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

doi:10.1016/j.corsci.2019.02.003

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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

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 language	English
Pages (from-to)	268-278
Number of pages	11
Journal	Corrosion Science
Volume	150
DOIs	https://doi.org/10.1016/j.corsci.2019.02.003
State	Published - 15 Apr 2019
Externally published	Yes

Keywords

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

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10.1016/j.corsci.2019.02.003

Cite this

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

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.",

keywords = "A. Aluminium, B. Modelling studies, B. SEM, B. TEM, C. Intergranular corrosion",

author = "Ruifeng Zhang and Jinfeng Li and Qian Li and Yuanshen Qi and Zhuoran Zeng and Yao Qiu and Xiaobo Chen and Kairy, {Shravan K.} and Sebastian Thomas and Nick Birbilis",

note = "Publisher Copyright: {\textcopyright} 2019 Elsevier Ltd",

year = "2019",

month = apr,

day = "15",

doi = "10.1016/j.corsci.2019.02.003",

language = "英语",

volume = "150",

pages = "268--278",

journal = "Corrosion Science",

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TY - JOUR

T1 - Analysing the degree of sensitisation in 5xxx series aluminium alloys using artificial neural networks

T2 - A tool for alloy design

AU - Zhang, Ruifeng

AU - Li, Jinfeng

AU - Li, Qian

AU - Qi, Yuanshen

AU - Zeng, Zhuoran

AU - Qiu, Yao

AU - Chen, Xiaobo

AU - Kairy, Shravan K.

AU - Thomas, Sebastian

AU - Birbilis, Nick

PY - 2019/4/15

Y1 - 2019/4/15

N2 - 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.

AB - 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.

KW - A. Aluminium

KW - B. Modelling studies

KW - B. SEM

KW - B. TEM

KW - C. Intergranular corrosion

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U2 - 10.1016/j.corsci.2019.02.003

DO - 10.1016/j.corsci.2019.02.003

M3 - 文章

AN - SCOPUS:85061603499

SN - 0010-938X

VL - 150

SP - 268

EP - 278

JO - Corrosion Science

JF - Corrosion Science

ER -

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

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Keywords

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