  In additive manufacturing (AM), particularly for laser-based metal AM,
process optimization is crucial to the quality of products and the efficiency
of production. The identification of optimal process parameters out of a vast
parameter space, however, is a daunting task. Despite advances in simulations,
the process optimization for specific materials and geometries is developed
through a time-consuming trial-and-error approach, which often lacks the
versatility to address multiple optimization objectives. Machine learning (ML)
provides a powerful tool to accelerate the optimization process, but most
current studies focus on simple single-track prints, which hardly translate to
manufacturing 3D components for engineering applications. In this study, we
develop an Accurate Inverse process optimization framework in laser Directed
Energy Deposition (AIDED), based on machine learning models and a genetic
algorithm, to aid process optimization in laser DED processes. Using the AIDED,
we demonstrate the following: (i) Accurately predict single-track (R2 score
0.995), multi-track (R2 score 0.969), and multi-layer (1.07% and 10.75% error
in width and height, respectively) cross-sectional melt pool geometries
directly from process parameters; (ii) Determine appropriate hatch spacing and
layer thickness for fabricating fully dense (density > 99.9%) multi-track and
multi-layer prints; (iii) Inversely identify optimal process parameters
directly from customizable application objectives within 1-3 hours. We also
validate the effectiveness of the AIDED experimentally by achieving two
exemplary targets: fast print speed and fine print resolution. Furthermore, we
show the high transferability of the framework from stainless steel to pure
nickel. With AIDED, we pave a new way for ''aiding'' the process optimization
in the laser-based AM processes that is applicable to a wide range of
materials.
