Name
Title | ||
|---|---|---|
Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/FUZZY.2011.6007731 | FUZZ-IEEE |
Keywords | Field | DocType |
mechanical property,fuzzy set theory,aluminium alloy,solid state joining technique,production engineering computing,traverse speed,surface treatment,systematic data-driven fuzzy modelling approach,welded materials,aluminium alloys,tool rotation speed,friction welding,fuzzy,multiple characterisation modelling,multi-objective,rotation,joining processes,welds,transparent fuzzy models,genetic multiobjective data-driven fuzzy modelling approach,optimal combinations,fsw behaviour,interpretability attributes,optimal design,microstructural features,fuzzy systems,nsga-ii,mechanical properties,aa5083 aluminium alloy,process parameters,friction stir welding,genetic algorithms,fsw process,heat treatment,overall weld quality,weld quality,prediction models,modelling,microstructure,welding property,fuzzy system,microstructures,predictive models,genetics,algorithm design,prediction model,fuzzy set,welding,materials,mathematical model,algorithm design and analysis,fuzzy sets,optimization | Friction welding,Friction stir welding,Computer science,Fuzzy logic,Fuzzy set,Optimal design,Control engineering,Artificial intelligence,Fuzzy control system,Mathematical model,Welding,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7584 E-ISBN : 978-1-4244-7316-8 | 978-1-4244-7316-8 | 2 |
PageRank | References | Authors |
0.38 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Zhang | 1 | 38 | 4.41 |
| Mahdi Mahfouf | 2 | 235 | 33.17 |
| George Panoutsos | 3 | 57 | 7.59 |
| Kathryn Beamish | 4 | 2 | 0.72 |
| Ian Norris | 5 | 2 | 1.06 |