Al-Zuriqat, ThamerThamerAl-ZuriqatNoufal, MahmoudMahmoudNoufalPeralta Abadia, PatriciaPatriciaPeralta AbadiaDragos, KosmasKosmasDragosSmarsly, KayKaySmarsly2025-11-282025-11-282025European Conference on Computing in Construction & CIB W78 Conference on IT in Construction 2025 & 13th Linked Data in Architecture and Construction Workshop, LDAC 2025https://hdl.handle.net/11420/59263Fused 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.enAdditive manufacturingfused filament fabrication3D printingartificial intelligencecomputer visiondeep learningdefect detectionComputer Science, Information and General Works::005: Computer Programming, Programs, Data and SecurityTechnology::620: Engineering::620.1: Engineering Mechanics and Materials ScienceConference Paper10.35490/EC3.2025.176Conference Paper