Classification of operation cases in electric arc welding wachine by using deep convolutional neural networks

Noğay H. S., Akıncı T. Ç.

NEURAL COMPUTING & APPLICATIONS, cilt.33, sa.12, ss.6657-6670, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 12
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s00521-020-05436-y
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.6657-6670
  • Anahtar Kelimeler: Welding, Deep convolutional neural network, Graphical image, Pre-trained, Alexnet, Googlenet, Squeezenet, Resnet18, OPTIMIZATION, PREDICTION, PARAMETERS, GEOMETRY, SYSTEM, BEAD
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

Electric arc welding machines are widely used in industry in metal technology. In parallel with the advancement of technology for the development and automation of electric arc welding machines, it is necessary to conduct scientific studies on the determination of optimal operation cases and control for optimal welding process. In this study, operating zones were classified and determined according to the measured welding current graph during the 5-s operation of the MAG electric arc welding machine. Five deep convolutional neural networks were used for this purpose. Four of these deep learning methods are pre-trained models. We used the concept of "ransfer learning" to use pre-trained models. According to the results we obtained from five different models, we were able to estimate the operating range of the electric arc welding machine, with 93.5% accuracy with the designed model and 95-100% accuracy with pre-trained models.