Generalizable process monitoring for FFF 3D printing with machine vision

Abstract

Additive manufacturing has experienced a surge in popularity in both commercial and private sectors over the past decade due to the growing demand for affordable and highly customized products, which are often in direct opposition to the requirements of traditional subtractive manufacturing. Fused Filament Fabrication (FFF) has emerged as the most widely-used additive manufacturing technology, despite challenges associated with achieving contour accuracy. To address this issue, the authors have developed a novel camera-based process monitoring method that enables the detection of errors in the printing process through a layer-by-layer comparison of the actual contour and the target contour obtained via G-Code processing. This method is generalizable and can be applied to different printer models with minimal hardware adjustments using off-the-shelf components. The authors have demonstrated the effectiveness of this method in automatically detecting both coarse and small contour deviations in 3D-printed parts.