FARKLI YÜZEY TÜRLERİNE SAHİP ÜRÜNLERİN HATA TESPİTİNDE EVRİŞİMSEL SİNİR AĞI MİMARİLERİNİN ETKİSİ

Betül Karakaş; Sinem Kulluk

doi:10.17780/ksujes.1640228

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FARKLI YÜZEY TÜRLERİNE SAHİP ÜRÜNLERİN HATA TESPİTİNDE EVRİŞİMSEL SİNİR AĞI MİMARİLERİNİN ETKİSİ

Öz

Üretim sistemlerinde üretilen ürünlerin hatalarının hızlı ve doğru bir şekilde tespit edilmesi maliyet, kalite ve müşteri memnuniyeti açısından önem teşkil etmektedir. Ürün yüzey hataları üretim hatalarının büyük bir kısmını kapsamaktadır. Yüzey hatalarının tespiti derin öğrenme yöntemleri ile başarılı bir şekilde gerçekleştirilebilmektedir. Bu çalışmada derin öğrenme yöntemlerinden Evrişimsel Sinir Ağı mimarileri kullanılmıştır. Farklı türdeki yüzey hataları üzerinde sınıflandırma işlemi gerçekleştirerek, değişen endüstriyel süreçlere uyum sağlamayı kolaylaştırmak ve farklı mimarilerin farklı yüzey türleri üzerindeki etkilerinin incelenmesi amaçlanmıştır. Metal, Ahşap, Gıda ve Kumaş türlerine sahip dört veri kümesi GoogleNet, SqueezeNet ve VGG 19 olmak üzere üç farklı CNN mimarisi ile sınıflandırılmış, başarıları incelenmiştir. Sınıflandırma işleminde validasyon doğruluk değeri göz önünde bulundurulmuştur. Elde edilen sonuçlara göre siyah-beyaz tonlamaya sahip Metal ve Kumaş veri kümelerinde GoogleNet mimarisi sırasıyla %99,89 ve %96,86 ile diğer mimarilerden daha yüksek sınıflandırma başarısı ortaya koyarken, renkli tonlamaya sahip veri kümeleri olan Ahşap ve Gıda veri kümelerinde VGG 19 mimarisi sırasıyla %94,39 ve %99,96 ile daha yüksek sınıflandırma başarısı göstermiştir. Yapılan çalışma sonucunda Ahşap ve Kumaş veri kümeleri için elde edilen sınıflandırma başarıları literatürdeki çalışmalara göre ortalamanın üzerinde elde edilirken, Metal ve Gıda veri kümeleri için sınıflandırma başarıları literatürdeki çalışmalardan daha yüksek olarak elde edilmiştir.

Anahtar Kelimeler

EFFECT OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES ON DEFECT DETECTION OF PRODUCTS WITH DIFFERENT SURFACE TYPES

Abstract

Fast and accurate detection of product defects in manufacturing systems is crucial for cost, quality and customer satisfaction. Surface defects comprise a large portion of production defects and can be successfully detected using deep learning methods. This study employed Convolutional Neural Network (CNN) architectures to classify different types of surface defects and examine the effects of different architectures on various surface types. Four datasets (Metal, Wood, Food, and Fabric) were classified using three CNN architectures: GoogleNet, SqueezeNet, and VGG 19. Validation accuracy was considered in the classification process. According to the results, for the grayscale Metal and Fabric datasets, the GoogleNet architecture achieved higher classification accuracies than the other architectures, with 99,89% and 96,86%, respectively. In contrast, for the color datasets, namely Wood and Food, the VGG19 architecture demonstrated higher classification performance, achieving 94,39% and 99,96%, respectively. As a result of this study, the classification accuracies obtained for the Wood and Fabric datasets were above the average reported in the literature, while the accuracies for the Metal and Food datasets surpassed those reported in previous studies.

Keywords

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Derin Öğrenme , Endüstri Mühendisliği

Bölüm

Araştırma Makalesi

Yazarlar

Betül Karakaş ^*
0000-0001-5524-9864
Türkiye

Sinem Kulluk
0000-0002-0675-3113
Türkiye

Yayımlanma Tarihi

3 Aralık 2025

Gönderilme Tarihi

17 Şubat 2025

Kabul Tarihi

12 Eylül 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1640228

IZ

https://izlik.org/JA63TF92JZ

Kaynak Göster

RIS / Bibtex

APA

Karakaş, B., & Kulluk, S. (2025). FARKLI YÜZEY TÜRLERİNE SAHİP ÜRÜNLERİN HATA TESPİTİNDE EVRİŞİMSEL SİNİR AĞI MİMARİLERİNİN ETKİSİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 1673-1687. https://doi.org/10.17780/ksujes.1640228