Modelling the influence of pH, fluoride, and caffeine on the corrosion resistance of TiMo alloys by artificial neural networks developed with differential evolution algorithm

Titanium (Ti) and its alloys are widely used in dental applications due to the excellent corrosion resistance and mechanical properties. However, it has been reported that Ti is sensitive to fluor ions (F‐) and lactic acid. Corrosion behaviour of the TiMo alloys, together with the currently used met...

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Bibliographic Details
Published in:Materials and corrosion 2015-09, Vol.66 (9), p.982-994
Main Authors: Mareci, D., Dragoi, E.-N., Bolat, G., Chelariu, R., Gordin, D., Curteanu, S.
Format: Article
Language:English
Subjects:
acidic artificial saliva
Alloys
Artificial neural networks
Biomedical materials
Caffeine
Chemical Sciences
Corrosion effects
Corrosion resistance
Corrosion resistant alloys
Dental alloys
Dental materials
differential evolution algorithm
Electrochemical impedance spectroscopy
Equivalent circuits
Evolutionary algorithms
Evolutionary computation
Lactic acid
Mathematical models
Mechanical properties
Modelling
Neural networks
TiMo alloys
Titanium
Titanium base alloys
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Summary:Titanium (Ti) and its alloys are widely used in dental applications due to the excellent corrosion resistance and mechanical properties. However, it has been reported that Ti is sensitive to fluor ions (F‐) and lactic acid. Corrosion behaviour of the TiMo alloys, together with the currently used metallic biomaterial commercial pure titanium (Cp‐Ti), was investigated considering the use of alloys for dental applications. All the samples were examined using electrochemical impedance spectroscopy (EIS) in acidic artificial saliva with NaF and/or caffeine, at 37 °C. Equivalent circuits were used for modeling EIS data, in order to characterize samples surface and better understanding the effect of Mo addition on Cp‐Ti. The TiMo alloys appear to possess superior corrosion resistance than Cp‐Ti in all electrochemical media. In addition, a modelling technique based on differential evolution and artificial neural network was applied. The scope of this procedure was to determine an efficient model of the process and to eliminate the need for new experiments based on the predictions provided by the developed models.
ISSN:0947-5117
1521-4176
DOI:10.1002/maco.201408077