A closed-loop in-process warping detection system for fused filament fabrication using convolutional neural networks

摘要

Fused Filament Fabrication (FFF) is an additive manufacturing technology that can produce complicated structures in a simple-to-use and cost-effective manner. Although promising, the technology is prone to defects, e.g. warping, compromising the quality of the manufactured component. To avoid the adverse effects caused by warping, this paper utilizes deep-learning algorithms to develop a warping detection system using Convolutional Neural Networks (CNN). To create such a system, a real-time data acquisition and analysis pipeline laid out. The system is responsible for capturing the print layer-by-layer and simultaneously extracting the corners of the component. The extracted region-of-interest s then passed through a CNN outputting the probability of a corner being warped. If a warp s identified, a signal is sent to pause the print, creating a closed-loop in-process detection system. The underlying model tested in an experimental set-up yielding a mean accuracy of 99.3 %.