Automated Non-Destructive Inspection of Fused Filament Fabrication Components Using Thermographic Signal Reconstruction
This enables automated, low-cost non-destructive testing for additive manufacturing, supporting its use in critical applications, though it is incremental as it applies existing AI methods to a specific domain problem.
The paper tackled the problem of detecting interlayer delamination defects in Fused Filament Fabrication components using Flash Thermography and AI, achieving 95.4% per-pixel accuracy for thickness differentiation and 98.6% accuracy for condition classification.
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.