A method of step-by-step packing and its application in generating 3D microstructures of polycrystalline and composite materials

Романова, В. А.; Балохонов, Р. Р.

doi:10.1007/s00366-019-00820-2

Cited by 48 publications

(9 citation statements)

References 39 publications

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“…Being statistics/empirically guided, packing tools enable designing 3D microstructure models of variable complexity and with a wide variety of geometrical features (Figure 7d-f). The packing concept implies filling a discretised computational domain with microstructural elements in a stepwise manner following geometrically based algorithms which are specifically defined for the microstructure type to be generated [149]. Compared to the tessellation-based approaches, [146,147] and (c) IN718 [31], which were generated using Voronoi tesselation; (d) polycrystalline model of an LPBF AlSi10Mg alloy simulated by SSP [29]; (e) RVEs of LPBF Ti-6Al-4V containing prior β grains without and with lamellar martensite α' microstructure [36], and (f) 2D domains combining synthetic grain structures with pores obtained using image reconstruction [148].…”

Section: Geometrically Based Microstructure Modelling and Image Recon...mentioning

confidence: 99%

“…An efficient option is to generate synthetic microstructures that statistically match real PBF-produced ones by the geometrical characteristics of microstructural elements. The synthetic microstructure generation approaches include spatial tessellation methods (e.g., Voronoi tessellation [31,146]) and semi-analytical packing tools (e.g., the step-by-step packing (SSP) method [29,30,149] or the Dream.3D packing algorithm [150]). While these methods are not physically based, they can still be successfully utilised in micromechanical simulations of AM materials [29][30][31]146,151].…”

Section: Geometrically Based Microstructure Modelling and Image Recon...mentioning

confidence: 99%

Section: Geometrically Based Microstructure Modelling and Image Recon...mentioning

confidence: 99%

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A Review of Computational Approaches to the Microstructure-Informed Mechanical Modelling of Metals Produced by Powder Bed Fusion Additive Manufacturing

Zinovieva,

Romanova,

Dymnich

et al. 2023

Materials

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In the rapidly evolving field of additive manufacturing (AM), the predictability of part properties is still challenging due to the inherent multiphysics complexity of the technology. This results in time-consuming and costly experimental guess-and-check approaches for manufacturing each individual design. Through synthesising advancements in the field, this review argues that numerical modelling is instrumental in mitigating these challenges by working in tandem with experimental studies. Unique hierarchical microstructures induced by extreme AM process conditions– including melt pool patterns, grains, cellular–dendritic substructures, and precipitates—affect the final part properties. Therefore, the development of microstructure-informed mechanical models becomes vital. Our review of numerical studies explores various modelling approaches that consider the microstructural features explicitly and offers insights into multiscale stress–strain analysis across diverse materials fabricated by powder bed fusion AM. The literature indicates a growing consensus on the key role of multiscale integrated process–structure–property–performance (PSPP) modelling in capturing the complexity of AM-produced materials. Current models, though increasingly sophisticated, still tend to relate only two elements of the PSPP chain while often focusing on a single scale. This emphasises the need for integrated PSPP approaches validated by a solid experimental base. The PSPP paradigm for AM, while promising as a concept, is still in its infantry, confronting multifaceted challenges that require in-depth, multidisciplinary expertise. These challenges range from accounting for multiphysics phenomena (e.g., advanced laser–material interaction) and their interplay (thermo-mechanical and microstructural evolution for simulating Type II residual stresses), accurately defined assumptions (e.g., flat molten surface during AM or purely epitaxial solidification), and correctly estimated boundary conditions for each element of the PSPP chain up to the need to balance the model’s complexity and detalisation in terms of both multiphysics and discretisation with efficient multitrack and multilayer simulations. Efforts in bridging these gaps would not only improve predictability but also expedite the development and certification of new AM materials.

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Section: Geometrically Based Microstructure Modelling and Image Recon...mentioning

confidence: 99%

Section: Geometrically Based Microstructure Modelling and Image Recon...mentioning

confidence: 99%

Section: Geometrically Based Microstructure Modelling and Image Recon...mentioning

confidence: 99%

See 1 more Smart Citation

A Review of Computational Approaches to the Microstructure-Informed Mechanical Modelling of Metals Produced by Powder Bed Fusion Additive Manufacturing

Zinovieva,

Romanova,

Dymnich

et al. 2023

Materials

View full text Add to dashboard Cite

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“…In the numerical examples presented in this paper the grain structures were constructed with the step-by-step packing method providing fast generation of 3D microstructures on a hexahedral mesh [16]. A series of calculations was performed for polycrystals containing different numbers of equiaxed grains approximated by hexahedral meshes with different resolutions.…”

Section: Microstructure-based Simulationmentioning

confidence: 99%

Mesoscale Deformation-Induced Surface Phenomena in Loaded Polycrystals

2021

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The paper reviews the results of numerical analyses for the micro-and mesoscale deformation-induced surface phenomena in three-dimensional polycrystals with the explicit account for the grain structure. The role of the free surface and grain boundaries in the appearance of the grain-scale stress concentrations and plastic strain nucleation is illustrated on the examples of aluminum polycrystals. Special attention is paid to the discussion of mesoscale deformation-induced surface roughening under uniaxial tension.

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“…Some reports are dedicated just to the computational design of microstructural models. Romanova et al [71] presented the step-by-step packing (SSP) method, while Naderi et al [69] introduced a package that generates realistic microstructures using the Voronoi tessellation method, and Laguerre-Voronoi tessellation was proposed by Falco et al [70]. Notwithstanding, they all represent a granular microstructure with some modifications, e.g., regular, random, or weighted random distribution.…”

Section: Introductionmentioning

confidence: 99%

Kinetic Modeling of Grain Boundary Diffusion: Typical, Bi-Modal, and Semi-Lamellar Polycrystalline Coating Morphologies

2022

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Polycrystalline coatings and materials are widely used in engineering applications. Therefore, it is important to know their kinetics and mass transport mechanisms. The effect of grain boundaries (GBs) on diffusion in thin films with different morphologies lacks understanding. Numerical studies are necessary to study GB kinetics but are limited to simplified cases. The present work addresses the lack of diffusion studies in more complex morphologies. Diffusion in two-dimensional polycrystalline coatings of typical, bi-modal, and semi-lamellar morphologies was modeled and the influence of the microstructure on the diffusion regimes and the overall rate was identified. Different morphologies with similar diffusion coefficients provided different regimes. The regime depends not only on the total diffusivity and grain/GB diffusivities, but also on the morphological features of the surface. While the fast diffusion pathways of GBs accelerated diffusion, the level of acceleration depends on the morphology since fast pathways and flux areas are limited to GBs. GB distribution is important to the mass transfer process, as GBs accelerate diffusion locally. The overall diffusion rate is generally dependent on the diffusion coefficients ratio. Nevertheless, the level of this dependence relies on the morphology.

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A method of step-by-step packing and its application in generating 3D microstructures of polycrystalline and composite materials

Cited by 48 publications

References 39 publications

A Review of Computational Approaches to the Microstructure-Informed Mechanical Modelling of Metals Produced by Powder Bed Fusion Additive Manufacturing

A Review of Computational Approaches to the Microstructure-Informed Mechanical Modelling of Metals Produced by Powder Bed Fusion Additive Manufacturing

Mesoscale Deformation-Induced Surface Phenomena in Loaded Polycrystals

Kinetic Modeling of Grain Boundary Diffusion: Typical, Bi-Modal, and Semi-Lamellar Polycrystalline Coating Morphologies

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