  Melt pool dynamics in metal additive manufacturing (AM) is critical to
process stability, microstructure formation, and final properties of the
printed materials. Physics-based simulation including computational fluid
dynamics (CFD) is the dominant approach to predict melt pool dynamics. However,
the physics-based simulation approaches suffer from the inherent issue of very
high computational cost. This paper provides a physics-informed machine
learning (PIML) method by integrating neural networks with the governing
physical laws to predict the melt pool dynamics such as temperature, velocity,
and pressure without using any training data on velocity. This approach avoids
solving the highly non-linear Navier-Stokes equation numerically, which
significantly reduces the computational cost. The difficult-to-determine model
constants of the governing equations of the melt pool can also be inferred
through data-driven discovery. In addition, the physics-informed neural network
(PINN) architecture has been optimized for efficient model training. The
data-efficient PINN model is attributed to the soft penalty by incorporating
governing partial differential equations (PDEs), initial conditions, and
boundary conditions in the PINN model.
