(吉林大学 南岭校区 材料科学与工程学院，长春 130025)
摘 要: 采用人工神经网络研究了在不同型温、浇温和转速条件下以离心法制备Al16%Si FGM时初晶硅的分布规律，并通过实验进行了验证。在建立神经网络模型时，以型温、浇温、转速等工艺参数作为人工神经网络的输入，以内生初晶硅分布的相对厚度作为输出。实验表明，预测结果与实际测定结果比较吻合，说明采用神经网络预测离心法制备梯度功能材料中内生颗粒的分布是可行的。
关键字: 离心法； 梯度功能材料； 人工神经网络；内生颗粒；颗粒分布
（College of Materials Science and Engineering,
Nanling Campus, Jilin University, Changchun 130025， P.R.China）
Abstract: Artificial neural network has been applied to acquire the constitutive relationships of endogenetic particle distribution in FGM prepared by centrifugal casting at different mould temperature, pouring temperature and rotating speed. Building up the neural network model of the constitutive relationship for the alloy, mould temperature, pouring temperature and rotating speed are taken as the inputs and relative thickness of endogenetic particle distribution in FGM is taken as the output. At the same time, four layers are constructed, six neurons are used in the first hidden layer and four neurons are used in the second hidden layer. The activation function in the output layer of the model obeys a linear function, while the activation function in the hidden layer is a sigmoid function. Comparison of the predicted and experimental results shows that the neural network model used to predict the constitutive relationship of the endogenetic particle distribution in FGM has good learning precision and good generalization. It's available to forecast endogenetic particle distribution in FGM prepared by centrifugal casting based on artificial neural network.
Key words: centrifugal casting； functionally gradient material； artificial neural network； endogenetic particle； particle distribution
