The Impact of Coating Ingredients on the Aging Resistance of Topcoat Paints by Model Trees


  • Tzu-Tsung Wong Institute of Information Management, National Cheng Kung University, Tainan, Taiwan
  • Shih-Hsuan Hung Institute of Information Management, National Cheng Kung University, Tainan, Taiwan



accelerated aging test, linear regression, model tree, numeric prediction, topcoat paint


Topcoat paint is mainly composed of resin and pigment and hence its quality highly depends on the type and proportion of these two ingredients. This study aims at testing the formula of the topcoat paint for finding one that can achieve better quality for anti-aging. Various formulas of paint are applied on boards that will be put into ultraviolet accelerated test machines to simulate weathering tests. The gloss and color, before and after the tests, are collected and numerical prediction method M5P is used to grow model trees for discovering the key factors affecting aging. Based on the structure and the linear regression models in the trees, a better topcoat paint should be composed of a high proportion of resin and generally a low proportion of pigment. Good types of resin and pigment are also identified for keeping color and gloss.


A. Boubault, C. K. Ho, A. Hall, T. N. Lambert, and A. Ambrosini, “Durability of Solar Absorber Coatings and Their Cost-Effectiveness,” Solar Energy Materials and Solar Cells, vol. 166, pp. 176-184, July 2017.

K. Simunkova, M. Panek, and A. Zeidler, “Comparison of Selected Properties of Shellac Varnish for Restoration and Polyurethane Varnish for Reconstruction of Historical Artefacts,” Coatings, vol. 8, no. 4, March 2018.

J. Wu, K. Niu, B. Su, and Y. Wang, “Effect of Combined UV Thermal and Hydrolytic Aging on Micro-Contact Properties of Silicone Elastomer,” Polymer Degradation and Stability, vol. 151, pp. 126-135, May 2018.

E. Kizilkonca and F. B. Erim, “Development of Anti-Aging and Anticorrosive Nanoceria Dispersed Alkyd Coating for Decorative and Industrial Purposes,” Coatings, vol. 9, no. 10, September 2019.

P. M. Carmona-Quiroga, R. M. J. Jacobs, S. Martinez-Ramirez, and H. A. Viles, “Durability of Anti-Graffiti Coatings on Stone: Natural vs Accelerated Weathering,” PLoS One, vol. 12, no. 2, February 2017.

P. Sanmartin and F. Cappitelli, “Evaluation of Accelerated Ageing Tests for Metallic and Non-Metallic Graffiti Paints Applied to Stone,” Coatings, vol. 7, no. 11, October 2017.

A. K. Soares, R. L. Pereira, P. H. Gonzalez de Cademartori, H. W. Dalla Costa, and D. A. Gatto, “Artificial Weathering of Four Coatings Applied on Woods of Two Forest Species,” Nativa: Pesquisas Agrárias e Ambientais, vol. 6, no. 3, pp. 313-320, 2018.

R. David, V. S. Raja, S. K. Singh, and P. Gore, “Development of Anti-Corrosive Paint with Improved Toughness Using Carboxyl Terminated Modified Epoxy Resin,” Progress in Organic Coatings, vol. 120, pp. 58-70, July 2018.

X. X. Yan, “Effect of Black Paste on the Property of Fluorine Resin/Aluminum Infrared Coating,” Coatings, vol. 9, no. 10, September 2019.

T. Ramde, L. G. Ecco, and S. Rossi, “Visual Appearance Durability as Function of Natural and Accelerated Ageing of Electrophoretic Styrene-Acrylic Coatings: Influence of Yellow Pigment Concentration,” Progress in Organic Coatings, vol. 103, pp. 23-32, February 2017.

S. N. Roselli, R. Romagnoli, and C. Deyda, “The Anti-Corrosion Performance of Water-Borne Paints in Long Term Tests,” Progress in Organic Coatings, vol. 109, pp. 172-178, August 2017.

M. Kohl, A. Kalendova, E. Cernoskova, M. Blaha, J. Stejskal, and M. Erben, “Corrosion protection by organic coatings containing polyaniline salts prepared by oxidative polymerization,” Journal of Coatings Technology and Research, vol. 14, no. 6, pp. 1397-1410, November 2017.

N. M. Ahmed, M. G. Mohamed, R. H. Tammam, and M. R. Mabrouk, “Performance of Coatings Containing Treated Silica Fume in the Corrosion Protection of Reinforced Concrete,” Pigment & Resin Technology, vol. 47, no. 4, pp. 350-359, July 2018.

Q. M. Abd El-Gawad, N. M. Ahmed, M. M. Selim, E. Hamed, and E. R. Souaya, “The Anticorrosive Performance of Cost Saving Zeolites,” Pigment & Resin Technology, vol. 48, no. 4, pp. 317-328, July 2019.

R. S. Peres, A. V. Zmozinski, F. R. Brust, A. J. Macedo, E. Armelin, C. Aleman, and C. A. Ferreira, “Multifunctional Coatings Based on Silicone Matrix and Propolis Extract,” Progress in Organic Coatings, vol. 123, pp. 223-231, October 2018.

W. H. Li, D. C. Franco, M. S. Yang, X. P. Zhu, H. P. Zhang, Y. Y. Shao, H. Zhang, and J. X. Zhu, “Investigation of the Performance of ATH Powders in Organic Powder Coatings,” Coatings, vol. 9, no. 2, February 2019.

T. Kanbayashi, A. Ishikawa, M. Matsunaga, M. Kobayashi, and Y. Kataoka, “Application of Confocal Raman Microscopy for the Analysis of the Distribution of Wood Preservative Coatings,” Coatings, vol. 9, no. 10, September 2019.

A. Karalic, “Employing Linear Regression in Regression Tree Leaves,” Proc. of the 10th European Conference on Artificial Intelligence, August 1992, pp. 440-441.

J. R. Quinlan, “Learning with Continuous Classes,” Proc. of the 5th Australian Joint Conference on Artificial Intelligence, November 1992, pp. 343-348.

P. Chaudhuri, M. C. Huang, W. Y. Loh, and R. Yao, “Piecewise-Polynomial Regression Trees,” Statistica Sinica, vol. 4, no. 1, pp. 143-167, January1994.

W. Y. Loh, “Regression Trees with Unbiased Variable Selection and Interaction Detection,” Statistica Sinica, vol. 12, no. 2, pp. 361-286, April 2002.

E. Ghasemi, H. Kalhori, R. Bagherpour, and S. Yagiz, “Model Tree Approach for Predicting Uniaxial Compressive Strength and Young's Modulus of Carbonate Rocks,” Bulletin of Engineering Geology and The Environment, vol. 77, no. 1, pp. 331-343, February 2018.

F. T. Matthew, A. I. Adepoju, O. Ayodele, O. Olumide, O. Olatayo, E. Adebimpe, O. Bolaji, and E. Funmilola, “Development of Mobile-Interfaced Machine Learning-Based Predictive Models for Improving Students' Performance in Programming Courses,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 5, pp. 105-115, 2018.

H. P. Su, C. Lian, J. C. Liu, and H. L. Liu, “Machine Learning Models for Solvent Effects on Electric Double Layer Capacitance,” Chemical Engineering Science, vol. 202, pp. 186-193, July 2019.

H. S. Yi, B. Lee, S. Park, K. C. Kwak, and K. G. An, “Prediction of Short-Term Algal Bloom Using the M5P Model-Tree and Extreme Learning Machine,” Environmental Engineering Research, vol. 24, no. 3, pp. 404-411, 2018.

Z. S. Khozani, K. Khosravi, B. T. Binh, B. Klove, W. H. M. W. Mohtar, and Z. M. Yaseen, “Determination of Compound Channel Apparent Shear Stress: Application of Novel Data Mining Models,” vol. 21, no. 5, pp. 798-811, 2019.




How to Cite

T.-T. Wong and S.-H. Hung, “The Impact of Coating Ingredients on the Aging Resistance of Topcoat Paints by Model Trees”, Adv. technol. innov., vol. 6, no. 1, pp. 39–46, Jan. 2021.