Compositional optimization of glass forming alloys based on critical dimension by using artificial neural network

Abstract An artificial neural network (ANN) model was developed for simulating and predicting critical dimension d c of glass forming alloys. A group of Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were designed based on the d c and their d c values were predicted by the ANN model. Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were prepared by injecting into copper mold. The amorphous structures and the determination of the d c of as-cast alloys were ascertained using X-ray diffraction. The results show that the predicted d c values of glass forming alloys are in agreement with the corresponding experimental values. Thus the developed ANN model is reliable and adequate for designing the composition and predicting the d c of glass forming alloy.