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Volume 15
Issue 7
10.3390/coatings15070852
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Dynamic Control of Coating Accumulation Model in Non-Stationary Environment Based on Visual Differential Feedback

by
Chengzhi Su
1,2,3,*,
Danyang Yu
4,
Wenyu Song
4,
Huilin Tian
5,
Haifeng Bao
4,
Enguo Wang
4 and
Mingzhen Li
6
1
College of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130022, China
2
Jilin Provincial Collaborative Innovation Center for Intelligent Robots, Changchun University of Science and Technology, Changchun 130022, China
3
Jilin Provincial University-Enterprise Joint Technological Innovation Laboratory for Intelligent Hybrid Robots, Changchun University of Science and Technology, Changchun 130022, China
4
College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun 130022, China
5
College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
6
PLA Army Academy of Artillery and Air Defense, Hefei 230000, China
*
Author to whom correspondence should be addressed.
Coatings 2025, 15(7), 852; https://doi.org/10.3390/coatings15070852 (registering DOI)
Submission received: 27 May 2025 / Revised: 17 July 2025 / Accepted: 17 July 2025 / Published: 19 July 2025
(This article belongs to the Special Issue Thermal Barrier Coatings: Materials, Processes, Properties and Applications)
Versions Notes

Abstract

To address the issue of coating accumulation model failure in unstable environments, this paper proposes a dynamic control method based on visual differential feedback. An image difference model is constructed through online image data modeling and real-time reference image feedback, enabling real-time correction of the coating accumulation model. Firstly, by combining the Arrhenius equation and the Hagen–Poiseuille equation, it is demonstrated that pressure regulation and temperature changes are equivalent under dataset establishment conditions, thereby reducing data collection costs. Secondly, online paint mist image acquisition and processing technology enables real-time modeling, overcoming the limitations of traditional offline methods. This approach reduces modeling time to less than 4 min, enhancing real-time parameter adjustability. Thirdly, an image difference model employing a CNN + MLP structure, combined with feature fusion and optimization strategies, achieved high prediction accuracy: <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ -->

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MDPI and ACS Style

Su, C.; Yu, D.; Song, W.; Tian, H.; Bao, H.; Wang, E.; Li, M. Dynamic Control of Coating Accumulation Model in Non-Stationary Environment Based on Visual Differential Feedback. Coatings 2025, 15, 852. https://doi.org/10.3390/coatings15070852

AMA Style

Su C, Yu D, Song W, Tian H, Bao H, Wang E, Li M. Dynamic Control of Coating Accumulation Model in Non-Stationary Environment Based on Visual Differential Feedback. Coatings. 2025; 15(7):852. https://doi.org/10.3390/coatings15070852

Chicago/Turabian Style

Su, Chengzhi, Danyang Yu, Wenyu Song, Huilin Tian, Haifeng Bao, Enguo Wang, and Mingzhen Li. 2025. "Dynamic Control of Coating Accumulation Model in Non-Stationary Environment Based on Visual Differential Feedback" Coatings 15, no. 7: 852. https://doi.org/10.3390/coatings15070852

APA Style

Su, C., Yu, D., Song, W., Tian, H., Bao, H., Wang, E., & Li, M. (2025). Dynamic Control of Coating Accumulation Model in Non-Stationary Environment Based on Visual Differential Feedback. Coatings, 15(7), 852. https://doi.org/10.3390/coatings15070852

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