Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Citation - Scopus: 1Enhancing Quality Control in Plastic Injection Production: Deep Learning-Based Detection and Classification of Defects(IEEE, 2023-09-13) Mutlu, İsmail; Çakar, Tuna; Aslan, Yeşim; Yıldız, Ahmet; Sayar, Alperen; Şimsek, Kamil; Tunalı, Mustafa MertThis study investigates the applicability of diverse deep learning techniques in detecting and classifying defects within plastic injection manufacturing processes. The findings derived from the models yield several feasible solutions that hold potential practical implications. Notably, the implementation of the Xception model as a classification framework presents a potential domain for enhancing quality control procedures. The developed models, trained on the prepared data sets, provide compelling evidence for the potential utilization of artificial intelligence technologies in the manufacturing industry. Consequently, this study represents a noteworthy contribution to the limited yet auspicious academic research in the field.Conference Object Citation - Scopus: 10Steel Surface Defect Classification Via Deep Learning(IEEE, 2022-09-14) Yildiz, Ahmet; Çakar, Tuna; Tunal, Mustafa MertDeep learning and image processing methods have taken place in many parts of our lives, as well as in the quality control stages of production lines. The aim of this study is to train and use a deep learning model to improve quality management using limited data and computing power. To achieve that, deep learning for quality control models were trained by classifying six different steel surface defect images in the NEU-DET dataset. Xception, ResNetV2 152, VGG19 and InceptionV3 architectures were used to train the model. High accuracy was obtained with both Xception and ResNetV2 152. © 2022 IEEE.