Knowledge discovery for friction stir welding via data driven approaches: Part 2 – multiobjective modelling using fuzzy rule based systems

Zhang, Qian and Mahfouf, Mahdi and Panoutsos, George and Beamish, Kathryn and Norris, Ian (2012) Knowledge discovery for friction stir welding via data driven approaches: Part 2 – multiobjective modelling using fuzzy rule based systems. Science and Technology of Welding and Joining, 17 (8). pp. 681-693. ISSN 1362-1718. (doi:https://doi.org/10.1179/1362171812Y.0000000062) (Full text available)

Abstract

In this final part of this extensive study, a new systematic data-driven fuzzy modelling approach has been developed, taking into account both the modelling accuracy and its interpretability (transparency) as attributes. For the first time, a data-driven modelling framework has been proposed designed and implemented in order to model the intricate FSW behaviours relating to AA5083 aluminium alloy, consisting of the grain size, mechanical properties, as well as internal process properties. As a result, ‘Pareto-optimal’ predictive models have been successfully elicited which, through validations on real data for the aluminium alloy AA5083, have been shown to be accurate, transparent and generic despite the conservative number of data points used for model training and testing. Compared with analytically based methods, the proposed data-driven modelling approach provides a more effective way to construct prediction models for FSW when there is an apparent lack of fundamental process knowledge.

Item Type: Article
Uncontrolled keywords: Friction stir welding; Aluminium alloy; Data-driven modelling; Fuzzy system; Multi-objective optimisation; Reduced space searching algorithm
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 01:56 UTC
Last Modified: 22 Sep 2015 10:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50510 (The current URI for this page, for reference purposes)
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