RAPID DENDRITIC GROWTH INVESTIGATED WITH ARTIFICIAL NEURAL NETWORK METHOD |
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Authors: | Wang Nan Zhang Jun Wei Bing-bo and Dai Guan-zhong |
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Affiliation: | Department of Applied Physics, and Northwestern Polytechnical University, Xi'an 710072, China |
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Abstract: | Rapid dendritic growth of γ-(Ni, Fe) phase, β-CoSb intermetallic compound and α-Fe phase was realized by undercooling Ni-10%Fe single phase alloy, Co-60.5%Sb intermetallic alloy and Fe-40%Sn hypomonotectic alloy to a substantial extent. Their experimentally measured dendrite growth velocities were 79.5m/s, 12m/s and 0.705m/s, corresponding to undercooling levels of 303K(0.18TL), 168K(0.11 TL) and 219K(0.15 TL) respectively. Since the usual dendrite growth theory deviates significantly from reality at great undercoolings, an artificial neural network incorporated with stochastic fuzzy control was developed to explore rapid dendrite growth kinetics. It leads to the reasonable prediction that dendritic growth always exhibits a maximum velocity at a certain undercooling, beyond which dendrite growth slows down as undercooling increases still further. In the case of Fe-Sn monotectic alloys, α-Fe dendrite growth velocity was found to depend mainly on undercooling rather than alloy composition. |
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Keywords: | dendritic growth neural network undercooling solidification |
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