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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/4231

Title: Formulating polyurethanes using case based reasoning
Authors: Segura-Velandia, Diana M.
Heath, R.J.
West, Andrew A.
Keywords: Case based reasoning
Polyurethane formulation
Artificial neural network
Issue Date: 2007
Publisher: © Maney
Citation: SEGURA-VELANDIA, D.M., HEATH, R.J. and WEST, A.A., 2007. Formulating polyurethanes using case based reasoning. Plastics, Rubber and Composites, 36 (6), pp.241-247
Abstract: A large amount of historical knowledge exists in the form of ‘formulation experiences’ across polyurethane manufacturing companies. This knowledge is difficult to formalise, share and use in new formulations. As a part of an effort to support the polyurethane formulating problem, the use of case based reasoning (CBR) has been assessed. Two basic problems in the development of the proposed hybrid tool that uses past formulations to solve new problems are studied. The problems investigated are related to the retrieval of former formulations that are similar to a new problem description by the CBR module, and the adaptation of the retrieved case to meet the problem constraints using an artificial neural network (ANN). Results indicated that the CBR-ANN system is useful for reusing historical data. Although the obtained ANN is unable to generalise well when presented with more data independent from the original data set, results proved that real formulation data can be used as a ‘knowledge repository’ that can guide CBR adaptation without human expert intervention.
Description: This article was published in the journal, Plastics, Rubber and Composites [© Maney Publishing] and is available at: http://www.maney.co.uk/search?fwaction=show&fwid=199
Version: Published
DOI: 10.1179/174328907X229691
URI: https://dspace.lboro.ac.uk/2134/4231
ISSN: 1465-8011
Appears in Collections:Published Articles (Materials)

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