Abstract
CNC milling is widely used in manufacturing complex parts of aerospace fields, and the development of the intelligent tool wear monitoring can improve the utilization of the tool during the milling process while ensuring the surface quality of the processed parts. In this paper, a novel method based on wavelet packet analysis and RBF neural network was proposed for monitoring the tool wear condition during milling. Firstly, cutting force signals were measured during milling, and filtered by filter function. Secondly, the cutting vibration signals caused by tool wear were separated by the wavelet packet decomposition from initial data, and the energy of the reconstructed signals was characterized for analyzing tool wear during the milling process. Then, the filtered cutting force and the cutting vibration features were trained by RBF neural network. Fifteen groups of features were trained by RBF neural network, and three groups of features were used to test RBF neural network. Finally, the results show that the method can accurately monitor the flank wear of milling cutter within a short time, which provides a theoretical basis and experimental scheme for further implementing the on-line tool wear monitoring.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ertunc, H.M., Loparo, K.A.: A decision fusion algorithm for tool wear condition monitoring in drilling. Int. J. Mach. Tools Manufact. 41(9), 1347–1362 (2001)
Li, X.: A brief review: acoustic emission method for tool wear monitoring during turning. Int. J. Mach. Tools Manufact. 42(2), 157–165 (2002)
Rehorn, A.G., Jiang, J., Orban, P.E.: State-of-the-art methods and results in tool condition monitoring: a review. Int. J. Adv. Manufact. Technol. 26(7–8), 693–710 (2005)
Haber, R.E., Jiménez, J.E., Peres, C.R., et al.: An investigation of tool-wear monitoring in a high-speed machining process. Sens. Actuators A Phys. 116(3), 539–545 (2004)
Kopač, J., Šali, S.: Tool wear monitoring during the turning process. J. Mater. Process. Technol. 113(1), 312–316 (2001)
Zhu, K., San, W.Y., Hong, G.S.: Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results. Int. J. Mach. Tools Manufact. 49(7), 537–553 (2009)
Wang, G., Cui, Y.: On line tool wear monitoring based on auto associative neural network. J. Intell. Manufact. 24(6), 1085–1094 (2013)
Elanagar, V.T.S., Shin, Y.C.: Design and implementation of tool wear monitoring with radial basis function neural networks. In: Proceedings of the 1995 American Control Conference, vol. 3, pp. 1722–1726. IEEE (1995)
Srinivasa, P., Nagabhushana, T.N., Rao, P.K.R.: Flank wear estimation in face milling based on radial basis function neural networks. Int. J. Adv. Manufact. Technol. 20(4), 241–247 (2002)
Wu, Y., Du, R.: Feature extraction and assessment using wavelet packets for monitoring of machining processes. Mech. Syst. Sig. Process. 10(1), 29–53 (1996)
Rothweiler, J.: Polyphase quadrature filters–a new subband coding technique. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 8, pp. 1280–1283, ICASSP 1983. IEEE (1983)
Elanayar, S., Shin, Y.C.: Robust tool wear estimation with radial basis function neural networks. Trans. Am. Soc. Mech. Eng. J. Dyn. Syst. Measure. Control 117, 459–467 (1995)
Dimla, D.E., Lister, P.M., Leighton, N.J.: Neural network solutions to the tool condition monitoring problem in metal cutting—a critical review of methods. Int. J. Mach. Tools Manufact. 37(9), 1219–1241 (1997)
Kuo, R.J., Cohen, P.H.: Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network. Neural Netw. 12(2), 355–370 (1999)
Panda, S.S., Chakraborty, D., Pal, S.K.: Flank wear prediction in drilling using back propagation neural network and radial basis function network. Appl. Soft Comput. 8(2), 858–871 (2008)
Yao, Y., Li, X., Yuan, Z.: Tool wear detection with fuzzy classification and wavelet fuzzy neural network. Int. J. Mach. Tools and Manufact. 39(10), 1525–1538 (1999)
Acknowledgements
This project is supported in part by National Basic Research Program of China (Grant No. 2013CB035802) and National Natural Science Foundation of China (Grant No. 51575453).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, T., Zhang, D., Luo, M., Wu, B. (2017). Tool Wear Condition Monitoring Based on Wavelet Packet Analysis and RBF Neural Network. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10464. Springer, Cham. https://doi.org/10.1007/978-3-319-65298-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-65298-6_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65297-9
Online ISBN: 978-3-319-65298-6
eBook Packages: Computer ScienceComputer Science (R0)