新型机械轴承钢的锻造温度神经网络优化  

Neural Network Optimization of Forging Temperature for New Mechanical Bearing Steel

作　　者：赵海贤 韩彦龙 刘燕玲 ZHAO Haixian;HAN Yanlong;LIU Yanling(Hebei University of Petroleum Technology,Chengde 067000,China;School of Mechanical Engineering,North China University of Technology,Tangshan 063210,China)

机构地区：[1]河北石油职业技术大学,河北承德067000 [2]华北理工大学机械工程学院,河北唐山063210

出　　处：《热加工工艺》2022年第17期79-81,86,共4页Hot Working Technology

基　　金：河北省自然科学基金项目(E2016411008)。

摘　　要：锻造温度是新型机械轴承钢锻造过程中的最重要工艺参数之一。为了优化新型机械轴承钢的锻造温度,本文构建了3×18×1三层拓扑结构的神经网络优化模型。选择轴承钢牌号、始锻温度和终锻温度作为输入层参数,选择耐磨损性能作为输出层参数,选择tansig函数作为隐含层传递函数,选择purelin函数作为输出层传递函数,对神经网络优化模型进行了学习训练以及预测验证。结果表明:模型相对训练误差介于3.03%与6.67%之间,平均相对训练误差5.00%;相对预测误差介于3.13%与5.41%之间,平均相对预测误差4.22%。模型能准确反映轴承钢牌号、始锻温度和终锻温度对钢耐磨损性能的影响规律,模型预测能力强、预测精度高。Forging temperature is one of the most important process parameter in the forging process of new mechanical bearing steel. In order to optimize the forging temperature of new mechanical bearing steel, a neural network optimization model with 3×18×1 three-layer topological structure was constructed. Selecting the bearing steel brand number, initial forging temperature and final forging temperature as the input layer parameters, wear resistance as the output layer parameter, tansig function as the implied layer transfer function, and purelin function as the output layer transfer function, learning training and prediction validation were performed for the neural network optimization model. The results show that the relative training error of the model is between 3.03% and 6.67%, and the average relative training error is 5.00%;the relative prediction error is between 3.13% and 5.41%, and the average relative prediction error is 4.22%. The model can accurately reflect the influence of the brand number, initial forging temperature and final forging temperature on the wear resistance of the steel, and the model has strong prediction ability and high prediction accuracy.

关 键 词：神经网络优化 机械轴承钢 始锻温度 终锻温度 

分 类 号：TG316[金属学及工艺—金属压力加工]