Model-based prognosis for intergranular corrosion

LISHCHUK, S. V., AKID, R. and WORDEN, K. (2008). Model-based prognosis for intergranular corrosion. Proc. 4th European Workshop on Structural Health Monitoring, Krakow, Poland, 340-348.

[img]
Preview
PDF
art2008b.pdf - Accepted Version

Download (256kB) | Preview

Abstract

Among the advantages of Aluminium-based alloys for structural use is their corrosion resistance. However, while Aluminium alloys are highly resistant to uniform (general) corrosion, they are much more susceptible to types of localised corrosion, especially intergranular corrosion, which is a localised attack along the grain boundaries which leaves the grains themselves largely unaffected. In order to estimate the progress of such corrosion in a given sample, it is considered possible to generate a numerical model of some sort. While there has been much effort spent in the development of electrochemistry-based models, the use of grey and black-box models remains largely unexplored. One exception to this is the use of Cellular Automata (CA) models which have recently been exploited to model the progression of uniform corrosion. The object of the current paper is to apply the CA methodology to the case of intergranular corrosion. The first phase of the work has been concerned with generating appropriate CA rules which can qualitatively reproduce observed physics, and this work is reported here. A model is proposed which shows qualitative agreement with experimental data on the advance of the corrosion front.

Item Type: Article
Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Structural Materials and Integrity Research Centre > Centre for Corrosion Technology
Page Range: 340-348
Depositing User: Sergey Lishchuk
Date Deposited: 20 Apr 2010 16:26
Last Modified: 18 Mar 2021 13:31
URI: https://shura.shu.ac.uk/id/eprint/1469

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics