The classification of acoustic emission signals of 304 stainless steel during stress corrosion process based on K‐means clustering

Abstract

Purpose

The purpose of this paper is to classify and identify the acoustic emission (AE) signals of 304 stainless steel during stress corrosion process.

Design/methodology/approach

The corrosion behavior of a specimen during slow strain rate testing (SSRT) in acidic NaCl solution was studied. The AE signals during the corrosion process were classified based on K‐means cluster algorithms; meanwhile, the characteristics of different AE sources were analyzed.

Findings

The results indicated that the AE characteristics of different AE sources, such as pitting, cracking, and bubble break‐up, differ significantly. The 304 stainless steel was prone to the occurrence of stress corrosion cracking under the SSRT condition in acidic NaCl solution.

Originality/value

The characteristics of different AE sources during corrosion process were gained for the first time, which could be of much help in analyzing and judging the corrosion situation.

Citation

Li, J., Du, G., Jiang, C. and Jin, S. (2012), "The classification of acoustic emission signals of 304 stainless steel during stress corrosion process based on K‐means clustering", Anti-Corrosion Methods and Materials, Vol. 59 No. 2, pp. 76-80. https://doi.org/10.1108/00035591211210848