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 (h_av) with the immersion time (t) was established using 3D maps and statistic techniques. It was found that with n ≈ 0.5. Moreover, determination of the pit area allows estimating the curve current density (j) versus the immersion time and it was found that with m ≈ 1. 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.

中文翻译：

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用统计方法和人工神经网络检验316L钢表面的腐蚀损伤

介绍并讨论了316L钢表面（浸入5 wt％NaCl溶液）腐蚀引起的变化的详细检查。使用3D地图和统计技术确定了稳定凹坑深度（h _av）随浸入时间（t）的变化。结果发现，与ñ  ≈0.5。此外，坑区的确定允许估计曲线电流密度（Ĵ相对于浸渍时间），并发现，与米 ≈1.一种用于确定表面腐蚀程度的新技术是基于对2D灰度图像的分析，而不是表明腐蚀形态已被阐述的黑白图像的分析。为此，使用了带有Levenberg-Marquardt反向传播训练算法的三层前馈神经网络。结果表明，所提出的程序可以充分描述腐蚀程度与浸没时间的关系（S型曲线）。