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Improving the quality of die castings by using artificial neural networks for porosity defect modelling

Khan, M. Imad, Nahavandi, Saeid and Frayman, Yakov 2003, Improving the quality of die castings by using artificial neural networks for porosity defect modelling, in Proceedings of the 1st International Light Metals Technology Conference 2003 : 18-20 September 2003, Brisbane, Australia, CRC for CAST Metals Manafacturing, Brisbane, Qld., pp. 243-245.

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Title Improving the quality of die castings by using artificial neural networks for porosity defect modelling
Author(s) Khan, M. Imad
Nahavandi, Saeid
Frayman, Yakov
Conference name International Light Metals Technology Conference (1st : 2003 : Brisbane, Australia)
Conference location Brisbane, Australia
Conference dates 18-20 September 2003
Title of proceedings Proceedings of the 1st International Light Metals Technology Conference 2003 : 18-20 September 2003, Brisbane, Australia
Editor(s) Dahle, Arne
Publication date 2003
Start page 243
End page 245
Publisher CRC for CAST Metals Manafacturing
Place of publication Brisbane, Qld.
Summary The aim of this work is to improve the quality of castings by minimizing defects and scrap through the analysis of the data generated by High Pressure Die Casting (HPDC) Machines using computational intelligence techniques. Casting is a complex process that is affected by the interdependence of die casting process parameters on each other such that changes in one parameter results in changes in other parameters. Computational intelligence techniques have the potential to model accurately this complex relationship. The project has the potential to generate optimal configurations for HPDC Machines and explain the relationships between die casting process parameters.
Notes Reproduced with the specific permission of the copyright owner.
ISBN 0975132903
9780975132906
Language eng
Field of Research 091207 Metals and Alloy Materials
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009621

Document type: Conference Paper
Collections: School of Engineering and Technology
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