  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.
