An-hui Cai

Artificial neural network modeling of reduced glass transition temperature of glass forming alloys

A model based on radial base function artificial neural network (RBFANN) was designed for the simulation and prediction of reduced glass transition temperature Trg of glass forming alloys. Its performance is examined by the influences of different kinds of alloys and elements, large and minor change of element content on the Trg, and composition dependence of Trg for La–Al–Ni ternary alloy system. Moreover, a group of Zr–Al–Ni–Cu bulk metallic glasses is designed by RBFANN. The values of Trg predicted by RBFANN agree well with the experimental values, indicating that the model is reliable and adequate.

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.

Zr-Al-Ni-Cu glass forming alloys with low fragility parameters

Abstract Zr-Al-Ni-Cu bulk metallic glasses (BMGs) were developed and their fragility parameters ( m ) were calculated by Arrhenius and Vogel-Fulcher-Tammann (VFT) equations. The results show that the m values of the Zr-Al-Ni-Cu BMGs derived by Arrhenius equation are in agreement with the corresponding m values derived by VFT equation. These Zr-Al-Ni-Cu BMGs characterize in low m values. The low m values for these BMGs would be due to their network microstructures. In addition, the m values of Zr-Al-Cu-Ni BMGs could be obtained by regulating Zr content. The composition of Zr-Al-Cu-Ni BMGs with the lowest m value would be near 54%Zr (mole fraction) because the m value about 13 of Zr 54 Al 13 Cu 18 Ni 15 BMG is the lowest among these Zr-Al-Ni-Cu BMGs developed.

A Cu-based bulk amorphous alloy composite reinforced by carbon nanotube

Abstract Bulk Cu 50 Zr 40 Ti 10 amorphous alloy composites reinforced with carbon nanotube (CNT) were successfully fabricated by hot pressing technique. Their density, thermal conductivity, and mechanical properties were systemically investigated. The density and the compression strength of the compacts both decrease with increasing CNT content. The thermal conductivity of the compacts decreases when the CNT content is less than 0.10% or exceeds 0.60% (mass fraction), while increases when the CNT content is in the range of 0.1%–0.6%. The strain limit and the modulus of the compacts are obviously improved when the CNT content is less than 1.0% and then decrease significantly when the CNT content exceeds 1.00%. The optimum CNT addition is less than 0.20% at the comprehensive properties point of view.