Akıllı Üretim Sistemlerinde Çoklu Hata Teşhisi için Hibrit Derin Öğrenme Yaklaşımları

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Erzincan Binali Yıldırım Üniversitesi, Fen Bilimleri Enstitüsü, Türkiye

Tezin Onay Tarihi: 2026

Tezin Dili: Türkçe

Öğrenci: FEYZANUR ŞAHİN

Danışman: Canan Taştimur Temiz

Özet:

Early detection of surface defects in smart systems within industrial production processes is

critical for automating quality control processes, reducing costs, and maintaining quality

standards. Smart manufacturing systems enable the detection of different types of defects

through multiple defect diagnosis. With the increase in data volume on production lines due to

Industry 4.0, classical machine learning approaches have proven insufficient for defect

diagnosis. This has led to a growing need for more advanced analysis methods. In this thesis,

hybrid deep learning architectures are used to classify different types of defective data samples

with high accuracy. This thesis examines hybrid deep learning approaches for identifying

multiple defects on metal and fabric surfaces. In the proposed method, different deep learning

architectures are used together to perform both object detection and segmentation. In this study,

features obtained from defective images are extracted using convolutional neural networks, and

then these features are analyzed with different classifiers. The deep architectures used in the

classification phase are innovative approaches such as Vision Transformer (ViT) and attentionbased

deep neural networks. Experimental studies have shown that the proposed method

achieves high performance in detecting different types of failures. This study contributes to the

improvement of quality control processes in intelligent manufacturing systems.