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Automated defect detection in fused filament fabrication coupling deep learning and computer vision
Publikationstyp
Conference Paper
Date Issued
2025
Sprache
English
Citation
European Conference on Computing in Construction & CIB W78 Conference on IT in Construction 2025 & 13th Linked Data in Architecture and Construction Workshop, LDAC 2025
Publisher DOI
Fused filament fabrication (FFF) is an additive manufacturing technique, popular due to its versatility and cost-effectiveness. However, FFF machines, such as 3D printers, are prone to runtime errors, wasting time and material, while requiring constant human supervision. This paper presents a defect detection approach for FFF processes based on artificial intelligence, combining convolutional neural networks and computer vision. The defect detection approach is validated using 3D prints designed to mimic common FFF defects. The results demonstrate the capability of the proposed approach to automatically detect defects in FFF processes, thereby reducing time and material waste as well as the need for human supervision.
Subjects
Additive manufacturing
fused filament fabrication
3D printing
artificial intelligence
computer vision
deep learning
defect detection
DDC Class
005: Computer Programming, Programs, Data and Security
620.1: Engineering Mechanics and Materials Science