Online incremental learning for tool condition classification using modified Fuzzy ARTMAP network

Journal of Intelligent Manufacturing

Volumn 25, Issue 6, 2014, Pages 1403-1411

  Wang, Guofeng ^a   Guo, Zhiwei ^a   Qian, Lei ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

Incremental learning; Modified Fuzzy ARTMAP; Online; Tool condition monitoring

Indexed keywords

CONDITION MONITORING; CUTTING TOOLS; FUZZY NEURAL NETWORKS; LEARNING SYSTEMS; TITANIUM ALLOYS;

ACCURACY ANALYSIS; FUZZY ARTMAP; FUZZY-ARTMAP NETWORKS; INCREMENTAL LEARNING; ONLINE; ONLINE INCREMENTAL LEARNING; RESONANCE LAYERS; TOOL CONDITION MONITORING;

E-LEARNING;

EID: 84912008946     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-013-0738-x     Document Type: Article

References (21)

1. Abellan-Nebot, J. V., & Subirón, F. R. (2010). A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology, 47(1–4), 237–257.
2. Boutros, T., & Liang, M. (2011). Detection and diagnosis of bearing and cutting tool faults using hidden Markov models. Mechanical System and Signal Processing, 25(6), 2102–2124.
3. Brezak, D., Majetic, D., Udiljak, T., & Kasac, J. (2012). Tool wear estimation using an analytic fuzzy classifier and support vector machines. Journal of Intelligent Manufacturing, 23(3), 797–809.
4. Burke, L., & Rangwala, S. (1991). Tool condition monitoring in metal cutting: A neural network approach. Journal of Intelligent Manufacturing, 2(5), 269–280.
5. Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., & Rosen, D. B. (1992). Fuzzy ARTMAP: An adaptive resonance architecture for incremental learning of analog maps. International Joint Conference on Neural Networks, 3, 309–314.
6. Connolly, J. F., Granger, E., & Sabourin, R. (2008). Supervised incremental learning with the fuzzy ARTMAP neural network. ANNPR ’08 Proceedings of the 3rd IAPR workshop on artificial neural networks in pattern recognition (pp. 66–77). Berlin.
7. Dimla, D. E., Jr., Lister, P. M., & Leighton, N. J. (1997). Neural network solutions to the tool condition monitoring problem in metal cutting—a critical review of methods. International Journal of Machine Tools and Manufacture, 37(9), 1219–1241.
8. Jemielniak, K., Kwiatkowski, L., & Wrzosek, P. (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.
9. Kuo, R. J., & Cohen, P. H. (1999). Multi-sensor integration for on-line toolwear estimation through radialbasisfunction networks and fuzzy neural network. Neural Networks, 12(2), 355–370.
10. Marungsri, B., & Boonpoke, S. (2011). Applications of simplified fuzzy ARTMAP to partial discharge classification and pattern recognition. Wseas Transactions on Systems, 10(3), 69–80.
11. Orhan, S., Er, A. O., Camuscu, N., & Aslan, E. (2007). Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness. NDT & E International, 40(2), 121–126.
12. Polikar, R., Udpa, L., Udpa, S. S., & Honavar, V. (2001). Learn++: An incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, 31(4), 497–508.
13. 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.
14. 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.
15. Silva, R. G. (2010). Condition monitoring of the cutting process using a self-organizing spiking neural network map. Journal of Intelligent Manufacturing, 21(6), 823–829.
16. Tian, Z. G. (2012). An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring. Journal of Intelligent Manufacturing, 23(2), 227–237.
17. Venkatesh, K., Zhou, M., & Caudill, R. J. (1997). Design of artificial neural networks for tool wear monitoring. Journal of Intelligent Manufacturing, 8(3), 215–226.
18. Wang, G. F., & Cui, Y. H. (2012). On line tool wear monitoring based on auto associative neural network. Journal of Intelligent Manufacturing,. doi:10.1007/s10845-012-0636-7.
19. Xu, Z., Shi, X. J., Wang, L. Y., Luo, J., Zhong, C. J., & Lu, S. (2009). Pattern recognition for sensor array signals using fuzzy ARTMAP. Sensors and Actuators B-Chemical, 141(2), 458–464.
20. Yan, W., Wong, Y. S., Lee, K. S., & Ning, T. (1999). An investigation of indices based on milling force for tool wear in milling. Journal of Materials Processing Technology, 89–90, 245–253.
21. 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 & Manufacture, 49(7–8), 537–553.