Data Mining of Experimental Corrosion Data Using Neural Network

A supervised Neural Network (NN) method was used to predict the corrosion behavior (general and localized corrosion) and in turn, the life of metals and alloys over extended periods of time in specific environments, such that the developed NN model learns the underlying laws that map the alloy characteristics and environments to the alloy's corrosion behavior. The approach used in this work is the mining of existing experimental databases for both DC and AC corrosion experiments on metallic glasses, and grade-2 Titanium. The data mining results allow us to categorize and prioritize certain parameter sets (e.g. pH, temperature, time, electrolyte & metal compositions) that impact the alloy's corrosion rate and electrochemical potential. The developed NN models were tested by validating the predicted life of metals/alloys against available experimental data. The NN models were then used to predict future corrosion rates for user-specified environmental conditions and time frames.