基于BP神经网络的细晶Ti_2AlNb基合金粉末球磨工艺研究

应用BP神经网络算法分析并预测了高能行星式球磨过程中工艺参数和球磨后Ti_2AlNb基合金粉末的形貌特征之间的关系,建立了粉末参数预测模型。BP网络模型的输入参数为球磨转速,球磨时间,球料比;输出参数为球磨后Ti_2AlNb基合金粉末的晶粒尺寸。BP网络模型中间隐含层节点数为9,输入、输出函数分别为tansig、purelin。通过检验样本验证了所建立神经网络模型的准确性。结果表明:该模型在容错性和通用性等方面优点突出,可用于预测球磨法制备细晶Ti_2AlNb基合金粉末的晶粒尺寸,还可以弥补各种球磨过程物理模型应用与表述方面的不足,对于实际的粉末冶金工艺研究具有积极的应用价值和指导意义。

Research on Ball Milling Processing of Fine Crystal Ti2AlNb-based Alloy Powder Based on Back-propagation Neural Network

An artificial-neural-network (ANN) model which is used for the prediction of properties of the as-milled powder is developed for the analysis and prediction of correlations between processing (high-energy planetary ball milling) parameters and the morphological characteristics of Ti2AlNb-based alloy powder by applying the back-propagation (BP) neural network technique.In the BP model, the input parameters of the neural network model are milling speed, milling time and ball-to-powder weight ratio. The output of the model is the properties of the as-milled powder (specifically crystallite size). The number of node in the hidden layer is 9. Input and output functions are tansig and purelin, respectively. The accuracy of the established artificial neural network model was tested by the test data sample. It is shown that the predicted values coincide well with the test results owe to the advantages in fault-tolerance and commonality. Not only can the trained neural network model be used to predict the crystallite size of the as-milled Ti2AlNb-based alloy powder, but also can make up for deficiency of all kinds of physical model for ball milling process in application and expression, which has application value and far-reaching significance for the research work of the actual powder metallurgy process.