Skip to main content

Advertisement

SpringerLink
Log in

Cutting tool operational reliability prediction based on acoustic emission and logistic regression model

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Working status of cutting tools (CTs) is crucial to the products’ precision. If broken down, it may lead to waste product. Condition monitoring and life prediction are beneficial to the manufacturing process. In this research, Logistic regression models (LRMs) and acoustic emission (AE) signal are used to evaluate reliability. Based on different conditions estimation, CTs are investigated to determine the best maintenance time. Based on experimental data analysis, AE and cutting force signals have better linear relationship with CT wearing process. They can be used to demonstrate CT degradation process. Frequency band energy is determined as characteristic vector for AE signal using wavelet packet decomposition. Two reliability estimation models are constructed based on cutting force and AE signals. One uses both signals, while the other uses only AE signal. The reliability degree can be estimated using the two models, independently. AE feature extraction and LRM can effectively estimate CT conditions. As it is difficult to monitor cutting force in a practical working condition, it is an effective method for CT reliability analysis by the combination of AE and LRM method. Experimental investigation is used to verify the effectiveness of this method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Alonso, F. J., & Salgado, D. R. (2008). Analysis of the structure of vibration signals for tool wear detection. Mechanical Systems and Signal Processing, 22(3), 735–748. doi:10.1016/j.ymssp.2007.09.012.

    Article  Google Scholar 

  • Bender, R., & Kuss, O. (2010). Methods to calculate relative risks, risk differences, and numbers needed to treat from logistic regression. Journal of Clinical Epidemiology, 63(1), 7–8.

    Article  Google Scholar 

  • Caesarendra, W., Widodo, A., & Yang, B.-S. (2010). Application of relevance vector machine and logistic regression for machine degradation assessment. Mechanical Systems and Signal Processing, 24(4), 1161–1171. doi:10.1016/j.ymssp.2009.10.011.

    Article  Google Scholar 

  • Chen, B., Chen, X., & Li, B. (2011a). Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mechanical Systems and Signal Processing, 25(7), 2516–2537.

    Article  Google Scholar 

  • Chen, B. J., Chen, X. F., & Li, B. (2011b). Reliability estimation for cutting tool based on logistic regression model. Chinese Journal of Mechanical Engineering, 47(18), 158–164.

    Article  Google Scholar 

  • Dimla S, D. E. (2002). The correlation of vibration signal features to cutting tool wear in a metal turning operation. The International Journal of Advanced Manufacturing Technology, 19(10), 705–713.

    Article  Google Scholar 

  • Ding, F., He, Z. J., & Zi, Y. Y. (2009). Reliability assessment based on equipment condition vibration feature using proportional hazard model. Chinese Journal of Mechanical Engineering, 45(13), 89–94.

    Article  Google Scholar 

  • Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739.

    Article  Google Scholar 

  • Huang, S. N., Tan, K. K., Wong, Y. S., de Silva, C. W., Goh, H. L., & Tan, W. W. (2007). Tool wear detection and fault diagnosis based on cutting force monitoring. International Journal of Machine Tools and Manufacture, 47(3–4), 444–451. doi:10.1016/j.ijmachtools.2006.06.011.

    Article  Google Scholar 

  • Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.

  • Jemielniak, K., & Kwiatkowski, L. (1998). Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network. Journal of Intelligent Manufacturing, 9(5), 447–455.

  • Kious, M., Ouahabi, A., Boudraa, M., Serra, R., & Cheknane, A. (2010). Detection process approach of tool wear in high speed milling. Measurement, 43(10), 1439–1446.

    Article  Google Scholar 

  • Liu, B. (2005). Selection of wavelet packet basis for rotating machinery fault diagnosis. Journal of Sound and Vibration, 284(3–5), 567–582. doi:10.1016/j.jsv.2004.06.047.

    Article  Google Scholar 

  • Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking and Finance, 1(3), 249–276.

    Article  Google Scholar 

  • Mill Data Set. (2007). BEST lab. UC Berkeley.

  • Peng, C. Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. Journal of Educational Research, 96(1), 3–14.

    Article  Google Scholar 

  • Peng, Z. K., & Chu, F. L. (2004). Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing, 18(2), 199–221. doi:10.1016/s0888-3270(03)00075-x.

    Article  Google Scholar 

  • Sharma, V. S., & Sharma, S. (2007). Cutting tool wear estimation for turning. Journal of Intelligent Manufacturing, 19(1), 99–108.

    Article  Google Scholar 

  • Sukhomay, P., & Heyns, P. (2009). Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. Journal of Intelligent Manufacturing, 22(4), 491–504.

    Google Scholar 

  • Venkatesh, K., & Mengchu, Z. (1997). Design of artificial neural networks for tool wear monitoring. Journal of Intelligent Manufacturing, 8(3), 215–226.

    Article  Google Scholar 

  • Wang, Z., & Wlofhard, L. (1996). Feature-filtered fuzzy clustering for condition monitoring of tool wear. Journal of Intelligent Manufacturing, 7(1), 13–22.

    Article  Google Scholar 

  • Xiaoyu, W., & wen, W. (2008). Design of neural network-based estimator for tool wear modeling in hard turning. Journal of Intelligent Manufacturing, 19(4), 383–396.

    Article  Google Scholar 

  • Yan, J., Ko, M., & Lee, J. (2004). A prognostic algorithm for machine performance assessment and its application. Production Planning and Control, 15(8), 796–801. doi:10.1080/09537280412331309208.

    Article  Google Scholar 

  • Yan, J., & Lee, J. (2005). Degradation assessment and fault modes classification using logistic regression. Journal of Manufacturing Science and Engineering, 127(4), 912–914.

    Article  Google Scholar 

  • Zhu, K., Wong, Y. S., & Hong, G. S. (2009). Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. International Journal of Machine Tools and Manufacture, 49(7–8), 537–553. doi:10.1016/j.ijmachtools.2009.02.003.

    Article  Google Scholar 

  • Zio, E. (2009). Reliability engineering, old problems and new challenges. Reliability Engineering and System Safety, 94(2), 125–141. doi:10.1016/j.ress.2008.06.002.

    Article  Google Scholar 

Download references

Acknowledgments

The work was supported by the Natural Science Foundation of China under Grant No. 51175057 and National Science and Technology Major Project of China under Grant No. 2013zx04012071.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, People’s Republic of China

    Hongkun Li, Yinhu Wang, Pengshi Zhao & Xiaowen Zhang

  2. Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, G4 0LZ, UK

    Peilin Zhou

Authors
  1. Hongkun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yinhu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pengshi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaowen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peilin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongkun Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Wang, Y., Zhao, P. et al. Cutting tool operational reliability prediction based on acoustic emission and logistic regression model. J Intell Manuf 26, 923–931 (2015). https://doi.org/10.1007/s10845-014-0941-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-014-0941-4

Keywords

Search

Navigation