DMLS Process Modelling and Validation

N’Dri, Narcisse; Mindt, H.‐W.; Shula, B.; Megahed, Mustafa; Peralta, Alonso; Kantzos, Pete; Neumann, J.

doi:10.1007/978-3-319-48127-2_49

Cited by 8 publications

(12 citation statements)

References 17 publications

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“…The common formulation of free-surface tracking algorithms supports tracking of one or two material states, such as liquid and gas [18][19][20]. Vogel et al and N'Dri et al extended the formulation to support three material states: solid, liquid, and gas/vapor [12,13]. Two numerical approaches are pursued to solve conservation Eqs.…”

Section: Species Conservationmentioning

confidence: 99%

“…N'Dri et al assessed the reliability of equivalent powder property models by performing an uncertainty quantification study. They showed that the results are very sensitive to the accuracy of absorption and loose powder conductivity [13]. In spite of the efficiency of these models, their reliance on accurate equivalent property approximations render them non-predictive.…”

Section: Power Bed Micromodelingmentioning

confidence: 99%

“…Instead, the resolution of the powder particles enables the prediction of radiation absorption and penetration depth as well as the overall powder conductivity change as the powder starts to melt [13,19]. Figure 2 shows a powder bed consisting of uniform spherical particles arranged in a BCC structure.…”

Section: Power Bed Micromodelingmentioning

confidence: 99%

“…(3)-last term on the right-hand side). N'Dri et al [13] and Mindt et al [38] used a finite-volume formulation for the discrete ordinate radiation model [30]. The model was extended to track the position of a molten surface.…”

Section: Power Bed Micromodelingmentioning

confidence: 99%

“…Models can predict the as-built properties and eventually support rapid qualification of topologically optimized functional components. Length-and timescale considerations make it necessary to split models into micro-, meso-, and macroscale models [11][12][13] 1. Micromodels address the heat source feedstock interaction, the heat absorption, and the phase changes in a domain comparable in size to the melt pool and the heat-affected zone.…”

Section: Introductionmentioning

confidence: 99%

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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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