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, vol.36, no.6, pp.742-750, 2018 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 6
  • Publication Date: 2018
  • Doi Number: 10.1080/02726351.2017.1301607
  • Title of Journal : PARTICULATE SCIENCE AND TECHNOLOGY
  • Page Numbers: pp.742-750

Abstract

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.