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Prediction of critical cooling rate for glass forming alloys by artificial neural network
Highlights A RBFANN model was developed for the prediction of critical cooling rate. The RBFANN model characterizes in the whole, local and sorting performance. The RBFANN model can identify the type of alloys and elements. The RBFANN model is sensitive to large and minor change of elements’ content.
Abstract A radial basis function artificial neural network (RBFANN) model was established for the simulation and prediction of critical cooling rate Rc of glass forming alloys. The RBFANN model was trained, learned and examined using the data from the published literature as well as own experimental data. The performance of RBFANN model is examined by the linearly dependent coefficient between the predicted Rc and the corresponding experimental/calculated one; the influence of the type of alloys and elements and the large and minor change of element content on the Rc. In addition, a group of Zr–Al–Ni–Cu metallic glasses were designed and their Rcs were predicted by the RBFANN model. The results show that the established RBFANN model is reliable and adequate and can be used to design the composition and predict the Rc of glass forming alloys since the predicted Rc is inherent with the experimental/calculated one.
Prediction of critical cooling rate for glass forming alloys by artificial neural network
Highlights A RBFANN model was developed for the prediction of critical cooling rate. The RBFANN model characterizes in the whole, local and sorting performance. The RBFANN model can identify the type of alloys and elements. The RBFANN model is sensitive to large and minor change of elements’ content.
Abstract A radial basis function artificial neural network (RBFANN) model was established for the simulation and prediction of critical cooling rate Rc of glass forming alloys. The RBFANN model was trained, learned and examined using the data from the published literature as well as own experimental data. The performance of RBFANN model is examined by the linearly dependent coefficient between the predicted Rc and the corresponding experimental/calculated one; the influence of the type of alloys and elements and the large and minor change of element content on the Rc. In addition, a group of Zr–Al–Ni–Cu metallic glasses were designed and their Rcs were predicted by the RBFANN model. The results show that the established RBFANN model is reliable and adequate and can be used to design the composition and predict the Rc of glass forming alloys since the predicted Rc is inherent with the experimental/calculated one.
Prediction of critical cooling rate for glass forming alloys by artificial neural network
2013-06-02
6 pages
Article (Journal)
Electronic Resource
English
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