Modelling steel heat treatment data using granular data compression and multiple granularity modelling
by George Panoutsos, Mahdi Mahfouf
International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS), Vol. 1, No. 4, 2010

Abstract: In this paper, a systematic modelling approach is presented, involving two algorithmic procedures: a data pre-processing and data compression algorithm using granular computing and statistics; and a granular neural-fuzzy ensemble network consisting of multiple granularity models. Both algorithmic procedures aim to reduce the data and modelling scatter often found in real industrial complex data. The study focuses on the prediction of the mechanical property of heat treated steel, in particular Charpy Toughness. This mechanical property yields high data scatter caused by unknown underlying fractural dynamics. The proposed methodology is shown to successfully model the process under investigation using a real industrial dataset.

Online publication date: Sat, 20-Nov-2010

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