Prediction of corrosion behaviour of Alloy 22 using neural network as a data mining tool

Abstract Objective of this work was to develop an algorithm to predict behaviour of corrosion resistant metal alloys using a supervised neural network method as a data mining tool. We studied corrosion data on a nickel-based alloy, Alloy 22 which is of great industrial interest. This is an extension of a previously reported study on metallic glasses, carbon steel, and grade-2 titanium. The data mining results allow us to categorize and prioritize certain parameters (i.e. pH, temperature, time of exposure, electrolyte composition, metal composition, etc.) and help us understand the synergetic effects of the parameters and variables on corrosion behaviour.

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