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- In relation to this article, we declare that there is no conflict of interest.
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Received October 20, 2022
Revised February 7, 2023
Accepted March 14, 2023
- Acknowledgements
- Support from the Basic Science Research Program through the Korea Coating Technology Center is gratefully acknowledged.
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Automated paint coating using two consecutive images with CNN regression
1Dept. of Chem. & Energy Eng., Kyungnam Coll. of Info. & Tech., Busan 47011, Korea 2Dept. of Industrial Chemistry, Pukyong National University, Busan 48547, Korea
yhkim2@pknu.ac.kr
Korean Journal of Chemical Engineering, September 2023, 40(9), 2334-2341(8), 10.1007/s11814-023-1452-9
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Abstract
Although new coating development for improved surface protection is necessary, its manual application
has been a difficult problem to solve. In this study, a convolution neural network (CNN) was used for prediction of the
painting gun operation. Painting videos were converted to sequential images, of which two consecutive images were
associated with the gun position in the next time step. The inputs were implemented in a regression CNN training,
which was used to calculate the position of the spray gun at the next moment. Recursive image utilization provides the
prediction of spray gun movement in real-time applications. The statistical measures of the prediction and true values
of gun movement using test data indicate that the proposed CNN gives comparable outcomes to similar applications of
the CNN. The exhibition of simulated painting of a rectangle and a semicircle demonstrates the usefulness of the proposed CNN application for spray gun painting
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55. G. Ancione, R. Saitta, P. Bragatto, G. Fiumara and M. F. Milazzo,Sustainability, 14, 6156 (2022)
