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Journals / Journal of Materials and Mechatronics: A / 2026 / Cilt: 7 Sayı: 1

Deep Learning Application for Image-Based Defect Detection in 3D Printing Processes

3B Baskı Süreçlerinde Görüntü Tabanlı Hata Tespiti için Derin Öğrenme Uygulaması

Elif Aktepe(AFYON KOCATEPE ÜNİVERSİTESİ, AFYON MESLEK YÜKSEKOKULU)0000-0002-2375-2040
Uçman Ergün(AFYON KOCATEPE UNIVERSITY)0000-0002-9218-2192
Pages
109–121
Publication Date
2026-06-16
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the EfficientNetB0 model has an overall accuracy of 87.7%, while MobileNetV2 achieves 97%.

📌 3 Key Takeaways

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Objective & Hypothesis:Fused Deposition Modeling (FDM) is a widely preferred additive manufacturing technology due to its design flexibility and cost advantages.
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Key Findings & Data:However, structural and visual defects such as stringing, layer shifting, warping, or off-platform issues frequently occur in FDM-based three-dimensional (3D) printing processes.
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Conclusion & Impact:The results show that the EfficientNetB0 model has an overall accuracy of 87.7%, while MobileNetV2 achieves 97%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:3B Baskı · derin öğrenme · hata tespiti

Abstract

Fused Deposition Modeling (FDM) is a widely preferred additive manufacturing technology due to its design flexibility and cost advantages. However, structural and visual defects such as stringing, layer shifting, warping, or off-platform issues frequently occur in FDM-based three-dimensional (3D) printing processes. This results in losses of raw materials, energy, and time. This study aims to classify common defect types occurring in FDM-based 3D printing using image-based deep learning algorithms. In this study, a transfer learning approach is adopted using the EfficientNetB0 and MobileNetV2 architectures, both pre-trained on ImageNet, with a dataset of 1912 images. The model’s generalization capability is enhanced through data preprocessing and enhancement techniques. The model can successfully classify defect types using print images without additional sensors or hardware components. The results show that the EfficientNetB0 model has an overall accuracy of 87.7%, while MobileNetV2 achieves 97%. The MobileNetV2 architecture demonstrates strong performance, particularly in error classes such as Layer Shifting and Stringing, with high F1 scores. The proposed architectures have the potential to reduce losses in FDM-based 3D printing processes by providing a low-cost and accessible visual quality control system.

Özet

Erimiş Katmanlı Modelleme (FDM), tasarım esnekliği ve maliyet avantajları nedeniyle yaygın olarak tercih edilen bir eklemeli üretim teknolojisidir. Bununla birlikte, FDM tabanlı üç boyutlu (3B) baskı süreçlerinde sıklıkla ipliklenme, katman kayması, bükülme veya platform dışı sorunlar gibi yapısal ve görsel kusurlar meydana gelir. Bu durum, hammadde, enerji ve zaman kayıplarına yol açar. Bu çalışma, görüntü tabanlı derin öğrenme algoritmaları kullanarak FDM tabanlı 3B baskıda meydana gelen yaygın kusur türlerini sınıflandırmayı amaçlamaktadır. Bu çalışmada, 1912 görüntüden oluşan bir veri kümesiyle ImageNet üzerinde önceden eğitilmiş EfficientNetB0 ve MobileNetV2 mimarileri kullanılarak bir transfer öğrenme yaklaşımı benimsenmiştir. Modelin genelleme yeteneği, veri ön işleme ve geliştirme teknikleri ile artırılmıştır. Model, ek sensörlere veya donanım bileşenlerine ihtiyaç duymadan, yalnızca baskı görüntülerini kullanarak kusur türlerini başarıyla sınıflandırabilir. Sonuçlar, EfficientNetB0 modelinin %87,7'lik genel doğruluk oranına sahip olduğunu, MobileNetV2'nin ise %97'lik bir doğruluk oranına ulaştığını göstermektedir. MobileNetV2 mimarisi, özellikle Katman Kayması ve İplikleme gibi hata sınıflarında yüksek F1 puanlarıyla güçlü bir performans sergilemektedir. Bu çalışmada önerilen başarılı mimariler, gelecekteki çalışmalarda ek donanım bileşenlerine ihtiyaç duymadan düşük maliyetli ve erişilebilir bir görsel kalite kontrol sistemi sağlayarak FDM tabanlı 3D baskı süreçlerindeki kayıpları azaltma potansiyeline sahiptir.

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Cite This Article

Aktepe, E., Ergün, U. (2026). Deep Learning Application for Image-Based Defect Detection in 3D Printing Processes. Journal of Materials and Mechatronics: A, Cilt: 7 Sayı: 1, 109–121. https://doi.org/10.55546/jmm.1910657

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