frayman-improvingthequalityofdiecasting-2003.pdf (222.38 kB)
Improving the quality of die castings by using artificial neural networks for porosity defect modelling
conference contribution
posted on 2003-01-01, 00:00 authored by M Khan, Saeid Nahavandi, Yakov FraymanThe 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 - 245Publisher
CRC for CAST Metals ManafacturingLocation
Brisbane, AustraliaPlace of publication
Brisbane, Qld.Start date
2003-09-18End date
2003-09-20ISBN-13
9780975132906ISBN-10
0975132903Language
engNotes
Reproduced with the specific permission of the copyright owner.Publication classification
E1 Full written paper - refereedEditor/Contributor(s)
A DahleTitle of proceedings
Proceedings of the 1st International Light Metals Technology Conference 2003 : 18-20 September 2003, Brisbane, AustraliaUsage metrics
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