Sistem Inspeksi Visual Berbasis Web-camera Menggunakan YOLO untuk Deteksi Real-time Spaghetti defect pada 3D Printing FDM
DOI:
https://doi.org/10.51903/elkom.v19i1.3976Keywords:
FDM 3D Printing, Raspberry Pi, Spaghetti Defect, Visual InspectionAbstract
Print failure remains a major challenge in fused deposition modeling (FDM) 3D printing because it may lead to material waste, production delays, and potential damage to printer components. One frequent failure mode is the spaghetti defect , in which extruded filament forms irregular strands due to poor bed adhesion, object displacement, or extrusion outside the intended printing path. This study develops a low-cost visual inspection system based on a web-camera and Raspberry Pi for real-time detection of spaghetti defect using a YOLO object detection model. The proposed system captures visual data from the printing area, processes image frames locally on the edge device, and displays the detected defect location using a bounding box, class label, and confidence score. The dataset consisted of 100 images, including 50 normal printing images and 50 spaghetti defect images, divided into training, validation, and testing sets using a 70:20:10 ratio. Defect images were annotated using bounding boxes to enable location-based defect detection. The evaluation results show that the model achieved a precision of 91.30%, recall of 88.24%, F1-score of 89.74%, [email protected] of 92.10%, and [email protected]:0.95 of 87.30%. Real-time testing also demonstrated that the system could detect spaghetti defect with confidence scores ranging from 0.87 to 0.90. These results indicate that the integration of a web-camera, Raspberry Pi, and YOLO has potential as an economical and portable early monitoring system for FDM 3D printing failures.
References
Agustina, F., & Sukron, M. (2022). Deteksi Kematangan Buah Pepaya Menggunakan Algoritma YOLO Berbasis Android. Jurnal Ilmiah Infokam, 18(2), 70–78.
Al Ahad Khan, A., Islam, M. S., Li, L., Jiang, L., & Ghaffari, N. (2026). Automated Pore Detection from In-Situ FDM 3D Printing Video: A Comparative Evaluation of Modern Segmentation Models. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 4673–4681.
Antić, J., Mišković, Ž., Mitrović, R., Stamenić, Z., & Antelj, J. (2023). The Risk assessment of 3D printing FDM technology. Procedia Structural Integrity, 48, 274–279.
Dewi, I. R., & Ramadhan, F. (2025). ANALISIS KOMPARATIF PENDEKATAN REKAYASA SISTEM EMBEDDED BERBASIS MIKROKONTROLER DALAM OPTIMALISASI KINERJA REAL-TIME DAN EFISIENSI ENERGI. Jurnal Teknologi Informasi, 11(1), 48–53.
Gamage, U. G., Huo, X., Zanatta, L., Delbruck, T., Cadena, C., Fumagalli, M., & Tolu, S. (2026). Event-based civil infrastructure visual defect detection: ev-CIVIL dataset and benchmark. Structural Health Monitoring, 14759217251411320.
He, H., Zhu, Z., Zhang, Y., Zhang, Z., Famakinwa, T., & Yang, R. (2024). Machine condition monitoring for defect detection in fused deposition modelling process: a review. The International Journal of Advanced Manufacturing Technology, 132(7), 3149–3178.
Liu, Z., Han, J., Wang, S., Li, J., Yi, H., & Long, T. (2025). Research Progress and Future Perspectives of 3D Printing Polymer‐Based Materials Whole Life Cycle Frameworks: Material Genetic Design–Intelligent Manufacturing–Recycling. Advanced Engineering Materials, 27(22), e202501538.
Mahendra, B., Pratama, D., Faqih, A., & Kurniawan, R. (2026). Evaluasi Pengaruh Kualitas Data Terhadap Performa Model Machine Learning Menggunakan Pendekatan Data-Centric AI. Jurnal Sistem Informasi dan Teknologi (SINTEK), 6(1), 107–113.
Nasikhin, Y. I., Rahmat, B., & Putra, C. A. (2025). PENGARUH RASIO PEMBAGIAN DATASET PADA KINERJA CNN KLASIFIKASI TUBERKULOSIS. Prosiding Seminar Sosial Politik, Bisnis, Akuntansi dan Teknik, 7, 303–311.
Omiyale, B. O., Ogedengbe, I. I., Odeyemi, J., Ogbeyemi, A., Olorunsogbon, F., & Zhang, W. C. (2025). Mitigating 3D printing defects via cyber-physical systems: a process for fabricating defect-free components. The International Journal of Advanced Manufacturing Technology, 139(7), 3175–3196.
Özsoy, K., & Aksoy, B. (2022). Real-time data analysis with artificial intelligence in parts manufactured by FDM printer using image processing method. Journal of Testing and Evaluation, 50(1), 629–645.
Pramanda, W., Zaqi, S. Y. B., & Utama, S. D. (2025). Inovasi media pembelajaran alat berat: Prototipe simulator excavator hidrolik air berbiaya rendah menggunakan teknologi fused deposition modeling (FDM). Jurnal Inovator, 8(2), 139–144.
Pristiansyah, P., Hasdiansah, H., Subhan, M., Sukanto, S., Aswin, F., Masdani, M., & Ariansyah, A. (2025). OPTIMIZATION OF FDM 3D PRINTING PARAMETERS ON THE TENSILE STRENGTH OF FOOD-GRADE PLA FILAMENT USING THE TAGUCHI METHOD. Jurnal Rekayasa Mesin, 16(3), 1169–1181.
RICO, A. (2026). EVALUASI KINERJA DEEP LEARNING UNTUK IDENTIFIKASI SPESIES KUPU-KUPU SUMATERA BERBASIS AUGMENTASI DATA.
Sani, A. R., Kouzani, A. Z., & Zolfagharian, A. (2026). Real-time defect monitoring in material extrusion 3D printing using optimized YOLO models. Progress in Additive Manufacturing, 11(1), 1115–1137.
Sobirin, M., & Wijonarko, P. (2025). Pemantauan Keamanan Real-Time pada Base Transceiver Station (BTS) Menggunakan YOLOv8 dan Integrasi Telegram: Optimasi Model dan Evaluasi Performa. ILKOMNIKA, 7(3), 533–547.
Suwandi, D. A. S. (2025). OTOMATISASI PENGENDALIAN KUALITAS KETAHANAN FILAMEN PET MENGGUNAKAN FUZZY LOGIC BERBASIS IOT. Politeknik Perkapalan Negeri Surabaya.
Tjahyanti, L. P. A. S., & Pratama, P. A. (2025). Deteksi Objek Menggunakan Convolutional Neural Network (CNN) pada Pengolahan Citra Digital. KOMTEKS, 4(2), 35–40.
Yadav, A., Rohru, P., Babbar, A., Kumar, R., Ranjan, N., Chohan, J. S., Kumar, R., & Gupta, M. (2023). Fused filament fabrication: A state-of-the-art review of the technology, materials, properties and defects. International Journal on Interactive Design and Manufacturing (IJIDeM), 17(6), 2867–2889.
Yean, F. P., & Chew, W. J. (2024). Detection of spaghetti and stringing failure in 3D printing. 2024 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), 293–298.
Published
Issue
Section
License
Copyright (c) 2026 Elkom: Jurnal Elektronika dan Komputer
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
