Hierarchical artificial neural network modelling of aluminum alloy properties used in die casting

This study aimed to develop a semi non-parametric model of the die casting process of aluminum alloys. This model uses a hierarchical artificial neural network (HANN), with a structure motivated by the relationships of the metals which define the characteristics of the aluminum alloy. These settings depend on the content of seven metals (Sn, Zn, Mn, Cu, Si, Ni, and Mg). The relation between these metals and the alloy characteristics oriented the HANN structure. A distributed back-propagation learning modified with the Levenberg-Marquardt method served to adjust the HANN weights. Two complementary validation methods justified the application of this novel hybrid non-parametric modelling structure. The training set came from standards composition proposed by different international organizations. A set of real aluminum alloys and the experimental results describing their characteristics formed the validation test. An average accuracy value of 3.65% confirmed the ability of the HANN to reproduce the relation between the metal content and the alloy characteristics. These values confirmed how the oriented HANN may predict the aluminum alloy characteristics as function of the metal distribution. This result offers a different alternative to the prediction of aluminum alloy properties using the metal composition as input information.