Pekka Siirtola
Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
Satu Tamminen
Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
Eija Ferreira
Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
Henna Tiensuu
Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
Elina Prokkola
SSAB Europe, Raahe plate mill, Finland
Juha Röning
Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
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http://dx.doi.org/10.3384/ecp17142503Published in: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Linköping Electronic Conference Proceedings 142:73, p. 503-510
Published: 2018-12-19
ISBN: 978-91-7685-399-3
ISSN: 1650-3686 (print), 1650-3740 (online)
In the steel plate production process it is important to minimize the wastage piece produced when cutting a mother steel plate to the size ordered by a customer. In this study, we build classi?cation and regression models to recognize the steel plate side edge shape, if it is curved or not and the amount of curvature. This is done based on time series data collected at the manufacturing line. In addition, this information needs to be presented in a way that enables fast analysis and long-term statistical monitoring. It can then be used to tune the parameters of the manufacturing process so that optimal curvature can be found and the size of the wastage piece can be reduced. The results show that using the classi?cation and linear regression methods, the side edge shape can be recognized reliably and the amount of curvature can be estimated with high accuracy as well.
steel manufacturing, classi?cation, regression, plate plan pattern control, plate side edge
