<p>Fused filament fabrication (FFF) is a material extrusion additive manufacturing technology that produces a part through the controlled melting and extrusion of thermoplastic filament. While popular for small-scale 3D printing, its wide-scale adoption for industrial applications still faces significant challenges due to the process’s inherent uncertainties. In this research, an in situ defect detection system is developed on a common consumer-grade 3D printer with an optical camera. The developed defect detection system links the completed part’s structural properties to the defect measurements taken during the layup process, rather than just focusing on surface defect detection during the printing process. Experimental results obtained from structural testing post-printing are used to label a set of optical images taken during the printing process. These labeled images are used to train a deep learning model for detecting defects. The final model’s performance is quantified using a variety of metrics, such as minimum detectable defect size, defect detection accuracy, and computation speed. The contribution of this research is the study of defect size vs. model detection accuracy. Particularly, the minimum detectable defect size for the considered structural part is quantified. The effects of signal noise and uncertainties in the printing process are also investigated in this work.</p>

Towards Online Structural Validation for Fused Filament Fabrication

Paper Proceedings: https://asmedigitalcollection.asme.org/qnde/QNDE2021/volume/85529

ASME 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE2021)

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