AUTHORS: Ahmed Bufardi, Olcay Akten, Muhammad Arif, Paul Xirouchakis, Roberto Perez

Download as PDF

ABSTRACT: In this paper, we present for the first time a general multiple-input multiple output (MIMO) fuzzy fuzzy-nets framework where the output parameters have different sets of inputs parameters and conflicting rules are generated. This is a significant departure from current fuzzy net approaches which are restricted to only one output. The proposed framework is based on a new model of the rule base architecture and of the predictive algorithm facilitating the resolution of conflicting rules. We demonstrate the application of the new framework for part quality prediction and input process parameter selection in Wire Electrical Discharge Machining (WEDM) with two part quality characteristics (surface roughness and average recast layer thickness) and three input process parameters (pulse-off time, feed rate and voltage); as a result a new technological paradigm for process planning in WEDM has been validated where not only surface roughness (the current situation) but also the average recast layer thickness is optimally planned.

KEYWORDS: Fuzzy nets, WEDM, surface roughness, recast layer thickness

REFERENCES:

[1] G. Chryssolouris, Manufacturing Systems, Theory and Practice, Springer-Verlag, NewYork, 1992.

[2] Aspinwall, D. K., Soo, S. L., Berrisford, A. E., Walder, G., Workpiece surface roughness and integrity after WEDM of Ti-6Al-4V and Inconel 718 using minimum damage generator technology, CIRP Annals - Manufacturing Technology, Vol. 57 (2008), pp. 187–190.

[3] R. J. Hammell II, T. Sudkamp. Learning fuzzy rules from data, Proc. NATO RTO Meeting 3: Appl. Inf. Technol. (Comput. Sci.) Mission Syst., 1998, pp. 8-1..8-10.

[4] L.A. Zadeh, Fuzzy sets, Information and Control 8 (1965) 338-353.

[5] Zimmermann, H.-J. (1996). Fuzzy set theory and its applications. Boston. Kluwer Academic Publishers.

[6] Li-Xin Wang and Jerry M. Mendel, 1992, Generating fuzzy rules by learning from examples, IEEE Transactions on Systems, Man and Cybernetics 22 (6), pp. 1414-1427.

[7] J. C. Chen, J. T. Black. A fuzzy-nets in-process (FNIP) system for tool-breakage monitoring in end-milling operations, International Journal of Machine Tools and Manufacture 37 (1997), pp. 783–800.

[8] J. C. Chen, M. S. Lou. Fuzzy-nets based approach using an accelerometer for in-process surface roughness prediction system in milling operations, Journal of Computer Integrated Manufacturing Systems 13 (2000), pp. 358-368.

[9] J. C. Chen, M. Savage. Fuzzy-net-based multilevel in-process surface roughness recognition system in milling operations. Journal of Advanced Manufacturing Technology 17 (2001), pp. 670-676.

[10] Kirby, E. D. and Chen, J. C., (2007) Development of a fuzzy-nets-based surface roughness prediction system in turning operations. International Journal of Computer & Industrial Engineering, 53, pp. 30-42.

[11] Kirby, E. D. & Chen, J. C. & J. Zhang, (2006) Development of a fuzzy-nets-based in-process surface roughness adaptive control system in turning operations. Expert Systems with Applications, 30, pp. 592-604.

[12] J.L. Yang (2002) Development of a fuzzy-netsbased adapted surface roughness control (FNASRC) system in end-milling operations. Dissertation, Iowa State University.

[13] Yang J. L. and Chen, J.C. (2003) Statistical assisted fuzzy-nets based in-process surface roughness prediction (S-FNIPSRP) system in end milling operations. Journal of Chinese Institution of Industrial Engineering, 20(5), 494- 510.

[14] L.D. Yang, J.C. Chen, H.M. and C.T. Lin, Fuzzy-nets based in process surface roughness adaptive control system in end-milling operations, Int. J. Adv. Manuf. Technol. (2006), 28: 236-248.

[15] Zhu, J. and Chen, J.C. (2006) In-process mixed materials caused flash prediction (FNN-IPMFP) system in injection moulding operations. International Journal of Advanced Manufacturing Technology, 29, 308-316.

[16] J.C. Chen, An effective fuzzy-nets training scheme for monitoring tool breakage, Journal of Intelligent Manufacturing (2000) 11, 85-101.

[17] J. Jantzen. Design Of Fuzzy Controllers, Tech. Rep. no 98-E864, Technical University of Denmark, 1998.

[18] H. Li, C.L. Philip Chen, H.-. Huang. Fuzzy Neural Intelligent Systems: Mathematical Foundation and the Applications in Engineering, CRC Press, 2000.

[19] Mamdani, E.H. and Assilian, S., (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of ManMachine Studies, 7(1), 1–13.

[20] K.M. Passino and S. Yurkovich, Fuzzy control, Addison Wesley Longman Inc. (1998) ISBN 0- 201-18074-X.

[21] Kovačić, Z. and Bogdan, S., (2006). Fuzzy Controller Design: Theory and Applications. CRC Press, Boca Raton, FL.

[22] C.C. Lee, (1990), Fuzzy Logic in control systems: Fuzzy Logic controller part I', IEEE Transactions on Systems, Man and Cybernetics 20 (2), pp. 404 - 418.

[23] T. Takagi, M. Sugeno. Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man and Cybernetics 15 (1985), pp. 116-132.

[24] M.-P. Morand, Experimental investigation of surface integrity and defects for a quality assurance system of WEDMed aerospace parts, EPFL, 2014.