arXiv:2606.07416v1 Announce Type: new Abstract: Atmospheric plasma spraying (APS) is a widely used coating process in which in-flight particle temperature and velocity strongly influence coating quality. However, these particle characteristics are difficult to monitor continuously during operation, motivating the development of non-invasive data-driven diagnostic methods. In this work, we investigate the predictive potential of high-speed video observations of the plasma plume for estimating in-flight particle characteristics in APS. We introduce three different video-derived feature represent
The increasing sophistication of AI and computer vision allows for non-invasive, data-driven diagnostic methods in industrial processes that were previously difficult to monitor continuously.
This development can significantly improve the quality control and efficiency of advanced manufacturing processes like atmospheric plasma spraying, reducing material waste and enabling more reliable high-performance coatings.
Manufacturing quality control shifts towards real-time, AI-driven video analysis, potentially enabling adaptive process adjustments without direct sensor contact with extreme environments.
- · Advanced manufacturing sector
- · Materials science
- · AI/Computer Vision developers
- · Industrial automation companies
- · Traditional QC methods
- · Manufacturing processes reliant on manual inspection
Improved and more consistent coating quality in industries utilizing plasma spraying.
Reduced operational costs and higher throughput in manufacturing due to real-time process optimization.
Acceleration of new material development and application where precise coating control is critical for novel functionalities.
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