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On-Line Prediction of Product Quality With IoT Technologies

Industrial Internet of Things technology significantly impacts quality management by predicting mechanical properties of steel products. A data-driven approach has been used to predict correlations between properties of final products and their chemical and process parameters, applied to long and flat rolling. A clustering algorithm has been used to highlight products out of standard. The model runs as an application interfaced with the plant automation. Achieved results show high prediction accuracy, especially reached by a high-frequency sampling rate, thus enabling high resolution along the length of the rolled product. The target is a drastic reduction of clients’ rejection rate.
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2020 AISTech Conference Proceedings
PR-380-177
C. Licata, T. Kenneth Mikael Nordell, S. Nova
September 01, 2020
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