  The use of additive manufacturing (AM) processes for industrial fabrication
has grown rapidly over the last ten years. The most well-known AM technologies
are fused deposition modelling and stereolithography techniques. One particular
industry where 3D printing is advantageous over traditional fabrication
techniques is within ceramic components due to its flexibility. To establish a
new and improved level of print quality and reduce resource consumption in the
3D printing ceramics industry, there is a need for fast integrated, sub-surface
and non-destructive inspection (NDI) with high resolution. Several techniques
have already been developed for high-resolution NDI, such as X-ray computed
tomography (XCT), but none of them are both fast, integrable, and
non-destructive while allowing deep penetration with high resolution. In this
study, we demonstrate sub-surface monitoring of 3D printed alumina parts to a
depth of $\sim$0.7 mm in images of 400$\times$2048 pixels with a lateral
resolution of 30$~\mu$m and depth (or axial) resolution of 7$~\mu$m . The
results were achieved using mid-infrared optical coherence tomography (MIR OCT)
based on a MIR supercontinuum laser with a 4$~\mu$m center wavelength. We find
that it is possible to detect individual printed ceramic layers and track
predefined defects through all four processing steps: green, preconditioned,
debinded, and sintered. Our results also demonstrate how a defect in the green
phase could affect the final product. Based on the understanding of how defects
develop in maturing printed parts, we pave the way for NDI integration in AM,
which can be combined with artificial intelligence and machine learning
algorithms for automatic defect classification in volume production of a new
standard of high quality ceramic components.
