A closed-loop in-process warping detection system for fused filament fabrication using convolutional neural networks. (October 2020)

• Record Type: Journal Article
• Authors: Saluja, Aditya; Xie, Jiarui; Fayazbakhsh, Kazem
• Is Part Of: Journal of manufacturing processes. Volume 58(2020)
• Page Start: 407
• Page End: 415
• Publication Date: 2020-10
• Subjects: Convolutional Neural Networks (CNN) -- Fused Filament Fabrication (FFF) -- Warping -- Detection -- Image analysis; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2020.08.036
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: 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 %.