An ANFIS model to prediction of corrosion resistance of coated implant materials

作者：Tuntas Remzi; Dikici Burak^*

来源：Neural Computing & Applications, 2017, 28(11): 3617-3627.

DOI：10.1007/s00521-017-3103-8

• Adaptive neuro-fuzzy inference system
• ANFIS
• Corrosion
• Modelling
• Potentiodynamic polarization
• Biomaterials

In the present study, an adaptive neuro-fuzzy inference system (ANFIS) model has been used for predicting the corrosion resistance of AA6061-T4 alloy coated with micro-/nano-hydroxyapatite (HA) powders by sol-gel technique. The input parameters of the model consist of the HA powder size (micro-/nanoscale, 35 mu m/20 nm), coating thickness (30, 60 and 85 mu m) and potential values, while the output parameter is corrosion current density. The performance of proposed ANFIS model was tested on the potentiodynamic polarization scanning (PDS) curves by comparing experimental and the theoretical results of the coatings. The results showed that the generated PDS curves of the coatings are in definitely acceptable levels with obtained results in our experimental reference study. Then, the combined effect of arbitrary selected coating thickness and HA powder size on corrosion behaviour of the coatings was also predicted by trained ANFIS model without using any experimental data. And finally, the predicted results for the arbitrary selected coating thicknesses were compared by validation tests. The results showed that the ANFIS has potential to be used in industrial applications of biomedical implant materials coated with HA without performing any experiments after detailed systematic studies in the near future.

• 出版日期2017-11