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Analyze the Performance of Electroactive Anticorrosion Coating of Medical Magnesium Alloy Using Deep Learning

Yashan Feng1, Yafang Tian1, Yongxin Yang1, Yufang Zhang1, Haiwei Guo1, Jing’an Li2,*

1 Advanced Functional Materials Laboratory, Zhengzhou Railway Vocational and Technical College, Zhengzhou, 450000, China
2 School of Materials Science and Engineering, Zhengzhou University, Zhengzhou, 450000, China

* Corresponding Author: Jing’an Li. Email: email

(This article belongs to the Special Issue: Application of Soft Computing in Techniques in Materials Development)

Computers, Materials & Continua 2024, 79(1), 263-278. https://doi.org/10.32604/cmc.2024.047004

Abstract

Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. The study employs strategic thermodynamic equilibrium calculations to pioneer a novel factor in corrosion protection. A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medical magnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoretical insights and practical applications to enhance our grasp of corrosion prevention mechanisms and establish a systematic approach to coating design. The groundbreaking significance of this study lies in its innovative integration of the TA/pH concept, which encompasses the TA/pH ratio of the chemical environment. This approach surpasses convention by acknowledging the intricate interplay between the acidity and pH levels within the coating formulation, thereby optimizing metal-phosphate-based conversion coatings and transforming corrosion mitigation strategies. To authenticate the TA/pH concept, the study comprehensively compares its findings with existing research, rigorously validating the theoretical framework and reinforcing the correlates among TA/pH values and observed corrosion resistance in the coatings. The influence of mutations that occur naturally in the detergent solution on persistent phosphorus changes is shown by empirical confirmation, which improves corrosion resistance. This realization advances the field of materials and the field’s knowledge of coated generation, particularly anticorrosion converter layers.

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APA Style
Feng, Y., Tian, Y., Yang, Y., Zhang, Y., Guo, H. et al. (2024). Analyze the performance of electroactive anticorrosion coating of medical magnesium alloy using deep learning. Computers, Materials & Continua, 79(1), 263-278. https://doi.org/10.32604/cmc.2024.047004
Vancouver Style
Feng Y, Tian Y, Yang Y, Zhang Y, Guo H, Li J. Analyze the performance of electroactive anticorrosion coating of medical magnesium alloy using deep learning. Comput Mater Contin. 2024;79(1):263-278 https://doi.org/10.32604/cmc.2024.047004
IEEE Style
Y. Feng, Y. Tian, Y. Yang, Y. Zhang, H. Guo, and J. Li "Analyze the Performance of Electroactive Anticorrosion Coating of Medical Magnesium Alloy Using Deep Learning," Comput. Mater. Contin., vol. 79, no. 1, pp. 263-278. 2024. https://doi.org/10.32604/cmc.2024.047004



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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