Artificial neural network-based prediction technique for coating thickness in Fe-Al coatings fabricated by mechanical milling

VAROL T., ÇANAKÇI A., ÖZŞAHİN Ş., ERDEMİR F., ÖZKAYA S.

PARTICULATE SCIENCE AND TECHNOLOGY, cilt.36, sa.6, ss.742-750, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 6
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1080/02726351.2017.1301607
  • Dergi Adı: PARTICULATE SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.742-750
  • Anahtar Kelimeler: Artificial neural network, coating, coating thickness, Fe-Al intermetallics, mechanical milling, NANO-CRYSTALLINE NICKEL, PROCESS-CONTROL AGENT, ALLOYING METHOD, IRON ALUMINIDES, COMPOSITE, KINETICS, BEHAVIOR, POWDERS, MICROSTRUCTURE, EVOLUTION
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

The objective of this study was to evaluate the effect of milling time, milling speed and particle size of initial powders on the coating thickness of Fe-Al intermetallic coating by using artificial neural network (ANN). Coating morphology and cross-section microstructures were evaluated using a scanning electron microscope (SEM). It was found that an increase in the milling time provided an increase in the coating-layer thickness due to the cold welding process between particles and the steel substrate. The microstructure of the coating surface was refined by ball impacts in the milling process. As a result of this study, the ANN was found to be successful for predicting the coating thickness of Fe-Al intermetallic coatings. The correlation between the predicted values and the experimental data of the feed-forward back-propagation ANN was quite adequate. The mean absolute percentage error (MAPE) for the predicted values didn't exceed 7.46%. The ANN model can be used for predicting the coating thickness of Fe-Al intermetallic coating produced for different milling time, milling speed and particle size.