Colas-Marquez, R. and Mahfouf, M. orcid.org/0000-0002-7349-5396 (2017) Data mining and modelling of Charpy impact energy for alloy steels using fuzzy rough sets. In: Dochain, D., Henrion, D. and Peaucelle, D., (eds.) IFAC-PapersOnLine. 20th IFAC World Congress, 09-14 Jul 2017, Toulouse, France. Elsevier , pp. 14970-14975.
Abstract
When considering data-driven modelling, uncertainties, errors and inconsistencies in the data can more often than not lead to sub-optimal predictions. A new framework based on rough sets theory is proposed and applied to an industrial data set obtained from a Charpy impact energy test for alloy steels. The inconsistent/consistent data sets are then used to train a series of artificial neural networks (ANN) for Charpy impact energy prediction for alloy steels. A k-nearest neighbor is used to classify the data points; if an object is classified as consistent, the ANN trained with the consistent data set provides a single prediction while if the object is classified as inconsistent, several ANN trained with different sets of inconsistent data are used to provide an interval prediction. Experimental results show an improvement in the consistent data set compared with a benchmark model. Also, the interval prediction provided by the various ANNs in the inconsistent data set represents a better alternative to the single point prediction results.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2016 IFAC (International Federation of Automatic Control). |
Keywords: | Charpy impact energy; neural networks; classification; fuzzy sets; rough sets; data-driven model |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Jan 2020 10:25 |
Last Modified: | 21 Jan 2020 10:25 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.ifacol.2017.08.2555 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129128 |