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Volume 31 Issue 7
Jul.  2024
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文章导航 >  矿物冶金与材料学报（英文）  > 2024  >  31(7): 1617-1627
Yiran Li, Zhongheng Fu, Xiangyang Yu, Zhihui Jin, Haiyan Gong, Lingwei Ma, Xiaogang Li,  and Dawei Zhang, Developing an atmospheric aging evaluation model of acrylic coatings: A semi-supervised machine learning algorithm, Int. J. Miner. Metall. Mater., 31(2024), No. 7, pp. 1617-1627. https://doi.org/10.1007/s12613-024-2921-9
Cite this article as:
Yiran Li, Zhongheng Fu, Xiangyang Yu, Zhihui Jin, Haiyan Gong, Lingwei Ma, Xiaogang Li,  and Dawei Zhang, Developing an atmospheric aging evaluation model of acrylic coatings: A semi-supervised machine learning algorithm, Int. J. Miner. Metall. Mater., 31(2024), No. 7, pp. 1617-1627. https://doi.org/10.1007/s12613-024-2921-9
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研究论文

一种丙烯酸涂料大气老化评估模型： 半监督式机器学习算法

  • 1)

    北京材料基因工程高精尖创新中心, 新材料技术研究院, 北京科技大学, 北京 100083, 中国

  • 2)

    国家材料腐蚀与防护数据中心, 北京科技大学, 北京 100083, 中国

  • 3)

    "一带一路"东南亚环境材料腐蚀与防护教育部野外科学观测研究站, 北京科技大学顺德研究生院, 佛山 528399, 中国


  • 通讯作者:

    富忠恒    E-mail: fuzhongheng@ustb.edu.cn

    龚海燕    E-mail: ghaiyan@ustb.edu.cn

文章亮点

  • 1）在中国 13 个具有代表性的气候环境中进行了为期两年的老化暴露实验。
  • 2）研究了11种环境因素对丙烯酸涂料大气老化性能的影响。
  • 3）采用半监督式机器学习算法建立一种丙烯酸涂料大气老化评估模型。
  • 丙烯酸涂料因其优异的耐候性和装饰性而被广泛应用于建筑和汽车工业，然而，其在复杂多变的大气环境中的老化性能仍然是材料科学领域关注的重点。为了探索丙烯酸涂料在不同气候条件下的大气老化行为，本研究在中国13个具有代表性气候的环境中进行了为期两年的实地老化暴露实验。研究分析了567个城市的老化数据，包括11个关键环境因素，以此来建立丙烯酸涂料的大气老化评估模型。通过采用随机森林算法和斯皮尔曼相关性分析的组合，实现了对数据集的有效降维，减少了数据冗余和多重共线性问题。在建模过程中，以环境因素为输入变量，选取了丙烯酸涂层在3.5wt%氯化钠溶液中的电化学阻抗谱的低频阻抗模量值作为输出变量，构建了一个半监督协同训练的回归模型。与传统的监督学习算法（如支持向量机）相比，新模型具备了更高的预测准确性。此外，该模型不仅为快速评估丙烯酸涂料的老化性能提供了一种新的方法论，也为其他有机涂料的老化性能评估提供了可借鉴的技术路径。总体而言，这项研究为涂料工业提供了一种创新的、可靠的评估工具，有助于推动材料科学在涂料老化领域的研究和应用。
    • Research Article

    Developing an atmospheric aging evaluation model of acrylic coatings: A semi-supervised machine learning algorithm

    + Author Affiliations
      Abstract
    • To study the atmospheric aging of acrylic coatings, a two-year aging exposure experiment was conducted in 13 representative climatic environments in China. An atmospheric aging evaluation model of acrylic coatings was developed based on aging data including 11 environmental factors from 567 cities. A hybrid method of random forest and Spearman correlation analysis was used to reduce the redundancy and multicollinearity of the data set by dimensionality reduction. A semi-supervised collaborative trained regression model was developed with the environmental factors as input and the low-frequency impedance modulus values of the electrochemical impedance spectra of acrylic coatings in 3.5wt% NaCl solution as output. The model improves accuracy compared to supervised learning algorithms model (support vector machines model). The model provides a new method for the rapid evaluation of the aging performance of acrylic coatings, and may also serve as a reference to evaluate the aging performance of other organic coatings.
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    • [1]
      X.G. Li, D.W. Zhang, Z.Y. Liu, Z. Li, C.W. Du, and C.F. Dong, Materials science: Share corrosion data, Nature, 527(2015), No. 7579, p. 441. doi: 10.1038/527441a
      [2]
      L. Wang, J. Gao, X.G. Li, and J.W. Hu, Effect of photo-radiation on anti-corrosion and protection performance of acrylic polyurethane coating, J. Univ. Sci. Technol. Beijing, 30(2008), No. 2, p. 152.
      [3]
      N. Guermazi, K. Elleuch, and H.F. Ayedi, The effect of time and aging temperature on structural and mechanical properties of pipeline coating, Mater. Des., 30(2009), No. 6, p. 2006. doi: 10.1016/j.matdes.2008.09.003
      [4]
      D.Y. Perera, Effect of thermal and hygroscopic history on physical ageing of organic coatings, Prog. Org. Coat., 44(2002), No. 1, p. 55. doi: 10.1016/S0300-9440(01)00241-7
      [5]
      V.O. Startsev, M.P. Lebedev, K.A. Khrulev, M.V. Molokov, A.S. Frolov, and T.A. Nizina, Effect of outdoor exposure on the moisture diffusion and mechanical properties of epoxy polymers, Polym. Test., 65(2018), p. 281. doi: 10.1016/j.polymertesting.2017.12.007
      [6]
      S. Geng, J. Gao, X.G. Li, and Q.L. Zhao, Aging behaviors of acrylic polyurethane coatings during UV irradiation, J. Univ. Sci. Technol. Beijing, 31(2009), No. 6, p. 752.
      [7]
      M.D. Yu, C.Q. Fan, F. Ge, Q.Y. Lu, X. Wang, and Z.Y. Cui, Anticorrosion behavior of organic offshore coating systems in UV, salt spray and low temperature alternation simulated Arctic offshore environment, Mater. Today Commun., 28(2021), art. No. 102545. doi: 10.1016/j.mtcomm.2021.102545
      [8]
      K.Y. Che, P. Lyu, F. Wan, and M.L. Ma, Investigations on aging behavior and mechanism of polyurea coating in marine atmosphere, Materials, 12(2019), No. 21, art. No. 3636. doi: 10.3390/ma12213636
      [9]
      J. Gao, C. Li, Z. Lv, R. Wang, D.Q. Wu, and X.G. Li, Correlation between the surface aging of acrylic polyurethane coatings and environmental factors, Prog. Org. Coat., 132(2019), p. 362. doi: 10.1016/j.porgcoat.2019.04.009
      [10]
      T.C. da Silva, S. Mallarino, S. Touzain, and I.C.P. Margarit-Mattos, DMA, EIS and thermal fatigue of organic coatings, Electrochim. Acta, 318(2019), p. 989. doi: 10.1016/j.electacta.2019.06.066
      [11]
      P.L. Gac, D. Choqueuse, D. Melot, B. Melve, and L. Meniconi, Life time prediction of polymer used as thermal insulation in offshore oil production conditions: Ageing on real structure and reliability of prediction, Polym. Test., 34(2014), p. 168. doi: 10.1016/j.polymertesting.2014.01.011
      [12]
      Y.D. Lv, Y.J. Huang, J.L. Yang, et al., Outdoor and accelerated laboratory weathering of polypropylene: A comparison and correlation study, Polym. Degrad. Stab., 112(2015), p. 145. doi: 10.1016/j.polymdegradstab.2014.12.023
      [13]
      D.Q. Wu, D.W. Zhang, S.P. Liu, et al., Prediction of polycarbonate degradation in natural atmospheric environment of China based on BP-ANN model with screened environmental factors, Chem. Eng. J., 399(2020), art. No. 125878. doi: 10.1016/j.cej.2020.125878
      [14]
      W. Sai, G.B. Chai, and N. Srikanth, Fatigue life prediction of GLARE composites using regression tree ensemble-based machine learning model, Adv. Theory Simul., 3(2020), No. 6, art. No. 2000048. doi: 10.1002/adts.202000048
      [15]
      J.G. Kuang and Z.L. Long, Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms, Int. J. Miner. Metall. Mater., 31(2024), No. 2, p. 337. doi: 10.1007/s12613-023-2679-5
      [16]
      M.W. Wu, W. Yong, C.Q. Fu, C.M. Ma, and R.P. Liu, Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature, Int. J. Miner. Metall. Mater., 31(2024), No. 4, p. 773. doi: 10.1007/s12613-023-2767-6
      [17]
      X.J. Yang, J.K. Yang, Y. Yang, et al., Data-mining and atmospheric corrosion resistance evaluation of Sn- and Sb-additional low alloy steel based on big data technology, Int. J. Miner. Metall. Mater., 29(2022), No. 4, p. 825. doi: 10.1007/s12613-022-2457-9
      [18]
      Z.B. Pei, D.W. Zhang, Y.J. Zhi, et al., Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning, Corros. Sci., 170(2020), art. No. 108697. doi: 10.1016/j.corsci.2020.108697
      [19]
      Y.P. Diao, L.C. Yan, and K.W. Gao, Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features, Mater. Des., 198(2021), art. No. 109326. doi: 10.1016/j.matdes.2020.109326
      [20]
      Y.J. Zhi, Z.H. Jin, L. Lu, et al., Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model, Corros. Sci., 178(2021), art. No. 109084. doi: 10.1016/j.corsci.2020.109084
      [21]
      A. Roy, M.F.N. Taufique, H. Khakurel, R. Devanathan, D.D. Johnson, and G. Balasubramanian, Machine-learning-guided descriptor selection for predicting corrosion resistance in multi-principal element alloys, NPJ Mater. Degrad., 6(2022), art. No. 9. doi: 10.1038/s41529-021-00208-y
      [22]
      J.E. van Engelen and H.H. Hoos, A survey on semi-supervised learning, Mach. Learn., 109(2020), No. 2, p. 373. doi: 10.1007/s10994-019-05855-6
      [23]
      Z.H. Zhou and M. Li, Semisupervised regression with cotraining-style algorithms, IEEE Trans. Knowl. Data Eng., 19(2007), No. 11, p. 1479. doi: 10.1109/TKDE.2007.190644
      [24]
      L. Ma and X.L. Wang, Semi-supervised regression based on support vector machine co-training, Comput. Eng. Appl., 47(2011), No. 3, p. 177.
      [25]
      L. Breiman, Random forests, Mach. Learn., 45(2001), No. 1, p. 5. doi: 10.1023/A:1010933404324
      [26]
      C.W. Xiao, J.Q. Ye, R.M. Esteves, and C.M. Rong, Using Spearman’s correlation coefficients for exploratory data analysis on big dataset, Concurr. Comput. Pract. Exp., 28(2016), No. 14, p. 3866. doi: 10.1002/cpe.3745
      [27]
      J.W. Liu, Y. Liu, and X.L. Luo, Semi-supervised learning methods, Chin. J. Comput., 38(2015), No. 8, p. 1592.
      [28]
      Z.H. Zhou, Machine Learning, Tsinghua University Publishing House Co., ltd, Beijing, 2016, p. 225.
      [29]
      S.F. Ding, B.J. Qi, and H.Y. Tan, An overview on theory and algorithm of support vector machines, J. Univ. Electron. Sci. Technol. China, 40(2011), No. 1, p. 2.
      [30]
      J.K. Wang, L.W. Ma, X. Guo, et al., Two birds with one stone: Nanocontainers with synergetic inhibition and corrosion sensing abilities towards intelligent self-healing and self-reporting coating, Chem. Eng. J., 433(2022), art. No. 134515. doi: 10.1016/j.cej.2022.134515
      [31]
      Y.X. Xu, C.W. Yan, J. Ding, Y.M. Gao, and C.N. Cao, UV photo-degradation of coatings, J. Chin. Soc. Corros. Prot., 24(2004), No. 3, p. 168.
      [32]
      X.F. Yang, J. Li, S.G. Croll, D.E. Tallman, and G.P. Bierwagen, Degradation of low gloss polyurethane aircraft coatings under UV and prohesion alternating exposures, Polym. Degrad. Stab., 80(2003), No. 1, p. 51. doi: 10.1016/S0141-3910(02)00382-8
      [33]
      D. Feldman, Polymer weathering: Photo-oxidation, J. Polym. Environ., 10(2002), No. 4, p. 163. doi: 10.1023/A:1021148205366
      [34]
      H.L. Qin, S.M. Zhang, H.J. Liu, S.B. Xie, M.S. Yang, and D.Y. Shen, Photo-oxidative degradation of polypropylene/montmorillonite nanocomposites, Polymer, 46(2005), No. 9, p. 3149. doi: 10.1016/j.polymer.2005.01.087
      [35]
      B.W. Johnson and R. McIntyre, Analysis of test methods for UV durability predictions of polymer coatings, Prog. Org. Coat., 27(1996), No. 1-4, p. 95. doi: 10.1016/0300-9440(94)00525-7
      [36]
      K. Zhang, L. Hao, M. Du, J. Mi, J.N. Wang, and J.P. Meng, A review on thermal stability and high temperature induced ageing mechanisms of solar absorber coatings, Renewable Sustainable Energy Rev., 67(2017), p. 1282. doi: 10.1016/j.rser.2016.09.083
      [37]
      C.V. Lacombre, G. Bouvet, D. Trinh, S. Mallarino, and S. Touzain, Effect of pigment and temperature onto swelling and water uptake during organic coating ageing, Prog. Org. Coat., 124(2018), p. 249. doi: 10.1016/j.porgcoat.2017.11.022
      [38]
      P. Sivakumar, S.M. Du, M. Selter, I. Ballard, J. Daye, and J. Cho, Long-term thermal aging of parylene conformal coating under high humidity and its effects on tin whisker mitigation, Polym. Degrad. Stab., 191(2021), art. No. 109667. doi: 10.1016/j.polymdegradstab.2021.109667
      [39]
      S.R. Taylor and P. Moongkhamklang, The delineation of local water interaction with epoxy coatings using fluorescence microscopy, Prog. Org. Coat., 54(2005), No. 3, p. 205. doi: 10.1016/j.porgcoat.2005.05.006
      [40]
      Y.J. Guo, H. Yan, and F. Xiao, Accelerated thermal-oxygen aging test for epoxy resin, J. Tsinghua Univ. (Sci. Tech.), 40(2000), No. 7, p. 1.
      [41]
      S.Y. Zhang, S.J. Li, X.W. Luo, and W.F. Zhou, Mechanism of the significant improvement in corrosion protection by lowering water sorption of the coating, Corros. Sci., 42(2000), No. 12, p. 2037. doi: 10.1016/S0010-938X(00)00042-1
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