徐良,陈燕,韩冰,程海东,刘文浩.基于进化神经网络的磁粒研磨表面粗糙度预测方法研究[J].表面技术,2021,50(12):94-100, 118.
XU Liang,CHEN Yan,HAN Bing,CHENG Hai-dong,LIU Wen-hao.Research on Surface Roughness Prediction Method of Magnetic Abrasive Finishing Based on Evolutionary Neural Network[J].Surface Technology,2021,50(12):94-100, 118
基于进化神经网络的磁粒研磨表面粗糙度预测方法研究
Research on Surface Roughness Prediction Method of Magnetic Abrasive Finishing Based on Evolutionary Neural Network
投稿时间:2021-09-29  修订日期:2021-12-05
DOI:10.16490/j.cnki.issn.1001-3660.2021.12.009
中文关键词:  磁粒研磨  表面粗糙度  预测模型  遗传算法  BP神经网络  5052铝合金
英文关键词:magnetic abrasive finishing  surface roughness  prediction model  genetic algorithm  BP neural network  5052 aluminum alloy
基金项目:国家自然科学基金(51775258)
作者单位
徐良 辽宁科技大学 机械工程与自动化学院,辽宁 鞍山 114051 
陈燕 辽宁科技大学 机械工程与自动化学院,辽宁 鞍山 114051 
韩冰 辽宁科技大学 机械工程与自动化学院,辽宁 鞍山 114051 
程海东 辽宁科技大学 机械工程与自动化学院,辽宁 鞍山 114051 
刘文浩 辽宁科技大学 机械工程与自动化学院,辽宁 鞍山 114051 
AuthorInstitution
XU Liang School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China 
CHEN Yan School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China 
HAN Bing School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China 
CHENG Hai-dong School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China 
LIU Wen-hao School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan 114051, China 
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中文摘要:
      目的 为实现磁粒研磨光整加工的表面粗糙度精准预测,提出一种遗传算法优化表面粗糙度BP神经网络的预测模型。方法 将表面粗糙度作为预测的目标,影响磁粒研磨5052铝合金管内表面粗糙度的五个主要工艺参数作为神经网络的输入。合理设计正交试验,得到不同工艺参数配置下的表面粗糙度值,将其作为神经网络的输出。通过建立非线性预测模型,对比遗传算法优化后和传统BP神经网络的均方差与仿真时间,分析优化前后表面粗糙度的预测效果。结果 通过试验数据建立了结构为5-11-1的神经网络,进化BP神经网络预测模型均方差为0.044,建模仿真时间为0.187 s,其平均相对误差率为13.2%。传统的BP神经网络预测模型均方差为0.231,建模仿真时间为1.840 s。结论 通过遗传算法优化后的BP神经网络均方差更小,建模仿真时间更短,进化BP神经网络可以实现更为精准的预测,同时能够极大地避免传统BP神经网络易陷入局部极小值的弊端。
英文摘要:
      In order to achieve accurate prediction of surface roughness of magnetic abrasive finishing, the precision prediction model of surface roughness by BP neural network optimized by genetic algorithm was established. Taking the surface roughness as the target of prediction, five main process parameters affecting the inner surface quality of 5052 Al alloy tube by magnetic abrasive finishing as inputs of neural network. Through the orthogonal experimental design, the surface roughness under different process parameters was obtained as the output of the neural network. By establishing a nonlinear prediction model, the prediction effect of surface roughness before and after optimization was analyzed by comparing the mean square error and simulation time of the optimized genetic algorithm and the traditional BP neural network. Based on experimental data, the BP neural network with 5-11-1 topology structure is established. The mean square deviation of the prediction model is 0.044, the simulation time is 0.187 s, and the average relative error rate is 13.2%. The mean square deviation of the unoptimized BP neural network prediction model is 0.231, and the simulation time is 1.840 s. The mean square error of evolutionary BP neural network is smaller, and the modeling and simulation time is shorter, and more accurate prediction by evolutionary BP neural network can be achieved. What’s more, the BP neutral network can greatly avoid the disadvantage of traditional BP neural network easily falling into local minimum.
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