【摘要】 利用文献提供的实验数据和优化方法,得到Sn-15%Pb合金半固态表观粘度(冷却速率G=0.33℃/min)的指数与幂函数的拟合公式和两段半抛物线拟合插值法的拟合公式。分别用BP神经网络和拟合的数学公式预测在剪切速率γ=180s-1,冷却速率G=0.33℃/min的Sn-15%Pb合金半固态的表观粘度与固相体积分数的关系曲线,以及用BP神经网络预测冷却速率G=10℃/min的Sn-15%Pb合金半固态的表观粘度等值线曲线。其表观粘度的拟合公式的计算值和BP网络仿真值与文献提供的实验值有较好的吻合。更多还原

【Abstract】 The fitted mathematical formula of Sn-15%Pb alloy apparent viscosity under the condition of the cooling rate of G=0.33 ℃/min, which are related to popular exponential function, a classical power equation and the fitting interpolation method of two semi-parabolic segments, are obtained by using experimental values given in literatures and optimization. The curves of Sn-15%Pb alloy semisolid apparent viscosity versus solid volume fraction under the condition of shear rate,γ=180 s^-1, and cooling rate of G=（0.33 ）℃/min, are forecasted by using respectively the BP neural network and the fitted mathematical formula. The contours of Sn-15%Pb alloy apparent viscosity, at the cooling rate G=10 ℃/min, are forecasted by the trained BP neural network. Its apparent viscosity data got from the fitted mathematical formula and simulated data from BP neural network agree with the experimental values given in literatures very well.更多还原