Topological neural network of combined AE and EN signals for assessment of SCC damage. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Calabrese, Luigi; Galeano, Massimiliano; Proverbio, Edoardo; Di Pietro, Domenico; Donato, Angelo
• Is Part Of: Nondestructive testing and evaluation. Volume 35:Issue 1(2020)
• Page Start: 98
• Page End: 119
• Publication Date: 2020-01-02
• Subjects: Acoustic emission -- electrochemical noise -- stress corrosion cracking -- self organising map -- pitting; Non-destructive testing -- Periodicals; 620.112705
• DOI: 10.1080/10589759.2019.1652294
• Languages: English
• ISSNs: 1058-9759

Abstract

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.