Physics‐based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales
Abstract
Powder bed fusion additive manufacturing (PBFAM) of metals has the potential to enable new paradigms of product design, manufacturing and supply chains while accelerating the realization of new technologies in the medical, aerospace, and other industries. Currently, wider adoption of PBFAM is held back by difficulty in part qualification, high production costs and low production rates, as extensive process tuning, post‐processing, and inspection are required before a final part can be produced and deployed. Physics‐based modeling and predictive simulation of PBFAM offers the potential to advance fundamental understanding of physical mechanisms that initiate process instabilities and cause defects. In turn, these insights can help link process and feedstock parameters with resulting part and material properties, thereby predicting optimal processing conditions and inspiring the development of improved processing hardware, strategies and materials. This work presents recent developments of our research team in the modeling of metal PBFAM processes spanning length scales, namely mesoscale powder modeling, mesoscale melt pool modeling, macroscale thermo‐solid‐mechanical modeling and microstructure modeling. Ongoing work in experimental validation of these models is also summarized. In conclusion, we discuss the interplay of these individual submodels within an integrated overall modeling approach, along with future research directions.
The authors would like to thank Gareth McKinley and Crystal Owens from MIT, Marvin Ochsenius from TUM as well as Jonah Myerberg from Desktop Metal for fruitful discussions and their support in the context of powder‐rheological experiments. Furthermore, the authors would like to thank their TUM colleagues Peter Munch for his valuable support in the code implementation of the (FEM) melt pool model, Niklas Fehn for his valuable contributions to the DEM code implementation, and Martin Kronbichler for fruitful discussions in the context of DEM as well as SPH and FEM (melt pool model) code implementations. C. Meier acknowledges funding by a postdoc fellowship of the German Academic Exchange Service (DAAD). Furthermore, the TUM authors acknowledge funding of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within project 437616465 and project 414180263. Magdalena Schreter was supported by the Austrian Science Fund (FWF) via a FWF Schrödinger scholarship, FWF project number J‐4577‐N. Y. Sun acknowledges funding by the CSC scholarship with number 201909110. For the cited experimental work on X‐ray microscopy of powder layers and MWIR microscopy of LPBF, the MIT authors acknowledge prior and current financial support by Robert Bosch, LLC; Honeywell Federal Manufacturing and Technologies (FM&T); and ArcelorMittal. Open Access funding enabled and organized by Projekt DEAL.
