Intelligent methods for root cause analysis behind the center line deviation of the steel strip
Tiensuu, Henna; Tamminen, Satu; Haapala, Olli; Röning, Juha (2020-05-30)
Tiensuu, H., Tamminen, S., Haapala, O., & Röning, J. (2020). Intelligent methods for root cause analysis behind the center line deviation of the steel strip, Open Engineering, 10(1), 386-393. doi: https://doi.org/10.1515/eng-2020-0041
© 2020 H. Tiensuu et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 License.
This article presents a statistical prediction model-based intelligent decision support tool for center line deviation monitoring. Data mining methods enable the data driven manufacturing. They also help to understand the manufacturing process and to test different hypotheses. In this study, the original assumption was that the shape of the strip during the hot rolling has a strong effect on the behaviour of the steel strip in Rolling, Annealing and Pickling line (RAP). Our goal is to provide information that enables to react well in advance to strips with challenging shape. In this article, we show that the most critical shape errors arising in hot rolling process will be transferred to critical errors in RAP-line process as well. In addition, our results reveal that the most critical feature characterizes the deviation better than the currently used criterion for rework. The developed model enables the user to understand better the quality of the products, how the process works, and how the quality model predicts and performs.
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