  In metals additive manufacturing (AM), materials and components are
concurrently made in a single process as layers of metal are fabricated on top
of each other in the near-final topology required for the end-use product.
Consequently, tens to hundreds of materials and part design degrees of freedom
must be simultaneously controlled and understood; hence, metals AM is a highly
interdisciplinary technology that requires synchronized consideration of
physics, chemistry, materials science, physical metallurgy, computer science,
electrical engineering, and mechanical engineering. The use of modern machine
learning approaches to model these degrees of freedom can reduce the time and
cost to elucidate the science of metals AM and to optimize the engineering of
these complex, multidisciplinary processes. New machine learning techniques are
not needed for most metals AM development; those used in other sects of
materials science will also work for AM. Most prolifically, the density
functional theory (DFT) community has used many of them since the early 2000s
for evaluating numerous combinations of elements and crystal structures to
discover new materials. This materials technologies-focused review introduces
the basic mathematics and terminology of machine learning through the lens of
metals AM, and then examines potential uses of machine learning to advance
metals AM, highlighting the many parallels to previous efforts in materials
science and manufacturing while also discussing new challenges and adaptations
specific to metals AM.
