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Prediction of architectural coating performance using titanium dioxide characterization applying artificial neural networks

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Abstract

Prediction of paint properties is a critical issue for the coatings industry, since experimentation is time consuming and a lot of financial and human resources are needed to test or develop new products. In current market conditions, cost savings and product innovation are critical issues. In this article, an artificial neural network, of the feed forward type, was trained using as inputs key properties of titanium dioxide and two formulation parameters (pigment volume concentration and titanium dioxide content) for a water-based architectural coating. The outputs of this research were spread rate, color (L*, a*, b*) and tinting strength. Test data were used to check the accuracy of the model, demonstrating the viability of paint properties prediction related to the properties of the titanium dioxide formulation with high correlation (>95%).

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Acknowledgments

The authors extend their gratitude to Juan Gomez and Elizabeth Rauda for the laboratory work performed. Gratitude is also expressed for their continuous support in this study to Carlos Verdejo, Miguel Gama, and Roberto Giesemann. Special mention to Dr. Michael Diebold for his comments on the paper.

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Authors and Affiliations

  1. DuPont Titanium Technologies, México, México

    Pablo René Aragón Candelaria

  2. Universidad Anáhuac, Mexico Sur, México

    Pablo René Aragón Candelaria

  3. DuPont Engineering Research and Technology, Wilmington, DE, USA

    Aaron J. Owens

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  1. Pablo René Aragón Candelaria
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  2. Aaron J. Owens
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Correspondence to Pablo René Aragón Candelaria.

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The information set forth herein is furnished free of charge and based on technical data that DuPont believes to be reliable. It is intended for use by persons having technical skill, at their own risk. Since conditions of use are outside our control, we make no warranties, expressed, or implied and assume no liability in connection with any use of this information. Nothing herein is to be taken as license to operate under or a recommendation to infringe any patents.

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Aragón Candelaria, P.R., Owens, A.J. Prediction of architectural coating performance using titanium dioxide characterization applying artificial neural networks. J Coat Technol Res 7, 431–440 (2010). https://doi.org/10.1007/s11998-009-9215-z

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  • DOI: https://doi.org/10.1007/s11998-009-9215-z

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