  Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method
of metal additive manufacturing (AM) that has benefits of high material
utilization and deposition rate, but parts manufactured by DED-LB/w suffer from
a substantial heat input and undesired surface finish. Hence, monitoring and
controlling the process parameters and signatures during the deposition is
crucial to ensure the quality of final part properties and geometries. This
paper explores the dynamic modeling of the DED-LB/w process and introduces a
parameter-signature-property modeling and control approach to enhance the
quality of modeling and control of part properties that cannot be measured in
situ. The study investigates different process parameters that influence the
melt pool width (signature) and bead width (property) in single and multi-layer
beads. The proposed modeling approach utilizes a parameter-signature model as
F_1 and a signature-property model as F_2. Linear and nonlinear modeling
approaches are compared to describe a dynamic relationship between process
parameters and a process signature, the melt pool width (F_1). A fully
connected artificial neural network is employed to model and predict the final
part property, i.e., bead width, based on melt pool signatures (F_2). Finally,
the effectiveness and usefulness of the proposed parameter-signature-property
modeling is tested and verified by integrating the parameter-signature (F_1)
and signature-property (F_2) models in the closed-loop control of the width of
the part. Compared with the control loop with only F_1, the proposed method
shows clear advantages and bears potential to be applied to control other part
properties that cannot be directly measured or monitored in situ.
