  Directed energy deposition (DED) is a promising metal additive manufacturing
technology capable of 3D printing metal parts with complex geometries at lower
cost compared to traditional manufacturing. The technology is most effective
when process parameters like laser scan speed and power are optimized for a
particular geometry and alloy. To accelerate optimization, we apply a
data-driven, parameterized, non-linear reduced-order model (ROM) called
Gaussian Process Latent Space Dynamics Identification (GPLaSDI) to
physics-based DED simulation data. With an appropriate choice of
hyperparameters, GPLaSDI is an effective ROM for this application, with a
worst-case error of about 8% and a speed-up of about 1,000,000x with respect to
the corresponding physics-based data.
