Abstract Stainless steel has proved to be an important material to be used in a wide range of applications. For this reason, ensuring the durability of this alloy is essential. In this work, pitting corrosion behaviour of EN 1.4404 stainless steel is evaluated in marine environment in order to develop a model capable of predicting its pitting corrosion status by an automatic way. Although electrochemical techniques and microscopic analysis have been shown to be very useful tools for corrosion studies, these techniques may present some limitationus. With the aim to solve these drawbacks, a three-step model based on Artificial Neural Networks (ANNs) is proposed. The results reveal that the model can be used to predict pitting corrosion status of this alloy with satisfactory sensitivity and specificity with no need to resort to electrochemical tests or microscopic analysis. Therefore, the proposed model becomes a useful tool to predict the behaviour of the material against pitting corrosion in saline environment automatically.

中文翻译：

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使用人工神经网络模拟 EN 1.4404 不锈钢在海洋环境中的点蚀行为：数据分析和新进展

摘要 不锈钢已被证明是一种用途广泛的重要材料。因此，确保这种合金的耐用性至关重要。在这项工作中，对 EN 1.4404 不锈钢在海洋环境中的点腐蚀行为进行了评估，以开发一种能够通过自动方式预测其点腐蚀状态的模型。尽管电化学技术和显微分析已被证明是非常有用的腐蚀研究工具，但这些技术可能存在一些局限性。为了解决这些缺点，提出了一种基于人工神经网络 (ANN) 的三步模型。结果表明，该模型可用于预测该合金的点蚀状态，具有令人满意的灵敏度和特异性，无需借助电化学测试或显微分析。因此，所提出的模型成为自动预测材料在盐水环境中抗点蚀行为的有用工具。