Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning
Anti-Corrosion Methods and Materials
ISSN: 0003-5599
Article publication date: 11 March 2022
Issue publication date: 14 April 2022
Abstract
Purpose
Cobalt-based alloys exhibit a unique combination of wear resistance, strength and corrosion resistance. Localized corrosion of such alloys in seawater system can be several orders of magnitude faster than general corrosion, and direct experimental evidence of the local activation process is still lacking, which makes the accurate prediction for properties difficult, especially for long-term corrosion. The purpose of this study is revealing the relationship between multiple environments and corrosion properties to predict the corrosion of cobalt-based alloys.
Design/methodology/approach
A data-driven method for the prediction of the corrosion behavior of cast and hot isostatic-pressed CoCrMo/W alloys in seawater is proposed. The gradient boosting regression models calculate mean relative errors (MREs) of 0.160 and 0.435 by evaluating a hold-out set for breakdown potential (Eb) and maximum current density (imax), respectively, considering various compositions, synthesis methods and corrosion environments.
Findings
The models can be used to estimate the “unseen” cobalt-based alloy after immersion in 3.5 Wt.% NaCl solution for one, two, four and eight months to obtain high precision with MREs of 7.8% and 9.8% for Eb and imax, respectively.
Originality/value
Machine learning method provides novel and promising insights for the prediction of localized corrosion properties.
Keywords
Acknowledgements
This work is financially supported by the Major Projects on Basic and Applied Basic Research of Guangdong Province (2019B030302011), National Key Research and Development Program of China (2020YFB0704503, 2016YFB0700500), Guangdong Province Key Area RandD Program (2019B010940001), USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering.
Data availability: All data included in this study are available at a web-access database (https://www.mgedata.cn/search/#/152546/list).
Citation
Jiang, X., Yan, Y. and Su, Y. (2022), "Predicting the corrosion properties of cast and hot isostatic pressed CoCrMo/W alloys in seawater by machine learning", Anti-Corrosion Methods and Materials, Vol. 69 No. 3, pp. 288-294. https://doi.org/10.1108/ACMM-01-2022-2594
Publisher
:Emerald Publishing Limited
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