Automatic Detection of Welding Defects Using Image Processing and Deep Learning

Mehmet Emin Örs; Ziya Özçelik

Makale detayları

Automatic Detection of Welding Defects Using Image Processing and Deep Learning

Yazarlar:

Mehmet Emin Örs | Ziya Özçelik

Yayıncı:

Karabuk University

Ana Başlıklar:

Kaynak Teknolojileri

Dergi:

DCE Doğa Bilimleri Dergisi Vol.5 Issue 2, pp.83-97

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001344588

Anahtar kelimeler:

Özet:

 This study developed a model based on Convolutional Neural Networks CNNs to achieve good performance for weld defect detection with 95.3 accuracy and 91.3 F1-score. The models success was achieved by optimizing the dataset of 800 high-resolution images with preprocessing techniques such as noise removal and segmentation, separating 80 for training and 20 for testing. Transfer learning techniques improved performance even on small datasets, and the analysis time of 0.25 seconds proved the usability of the model in real-time applications. The base model in this work is compared with popular architectures such as DenseNet, YOLOv5, and ResNet. Compared to traditional methods, a 15 increase in accuracy and a halving in processing time were achieved. This system brings a new standard to automated quality control processes within the scope of Industry 4.0 and stands out with its applicability, especially in sectors such as automotive and aviation.

 This study developed a model based on Convolutional Neural Networks CNNs to achieve good performance for weld defect detection with 95.3 accuracy and 91.3 F1-score. The models success was achieved by optimizing the dataset of 800 high-resolution images with preprocessing techniques such as noise removal and segmentation, separating 80 for training and 20 for testing. Transfer learning techniques improved performance even on small datasets, and the analysis time of 0.25 seconds proved the usability of the model in real-time applications. The base model in this work is compared with popular architectures such as DenseNet, YOLOv5, and ResNet. Compared to traditional methods, a 15 increase in accuracy and a halving in processing time were achieved. This system brings a new standard to automated quality control processes within the scope of Industry 4.0 and stands out with its applicability, especially in sectors such as automotive and aviation.

 This study developed a model based on Convolutional Neural Networks CNNs to achieve good performance for weld defect detection with 95.3 accuracy and 91.3 F1-score. The models success was achieved by optimizing the dataset of 800 high-resolution images with preprocessing techniques such as noise removal and segmentation, separating 80 for training and 20 for testing. Transfer learning techniques improved performance even on small datasets, and the analysis time of 0.25 seconds proved the usability of the model in real-time applications. The base model in this work is compared with popular architectures such as DenseNet, YOLOv5, and ResNet. Compared to traditional methods, a 15 increase in accuracy and a halving in processing time were achieved. This system brings a new standard to automated quality control processes within the scope of Industry 4.0 and stands out with its applicability, especially in sectors such as automotive and aviation.

 Bu çalışmada, Convolutional Neural Networks CNN tabanlı bir model geliştirilerek kaynak kusurlarının tespitinde 93,5 doğruluk ve 91,3 F1-skoru ile oldukça iyi bir performans elde edilmiştir. Modelin başarısı, 800 yüksek çözünürlüklü görüntüden oluşan veri setinin 80 eğitim ve 20 test için ayrılarak, gürültü giderme ve segmentasyon gibi ön işleme teknikleriyle optimize edilmesiyle sağlanmıştır. Transfer öğrenme teknikleri, küçük veri setlerinde dahi performansı artırmış; 0,25 saniyelik analiz süresi, modelin gerçek zamanlı uygulamalarda kullanılabilirliğini kanıtlamıştır. DenseNet, YOLOv5 ve ResNet gibi popüler mimarilerle çalışmada kullanılan model karşılaştırılmıştır. Geleneksel yöntemlere kıyasla 15 doğruluk artışı ve işlem süresinde yarı yarıya azalma sağlanmıştır. Bu sistem, Endüstri 4.0 kapsamında otomatik kalite kontrol süreçlerine yeni bir standart getirmekte ve özellikle otomotiv, havacılık gibi sektörlerde uygulanabilirliği ile öne çıkmaktadır.

Makale detayları

Automatic Detection of Welding Defects Using Image Processing and Deep Learning

Yazarlar:

Mehmet Emin Örs | Ziya Özçelik

Yayıncı:

Karabuk University

Ana Başlıklar:

Kaynak Teknolojileri

Dergi:

DCE Doğa Bilimleri Dergisi Vol.5 Issue 2, pp.83-97

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001344588

Convolutional Neural Networks (CNN)

Weld Defect Detection

Transfer Learning

Real-Time Applications

Automated Quality Control

Industry 4.0Evrişimli Sinir Ağları (CNN)

Kaynak Hatası Tespiti

Transfer Öğrenimi

Gerçek Zamanlı Uygulamalar

Otomatik Kalite Kontrol

Endüstri 4.0

Sayı

Makale detayları

Yazarlar: Mehmet Emin Örs | Ziya Özçelik

Yayıncı: Karabuk University

Ana Başlıklar: Kaynak Teknolojileri

Dergi: DCE Doğa Bilimleri Dergisi Vol.5 Issue 2, pp.83-97

Belge Tipi: Makale

Yayın Tarihi: 3/2025

Belge Dili: Türkçe

Document ID (Org No.): 2001344588

Sayı

Yazarlar:

Mehmet Emin Örs | Ziya Özçelik

Yayıncı:

Karabuk University

Ana Başlıklar:

Kaynak Teknolojileri

Dergi:

DCE Doğa Bilimleri Dergisi Vol.5 Issue 2, pp.83-97

Belge Tipi:

Makale

Yayın Tarihi:

3/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001344588