  A digital twin (DT), with the components of a physics-based model, a
data-driven model, and a machine learning (ML) enabled efficient surrogate,
behaves as a virtual twin of the real-world physical process. In terms of Laser
Powder Bed Fusion (L-PBF) based additive manufacturing (AM), a DT can predict
the current and future states of the melt pool and the resulting defects
corresponding to the input laser parameters, evolve itself by assimilating
in-situ sensor data, and optimize the laser parameters to mitigate defect
formation. In this paper, we present a deep neural operator enabled
computational framework of the DT for closed-loop feedback control of the L-PBF
process. This is accomplished by building a high-fidelity computational model
to accurately represent the melt pool states, an efficient surrogate model to
approximate the melt pool solution field, followed by an physics-based
procedure to extract information from the computed melt pool simulation that
can further be correlated to the defect quantities of interest (e.g., surface
roughness). In particular, we leverage the data generated from the
high-fidelity physics-based model and train a series of Fourier neural operator
(FNO) based ML models to effectively learn the relation between the input laser
parameters and the corresponding full temperature field of the melt pool.
Subsequently, a set of physics-informed variables such as the melt pool
dimensions and the peak temperature can be extracted to compute the resulting
defects. An optimization algorithm is then exercised to control laser input and
minimize defects. On the other hand, the constructed DT can also evolve with
the physical twin via offline finetuning and online material calibration.
Finally, a probabilistic framework is adopted for uncertainty quantification.
The developed DT is envisioned to guide the AM process and facilitate
high-quality manufacturing.
