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基于BP神经网络的粉末冶金铜基滑动轴承材料磨损量预测
引用本文:董文文,尹延国,张国涛,李蓉蓉,陈祥雨. 基于BP神经网络的粉末冶金铜基滑动轴承材料磨损量预测[J]. 轴承, 2021, 0(2): 52-56
作者姓名:董文文  尹延国  张国涛  李蓉蓉  陈祥雨
作者单位:合肥工业大学机械工程学院;西南交通大学外国语学院;安徽工业大学机械工程学院
基金项目:国家自然科学基金项目(51575151)。
摘    要:从FeS/Cu-Bi铜基滑动轴承材料组元配置、工艺处理、工况条件入手探究影响铜基滑动轴承材料磨损量的因素,发现材料的磨损量受所添加固体润滑剂Bi的含量、球磨工艺以及加载载荷的影响.针对铜基滑动轴承材料磨损量的3个主要影响因素,建立了结构为3-9-1的BP神经网络模型,对铜基滑动轴承材料的磨损量进行了预测.通过实测数据对...

关 键 词:滑动轴承  BP神经网络  铜基材料  固体润滑剂  磨损量

Wear Volume Prediction of Powder Metallurgy Copper-Based Sliding Bearing Material Based on BP Neural Network
DONG Wenwen,YIN Yanguo,ZHANG Guotao,LI Rongrong,CHEN Xiangyu. Wear Volume Prediction of Powder Metallurgy Copper-Based Sliding Bearing Material Based on BP Neural Network[J]. Bearing, 2021, 0(2): 52-56
Authors:DONG Wenwen  YIN Yanguo  ZHANG Guotao  LI Rongrong  CHEN Xiangyu
Affiliation:(Institute of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China;Institute of Foreign Language, Southwest Jiaotong University, Chengdu 611756, China;Institute of Mechanical Engineering, Anhui University of Technology, Ma′anshan 243002, China)
Abstract:From the material composition,treatment process,and working conditions of the copper-based sliding bearing material,the factors that affect the wear volume of the material are investigated.It is found that the wear volume of the material is affected by the content of the added solid lubricant Bi,the ball milling process and the test load.Based on the three vital factors that affect the wear volume of the material,a BP neural network model with a structure of 3-9-1 is established to predict the wear volume of the copper-based sliding bearing material.By training the neural network based on the measured data,and five sets of test data are randomly selected to examine.The results show that the maximum relative error between the predicted data and the measured data is 10.24%,which verify the correctness of the model.
Keywords:sliding bearing  BP neural network  copper-based material  solid lubricant  wear volume
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