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Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach

Zhang, Qian and Mahfouf, Mahdi and Panoutsos, George and Beamish, Kathryn and Norris, Ian (2011) Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011). IEEE, pp. 2288-2295. ISBN 978-1-4244-7315-1. E-ISBN 978-1-4244-7316-8. (doi:10.1109/FUZZY.2011.6007731) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:50542)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://doi.org/10.1109/FUZZY.2011.6007731

Abstract

Friction Stir Welding (FSW) is a relatively new solid state joining technique, which is versatile, environment friendly, and energy and time efficient. For a comprehensive understanding of the effects of process conditions, such as tool rotation speed and traverse speed, on characterisations of welded materials, it is essential to establish prediction models for different aspects of the materials' behaviours. Because of the high complexity of the FSW process, it is often difficult to derive accurate and yet transparent enough mathematical models. In such a situation, a systematic data-driven fuzzy modelling approach is developed and implemented in this paper to model FSW behaviour relating to AA5083 aluminium alloy, consisting of microstructural features, mechanical properties, as well as overall weld quality. This methodology allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The elicited models proved to be accurate, interpretable and robust and can be further applied to facilitate the optimal design of process parameters, with the aim of finding the optimal combinations of process parameters to achieve desired welding properties.

Item Type: Book section
DOI/Identification number: 10.1109/FUZZY.2011.6007731
Uncontrolled keywords: welding; fuzzy systems; optimization; fuzzy sets; materials; algorithm design and analysis; predictive models
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 15:33 UTC
Last Modified: 16 Nov 2021 10:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50542 (The current URI for this page, for reference purposes)

University of Kent Author Information

Zhang, Qian.

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