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Journal of Intelligent Manufacturing

, Volume 30, Issue 1, pp 113–122 | Cite as

A new tool wear monitoring method based on multi-scale PCA

  • Guofeng WangEmail author
  • Yanchao Zhang
  • Chang Liu
  • Qinglu Xie
  • Yonggang Xu
Article

Abstract

A multi-scale principal component analysis (MSPCA) method is presented to realize online tool wear monitoring of milling process. In this method, the training sample set of normal operational condition is decomposed into different scales using wavelet multi resolution analysis. The statistical indices and the corresponding control limits are constructed to monitor the tool wear based on principal component analysis (PCA). By integration of PCA with wavelet transformation, the accuracy and robustness of tool wear monitoring model can be improved greatly. To test the effectiveness of the proposed method, a Ti–6Al–4V milling experiment was carried out. Force and vibration signals during the machining process were collected simultaneously to depict the characteristics of the tool wear variation. Based on the extracted root mean square and kurtosis features, the tool wear monitoring is realized by MSPCA and PCA respectively. The analysis and comparison results show that MSPCA can produce higher accuracy in comparison with PCA.

Keywords

Tool wear monitoring Multi-scale principal component analysis Milling process Wavelet transformation 

Notes

Acknowledgments

This project is supported by National Natural Science Foundation of China (51175371 and 51420105007), National Science and Technology Major Projects (2014ZX04012-014), and Tianjin Science and Technology Support Program (13ZCZDGX04000) and (14ZCZDGX00021).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Guofeng Wang
    • 1
    Email author
  • Yanchao Zhang
    • 1
  • Chang Liu
    • 1
  • Qinglu Xie
    • 1
  • Yonggang Xu
    • 1
  1. 1.Key Laboratory of Mechanism Theory and Equipment Design of Ministry of EducationTianjin UniversityTianjinChina

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