Research on the Technique of Tool Wear Monitoring in Plunge Milling

摘要：

The technique of tool wear monitoring in plunge milling is studied. The mean of cutting force signals and the root mean square (RMS) of vibration signals are selected as characteristic quantities. The model between tool wear and the characteristic quantities is built using BP artificial neural network. The result of experiment shows that the module is fit for plunge milling wears testing under cutting condition, and it is helpful to monitoring plunge milling tool strong wear.

关键词： Plunge milling Tool wear monitoring BP artificial neural network

作者: X.D.Qin X.L.Ji X.Yu S.Hua W.C.Liu W.Y.Ni Y.X.Liu

作者单位: Department of Mechanical Engineering,Tianjin University,Tianjin,300072,China Kennametal Co.,Ltd,Latrobe,PA,15650,USA

会议类型: 国际会议

会议名称: The 3rd Conference of Cross-Strait Engineering Education and Ceeusro & 1st International Conference on Engineering Technologies and Ceeusro(ICETC2009)(第三届工程技术与产学研研讨会暨第一届国际功能制造技术学术会议)

会议地点: 常州

会议语种:英文

页码: 468-471

在线出版日期: 2009-11-19（万方平台首次上网日期，不代表论文的发表时间）