Abstract
Machine learning models trained to predict certain outcomes bear great potential in a variety of applications. This research takes a step to elevate the hot forming technology of radial-axial ring rolling towards a fully digitalized and even more efficient forming technology. For successful machine learning the preprocessing step is essential. This paper presents current research regarding the most promising preprocessing approaches of time series data for the specific use case of classifying form errors of the radial-axial ring rolling process. By predicting form errors (in-situ), scrap and rework rates can be lowered due to an alert by the model for form errors in advance of a potential error, thus contributing to a more efficient industry. The data used exists in form of time series from log-data of an industrial used, single ring rolling machine. Concluding, the proposed preprocessing approaches are evaluated by comparing different model performances, trained on actual production data.
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Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 404517758
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Fahle, S., Kneißler, A., Glaser, T., Kuhlenkötter, B. (2021). Research on Preprocessing Methods for Time Series Classification Using Machine Learning Models in the Domain of Radial-Axial Ring Rolling. In: Behrens, BA., Brosius, A., Hintze, W., Ihlenfeldt, S., Wulfsberg, J.P. (eds) Production at the leading edge of technology. WGP 2020. Lecture Notes in Production Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62138-7_49
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