Prediction of corrosion susceptibilities of Al-based metal matrix composites reinforced with SiC particles using artificial neural network

Tuntaş R., Dikici B.

JOURNAL OF COMPOSITE MATERIALS, vol.49, no.27, pp.3431-3438, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 27
  • Publication Date: 2015
  • Doi Number: 10.1177/0021998314565430
  • Journal Name: JOURNAL OF COMPOSITE MATERIALS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.3431-3438
  • Keywords: Artificial neural network, metal matrix composite, corrosion, modeling, SURFACE-ROUGHNESS, BEHAVIOR, DENSITY, STEEL
  • Van Yüzüncü Yıl University Affiliated: Yes

Abstract

In this theoretical study, the prediction of the corrosion resistance of Al-Si-Mg-based metal matrix composites reinforced with SiC particles has been studied, using an artificial neural network. Four input vectors were used in the construction of the proposed network; namely, volume fraction of SiC reinforcement, aging time of the composites, environmental conditions, and potential. Current was used as the one output in the proposed network. Test results indicate that the proposed network can be used efficiently for the prediction of the corrosion resistance of Al-Si-Mg-based metal matrix composites reinforced with SiC particles, and the methodology is suitable for engineers to study the corrosion of metal matrix composites. In addition, a few forecasts regarding the polarization response for different SiC volume fractions and aging conditions have also been generated without using any experimental data.