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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Proceedings of the 1st International Light Metals Technology Conference 2003 : 18-20 September 2003, Brisbane, Australia
CRC for CAST Metals Manafacturing
Place of publication
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.
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