  Manufacturers struggle to produce low-cost, robust and complex components at
manufacturing lot-size one. Additive processes like Fused Filament Fabrication
(FFF) inexpensively produce complex geometries, but defects limit viability in
critical applications. We present an approach to high-accuracy, high-throughput
and low-cost automated non-destructive testing (NDT) for FFF interlayer
delamination using Flash Thermography (FT) data processed with Thermographic
Signal Reconstruction (TSR) and Artificial Intelligence (AI). A Deep Neural
Network (DNN) attains 95.4% per-pixel accuracy when differentiating four
delamination thicknesses 5mm subsurface in PolyLactic Acid (PLA) widgets, and
98.6% accuracy in differentiating acceptable from unacceptable condition for
the same components. Automated inspection enables time- and cost-efficient 100%
inspection for delamination defects, supporting FFF's use in critical and
small-batch applications.
