Kanapy: A Python package for generating complex synthetic polycrystalline microstructures

Prasad, Mahesh R. G.; Vajragupta, Napat; Hartmaier, Alexander

doi:10.21105/joss.01732

Cited by 20 publications

(15 citation statements)

References 5 publications

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“…Thus, the method is able to handle a large number of collision checks in an efficient way. For a more detailed description regarding the collision detection scheme and its implementation, refer to Prasad et al [28] The user-defined simulation box size and the ellipsoid size distribution are used for creating the simulation box and ellipsoids. The simulation begins by randomly placing ellipsoids of null volume inside the box, and each ellipsoid is given a random velocity vector for movement.…”

Section: Rve Generationmentioning

confidence: 99%

“…Furthermore, the two directions orthogonal to the BD are referred to as normal (ND) and transverse (TD) directions, respectively. To construct an RVE that consists of these complexly shaped grains, the synthetic microstructure generation tool Kanapy [ 28 ] is used. Kanapy approximates grains as ellipsoidal particles following a particular size distribution and packs them into a pre‐defined simulation box representing the RVE.…”

Section: Micromechanical Modelingmentioning

confidence: 99%

Section: Micromechanical Modelingmentioning

confidence: 99%

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Influence of Pore Characteristics on Anisotropic Mechanical Behavior of Laser Powder Bed Fusion–Manufactured Metal by Micromechanical Modeling

et al. 2020

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In recent times, additive manufacturing (AM) has proven to be an indispensable technique for processing complex 3D parts because of the versatility and ease of fabrication it offers. However, the generated microstructures show a high degree of complexity due to the complex solidification process of the melt pool. In this study, micromechanical modeling is applied to gain deeper insight into the influence of defects on plasticity and damage of 316L stainless steel specimens produced by a laser powder bed fusion (L‐PBF) process. With the statistical data obtained from microstructure characterization, the complex AM microstructures are modeled by a synthetic microstructure generation tool. A damage model in combination with an element deletion technique is implemented into a nonlocal crystal plasticity model to describe anisotropic mechanical behavior, including damage evolution. The element deletion technique is applied to effectively model the growth and coalescence of microstructural pores as described by a damage parameter. Numerical simulations show that the shape of the pores not only affects the yielding and hardening behavior but also influences the porosity evolution itself.

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Section: Rve Generationmentioning

confidence: 99%

Section: Micromechanical Modelingmentioning

confidence: 99%

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Influence of Pore Characteristics on Anisotropic Mechanical Behavior of Laser Powder Bed Fusion–Manufactured Metal by Micromechanical Modeling

et al. 2020

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“…To model the complexly shaped morphologies of the grains, as observed in the EBSD map of Figure 1, the in-house tool for generating synthetic microstructures, Kanapy [22] was used. From EBSD characterization, the equivalent diameter, the aspect ratio, and the tilt angle of the grains are extracted and used as input for RVE generation.…”

Section: Microstructure Generator: Kanapymentioning

confidence: 99%

“…For a more detailed description regarding the collision detection scheme and its implementation, kindly refer to the study by Prasad et al [22] The packing simulation terminates itself once the ellipsoidal particles are tightly packed with minimum acceptable overlaps. As the ellipsoidal particles are defined by their major and their minor diameters, classical Voronoi tessellation cannot be used here.…”

Section: Microstructure Generator: Kanapymentioning

confidence: 99%

Effect of Grain Statistics on Micromechanical Modeling: The Example of Additively Manufactured Materials Examined by Electron Backscatter Diffraction

et al. 2020

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Micromechanical modeling is one of the prominent numerical tools for the prediction of mechanical properties and the understanding of deformation mechanisms of metals. As input parameters, it uses data obtained from microstructure characterization techniques, among which the electron backscatter diffraction (EBSD) technique allows us to understand the nature of microstructural features, that are usually described by statistics. Because of these advantages, the EBSD dataset is widely used for synthetic microstructure generation. However, for the statistical description of microstructural features, the population of input data must be considered. Preferably, the EBSD measurement area must be sufficiently large to cover an adequate number of grains. However, a comprehensive study of this measurement area with a crystal plasticity finite element method (CPFEM) framework is still missing although it would considerably facilitate information exchange between experimentalists and simulation experts. Herein, the influence of the EBSD measurement area and the number of grains on the statistical description of the microstructural features and studying the corresponding micromechanical simulation results for 316L stainless steel samples produced by selective laser melting is investigated.

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Experimental Assessment and Micromechanical Modeling of Additively Manufactured Austenitic Steels under Cyclic Loading

et al. 2023

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The present work deals with the cyclic deformation behavior of additively manufactured austenitic stainless steel 316L. Since fatigue experiments are complex and time‐consuming, it is important to develop accurate numerical models to predict cyclic plastic deformation and extrapolate the limited experimental results into a wider range of conditions, considering also the microstructures obtained by additive manufacturing. Herein, specimens of 316L steel are produced by powder bed fusion of metals with laser beams (PBF‐LB/M) with different parameters, and cyclic strain tests are performed to assess their deformation behavior under cyclic loads at room temperature. Additionally, a micromechanical model is set up, based on representative volume elements (RVE) mimicking the microstructure of the experimentally tested material that is characterized by electron backscatter diffraction (EBSD) analysis. With the help of these RVEs, the deformation‐dependent internal stresses within the microstructure can be simulated in a realistic manner. The additively manufactured specimens are produced with their loading axis either parallel or perpendicular to the building direction, and the resulting anisotropic behavior under cyclic straining is investigated. Results highlight significant effects of specimen orientation and crystallographic texture and only a minor influence of grain shape on cyclic behavior.

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Kanapy: A Python package for generating complex synthetic polycrystalline microstructures

Cited by 20 publications

References 5 publications

Influence of Pore Characteristics on Anisotropic Mechanical Behavior of Laser Powder Bed Fusion–Manufactured Metal by Micromechanical Modeling

Influence of Pore Characteristics on Anisotropic Mechanical Behavior of Laser Powder Bed Fusion–Manufactured Metal by Micromechanical Modeling

Effect of Grain Statistics on Micromechanical Modeling: The Example of Additively Manufactured Materials Examined by Electron Backscatter Diffraction

Experimental Assessment and Micromechanical Modeling of Additively Manufactured Austenitic Steels under Cyclic Loading

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