Rapid eutectic growth of Sb-24%Cu alloy is realized in the drop tube during the free fall under the con-tainerless condition. Based on the analysis of crystal nuclea-tion and eutectic growth in the free fall condition, it is indicated that, with the increase of undercooling, microstruc-tural transition of Sb-24%Cu eutectic alloy proceeds from lamellar to anomalous eutectic structure. Undercoolings of 0 -154 K have been obtained in experiment. The maximum undercooling exceeds to 0.19TE. Calculated results exhibit that Cu2Sb compound is the primary nucleation phase, and that the primary Sb dendrite will grow more rapidly than the eutectic structure when undercooling is larger than 40 K. The eutectic coupled zone around Sb-24%Cu eutectic alloy leads strongly to the Cu-rich side and covers a composition range from 23.0% to 32.7%Sb.
Rapid solidification of liquid alloy is an importantsubject in both fundamental research and practical applications. However,theideal theoretical descriptionsofcrystal growth kineticsduring this processhave been still lacked up to now. Although several theories have been setup by using traditional methematical physics methods,the experimental results reveal that they cannot be universally applicable for different experimental conditions. The LKT model[1 ] ,for example,is quite successful to describe dendrite growth during rapid solidification. But it has been confirmed bo be only useful within medium undercooling regime[2 ,3 ] . With the developing of the materials science in space,the undercooling up to 2 0 0 to 50 0 K can be obtained by modern experimenttechniques. Therefore,itis highly desirable to develop a more universal theoretical model which can depictrapid dendrite growth within any attainable undercooling regime. The artificial neural network (ANN) technique is an important research field in automatic control engineering and hasalso found many application in otherscientific areas. By using such a method,Sun and coworkers[4] successfully predicated the thermophysical properties of high temperature metallurgical melts. Li and Xu[5 ] acquired satisfactory results when they used ANN technique to study the CVD/Si C coating formation processof C/C composites.However,there is no reporton dendrite growth investigation during rapid solidification by ANN technique.The objective of this paperis to directsome efforts to thisrespect. Since rapid solidification is a typical complex nonlinear dynamic process characterized by some random elements,a stochastic fuzzy neural network(SFNN model) which incorporates random control into ANN technique is developed and applied. The SFNN model is schematically presented in Fig.1 . This is a forward neural network with multi- inputs and single output. It consists of one input layer,one output layer and two hidden layers. Input parameters mainly involve such independent variables
