In-situ measurement of extrusion width for fused filament fabrication process using vision and machine learning models

Measuring geometry of the printing road is key for detection of anomalies in 3D printing processes. Although commercial 3D printers can measure the extrusion height using various distance sensors, measuring of the width in realtime remains a challenge. This paper presents a visual in-situ monitoring system to measure width of the printing filament road in 2D patterns. The proposed system is composed of a printable shroud with embedded camera setup and a visual
detection approach based on a two-stage instance segmentation method. Each of the segmentation and localization stages can use multiple computational approaches including Gaussian mixture model, color filter, and deep neural network models. The visual monitoring system is mounted on a standard 3D printer and validated with the measurement of printed filament roads of
sub-millimeter widths. The results on accuracy and robustness reveal that combinations of deep models for both segmentation and localization stages have better performance. Particularly, fully connected CNN segmentation model combined with YOLO object detector can measure sub-millimeter extrusion width
with 90μm accuracy at 125ms speed. This visual monitoring system has potential to improve the control of printing processes by the real-time measurement of printed filament geometry.