Abstract
The use of information technologies in industry is growing year by year. More and more advanced devices are implemented and the software needed for them becomes more complex, which increases the risk of errors. To minimize them, it is necessary to constantly monitor the condition of the system and its components. This paper presents a part of a complex production support system for steel mill, responsible for monitoring and tracking the current state on the production hall. Data on currently performed melts and their condition, collected from two sensor layers - Level1 and Level2 - combining with a camera system that allows tracking the position of the main ladle in the hall, was used to create metamodel based on linear regression and neural network for the temperature drop which is occurring during the transport of liquid steel to the casting machine. This approach enables optimization of production volume and minimizes the risk associated with a temperature drop below the optimal one for casting. Several neural network models were used: YOLOv3 for object detection, CRAFT for text detection and CRNN for text recognition. This information is published to the sensor subsystem, enabling precise determination of the state of each performed melt. The system architecture, prediction accuracies and performance analysis were presented.
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Hajder, P. et al. (2023). Cyber-physical System Supporting the Production Technology of Steel Mill Products Based on Ladle Furnace Tracking and Sensor Networks. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10477. Springer, Cham. https://doi.org/10.1007/978-3-031-36030-5_36
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DOI: https://doi.org/10.1007/978-3-031-36030-5_36
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