High- vs. low-fidelity models for dynamic recrystallization in copper

Tutcuoglu, Abbas D.; Hollenweger, Yannick; Stoy, A.; Kochmann, Dennis M.

doi:10.1016/j.mtla.2019.100411

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…To this end, new methods [18,2] are being developed for operator learning, which aims to learn the mapping in the functional space and avoid mesh dependence. In computational solid mechanics, currently both PINNs and operator learning methods have been applied only to simple PDEs in regular domains but are promising for future application to complex material systems such as those with sharp discontinuities and localization, multiscale metallic systems, or loss of uniqueness in the solutions (e.g., see [16,17,27]).…”

Section: Learning To Solve Pdesmentioning

confidence: 99%

What machine learning can do for computational solid mechanics

Kumar,

Kochmann

2021

Preprint

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Section: Learning To Solve Pdesmentioning

confidence: 99%

What machine learning can do for computational solid mechanics

Kumar,

Kochmann

2021

Preprint

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“…The evolution of the grain microstructure is then employed by Monte Carlo sampling of the lattice sites, where a change to the state of a site is accepted or rejected based on a switching probability [299], [300]. Even though the MC Potts approach to model recrystallization is frequently used due to its versatility and flexibility to represent many different physical features of the recrystallization process (such as overall texture evolution [320]), until very recently [321], this methodology excluded the grain-scale micromechanical effects on recrystallization process in a full-field sense. Such effects have mostly been incorporated using crystal plasticity models coupled with either phase-field equations or the cellular automata approach.…”

Section: Modeling Recrystallizationmentioning

confidence: 99%

Survey of Modeling and Simulation Techniques for Advanced Manufacturing Technologies Volume I – Predicting Initial Microstructures

Nicolas

Chakraborty

Paulson

et al. 2020

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This report summarizes the current state of modeling and simulation methods for predicting the initial structure and properties of material in components assembled using advanced manufacturing technologies (AMTs). The report is the first volume in a two-volume series. This first volume focuses on predicting initial microstructures of AM material. The second volume discusses predicting material properties given the initial microstructure. The focus is on technologies of particular relevance to the design and manufacture of nuclear reactor components. The purpose of the report is to help develop the technical knowledge base to support regulatory decisions that will be needed to assess nuclear components manufactured with AMTs as these components are installed at nuclear power plants (NPPs). The report develops a list of AMTs of particular interest to the NRC and summarizes the key microstructural features and corresponding processing parameters relevant to each technology. Further sections describe available physically-based and data-driven prediction methods and survey widely available software tools. The report concludes with a summary of gaps that may be of particular interest to the NRC when evaluating modeling and simulation methods for AMTs. v

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“…Following the classification provided in [22,23], grain refinement models can be classified into three main types: continuum models based on the macrophenomenological approach [24][25][26][27]; physically-based continuum models [28][29][30][31][32][33][34][35]; and multilevel crystal plasticity models. Recent decades have been marked by the appearance of some modifications of statistical [36][37][38][39][40][41], self-consistent [42][43][44][45][46][47] and direct crystal plasticity CMs [48][49][50][51][52][53], designed to take grain refinement into consideration. Separately, let us note that to describe the processes in question at the microlevel, a popular approach is modeling using the method of molecular dynamics [54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Advanced Statistical Crystal Plasticity Model: Description of Copper Grain Structure Refinement during Equal Channel Angular Pressing

Romanov

Shveykin

Трусов

2023

Metals

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The grain structure of metals changes significantly during severe plastic deformation (SPD), and grain refinement is the main process associated with SPD at low homologous temperatures. Products made of ultrafine-grained materials exhibit improved performance characteristics and are of considerable industrial interest, which generates a need for the creation of comprehensive grain refinement models. This paper considers the integration of the ETMB (Y. Estrin, L.S. Toth, A. Molinari, Y. Brechet) model, which describes the evolution of an average cell size during deformation into the two-level statistical crystal plasticity constitutive model (CM) of FCC polycrystals. The original relations of the ETMB model and some of its modifications known from the literature were analyzed to obtain an accurate, physically admissible description of the grain refinement process. The characteristics of the grain substructure determined with the framework of the advanced ETMB model were taken into account in the CM in a hardening formula. By applying the CM with the integrated ETMB model, numerical experiments were performed to simulate the changes in the grain structure of copper during equal channel angular pressing (ECAP) at room temperature. The results obtained are in good agreement with the experimental data. The ideas about further development of the proposed model are outlined.

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High- vs. low-fidelity models for dynamic recrystallization in copper

Cited by 7 publications

References 42 publications

What machine learning can do for computational solid mechanics

What machine learning can do for computational solid mechanics

Survey of Modeling and Simulation Techniques for Advanced Manufacturing Technologies Volume I – Predicting Initial Microstructures

Advanced Statistical Crystal Plasticity Model: Description of Copper Grain Structure Refinement during Equal Channel Angular Pressing

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