PREDICTION OF FLUIDITY OF CASTING ALUMINUM ALLOYS USING ARTIFICIAL NEURAL NETWORK

摘要

Based on BP (back-propagation algorithm) neural network and training dataset of fluidity of casting aluminum alloys, a prediction model with a structure of 8-9-1 has been constructed to predict the fluidity of casting aluminum alloys. The model inputs are contents of Al, Si, Fe, Cu, Mn, Mg, Zn and pouring temperature, and the output is fluidity of casting aluminum alloys. The test dataset of fluidity of casting aluminum alloys was used to check the accuracy of the model. Results show that the developed fluidity model can well predict the fluidity of casting aluminum alloys, with a maximum error of 11.81% and an average error of 6.56%. Also, based on the prediction model of fluidity of casting aluminum alloys, how the compositions effect the fluidity of binary and multicomponent casting aluminum alloys has been studied.