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- W2031142546 abstract "High temperature oxidation property of Ni–Cr–W–Mo alloys is modelled as a function of alloy composition. A database has been constructed from cyclic oxidation experiments performed between 1150 °C and 400 °C of 27 alloys having various contents of Cr, W, and Mo. The Bayesian neural network technique was used for the modelling of cyclic oxidation experiments. With a 3-17-1 neural network architecture, the model shows a precise prediction of oxidation property of Ni–Cr–W–Mo alloys (R = 0.999). Automatic relevance determination (ARD) analysis reveals that the influence of alloying elements on the output is in the order of Cr, Mo, and W." @default.
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- W2031142546 date "2014-02-01" @default.
- W2031142546 modified "2023-09-26" @default.
- W2031142546 title "Modelling high temperature oxidation behaviour of Ni–Cr–W–Mo alloys with Bayesian neural network" @default.
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- W2031142546 doi "https://doi.org/10.1016/j.jallcom.2013.10.138" @default.
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