A Vision-based Monitoring System for Quality Assessment of Fused Filament Fabrication (FFF) 3D Printing

Abstract

As one of the most popular 3D printing technology, Fused Filament Fabrication (FFF) allows intricate structures to be produced without complex manufacturing processes. However, there is a limitation of the currently available FFF 3D printers which print blindly without an ability to detect and stop upon printing deviations, incurring additional running costs due to unnecessary waste of materials and time. This has led to a novel development reported in this paper of a vision-based monitoring system for the quality assessment of 3D printing by applying advanced computer vision algorithms and imaging processing techniques. The proposed approach is through comparison between actual images of the printed layer and simulated images created by slicing CAD model via G-code generation based on the calibrated camera pose. Also presented are feature extraction methods to yield object dimension, profile and infill for quality assessment, with the system performance demonstrated based on various object geometries. Using this system makes it possible to analyze and examine the quality of 3D printing during the print process, which could identify the defective printed parts, terminate the whole process and alert the users for time and cost-savings.