Estimation of cutting speed in abrasive water jet using an adaptive wavelet neural network

Journal of Intelligent Manufacturing

Volumn 26, Issue 2, 2015, Pages 403-413

  Ergur, H Sevil ^a   Oysal, Yusuf ^b  

^a ESKISEHIR OSMANGAZI UNIVERSITY   (Turkey)

^b ANADOLU UNIVERSITY   (Turkey)

Author keywords

ANFIS; AWJ cutting; Jet traverse rate; Modeling prediction; Neural network; Wavelet

Indexed keywords

FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; JETS; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; RELOCATION;

ANFIS; AWJ CUTTING; MODEL PREDICTION; TRAVERSE RATE; WAVELET;

CUTTING;

EID: 84925290514     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-013-0798-y     Document Type: Article

References (26)

1. Abiyev, R. H., & Kaynak, O. (2008). Fuzzy wavelet neural networks for identification and control of dynamic plants-A novel structure and comparative study. IEEE Transactions on Industrial Electronics, 55(8), 3133–3140.
2. Akkurt, A., Kulekci, K. M., Seker, U., & Ercan, F. (2004). Effect of feed rate on surface roughness in abrasive waterjet cutting applications. Journal of Materials Processing Technology, 147, 389–396.
3. Blickwedel, H., Guo, N. S., Haferkamp, H., & Louis, H. (1991). Prediction of abrasive jet cutting performance and quality. In Proceedings of 10th international conference (jet cutting technology), (pp. 163–179), Elsevier, London.
4. Gill, P. E., Murray, W., & Wright, M. H. (1982). Practical optimization. London: Academic Press Ltd.
5. Hloch, S., & Valicek, J. (2011). Prediction of distribution relationship of titanium surface topography created by abrasive waterjet. International Journal of surface Science and Engineering, 5(2/3), 152–168.
6. Ho, D. W. C., Ping-Au, Z., & Jinhua, X. (2001). Fuzzy wavelet networks for function learning. IEEE Transactions on Fuzzy Systems, 9(1), 200–211.
7. Horikawa, S., Furuhashi, T., & Uchikawa, Y. (1992). On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Transactions on Neural Networks, 3(5), 801–806.
8. Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on System, Man and Cybernetics, 23(3), 665–685.
9. Jianming, W., Na, G., & Wenjun, G. (2010). Abrasive waterjet machining simulation by SPH method. The International Journal of Advanced Manufacturing Technology, 50, 227–234.
10. Kim, J., & Kasabov, N. (1999). HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks, 12, 1301–1319.
11. Liu, H., Wang, J., Kelson, N., & Brown, J. R. (2004). A study of abrasive waterjet characteristics by CFD simulation. Journal of Materials Processing Technology, 153–154, 488–493.
12. Momber, A. W., & Kovacevic, R. (1997). Test parameters analysis in abrasive water jet cutting of rocklike materials. International Journal of Rock Mechanics and Mining Sciences, 34(1), 17–25.
13. Momber, A. W., & Kovacevic, R. (1998). Principles of abrasive water jet machining. New York: Springer.
14. Oysal, Y., & Yilmaz, S. (2010). An adaptive wavelet network for function learning. Neural Computing & Applications, 19, 383–392.
15. Parikh, P. J., & Lam, S. S. (2009). Parameter estimation for abrasive water jet machining process using neural networks. The International Journal of Advanced Manufacturing Technology, 40, 497–502.
16. Sanner, R., & Slotine, J.-J. E. (1992). Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, 13(6), 837–863.
17. Sharma, V. S., Dhiman, S., Sehgal, R., & Sharma, S. K. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19(4), 473–483.
18. Shukla, M., & Tambe, P. B. (2010). Predictive modeling of surface roughness and kerf widths in abrasive waterjet cutting of Kevlar composites using neural network. The International Journal of Machining and Machinability of Materials, 8(1/2), 226–246.
19. Srivastavaa, S., Singha, M., Hanmandlub, M., & Jha, A. N. (2005). New fuzzy wavelet neural networks for system identification and control. Applied Soft Computing, 6, 1–17.
20. Yang, L., Song, J., & Hu, B. (2007). Neural network parametric modeling of abrasive water jet cutting quality. The International Journal of Abrasive Technology, 1(2), 198–207.
21. Yusup, N., Sarkheyli, A., Zain, A. M., Hashim, S. Z. M., & Ithnin, N. (2013). Estimation of optimal machining control parameters using artificial bee colony. Journal of Intelligent Manufacturing, doi:10.1007/s10845-013-0753-y.
22. Yuyong, L., Jiang, P. T., & Kefu, L. D. (2010). Artificial neural network model of abrasive water jet cutting stainless steel process. In Proceedings of the 9th international conference on mechanic automation and control engineering (MACE), 26–28 June 2010, pp. 3507–3511, Wuhan, China.
23. Zekri, M., Sadri, S., & Sheikholeslam, F. (2008). Adaptive fuzzy wavelet network control design for nonlinear systems. Fuzzy Sets and Systems, 159, 2668–2695.
24. Zhang, Q., & Benveniste, A. (1992). Wavelet networks. IEEE Transactions on Neural Networks, 3(6), 889–898.
25. Zhang, J., Walter, G. G., & Lee, W. N. W. (1995). Wavelet neural networks for function learning. IEEE Transactions on Signal Processing, 43(6), 1485–1497.
26. Zhang, Q. (1997). Using wavelet networks in nonparametric estimation. IEEE Transactions on Neural Networks, 8, 227–336.