Real-time monitoring of molten zinc splatter using machine learning-based computer vision
Callum O’Donovan, Cinzia Giannetti, Cameron Pleydell-Pearce
TL;DR
This paper introduces a real-time monitoring system using computer vision to track molten zinc splatter during steel galvanization, improving quality and reducing defects.
Contribution
A novel machine learning-based system for real-time zinc splatter monitoring in harsh industrial environments is proposed.
Findings
The YOLOv5 model achieved high precision, recall, and mean average precision (mAP) values.
Discrepancy between operators was higher than between operators and the model, indicating model reliability.
The system enables real-time monitoring and quantification of splatter severity for process optimization.
Abstract
During steel galvanisation, immersing steel strip into molten zinc forms a protective coating. Uniform coating thickness is crucial for quality and is achieved using air knives which wipe off excess zinc. At high strip speeds, zinc splatters onto equipment, causing defects and downtime. Parameters such as knife positioning and air pressure influence splatter severity and can be optimised to reduce it. Therefore, this paper proposes a system that converges computer vision and manufacturing whilst addressing some challenges of real-time monitoring in harsh industrial environments, such as the extreme heat, metallic dust, dynamic machinery and high-speed processing at the galvanising site. The approach is primarily comprised of the Counting (CNT) background subtraction algorithm and YOLOv5, which together ensure robustness to noise produced by heat distortion and dust, as well as…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 1
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 27Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIndustrial Vision Systems and Defect Detection · Thermography and Photoacoustic Techniques · Currency Recognition and Detection