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

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conference contribution
posted on 2003-01-01, 00:00 authored by M Khan, Saeid Nahavandi, Yakov Frayman
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.

History

Event

International Light Metals Technology Conference (1st : 2003 : Brisbane, Australia)

Pagination

243 - 245

Publisher

CRC for CAST Metals Manafacturing

Location

Brisbane, Australia

Place of publication

Brisbane, Qld.

Start date

2003-09-18

End date

2003-09-20

ISBN-13

9780975132906

ISBN-10

0975132903

Language

eng

Notes

Reproduced with the specific permission of the copyright owner.

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

A Dahle

Title of proceedings

Proceedings of the 1st International Light Metals Technology Conference 2003 : 18-20 September 2003, Brisbane, Australia

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