Analysis and Control of High-Pressure Die-Casting Process Parameters with Use of Data Mining Tools

2019 ◽  
pp. 253-267
Author(s):  
Jacek Kozłowski ◽  
Michał Jakimiuk ◽  
Michał Rogalewicz ◽  
Robert Sika ◽  
Jakub Hajkowski
Keyword(s):  
Data Mining ◽  
High Pressure ◽  
Process Parameters ◽  
Die Casting ◽  
Casting Process ◽  
Use Of Data ◽  
Die Casting Process ◽  
And Control ◽  
Pressure Die Casting ◽  
Mining Tools

High-Pressure Die-Casting Process Modelling Using Neural Networks

2006 ◽  
pp. 182-198 ◽  
Author(s):  
M. Imad Khan ◽  
Saeid Nahavandi ◽  
Yakov Frayman
Keyword(s):  
High Pressure ◽  
Process Parameters ◽  
Current Knowledge ◽  
Die Casting ◽  
Process Condition ◽  
Industrial Process ◽  
Casting Process ◽  
High Pressure Die Casting ◽  
Die Casting Process ◽  
Pressure Die Casting

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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