Machine Learning-Guided Design and High-Temperature Corrosion Behavior of Pt-Modified Aluminide Coatings
编号:92 访问权限:仅限参会人 更新:2026-10-10 09:59:12 浏览:40次 口头报告

报告开始:2026年10月18日 15:15(Asia/Shanghai)

报告时间:15min

所在会场:[S7] Surface Engineering Early-Career Scholars Forum [S7] Parallel Session

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摘要
        Pt-modified aluminide coatings are widely used to protect gas turbine hot-section components against high-temperature oxidation and hot corrosion. However, their long-term durability remains a concern under aggressive marine conditions. This work combines machine learning-guided compositional design with experimental investigations of the high-temperature corrosion behavior of Pt-modified aluminide coatings. A Gaussian process regression model was trained on a hot-corrosion dataset compiled from published studies and experimental measurements to establish quantitative correlations among coating composition, test conditions, and corrosion-induced mass change. Based on a multilayer screening strategy, the target Al and Pt concentration ranges were identified, and a β-(Ni, Pt)Al coating with a composition of 48.71Al–3.12Co–2.22Cr–37.11Ni–8.84Pt (at.%) was successfully prepared. The coating exhibited good resistance to NaCl/Na2SO4-induced hot corrosion at 900℃. Microstructural characterization revealed the formation of a heterostructure oxide scale, in which the dense inner Al2O3 layer effectively inhibited the inward penetration of corrosive species and played a key role in the excellent hot corrosion resistance of the coating.
        Further investigations under high-temperature salt-fog conditions revealed substantially improved corrosion resistance of the β-(Ni, Pt)Al coating compared with the uncoated substrate, with additional improvements achieved through pre-oxidation. At 900℃, coating degradation was dominated by hot corrosion, while the evolution of the surface oxide scale was governed by the competition between oxidation and salt-induced corrosion, resulting in a layered structure consisting of an Al2O3-rich outer layer and an underlying nitride-containing inner layer. At 1000℃, scale growth and elemental diffusion became increasingly important in governing coating degradation, suggesting a shift in the dominant corrosion processes with increasing temperature. These results highlight the potential of data-driven compositional design for improving the hot-corrosion resistance of Pt-modified aluminide coatings and provide insights into their degradation mechanisms in marine environments.
关键词
High-temperature corrosion,β-(Ni, Pt)Al coating,Machine learning,Corrosion behavior,Simulation marine environment
报告人
Peng Deng
Engineer Institute of new materials, Guangdong academy of sciences

稿件作者
Peng Deng Institute of new materials, Guangdong academy of sciences
Bin Yin Institute of new materials, Guangdong academy of sciences
Jianbao Gao Central South University
Lijun Zhang Central South University
Chunming Deng Institute of new materials, Guangdong academy of sciences
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重要日期
  • 会议日期

    10月16日

    2026

    至

    10月18日

    2026

  • 10月14日 2026

    初稿截稿日期

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中国机械工程学会
承办单位
扬州大学
中国矿业大学
中国机械工程学会表面工程分会
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