Abstract
There are many Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) applications being used in real manufacturing plants. Digital twins with AI/ML/DL capabilities are among these applications. Steel is an important process industry, where such applications have vast potential to provide positive impacts on different aspects, including energy consumption and cost benefits. The Cognitive Plants Through Proactive Self-Learning Hybrid Digital Twins (CogniTwin) project aims at adding the cognitive elements to the existing process control systems, enabling their capability to self-organise and offer solutions to unpredicted behaviours. In NOKSEL pilot of CogniTwin, the main problem is related to very high costs of machines breakdowns. By developing digital, hybrid and cognitive twins, the pilot aims at reducing energy consumption and average duration of machine downtimes. Cognitive twins for Spiral Welded Steel Pipes (SWP) will enable predictive maintenance at the steel pipe plant. This study briefs the NOKSEL pilot’s purpose, scope, state and the results gained in the 1st year of the project.
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Acknowledgement
The CogniTwin project has partially been funded by the European Union’s Horizon 2020 research and innovation programme under GA No. 870130. We would like to thank the TEKNOPAR’s R&D team members, and the software engineers who work for the CogniTwin project. For the use case, we thank to all NOKSEL employees who participated in the CogniTwin.
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Albayrak, Ö., Ünal, P. (2021). Smart Steel Pipe Production Plant via Cognitive Digital Twins: A Case Study on Digitalization of Spiral Welded Pipe Machinery. In: Colla, V., Pietrosanti, C. (eds) Impact and Opportunities of Artificial Intelligence Techniques in the Steel Industry. ESTEP 2020. Advances in Intelligent Systems and Computing, vol 1338. Springer, Cham. https://doi.org/10.1007/978-3-030-69367-1_11
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