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Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations

  • Zhiwen Huang
  • Jianmin ZhuEmail author
  • Jingtao Lei
  • Xiaoru Li
  • Fengqing Tian
Article
  • 4 Downloads

Abstract

Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain feature fusion by deep convolutional neural network (DCNN) is proposed in this paper. In this method, multi-domain (including time-domain, frequency domain and time–frequency domain) features are respectively extracted from multisensory signals (e.g. three-dimensional cutting force and vibration) as health indictors of tool wear condition, then the relationship between these features and real-time tool wear is directly established based on the designed DCNN model to combine adaptive feature fusion with automatic continuous prediction. The performance of the proposed tool wear predicting method is experimentally validated by using three tool run-to-failure datasets measured from three-flute ball nose tungsten carbide cutter of high-speed CNC machine under dry milling operations. The experimental results show that the predicting accuracy of the proposed method is significantly higher than other advanced methods.

Keywords

Tool wear predicting Multi-domain Feature fusion Convolutional neural network Milling 

Notes

Acknowledgements

This research is supported by National Natural Science Foundation of China (Nos. 50975179, 51375289 and 51775323).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zhiwen Huang
    • 1
  • Jianmin Zhu
    • 1
    Email author
  • Jingtao Lei
    • 2
  • Xiaoru Li
    • 1
  • Fengqing Tian
    • 1
  1. 1.College of Mechanical EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
  2. 2.School of Mechatronic Engineering and AutomationShanghai UniversityShanghaiChina

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