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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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Research on a thin-walled part manufacturing method based on information-localizing technology
  • 点击次数:
  • 论文类型:期刊论文
  • 发表时间:2017-11-01
  • 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:22
  • 页面范围:4099-4109
  • ISSN号:0954-4062
  • 关键字:Machining accuracy; thin-walled parts; information-localizing; fiducials; milling
  • 摘要:A new information-localization strategy for machining large thin-walled parts is presented in this paper. This strategy uses sub-areas and fiducials calibration methods to improve processing precision of large thin-walled parts, which are of poor machinability, low rigidity, and poor deformation coupling. In our experiments, the large thin-walled part is firstly divided into several sub-areas based on the actual state of the workpiece sampled by a binocular vision sensor. And then each divided sub-area is calibrated by the machining benchmark fiducials. Finally, the machining error of each sub-area is automatically compensated based on the measurement results of fiducials in each sub-area by the touch probe. Two verification experiments show that the proposed strategy is feasible and efficient.
  • 发表时间:2017-11-01