ABSTRACT Stress corrosion cracking (SCC) is a common corrosion form that involves undetected premature failures during service life of structures. Since SCC is a combination of electrochemical and mechanical phenomena, coupled acoustic emission and electrochemical noise techniques were proposed to investigate the evolution of SCC damage tomartensitic stainless steel samples. Tests were carried out using a precipitation hardening martensitic stainless steel in an aqueous MgCl2 environment at 100°C, with an applied mechanical stress equal to the 90% of 0.2% yield strength. The synergistic use of the two non-destructive analysis technique was performed using a synchronisation process. The combination of two advanced multivariate analysis techniques (Principal Component Analysis and Self Organising Map neural network) highlighted damage-sensitive features. Characteristic clusters of variables related to specific damage mechanisms were identified discriminating electrochemical activation processes (i.e. pitting), SCC initiation and propagation until final failure phenomena during SCC.

中文翻译：

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用于评估 SCC 损伤的组合 AE 和 EN 信号的拓扑神经网络

摘要 应力腐蚀开裂 (SCC) 是一种常见的腐蚀形式，涉及结构使用寿命期间未检测到的过早失效。由于 SCC 是电化学和机械现象的结合，因此提出了耦合声发射和电化学噪声技术来研究 SCC 损伤对马氏体不锈钢样品的演变。使用沉淀硬化马氏体不锈钢在 100°C 的 MgCl2 水溶液环境中进行测试，施加​​的机械应力等于 0.2% 屈服强度的 90%。两种无损分析技术的协同使用是使用同步过程进行的。两种先进的多元分析技术（主成分分析和自组织图神经网络）的结合突出了损伤敏感特征。确定了与特定损伤机制相关的变量的特征集群，以区分电化学活化过程（即点蚀）、SCC 开始和扩展，直到 SCC 期间的最终失效现象。