Compositional optimization of glass forming alloys based on critical dimension by using artificial neural network
(1. College of Mechanical Engineering, Hunan Institute of Science and Technology, Yueyang 414000, China;
2. State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China)
2. State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China)
Abstract: An artificial neural network (ANN) model was developed for simulating and predicting critical dimension dc of glass forming alloys. A group of Zr-Al-Ni-Cu and Cu-Zr-Ti-Ni bulk metallic glasses were designed based on the dc and their dc 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 dc of as-cast alloys were ascertained using X-ray diffraction. The results show that the predicted dc 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 dc of glass forming alloy.
Key words: critical dimension; glass forming alloy; artificial neural network; metallic glasses