High-Pressure Die-Casting Process Modelling Using Neural Networks

High-Pressure Die-Casting Process Modelling Using Neural Networks

M. Imad Khan (Deakin University, Australia), Saeid Nahavandi (Deakin University, Australia) and Yakov Frayman (Deakin University, Australia)
Copyright: © 2006 |Pages: 17
DOI: 10.4018/978-1-59140-670-9.ch011
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This chapter presents the application of a neural network to the industrial process modeling of high-pressure die casting (HPDC). The large number of inter- and intradependent process parameters makes it difficult to obtain an accurate physical model of the HPDC process that is paramount to understanding the effects of process parameters on casting defects such as porosity. The first stage of the work was to obtain an accurate model of the die-casting process using a feed-forward multilayer perceptron (MLP) from the process condition monitoring data. The second stage of the work was to find out the effect of different process parameters on the level of porosity in castings by performing sensitivity analysis. The results obtained are in agreement with the current knowledge of the effects of different process parameters on porosity defects, demonstrating the ability of the MLP to model the die-casting process accurately.

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Table of Contents
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Joarder Kamruzzaman, Rezaul Begg, Ruhul Sarker
Chapter 1
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Chapter 10
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Chapter 11
M. Imad Khan, Saeid Nahavandi, Yakov Frayman
This chapter presents the application of a neural network to the industrial process modeling of high-pressure die casting (HPDC). The large number... Sample PDF
High-Pressure Die-Casting Process Modelling Using Neural Networks
Chapter 12
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Chapter 13
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