Artificial neural network modelling to predict the efficiency of aluminium sacrificial anode-Reference-Cited by-同舟云学术

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Artificial neural network modelling to predict the efficiency of aluminium sacrificial anode

Published:2023-09 Issue:8 Volume:58 Page:747-754
ISSN:1478-422X
Container-title:Corrosion Engineering, Science and Technology
language:en
Short-container-title:Corrosion Engineering, Science and Technology

Author:

Rezaei Amir¹^ORCID

Affiliation:

1. School of Metallurgy and Material Engineering, University of Tehran, Tehran, Iran

Publisher

SAGE Publications

Subject

General Materials Science,General Chemical Engineering,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/1478422X.2023.2252258

Reference38 articles.

1. Muazu A, Yaro SA. J Miner Mater Charact Eng. 2011;10:185–198.

2. Influence of Sn, Cd, and Si addition on the electrochemical performance of Al–Zn–In sacrificial anodes

3. Corrosion analysis of Al–Zn–In–Mg–Ti–Mn sacrificial anode alloy

4. Effects of Addition of Alkaline Earth Metal-Beryllium on the Dissolution Behaviour of Aluminium-Zinc Alloy

5. Effects of Gallium Addition on the Microstructure and Electrochemical Properties of Al-Zn-Bi Series Alloy Anode

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effect of Bi on the Performance of Al-Ga-In Sacrificial Anodes;Materials;2024-02-08

2. Effect of Alloy Elements on the Performance of Al Sacrificial Anode;Journal of Physics: Conference Series;2024-01-01

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