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: Fuzzy Systems (FUZZ), 2011 IEEE International Conference on. IEEE pp. 2288-2295. ISBN 978-1-4244-7315-1. E-ISBN 978-1-4244-7316-8. (doi:https://doi.org/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)

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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: Conference or workshop item (Paper)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 15:33 UTC
Last Modified: 22 Sep 2015 08:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50542 (The current URI for this page, for reference purposes)
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