  In-situ monitoring incorporating data from visual and other sensor
technologies, allows the collection of extensive datasets during the Additive
Manufacturing (AM) process. These datasets have potential for determining the
quality of the manufactured output and the detection of defects through the use
of Machine Learning during the manufacturing process. Open and annotated
datasets derived from AM processes are necessary for the machine learning
community to address this opportunity, which creates difficulties in the
application of computer vision-related machine learning in AM. This systematic
review investigates the availability of open image-based datasets originating
from AM processes that align with a number of pre-defined selection criteria.
The review identifies existing gaps among the current image-based datasets in
the domain of AM, and points to the need for greater availability of open
datasets in order to allow quality assessment and defect detection during
additive manufacturing, to develop.
