个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:机械工程学院
学科:机械电子工程. 机械制造及其自动化
办公地点:机械工程学院知方楼5051
联系方式:座机:0411-84707276
电子邮箱:hbliu@dlut.edu.cn
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 However, the traditional tool wear prediction strategy based on continuous cutting model is no longer applicable.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.