Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10418363 | Journal of Materials Processing Technology | 2005 | 7 Pages |
Abstract
In this study, artificial neural networks were used to model the hot deformation behavior of Zr-2.5Nb-0.5Cu alloy, in the strain rate range of 10â3 to 10 sâ1, temperature range of 650-1050 °C and to a strain of 0.5. Strain, log strain rate and inverse of temperature were used as inputs and stress was taken as the output of the network. The feed-forward network used consisted of two hidden layers containing four and three neurons each with a log-sigmoid activation function and Levenberg-Marquardt training algorithm. The network was successfully trained across phase regimes (α + β) to β and across different deformation domains. This trained network could predict the flow stress better than a constitutive equation of the type εË=Asinh(αâ²Ï)nexp(âQ/RT).
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Authors
R. Kapoor, D. Pal, J.K. Chakravartty,