科研之友
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摘要
A model based on radial base function artificial neural network (RBFANN) was designed for the simulation and prediction of reduced glass transition temperature T-rg 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 T-rg, and composition dependence of T-rg 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 T-rg predicted by RBFANN agree well with the experimental values, indicating that the model is reliable and adequate.
