Powder Bed Layer Characteristics: The Overseen First-Order Process Input

Mindt, H.‐W.; Megahed, Mustafa; Lavery, Nicholas; Holmes, Marc; Brown, S. G. R.

doi:10.1007/s11661-016-3470-2

Cited by 146 publications

(74 citation statements)

References 20 publications

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“…This is in agreement with observation made for other powders [7]. Figure 4 shows the particle size distribution obtained for two different table displacements, 35 and 50 µm, and compares them the input powder size distribution.…”

Section: Resultssupporting

confidence: 89%

“…When modelling the powder spreading process, we use discrete element methods (DEM) to study powder spreading and the distribution of powder on the processing table [7]. DEM is a Lagrangian approach where the modelled region considers the particles inside their own point of reference describing Newton's laws of motion for conservation of momentum.…”

Section: Computational Modelsmentioning

confidence: 99%

“…Since the spatial arrangement of the particles in the powder bed cannot be manipulated or easily measured in a production 3D printer, the study of the impact of those parameters on the SLM process is investigated by comparing the melting behaviour of a BCC arrangement of powder particles with that of a powder bed obtained by modelling the racking process [6]. The numerically predicted melt pool characteristics are compared with experimentally observations to assess the effect of powder distribution of material behaviour.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Powder Bed Characteristics on Material Quality in Additive Manufacturing

Zielinski

Vervoort

Mindt

et al. 2017

Berg Huettenmaenn Monatsh

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Abstract:In powder bed based Additive Manufacturing (AM) processes like Selective Laser Melting (SLM) or Electron Beam Melting (EBM), the spatial distribution of the individual powder particles is typically unknown. Nevertheless, the distribution of particles in the heat affected zone defines the thermophysical properties of the region being processed by the heat source and therefore plays a crucial role in heat transfer processes. In this work, the spatial distribution of individual particles and their influence on the AM process is numerically investigated. Two powder bed configurations are compared: One powder bed is generated using the discrete element method (DEM) to model the coating process; the second powder bed is arranged in the BCC structure. The melting and solidification of both configurations are modelled. The predicted melt pool dimensions are compared with experimentally determined values. The results indicate that modelling the coating process is necessary to ensure accurate modelling of the heat source powder bed interaction as well as an accurate prediction of the melt pool characteristics. Keywords

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Section: Resultssupporting

confidence: 89%

Section: Computational Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Powder Bed Characteristics on Material Quality in Additive Manufacturing

Zielinski

Vervoort

Mindt

et al. 2017

Berg Huettenmaenn Monatsh

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“…The coating arm is assumed to be a rigid body the velocity of which is described as a boundary condition. Once the particles are created, the coating arm is set into motion spreading the particles onto the processing table [35]. Figure 5 shows the evolution of the calculated powder packing density for multiple layers.…”

Section: Power Bed Micromodelingmentioning

confidence: 99%

Metal additive-manufacturing process and residual stress modeling

Megahed

Mindt

N’Dri

et al. 2016

Integr Mater Manuf Innov

225

113

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Additive manufacturing (AM), widely known as 3D printing, is a direct digital manufacturing process, where a component can be produced layer by layer from 3D digital data with no or minimal use of machining, molding, or casting. AM has developed rapidly in the last 10 years and has demonstrated significant potential in cost reduction of performance-critical components. This can be realized through improved design freedom, reduced material waste, and reduced post processing steps. Modeling AM processes not only provides important insight in competing physical phenomena that lead to final material properties and product quality but also provides the means to exploit the design space towards functional products and materials. The length-and timescales required to model AM processes and to predict the final workpiece characteristics are very challenging. Models must span length scales resolving powder particle diameters, the build chamber dimensions, and several hundreds or thousands of meters of heat source trajectories. Depending on the scan speed, the heat source interaction time with feedstock can be as short as a few microseconds, whereas the build time can span several hours or days depending on the size of the workpiece and the AM process used. Models also have to deal with multiple physical aspects such as heat transfer and phase changes as well as the evolution of the material properties and residual stresses throughout the build time. The modeling task is therefore a multi-scale, multi-physics endeavor calling for a complex interaction of multiple algorithms. This paper discusses models required to span the scope of AM processes with a particular focus towards predicting as-built material characteristics and residual stresses of the final build. Verification and validation examples are presented, the over-spanning goal is to provide an overview of currently available modeling tools and how they can contribute to maturing additive manufacturing.

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“…Using a discrete element model to obtain the powder bed characteristics, Mindt et al showed how the balance between powder size distribution and the slit height can affect the coating quality and powder recyclability. 9,10 The numerically obtained powder bed was transferred to the melting models to solve Navier-Stokes equations as described in Ref. 6.…”

Section: Numerical Modelsmentioning

confidence: 99%