  The paper describes an open source computer vision-based hardware structure
and software algorithm, which analyzes layer-wise the 3-D printing processes,
tracks printing errors, and generates appropriate printer actions to improve
reliability. This approach is built upon multiple-stage monocular image
examination, which allows monitoring both the external shape of the printed
object and internal structure of its layers. Starting with the side-view height
validation, the developed program analyzes the virtual top view for outer shell
contour correspondence using the multi-template matching and iterative closest
point algorithms, as well as inner layer texture quality clustering the
spatial-frequency filter responses with Gaussian mixture models and segmenting
structural anomalies with the agglomerative hierarchical clustering algorithm.
This allows evaluation of both global and local parameters of the printing
modes. The experimentally-verified analysis time per layer is less than one
minute, which can be considered a quasi-real-time process for large prints. The
systems can work as an intelligent printing suspension tool designed to save
time and material. However, the results show the algorithm provides a means to
systematize in situ printing data as a first step in a fully open source
failure correction algorithm for additive manufacturing.
