Development of viscosity model for aluminum alloys using BP neural network

基于BP神经网络算法的铝合金黏度预测模型

Viscosity is one of the important thermophysical properties of liquid aluminum alloys,which influences the characteristics of mold filling and solidification and thus the quality of castings.In this study,315 sets of experimental viscosity data collected from the literatures were used to develop the viscosity prediction model.Back-propagation(BP)neural network method was adopted,with the melt temperature and mass contents of Al,Si,Fe,Cu,Mn,Mg and Zn solutes as the model input,and the viscosity value as the model output.To improve the model accuracy,the influence of different training algorithms and the number of hidden neurons was studied.The initial weight and bias values were also optimized using genetic algorithm,which considerably improve the model accuracy.The average relative error between the predicted and experimental data is less than 5%,confirming that the optimal model has high prediction accuracy and reliability.The predictions by our model for temperature-and solute content-dependent viscosity of pure Al and binary Al alloys are in very good agreement with the experimental results in the literature,indicating that the developed model has a good prediction accuracy.

黏度是液体铝合金重要的热物性质之一,影响到液体充型与凝固的特征,继而铸件的质量。在本研究中,从文献中收集了315组实验测定的黏度数据,用来开发黏度预测模型。采用BP神经网络算法构建模型,以熔体温度和合金中Al、Si、Fe、Cu、Mn、Mg和Zn的含量作为模型输入,并以黏度值作为模型输出。为了改善模型精度,研究不同训练算法和隐含层神经元数的影响。使用遗传算法优化初始权重与赋值,这显著改善了模型精度。模型预测值与实验值间的相对误差小于5%,证明所构建的优化模型具有高的预测精度与可靠性。用建立的模型对纯Al和二元Al合金的黏度随温度和溶质含量变化的预测结果与文献中的实验结果非常一致,表明该模型在工程应用中具有较好的预测精度。