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

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.

This is a preview of subscription content, access via your institution.

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

References

  1. Aghazadeh, F., Tahan, A., & Thomas, M. (2018). Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process. The International Journal of Advanced Manufacturing Technology,98(9–12), 3217–3227.

    Article  Google Scholar 

  2. Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys,4, 40–79.

    Article  Google Scholar 

  3. Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2015). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing,26(2), 213–223.

    Article  Google Scholar 

  4. Chen, Y., Jin, Y., & Jiri, G. (2018). Predicting tool wear with multi-sensor data using deep belief networks. The International Journal of Advanced Manufacturing Technology, 99(5–8), 1917–1926.

  5. Dimla, D. E., Sr., & Lister, P. M. (2000). On-line metal cutting tool condition monitoring. I: Force and vibration analyses. International Journal of Machine Tools and Manufacture,40(5), 739–768.

    Article  Google Scholar 

  6. Dimla Snr, D. E. (2000). Sensor signals for tool-wear monitoring in metal cutting operations—A review of methods. International Journal of Machine Tools and Manufacture,40, 1073–1098.

    Article  Google Scholar 

  7. Duro, J. A., Padget, J. A., Bowen, C. R., Kim, H. A., & Nassehi, A. (2016). Multi-sensor data fusion framework for CNC machining monitoring. Mechanical Systems and Signal Processing,66–67, 505–520.

    Article  Google Scholar 

  8. El-Wardany, T. I., Gao, D., & Elbestawi, M. A. (1996). Tool condition monitoring in drilling using vibration signature analysis. International Journal of Machine Tools and Manufacture,36(6), 687–711.

    Article  Google Scholar 

  9. Fu, Y., Zhang, Y., Gao, Y., Gao, H., Mao, T., Zhou, H. M., et al. (2017). Machining vibration states monitoring based on image representation using convolutional neural networks. Engineering Applications of Artificial Intelligence,65, 240–251.

    Article  Google Scholar 

  10. García, P. E., & Núñez López, P. J. (2018). Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations. Mechanical Systems and Signal Processing,98, 902–919.

    Article  Google Scholar 

  11. Ghosh, N., Ravi, Y. B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A. R., et al. (2007). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing,21(1), 466–479.

    Article  Google Scholar 

  12. Gierlak, P., Burghardt, A., Szybicki, D., Szuster, M., & Muszyńska, M. (2016). On-line manipulator tool condition monitoring based on vibration analysis. Mechanical Systems and Signal Processing,89, 14–26.

    Article  Google Scholar 

  13. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science,313, 504–507.

    Article  Google Scholar 

  14. 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), 444–451.

    Article  Google Scholar 

  15. Javed, K., Gouriveau, R., Li, X., & Zerhouni, N. (2016). Tool wear monitoring and prognostics challenges: A comparison of connectionist methods toward an adaptive ensemble model. Journal of Intelligent Manufacturing,29, 1873–1890.

    Article  Google Scholar 

  16. Karandikar, J., McLeay, T., Turner S., Schmitz, T. (2015). Tool wear monitoring using naive Bayes classifiers. The International Journal of Advanced Manufacturing Technology, 77(9), 1613–1626.

  17. Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., & Tang, P. T. P. (2016). On large-batch training for deep learning: Generalization gap and sharp minima. arXiv preprint arXiv:1609.04836.

  18. Kong, D., Chen, Y., & Li, N. (2018). Gaussian process regression for tool wear prediction. Mechanical Systems and Signal Processing,104, 556–574.

    Article  Google Scholar 

  19. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In 21th annual conference on neural information processing systems (NIPS). Lake Tahoe, USA, December 3–8.

  20. Kuljanic, E., & Sortino, M. (2005). TWEM, a method based on cutting forces-monitoring tool wear in face milling. International Journal of Machine Tools and Manufacture,45(1), 29–34.

    Article  Google Scholar 

  21. Lecun, Y. L., Bottou, L., Bengio, Y., & Haffner, P. (1998a). Gradient-based learning applied to document recognition. Proceedings of the IEEE,86(11), 2278–2324.

    Article  Google Scholar 

  22. Lecun, Y., Bottou, L., Orr, G. B., & Müller, K. R. (1998b). Efficient backprop. Lecture Notes in Computer Science,1524(1), 9–50.

    Article  Google Scholar 

  23. Li, X., Lim, B. S., Zhou, J. H., & Huang, S. (2009). Fuzzy neural network modelling for tool wear estimation in dry milling operation. In Annual conference of the prognostics and health management society (pp. 1–11). PHM Society.

  24. Mali, R., Telsang, M. T., & Gupta, T. V. K. (2017). Real time tool wear condition monitoring in hard turning of Inconel 718 using sensor fusion system. Materials Today: Proceedings,4(8), 8605–8612.

    Google Scholar 

  25. Morgan, J., & O’Donnell, G. E. (2018). Cyber physical process monitoring systems. Journal of Intelligent Manufacturing,29, 1317–1328.

    Article  Google Scholar 

  26. Pandiyan, V., Caesarendra, W., Tjahjowidodo, T., & Tan, H. H. (2018). In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. Journal of Manufacturing Processes,31, 199–213.

    Article  Google Scholar 

  27. Purushothaman, S. (2010). Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns. Journal of Intelligent Manufacturing,21(6), 717–730.

    Article  Google Scholar 

  28. Rehorn, A. G., Jiang, J., & Orban, P. E. (2005). State-of-the-art methods and results in tool condition monitoring: A review. The International Journal of Advanced Manufacturing Technology,26(7–8), 693–710.

    Article  Google Scholar 

  29. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.

  30. Salehi, M., Albertelli, P., Goletti, M., Ripamonti, F., Tomasini, G., & Monno, M. (2015). Indirect model based estimation of cutting force and tool tip vibrational behavior in milling machines by sensor fusion. Procedia CIRP,33, 239–244.

    Article  Google Scholar 

  31. Tobon-Mejia, D. A., Medjaher, K., & Zerhouni, N. (2012). CNC machine tool’s wear diagnostic and prognostic by using dynamic Bayesian networks. Mechanical Systems and Signal Processing,28, 167–182.

    Article  Google Scholar 

  32. Wang, G., Guo, Z., & Qian, L. (2014). Online incremental learning for tool condition classification using modified fuzzy ARTMAP network. Journal of Intelligent Manufacturing,25(6), 1403–1411.

    Article  Google Scholar 

  33. Wang, J., Xie, J., Zhao, R., Zhang, L., & Duan, L. (2017). Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics and Computer Integrated Manufacturing,45(C), 47–58.

    Article  Google Scholar 

  34. Wu, J., Su, Y., Cheng, Y., Shao, X., Deng, C., & Liu, C. (2018). Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system. Applied Soft Computing,68, 13–23.

    Article  Google Scholar 

  35. Yu, J., Liang, S., Tang, D., & Liu, H. (2017). A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. International Journal of Advanced Manufacturing Technology,91(1–4), 1–11.

    Google Scholar 

  36. Zhang, W., Li, C. H., Peng, G. L., Chen, Y. H., & Zhang, Z. J. (2018). A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing,100, 439–453.

    Article  Google Scholar 

  37. Zhang, K. F., Yuan, H. Q., & Nie, P. (2015). A method for tool condition monitoring based on sensor fusion. Journal of Intelligent Manufacturing,26(5), 1011–1026.

    Article  Google Scholar 

  38. Zhao, R., Yan, R., Wang, J., & Mao, K. (2017). Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors,17(2), 273.

    Article  Google Scholar 

  39. Zhou, Y., & Xue, W. (2018). Review of tool condition monitoring methods in milling processes. International Journal of Advanced Manufacturing Technology,96(5–8), 2509–2523.

    Article  Google Scholar 

  40. Zhu, K. P., 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), 537–553.

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jianmin Zhu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Huang, Z., Zhu, J., Lei, J. et al. Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. J Intell Manuf 31, 953–966 (2020). https://doi.org/10.1007/s10845-019-01488-7

Download citation

Keywords

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