Showing 1 results for Neural Network Model.
M. Meratian, N. Saeidi,
Volume 28, Issue 1 (6-2009)
Abstract
In cast aluminum and its alloys, the microstructure varies under different solidification conditions, causing variations in their mechanical properties. These materials are basically produced in sand and metallic molds or through die casting, each of which is associated with a unique solidification regime with significantly different cooling rates so that the resulting microstructure strongly depends on the casting method used. In the present study, the effects of such important solidification parameters as cooling rate, solidification front velocity, and thermal gradient at the solid-liquid interface on secondary dendrite arm spacing were investigated. By a directional solidification system, the mathematical relation between cooling rate and dendrite spacing was extracted for several commercially important aluminum alloys. A neural network model was trained using the experimental values of cooling rates and secondary dendrite arm spacing. Reliable prediction of these values was made from the trained network and their corresponding diagrams were constructed. A good agreement was found between simulation and experimental values. It is concluded that the neural network constructed in this study can be employed to predict the relationship between cooling rate and dendrite arm spacing, which is difficult, if not iompossible, to accomplish experimentally.
