On the multiphysics modeling challenges for metal additive manufacturing processes

Michopoulos, John G.; Iliopoulos, Athanasios; Steuben, John C.; Birnbaum, Andrew J.; Lambrakos, S. G.

doi:10.1016/j.addma.2018.06.019

Cited by 47 publications

(29 citation statements)

References 52 publications

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“…As can be seen this covers the full range of scales in the flow problem. Multi-scale simulations [12,13] can combine several approaches such as Discrete Element Models (DEM) with Finite Element (FE) or Finite Volume (FV) continuum models in the same simulation. The FE/FV is employed to solve a boundary value problem, while using the DEM to derive the required nonlinear material responses at each Gauss integration point.…”

Section: Computational Modelling Of Granular Flowmentioning

confidence: 99%

Modelling of Powder Removal for Additive Manufacture Postprocessing

Roberts¹,

Kahraman²,

Bacheva³

et al. 2021

Preprint

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A critical challenge underpinning the adoption of Additive Manufacture (AM) as a technology is the postprocessing of manufactured components. For Selective Laser Sintering (SLS) this can involve the removal of powder from the interior of the component, often by vibrating the component to fluidise the powder to encourage drainage. In this paper we develop and validate a computational model of the flow of metal powder suitable for predicting powder removal from such AM components. The model is a continuum Eulerian multiphase model of the powder including models for the granular temperature; the effect of vibration can be included through appropriate wall boundaries for this granular temperature. We validate the individual sub-models appropriate for AM metal powders by comparison with in-house and literature experimental results, and then apply the full model to a more complex geometry typical of an AM Heat Exchanger. The model is shown to provide valuable and accurate results at a fraction of the computational cost of a particle-based model.

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Section: Computational Modelling Of Granular Flowmentioning

confidence: 99%

Modelling of Powder Removal for Additive Manufacture Postprocessing

Roberts¹,

Kahraman²,

Bacheva³

et al. 2021

Preprint

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“…Moreover, the part has to be manufacturable with one or more AM or SM capabilities [13]. Manufacturability constraints can be of both kinematic and physical types; for instance, accessibility in SM [7,8] and post-processing of AM (e.g., support removal [16]) are of predominantly kinematic nature, whereas achieving desired material properties in AM requires in situ physical analysis [17]. With few exceptions (e.g., TO for AM with minimized support [18]) TO algorithms are not developed with manufacturability provisions built into their objective functions.…”

Section: Kinematic Physical and Manufacturing Constraintsmentioning

confidence: 99%

Exploring feasible design spaces for heterogeneous constraints

Mirzendehdel

Behandish

Nelaturi

2019

Computer-Aided Design

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We demonstrate an approach of exploring design spaces to simultaneously satisfy kinematics-and physics-based requirements. We present a classification of constraints and solvers to enable postponing optimization as far down the design workflow as possible. The solvers are organized into two broad classes of design space 'pruning' and 'exploration' by considering the types of constraints they can satisfy. We show that pointwise constraints define feasible design subspaces that can be represented and computed as first-class entities by their maximal feasible elements. The design space is pruned upfront by intersecting maximal elements, without premature optimization. To solve for other constraints, we apply topology optimization (TO), starting from the pruned feasible space. The optimization is steered by a topological sensitivity field (TSF) that measures the global changes in violation of constraints with respect to local topological punctures. The TSF for global objective functions is augmented with TSF for global constraints, and penalized/filtered to incorporate local constraints, including set constraints converted to differentiable (in)equality constraints. We demonstrate application of the proposed workflow to nontrivial examples in design and manufacturing. Among other examples, we show how to explore pruned design spaces via TO to simultaneously satisfy physics-based constraints (e.g., minimize compliance and mass) as well as kinematics-based constraints (e.g., maximize accessibility for machining).

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“…The simulation gives many details about thermal history during printing [7,[14][15][16][17], and the computational results could be used for the optimization of the manufacturing process. There are some researches that coupled macroscale thermal simulation coupled with mesoscale microstructural evaluation [18][19][20][21]. There are several published kinds of research on mesoscale microstructural simulations for powder bedbased additive manufacturing in the first layer of the print [22][23][24][25][26][27].…”

Section: -Introductionmentioning

confidence: 99%

Direct Laser Deposition Metal 3D Printing at Different Temperatures of the Printer’s Chamber: Computer Simulation

Hosseinzadeh¹

2020

Preprint

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Metal 3D printing technology is a promising manufacturing method. The quality of the printed product can pass for mechanical application, if the anisotropy of the microstructure, imperfections, deformation, and residual stress of the printed sample could be lower than the appropriate level or if they are fully illuminated. Thermal stress is one of the significant reasons for deformation in the 3D printed samples. Thermal stresses are the direct consequence of the local temperature gradient. In this research, the effect of the temperature printer’s chamber (from room temperature to 900 C) was studied on thermal stress and subsequent total deformation in the printed sample. The printed sample is a six-layers-printed walk, which could be considered as a building block of other complex shapes and give us inside about deformation. The computational results show a meaningful reduction in thermal stress and deformation at the higher temperature of the printer’s chamber. The lower final deformation of the printed sample is an important subject, especially for samples with complex shapes.

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On the multiphysics modeling challenges for metal additive manufacturing processes

Cited by 47 publications

References 52 publications

Modelling of Powder Removal for Additive Manufacture Postprocessing

Modelling of Powder Removal for Additive Manufacture Postprocessing

Exploring feasible design spaces for heterogeneous constraints

Direct Laser Deposition Metal 3D Printing at Different Temperatures of the Printer’s Chamber: Computer Simulation

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