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  • 教师姓名:王永青
  • 性别:
  • 主要任职:Dean of School of Mechanical Engineering
  • 电子邮箱:yqwang@dlut.edu.cn
  • 职称:教授
  • 所在单位:机械工程学院
  • 学位:博士
  • 学科:机械电子工程. 机械制造及其自动化
  • 毕业院校:大连理工大学
  • 曾获荣誉:国家技术发明一等奖1项、国家技术发明二等奖1项、教育部技术发明一等奖2项、教育部科技进步一等奖1项、中国机械工业科学技术一等奖1项,第九届辽宁省优秀科技工作者
  • 办公地点:机械工程学院1#楼346-2房间
  • 联系方式:yqwang@dlut.edu.cn; 0411-84708420
论文成果
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Mesh node rigid moving algorithm for the uncoated milling cutter tool wear prediction considering periodic process variables
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  • 论文类型:期刊论文
  • 发表时间:2017-10-01
  • 发表刊物:PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
  • 收录刊物:Scopus、SCIE、EI
  • 卷号:231
  • 期号:19
  • 页面范围:3635-3648
  • ISSN号:0954-4062
  • 关键字:Tool wear; prediction; periodic process variables; uncoated tool; milling
  • 摘要:Milling is a typical intermittent cutting process. As a result, tool wear is generated cyclically due to periodic process variables. However, the traditional tool wear prediction strategy based on continuous cutting model is no longer applicable. In this paper, a novel geometric approach through mesh node rigid moving for the milling cutter tool wear prediction has been developed. Firstly, a unified tool wear predictive model is established through bridging the two wear configurations before and after worn. A coupled abrasive-diffusive model is employed to calculate the tool wear volume of each point on tool face. Further, a novel iterative algorithm for tool wear prediction through mesh node rigid moving layer-by-layer and process variables redistribution is designed in discrete-time domain, which is generally decomposed into two phases according to cutting heat equilibrium state, FEM simulation and offline calculation. Last, a series of numerical and saw-milling experiments for flank wear prediction were implemented to verify the developed approach. The AISI304 and the high vanadium high-speed steel tool without coating were adopted. By comparison, the predicted results were consistent with the experimental overall. It has been proved that the proposed approach is more effective than pure FEM simulation and is suitable for long-term milling tool wear prediction.
  • 发表时间:2017-10-01