Multiscale Modeling of Powder Bed–Based Additive Manufacturing

By Matthias Markl, Carolin Körner. 2016

377 citations recorded by the source corpus.

Subfields: Automotive Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/58fbcd0d412e52627321f71ccbfda9b9589d6d19

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. T. DebRoy
2. T. Mukherjee
3. H. Wei
4. Amitava De
5. Wei Zhang
6. J. Zuback
7. J. Elmer
8. Allison M. Beese

In Mechanical Engineering

1. T. DebRoy
2. T. Mukherjee
3. H. Wei
4. Amitava De
5. Wei Zhang
6. J. Elmer
7. J. Zuback
8. T. Rodgers

Citations in the corpus

• Material-structure-performance integrated laser-metal additive manufacturing (2021, background): “Second, the point-by-point, line-by-line, and layer-by-layer localized forming characteristics of AM require cross-scale coordinationacross at least six orders of magnitude, including nano/microscale phase and microstructure control (material-related) (19, 20), mesoscale interaction between laser an”
• Additive manufacturing of metallic components – Process, structure and properties (2018, background): “The circulation pattern has a major effect on the temperature distribution in the liquid alloy, heating and cooling rates, solidification pattern, and the microstructure and properties of the build [91].”
• Powder-based additive manufacturing – a review of types of defects, generation mechanisms, detection, property evaluation and metrology (2017, background): “The physical processes that occur during AM are very complex, and are just beginning to be fully understood and quantified (Collins et al., 2016; Markl and Korner, 2016; Matthews et al., 2016).”
• The effect of powder oxidation on defect formation in laser additive manufacturing (2019, methodology): “Although computer simulations can provide some physical understanding 41 of the additive manufacturing (AM) processes [12], they require experimental data for model validation 42 and verification, especially with regards to the molten pool and defect dynamics.”
• Additive manufacturing for energy: A review (2020, methodology): “A recent review was provided for melt-extrusion AM processes [21] and PBF processes [154].”
• Simulation of metal additive manufacturing microstructures using kinetic Monte Carlo (2017, background): “This approach allows for specification of molten zones with widely varying geometries including long tails and/or abbreviated ‘‘caps” as observed in many thermofluid simulations [25,27] without requiring specific knowledge of the material’s thermo-fluidic properties.”