Computer Vision Based Analysis for Fused Filament Fabrication Using a G-Code Visualization Comparison

Abstract

The first open-source and low-cost fused filament fabrication 3D printer of 2004 represented a new opportunity to manufacture mechanical components easily and affordably. 3D printers have been continuously developed since the first one entered the market and are equipped with various sensors that can monitor the printing process. Despite these improvements, a probability of 41.1% remains that the printed part will have errors. This can lead to an irreparable print that has to be canceled, but is often not noticed by common sensors, while costly time, electricity and filament continue to be consumed. This investigation provides an account of a camera based monitoring system developed to detect complex problems that are not easily recognizable with sensors commonly used for fused filament fabrication. Image segmentation was used to remove the background of the printed part and the result was compared to a visualization of the G-Code. By using an exclusive-or method it was possible to determine differences, which can indicate defects. Depending on the similarity, the printing process can be canceled promptly. Tests have demonstrated that this method works reliably even under changing lighting conditions in most cases but can lead to poor segmentation due to shadows being cast in the infill. The application is also able to recognize differences when printed parts detach or layers have shifted if they are not covered by lower layers. The use of a light source on top of the 3D printer and additional cameras, beside the build plate, could solve both problems in the future.