Powder Packing Density and Its Impact on SLM-Based Additive Manufacturing

2019 ◽  
pp. 355-367 ◽  
Author(s):  
Taher Abu-Lebdeh ◽  
Ransford Damptey ◽  
Vincent Lamberti ◽  
Sameer Hamoush
Keyword(s):  
Additive Manufacturing ◽  
Packing Density ◽  
Powder Packing

scholarly journals Influence of packing density and fillers on thermal conductivity of polymer powders for additive manufacturing

2021 ◽  
Author(s):  
Francesco Sillani ◽  
Fabian de Gasparo ◽  
Manfred Schmid ◽  
Konrad Wegener
Keyword(s):  
Thermal Conductivity ◽  
Additive Manufacturing ◽  
Packing Density ◽  
Heating System ◽  
Neat Polymer ◽  
Additional Tool ◽  
Powder Packing ◽  
Inhomogeneous Temperature ◽  
Thermal Phenomena ◽  
Polymer Powders

AbstractAdditive manufacturing of polymer powders is nowadays an industrial production technology. Complex thermal phenomena occur during processing, mainly related to the interaction dynamics among laser, powder, and heating system, and also to the subsequent cool-down phase from the melt to the parts. Thermal conductivity of the powder is a key property for material processing, since an inhomogeneous temperature field in the powder cake leads to uneven part properties and can strongly limit productivity because only a smaller portion of the build chamber can be used. Nevertheless, little is known about the relationship between thermal conductivity, packing density, and presence of fillers, which are used to enhance specific properties such as high temperature resistance or stiffness. The development and consequent validation of a device capable of measuring thermal conductivity as a function of powder packing density are then extremely important, providing an additional tool to characterize powders during the development process of new materials for PBF of polymers. The results showed a positive correlation between packing density and thermal conductivity for some commercially available materials, with an increase of the latter of about 10 to 40% with an increase of the packing density from 0 to 100%. Problems arose in trying to replicate the compaction state of the powder, since the same amount of taps led to a different packing density, but this is a known problem of measuring free-flowing powders such as the ones used for additive manufacturing. Regarding fillers, an increase of about 40 to 70% of thermal conductivity when inorganic fillers such as carbon fibers are added to the neat polymer was observed, and the expected behavior following the rule of mixture was only partially observed.

Optimization of tape width and powder packing density in the powder-in-tube processing of (Bi,Pb)-2223 tapes

1998 ◽  
Vol 309 (3-4) ◽  
pp. 203-207 ◽  
Author(s):  
R.P. Aloysius ◽  
A. Sobha ◽  
P. Guruswamy ◽  
K.G.K. Warrier ◽  
U. Syamaprasad
Keyword(s):  
Packing Density ◽  
Powder Packing ◽  
Powder In Tube

scholarly journals Analytical Modeling of In-Process Temperature in Powder Bed Additive Manufacturing Considering Laser Power Absorption, Latent Heat, Scanning Strategy, and Powder Packing

Materials ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 808 ◽  
Author(s):  
Jinqiang Ning ◽  
Daniel Sievers ◽  
Hamid Garmestani ◽  
Steven Liang
Keyword(s):  
Additive Manufacturing ◽  
Computational Efficiency ◽  
Molten Pool ◽  
Laser Power ◽  
Analytical Modeling ◽  
Single Track ◽  
Scanning Strategy ◽  
Powder Bed ◽  
Powder Packing ◽  
High Computational Efficiency

Temperature distribution gradient in metal powder bed additive manufacturing (MPBAM) directly controls the mechanical properties and dimensional accuracy of the build part. Experimental approach and numerical modeling approach for temperature in MPBAM are limited by the restricted accessibility and high computational cost, respectively. Analytical models were reported with high computational efficiency, but the developed models employed a moving coordinate and semi-infinite medium assumption, which neglected the part dimensions, and thus reduced their usefulness in real applications. This paper investigates the in-process temperature in MPBAM through analytical modeling using a stationary coordinate with an origin at the part boundary (absolute coordinate). Analytical solutions are developed for temperature prediction of single-track scan and multi-track scans considering scanning strategy. Inconel 625 is chosen to test the proposed model. Laser power absorption is inversely identified with the prediction of molten pool dimensions. Latent heat is considered using the heat integration method. The molten pool evolution is investigated with respect to scanning time. The stabilized temperatures in the single-track scan and bidirectional scans are predicted under various process conditions. Close agreements are observed upon validation to the experimental values in the literature. Furthermore, a positive relationship between molten pool dimensions and powder packing porosity was observed through sensitivity analysis. With benefits of the absolute coordinate, and high computational efficiency, the presented model can predict the temperature for a dimensional part during MPBAM, which can be used to further investigate residual stress and distortion in real applications.

On Characterizing Uncertainty Sources in Laser Powder Bed Fusion Additive Manufacturing Models

2019 ◽  
Author(s):  
Tesfaye Moges ◽  
Paul Witherell ◽  
Gaurav Ameta
Keyword(s):  
Additive Manufacturing ◽  
Computational Models ◽  
Input Parameter ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Flow Models ◽  
Uncertainty Sources ◽  
Powder Packing ◽  
Ontology Language

Abstract Tremendous effort has been dedicated to computational models and simulations of Additive Manufacturing (AM) processes to better understand process complexities and better realize high-quality parts. However, understanding whether a model is an acceptable representation for a given scenario is a difficult proposition. With metals, the laser powder bed fusion (L-PBF) process involves complex physical phenomena such as powder packing, heat transfer, phase transformation, and fluid flow. Models based on these phenomena will possess different degrees of fidelity as they often rely on assumptions that may neglect or simplify process physics, resulting in uncertainty in their prediction accuracy. Predictive uncertainty and its characterization can vary greatly between models. This paper characterizes sources of L-PBF model uncertainty, including those due to modeling assumptions (model form uncertainty), numerical approximation (numerical uncertainty), and model input parameters (input parameter uncertainty) for low and high-fidelity models. The characterization of input uncertainty in terms of probability density function (PDF) and its propagation through L-PBF models, is discussed in detail. The systematic representation of such uncertainty sources is achieved by leveraging the Web Ontology Language (OWL) to capture relevant knowledge used for interoperability and reusability. The topology and mapping of the uncertainty sources establish fundamental requirements for measuring model fidelity and guiding the selection of a model suitable for its intended purpose.

Induction Plasma Technology Applied to Powder Manufacturing: Example of Titanium-Based Materials

2016 ◽  
Vol 704 ◽  
pp. 282-286 ◽  
Author(s):  
Romain Vert ◽  
Remy Pontone ◽  
Richard Dolbec ◽  
Luc Dionne ◽  
M.I. Boulos
Keyword(s):  
Additive Manufacturing ◽  
Powder Metallurgy ◽  
Packing Density ◽  
Inductively Coupled Plasma ◽  
Manufacturing Industry ◽  
Critical Properties ◽  
Plasma Technology ◽  
High Quality ◽  
Inductively Coupled ◽  
Powder Manufacturing

Powder metallurgy technologies require specific powders to ensure a good quality to the manufactured parts. The critical properties are; the powder chemistry, flow ability, packing density, and the absence of porosity. This review highlights the capability of Tekna’s Inductively Coupled Plasma (ICP) technology for the production of high quality powders for the additive manufacturing industry.

Sign in / Sign up

Export Citation Format

Share Document