Forward and reverse mappings in green sand mould system using neural networks

The quality of castings in a green sand mould is influenced significantly by its properties, such as green compression strength, permeability, mould hardness, and others, which depend on input parameters. The relationships of these properties with the input parameters, like sand grain size and shape, binder, water, etc. are complex in nature. In the neural network based forward mapping, mould properties are expressed as the functions of input parameters, whereas attempts can also be made to determine an appropriate set of input parameters, to ensure a set of desired properties, in reverse mapping. In the present work, the problems related to both the forward as well as reverse mappings in green sand mould system were tackled by using a back-propagation neural network (BPNN) and a genetic-neural network (GA-NN). Batch mode of training had been provided to both the networks with the help of one thousand data, generated artificially from the regression equations obtained earlier by the authors. The performances of the developed models had been compared among themselves for 20 randomly generated test cases. The results show that GA-NN outperforms the BPNN and that both the NN approaches are able to carry out the reverse mapping effectively. # 2007 Elsevier B.V. All rights reserved.