j. mater. mechat. a Journal of Materials and Mechatronics: A 2717-8811 Yusuf KAYALI 10.55546/jmm.1512549 Deep Learning Derin Öğrenme Deep Learning Application and Analysis In Detection of Metal Plate Surface Defects METAL PLAKA YÜZEY KUSURLARININ TESPİTİNDE DERİN ÖĞRENME UYGULAMASI VE ANALİZİ https://orcid.org/0000-0003-0539-1381 Tuncer Can GESBEY A.Ş. Ar-Ge Merkezi https://orcid.org/0000-0003-1407-9867 Közkurt Cemil BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ https://orcid.org/0000-0001-9483-4425 Kılıçarslan Serhat BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ 12 20 2024 5 2 263 285 07 09 2024 11 18 2024 Copyright © 2020, Journal of Materials and Mechatronics: A 2020 Journal of Materials and Mechatronics: A

In industrial manufacturing processes, detection of defects on the surfaces of metal plates supplied from iron and steel main industry manufacturers to be processed by machining and non-machining methods has an important place in estimating the values of the relevant plate such as safety and maintenance cost. With the developing technology and computer vision and deep learning applications finding a place in the industry, it has become possible to detect and classify metal plate surface defects more quickly and effectively with a lower error rate at an advanced technological level. Within the scope of this study, a deep learning model was created by using the TensorFlow library in the Python environment with using NEU Metal Surface Defects Dataset to detect metal plate surface defects. Then as an industrial application, a device prototype developed using Nvidia Jetson Nano and USB Camera, in order to test this model under real conditions.

Endüstriyel imalat proseslerinde, demir çelik ana sanayi üreticilerinden talaşlı ve talaşsız yöntemlerle işlenmek üzere temin edilen metal levhaların yüzeylerindeki kusurların tespiti, ilgili levhanın güvenlik ve bakım maliyeti gibi değerlerinin tahmin edilmesinde önemli bir yer tutmaktadır. Gelişen teknolojiyle bilgisayarlı görü ve derin öğrenme uygulamalarının endüstride kendine yer bulması ile metal plaka yüzey kusurlarının ileri teknolojik düzeyde daha hızlı ve etkin bir şekilde daha düşük hata oranıyla tespit edilmesi ve sınıflandırılması mümkün hale gelmiştir. Bu çalışma kapsamında, metal plaka yüzey kusurlarını tespit etmek için NEU Metal Yüzey Kusurları Veri Seti kullanılarak Python ortamında TensorFlow kütüphanesi kullanılarak bir derin öğrenme modeli oluşturulmuştur. Daha sonra endüstriyel uygulama olarak bu modeli gerçek koşullar altında test etmek amacıyla Nvidia Jetson Nano ve USB Kamera kullanılarak bir cihaz prototipi geliştirilmiştir.

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