Corrosion Damage of 316L Steel Surface Examined Using Statistical Methods and Artificial Neural Network
Abstract
Detailed examination of corrosion-induced changes of the 316L steel surface (immersed in 5 wt% NaCl solution) is presented and discussed. The evolution of the stable pit depth (hav) with the immersion time (t) was established using 3D maps and statistic techniques. Moreover, determination of the pit area allows estimating the curve current density (j) versus the immersion time. A novel technique for surface corrosion degree determination is based on analysis of 2D grayscale images instead of black and white images showing that corrosion morphology was elaborated. For this purpose a three-layered, feed-forward neural network with the Levenberg–Marquardt backpropagation training algorithm was used. It was shown that a dependence corrosion degree versus immersion time (S-type curve) can be fully described by the proposed procedure.
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