14 ], or cellular automata [ 15 ] methods. The latter is often used in numerical model microstructure evolution during recrystallization that occurs under or after the deformation [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Scaling Scientific Cellular Automata Microstructure Evolution Model of Static Recrystallization toward Practical Industrial Calculations

2021

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An attempt to bridge the gap between capabilities offered by advanced full-field microstructure evolution models based on the cellular automata method and their practical applications to daily industrial technology design was the goal of the work. High-performance parallelization techniques applied to the cellular automata static recrystallization (CA-SRX) model were selected as a case study. Basic assumptions of the CA-SRX model and developed modifications allowing high-performance computing are presented within the paper. Particular attention is placed on the development of the parallel computation scheme allowing numerical simulations even for a large volume of material. The development of new approaches to handle communication within the distributed environment is also addressed in the paper as a means to obtain higher computational efficiency. Evaluation of model limits was based on the scalability analysis. The investigation was carried out for the 3D and 2D case studies. Therefore, the complex static recrystallization cellular automata simulation taking into account the influence of recovery, nucleation based on accumulated energy, and the progress of recrystallization as a function of stored energy and grain boundary mobility with high-performance computing capabilities is now possible. The research highlighted that parallelization is more effective with an increasing number of cellular automata cells processed during the entire simulation. It was also proven that the developed parallelization scheme and communication mechanism provides a possibility of obtaining scaled speedup over 700 times for 2D and over 800 times for 3D computational domains, which is crucial for future applications in industrial practice. Therefore, the presented approach’s main advantage is based on the possibility of running the calculation based on input data obtained directly from high-resolution 3D imaging of the microstructure. With that, the full immersion of the experimental results into the numerical model is possible. The second novelty aspect of this work is related to the identification of the quality of model predictions as a function of model size reductions.

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“…That creates new and often unrecognized opportunities for materials science applications that complement and extend experimental investigations. Designing virtual/digital shadows [10] that accurately describe materials concerning their actual microstructure with grains, phases, defects, or even atoms is now a broad and multidisciplinary research area [11][12][13][14]. With such digital engineering tools, scientists can analyse all microstructure components that are often invisible during experimental observations of processing and exploitation conditions.…”

Section: Introductionmentioning

confidence: 99%

Capturing Local Material Heterogeneities in Numerical Modelling of Microstructure Evolution

Madej¹,

Sitko²,

Fular³

et al. 2021

Journal of Machine Engineering

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The work focuses on developing the complex digital shadow of the metallic material microstructure that can predict its evolution during metal forming operations. Therefore, such a digital shadow has to consider all major physical mechanisms influencing the particular investigated phenomenon. The motivation for the work is directly related to the development of modern metallic materials, often of multiphase nature. Such microstructure types lead to local heterogeneities influencing material behaviour and eventually macroscopic properties of the final product. The concept of the digital material shadow, stages of the model development, and examples of practical applications to simulation of microstructure evolution are presented within the work. Capturing local heterogeneities that have a physical origin and eliminating numerical artefacts is particularly addressed. Obtained results demonstrate the capabilities of such a digital microstructure shadow approach in the numerical design of final product properties.

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Integrated crystal plasticity and phase field model for prediction of recrystallization texture and anisotropic mechanical properties of cold-rolled ultra-low carbon steels

Cited by 47 publications

References 73 publications

Advanced Crystal Plasticity Modeling of Multi-Phase Steels: Work-Hardening, Strain Rate Sensitivity and Formability

Advanced Crystal Plasticity Modeling of Multi-Phase Steels: Work-Hardening, Strain Rate Sensitivity and Formability

Scaling Scientific Cellular Automata Microstructure Evolution Model of Static Recrystallization toward Practical Industrial Calculations

Capturing Local Material Heterogeneities in Numerical Modelling of Microstructure Evolution

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