Rubio-Solis, A. and Panoutsos, G. (2017) An ensemble data-driven fuzzy network for laser welding quality prediction. In: 2017 IEEE International Conference on Fuzzy Systems. 2017 IEEE International Conference on Fuzzy Systems , 09-12 Jul 2017, Naples, Italy. IEEE ISBN 9781509060344
Abstract
This paper presents an Ensemble Data-Driven Fuzzy Network (EDDFN) for laser welding quality prediction that is composed of a number of strategically selected Data-Driven Fuzzy Models (DDFMs). Each model is trained by an Adaptive Negative Correlation Learning approach (ANCL). A monitoring system provides quality-relevant information of the laser beam spectrum and the geometry of the melt pool. This information is used by the proposed ensemble model to asist in the prediction of the welding quality. Each DDFM is based on three conceptual components, i.e. a selection procedure of the most representative welding information, a granular comprehesion process of data and the construction of a fuzzy reasoning mechanism as a series of Radial Basis Function Neural Networks (RBF-NNs). The proposed model aims at providing a fuzzy reasoning engine that is able to preserve a good balance between transparency and accuracy while improving its prediction properties. We apply the EDDFN to a real case study in manufacturing industry for the prediction of welding quality. The corresponding results confirm that the EDDFN provides better prediction properties compared to a single DDFM with an overal prediction performance > 78%.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
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: | 03 Nov 2017 14:33 |
Last Modified: | 19 Dec 2022 13:37 |
Published Version: | https://doi.org/10.1109/FUZZ-IEEE.2017.8015496 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/FUZZ-IEEE.2017.8015496 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:123416 |