  Machine learning (ML)-based monitoring systems have been extensively
developed to enhance the print quality of additive manufacturing (AM). In-situ
and in-process data acquired using sensors can be used to train ML models that
detect process anomalies, predict part quality, and adjust process parameters.
However, the reproducibility of the proposed AM monitoring systems has not been
investigated. There has not been a method to evaluate and improve
reproducibility in the joint domain of AM and ML. Consequently, some crucial
information for reproducing the research is usually missing from the
publications; thus, systems reproduced based on the publications often cannot
achieve the claimed performance. This paper establishes the definition of
reproducibility in this domain, proposes a reproducibility investigation
pipeline, and composes a reproducibility checklist. A research is reproducible
if a performance comparable to the original research can be obtained when
reproduced by a different team using a different experiment setup. The
reproducibility investigation pipeline sequentially guides the readers through
all the necessary reproduction steps, during which the reproducibility
checklist will help extract the reproducibility information from the
publication. A case study that reproduced a vision-based warping detection
system demonstrated the usage and validated the efficacy of the proposed
pipeline and checklist. It has been observed that the reproducibility checklist
can help the authors verify that all the information critical to
reproducibility is provided in the publications. The investigation pipeline can
help identify the missing reproducibility information, which should be acquired
from the original authors to achieve the claimed performance.
