Drilling and dimpling tool monitoring based on power signal and its system development

被引：0
|
作者：
Wan W. [1 ]
Li J. [1 ]
Bi Y. [1 ]
机构：
[1] School of Mechanical Engineering, Zhejiang University, Hangzhou
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 09期
关键词：
Adaptive network-based fuzzy inference system; Drilling and dimpling tool; Power signal; Support vector regression; Tool condition monitoring;
D O I：
10.13196/j.cims.2019.09.002
中图分类号：
学科分类号：
摘要：
The state of cutting tool directly affects the quality and efficiency of machining. In actual production line of flexible manufacturing and computer integrated manufacturing system, the threshold of every process should be reset in the condition of changing parameter drilling. For this problem, based on the power threshold monitoring, the model between power and processing parameters was established by using Adaptive Network-Based Fuzzy Inference System(ANFIS)and Support Vector Regression(SVR), which was used to update the threshold in real time and then compared with the actual cutting power for determining the state of drilling tools and executing the strategy of tool change. The development of Tool Condition Monitoring System(TCMS)was completed in Siemens Sinumerik 840Dsl, and the effectiveness of the proposed model in actually project was certified. © 2019, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：2140 / 2148
页数：8
相关论文
共 21 条
  • [1] Dong H., Cao G., Qu W., Et al., Processing research of industry robots drilling and countersinking automaticly, Journal of Zhejiang University: Engineering Science, 47, 2, pp. 201-208, (2013)
  • [2] Xu G., Automatic riveting technology and its application in digital assembly, Aeronautical Manufacturing Technology, 6, pp. 45-49, (2005)
  • [3] Byrne G., Dornfeld D., Inasaki I., Et al., Tool condition monitoring(TCM)-the status of research and industrial application, CIRP Annals-Manufacturing Technology, 44, 2, pp. 541-567, (1995)
  • [4] Snr D.E.D., Sensor signals for tool-wear monitoring in metal cutting operations-a review of methods, International Journal of Machine Tools & Manufacture, 40, 8, pp. 1073-1098, (2000)
  • [5] Jantunen E., A summary of methods applied to tool condition monitoring in drilling, International Journal of Machine Tools & Manufacture, 42, 9, pp. 997-1010, (2002)
  • [6] Dan L., Mathew J., Tool wear and failure monitoring techniques for turning-a review, International Journal of Machine Tools & Manufacture, 30, 4, pp. 579-598, (1990)
  • [7] Liu Y., Chen Y., Yang W., Et al., Recent development and study of online monitoring of tool-wear, Machine Tool & Hydraulics, 42, 19, pp. 174-180, (2014)
  • [8] Li X., Djordjevich A., Venuvinod P.K., Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring, IEEE Transactions on Industrial Electronics, 47, 3, pp. 697-702, (2000)
  • [9] Wang H., Weng D., Hu Z., Et al., Tool wear monitoring using fuzzy neural network, Journal of Shanghai Jiaotong University, 36, 8, pp. 1086-1090, (2002)
  • [10] Gao H., Xu M., Li D., Et al., On-line tool life measurement technique based on PCA and dynamic monitoring model, Chinese Journal of Scientific Instrument, 31, 11, pp. 2416-2421, (2010)
123