Title: Modelling steel heat treatment data using granular data compression and multiple granularity modelling

Authors: George Panoutsos, Mahdi Mahfouf

Addresses: Department of Automatic Control and Systems Engineering, The University of Sheffield, Amy Johnson Building, Mappin Street, Sheffield S1 3JD, UK; Institute for Microstructural and Mechanical Process Engineering, The University of Sheffield (IMMPETUS), Sheffield, UK. ' Department of Automatic Control and Systems Engineering, The University of Sheffield, Amy Johnson Building, Mappin Street, Sheffield S1 3JD, UK; Institute for Microstructural and Mechanical Process Engineering, The University of Sheffield (IMMPETUS), Sheffield, UK

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.

Keywords: GrC; granular computing; granular data compression; multiple granularity modelling; granular neural-fuzzy modelling; mechanical properties; heat treated steel; Charpy toughness; modelling; steel heat treatment; neural networks; fuzzy logic.

DOI: 10.1504/IJGCRSIS.2010.036980

International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2010 Vol.1 No.4, pp.382 - 392

Received: 02 Feb 2009
Accepted: 02 Feb 2009
Published online: 20 Nov 2010 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article