Abstract
Data generated by manufacturing processes can often be represented as a data stream. The main characteristics of these data are that it is not possible to store all the data in memory, the data are generated continuously at high speeds, and it may evolve over time. These characteristics of the data make it impossible to use ordinary machine learning techniques. Specially crafted methods are necessary to deal with these problems, which are capable of assimilation of new data and dynamic adjustment of the model. In this work, we consider a cold rolling mill, which is one of the steps in steel strip manufacturing, and apply data stream methods to predict distribution of rolling forces based on the input process parameters. The model is then used for the purpose of anomaly detection during online production. Three different machine learning scenarios are tested to determine an optimal solution that fits the characteristics of cold rolling. The results have shown that for our use case the performance of the model trained offline deteriorates over time, and additional learning is required after deployment. The best performance was achieved when the batch learning model was re-trained using a data buffer upon concept drift detection. We plan to use the results of this investigation as a starting point for future research, which will involve more advanced learning methods and a broader scope in relation to the cold rolling process.
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Acknowledgements
Project XPM is supported by the National Science Centre, Poland (2020/02/Y/ST6/00070), under CHIST-ERA IV programme, which has received funding from the EU Horizon 2020 Research and Innovation Programme, under Grant Agreement no 857925.
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Jakubowski, J., Stanisz, P., Bobek, S., Nalepa, G.J. (2023). Towards Online Anomaly Detection in Steel Manufacturing Process. 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 10476. Springer, Cham. https://doi.org/10.1007/978-3-031-36027-5_37
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