Abstract
The realization of physical modeling of the rolling process is proposed as a material hardness virtual sensor and represents a valid tool for data exploration. The use of unsupervised clustering technology is here proposed and explored so as ease the material grouping process that might be strictly required for technological maintenance purposes.
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Cuzzola, F.A., Aurora, C. (2017). A Data-Mining Technology for Tuning of Rolling Prediction Models: Theory and Application. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_46
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DOI: https://doi.org/10.1007/978-3-319-62392-4_46
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