Application of machine learning in material corrosion research

IF 2.7 4区 材料科学 Q3 ELECTROCHEMISTRY
Shuaijie Ma, Yanxia Du, Shasha Wang, Yanjing Su
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引用次数: 1

Abstract

Abstract The application of machine learning (ML) to corrosion research has become an important trend in corrosion science in recent years. In this paper, the feature extraction method for corrosion data and the ML algorithms commonly used (including artificial neural networks, support vector machines, ensemble learning and other widely used algorithms) in corrosion field is introduced. Then, the characteristics of different algorithms and their application scenarios in the corrosion prediction are summarized. Finally, the development trend of ML in material corrosion field is prospected.
机器学习在材料腐蚀研究中的应用
摘要近年来,机器学习在腐蚀研究中的应用已成为腐蚀科学的一个重要趋势。本文介绍了腐蚀数据的特征提取方法以及腐蚀领域常用的ML算法(包括人工神经网络、支持向量机、集成学习等广泛使用的算法)。然后,总结了不同算法的特点及其在腐蚀预测中的应用场景。最后,展望了机器学习在材料腐蚀领域的发展趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Corrosion Reviews
Corrosion Reviews 工程技术-材料科学:膜
CiteScore
5.20
自引率
3.10%
发文量
44
审稿时长
4.5 months
期刊介绍: Corrosion Reviews is an international bimonthly journal devoted to critical reviews and, to a lesser extent, outstanding original articles that are key to advancing the understanding and application of corrosion science and engineering in the service of society. Papers may be of a theoretical, experimental or practical nature, provided that they make a significant contribution to knowledge in the field.
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