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Citation - Scopus: 1
Enhancing Quality Control in Plastic Injection Production: Deep Learning-Based Detection and Classification of Defects
(IEEE, 2023) Mutlu, İsmail; Çakar, Tuna; Çakar, Tuna; Yıldız, Ahmet; Sayar, Alperen; Şimsek, Kamil; Tunalı, Mustafa Mert; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
This 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.

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