Analysing the degree of sensitisation in 5xxx series aluminium alloys using artificial neural networks

a tool for alloy design

Ruifeng Zhang, Jinfeng Li, Qian Li, Yuanshen Qi, Zhuoran Zeng, Yao Qiu, Xiaobo Chen, Shravan K. Kairy, Sebastian Thomas, Nick Birbilis

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)268-278
Number of pages11
JournalCorrosion Science
Volume150
DOIs
Publication statusPublished - 15 Apr 2019

Keywords

  • A. Aluminium
  • B. Modelling studies
  • B. SEM
  • B. TEM
  • C. Intergranular corrosion

Cite this

Zhang, Ruifeng ; Li, Jinfeng ; Li, Qian ; Qi, Yuanshen ; Zeng, Zhuoran ; Qiu, Yao ; Chen, Xiaobo ; Kairy, Shravan K. ; Thomas, Sebastian ; Birbilis, Nick. / Analysing the degree of sensitisation in 5xxx series aluminium alloys using artificial neural networks : a tool for alloy design. In: Corrosion Science. 2019 ; Vol. 150. pp. 268-278.
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Analysing the degree of sensitisation in 5xxx series aluminium alloys using artificial neural networks : a tool for alloy design. / Zhang, Ruifeng; Li, Jinfeng; Li, Qian; Qi, Yuanshen; Zeng, Zhuoran; Qiu, Yao; Chen, Xiaobo; Kairy, Shravan K.; Thomas, Sebastian; Birbilis, Nick.

In: Corrosion Science, Vol. 150, 15.04.2019, p. 268-278.

Research output: Contribution to journalArticleResearchpeer-review

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