基于神经网络算法的铝合金差压铸造工艺优化研究  

Optimization of Differential Pressure Casting Process of Aluminum Alloys Based on Neural Network Algorithm

作　　者：陈新林[1] 胡帅[1] 董燕飞[2] CHEN Xinlin;HU Shuai;DONG Yanfei(Henan Vocational College of Agriculture, Zhengzhou 451450, China;Henan University of Urban Construction, Pingdingshan 467036, China)

机构地区：[1]河南农业职业学院,河南郑州451450 [2]河南城建学院,河南平顶山467036

出　　处：《热加工工艺》2018年第7期99-101,共3页Hot Working Technology

基　　金：河南省教育厅项目(15A413008)

摘　　要：以11个差压铸造工艺参数作为输入层参数,以抗拉强度作为输出层参数,构建了11×55×5×1四层结构的铝合金差压铸造工艺优化神经网络模型。结果表明,神经网络模型的平均相对训练误差2.8%,平均相对预测误差为2.9%,模型具有较强的预测能力和较佳的预测精度。Taking 11 differential pressure casting process parameters as input layer parameters, and taking the tensile strength as the output layer parameter, the neural network model（four layers structure of 11×55×5×1） for differential pressure casting process optimization of aluminum alloys was built. The results show that the average relative training and prediction errors of the neural network model are 2.8% and 2.9%, respectively. The model has stronger prediction ability and better prediction accuracy.

关 键 词：神经网络算法 铝合金 差压铸造 工艺优化 抗拉强度 

分 类 号：TG249.9[金属学及工艺—铸造]