Analysing the degree of sensitisation in 5xxx series aluminium alloys using artificial neural networks: A tool for alloy design

Zhang, R, Li, J, Li, Q, Qi, Y, Zeng, Z, Qiu, Y, Chen, X, Kairy, S, Thomas, S and Birbilis, N 2019, 'Analysing the degree of sensitisation in 5xxx series aluminium alloys using artificial neural networks: A tool for alloy design', Corrosion Science, vol. 150, pp. 268-278.


Document type: Journal Article
Collection: Journal Articles

Title Analysing the degree of sensitisation in 5xxx series aluminium alloys using artificial neural networks: A tool for alloy design
Author(s) Zhang, R
Li, J
Li, Q
Qi, Y
Zeng, Z
Qiu, Y
Chen, X
Kairy, S
Thomas, S
Birbilis, N
Year 2019
Journal name Corrosion Science
Volume number 150
Start page 268
End page 278
Total pages 11
Publisher Pergamon Press
Abstract The 5xxx series aluminium alloys are susceptible to sensitisation during service at elevated temperatures. Sensitisation refers to deleterious grain boundary precipitation resulting in rapid intergranular corrosion in moist environments. A holistic understanding of the variables that can influence the degree of sensitisation in Al-Mg-Mn alloys is presented herein, including the exploration of some custom produced 5xxx series alloys that were prepared to create a significant dataset for which an artificial neural network (ANN) could be applied. An ANN model could reveal complex interactions between various factors that influence sensitisation, with the view to designing sensitisation resistant Al-Mg-Mn alloys.
Subject Metals and Alloy Materials
Keyword(s) A. Aluminium
B. Modelling studies
B. SEM
B. TEM
C. Intergranular corrosion
DOI - identifier 10.1016/j.corsci.2019.02.003
Copyright notice © 2019 Elsevier
ISSN 0010-938X
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