铝合金微观组织的智能预测模型

Intelligent Model for Predicting the Microstructure of Aluminum Alloys

提出了由多个NALU算法层构建适于铝合金微观组织智能预测的神经网络模型,简述了NALU算法的基本原理和模型,阐述了以局部晶粒特征表示的微观组织预测的建模和训练方法,将RMSProp与Adam优化算法相结合,并合理调整学习速率,可训练出满意的预测模型。以铝合金铸造为例,考虑合金元素、工艺条件和设备参数等26个影响因素,以包含18个局部晶粒特征的微观组织参数为目标,建立了包括输入和输出神经元数分别为(26,32)、(32,28)、(28,23)、(23,18)的4个NALU层的神经网络,模型训练后的均方误差达到0.0006。任选8组不同条件的铝合金平均晶粒尺寸的预测误差小于9%,适于微观组织的智能预测及新合金设计。研究表明,用多个NALU层构建合金微观组织与性能的智能预测模型是可行的,用局部晶粒特征对铝合金微观组织形貌特点的较细致表达的智能预测可以用多层NALU神经网络模型来实现。

A neural network model with multi-layer NALU algorithm for the prediction of microstructures of aluminum alloys was carried out.The principle and model of NALU algorithm were explained in detail,and the approaches of modeling and training for the prediction with local grain characteristics were introduced.A satisfying model can be trained with the combination of both optimization algorithm RMSProp and Adam,and appropriate adjustment of learning rate.Taking aluminum alloy casting as instance,26 input factors including alloying elements,processing conditions as well as equipment parameters were concerned,and microstructure parameters containing 18 local grain characteristics were targeted.Hence a neural network model containing 4 layers of NALU algorithm was established with(26,32)、(32,28)、(28,23)、(23,18)as the number of input/output neurons.The mean square error for the trained network model reaches 0.0006.Prediction error of average grain size of 8 groups of aluminum alloys under different conditions is less than 9%,suitable for intelligent prediction of microstructures and design of new aluminum alloys.The results indicate that it is feasible to construct an intelligent prediction model for the microstructure and properties of alloys by multiple-layer NALU.Multi-layer NALU neural network model can realize the intelligent prediction for exquisite expression of aluminum alloy microstructure by local grain characteristics.