Abstract
A deeper insight into the changing states of corrosion during certain exposure circumstances has been investigated by applying Kohonen networks. The Kohonen network has been trained by four sets of samples and tested using another sample. All the sample data were collected during accelerated corrosion experiments and the network took the changing rate of impedance of each cycle as an input. Compared with traditional classification, the Kohonen artificial network method classifies corrosion process into five sub-processes which is a refinement of three typical corrosion processes. The two newly defined sub-processes of corrosion—namely, pre-middle stage and post-middle stage—were introduced. The EIS data and macro-morphology for both sub-processes were analyzed through accelerated experiments. The classification results of the Kohonen artificial network are highly consistent with the predictions based on impedance magnitude at low frequency, which illustrates that the Kohonen network classification is an effective method for predicting the failure cycles of polymer coatings.
Highlights
There is major significance in judging the state of polymer coating in service to study the coating failure process under different conditions
Corrosion bubbling appears in part of the specimen visually and it can be observed in and this period is corresponds to in the the phenomena phenomenaof ofwhite whitecorrosion corrosionforming formingflow flowmarks, marks, and this period is corresponds the middle stage
The challenging feature that has been proposed for such intelligence-based assessments of coating corrosion deterioration appears to be the refinement of traditional three stages of the corrosion process into five sub-processes
Summary
There is major significance in judging the state of polymer coating in service to study the coating failure process under different conditions. Electrochemical impedance spectroscopy (EIS) is considered a very powerful technique for evaluating the protective properties of polymer coatings and their degradation during exposure to corrosive environments [1,2,3]. The experimental EIS data can be analyzed by appropriate models (e.g., equivalent circuits (EC)) and the fitting parameters can be used to reflect coating properties as well as the corrosion reaction at the metal/coating interface, such quantities as coating capacitance, coating resistance, and double layer capacitance, etc. Orders of impedance magnitudes are diverse for different coating systems. Considering this situation, some researchers proposed to make use of the minimum phase angle and its frequency [4], impedance at low frequencies [5], and phase angle at high frequencies [6] to get parameter assessment.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
