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.
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