TSK fuzzy modeling for tool wear condition in turning processes: An experimental study

Engineering Applications of Artificial Intelligence

Volumn 24, Issue 2, 2011, Pages 260-265

  Rena, Qun ^a   Balazinskia, Marek ^a   Barona, Luc ^a   Jemielniakb, Krzysztof ^b  

^a ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

^b WARSAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY INFERENCE; TURNING; WEAR OF MATERIALS;

ARTIFICIAL INTELLIGENCE METHODS; CUTTING CONDITIONS; HIGH RELIABILITY; INPUT-OUTPUT DATA; MULTI-SENSOR INFORMATION; TAKAGI SUGENO KANG FUZZY MODEL; TSK FUZZY MODEL; TURNING PROCESS;

CUTTING TOOLS;

EID: 79151484092     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2010.10.016     Document Type: Article

References (16)

1. Balazinski, M., Czogala, E., Jemielniak, K., Leski, J., 2002. Tool condition monitoring using artificial intelligence methods. Engineering Applications of Artificial Intelligence 15, 73-80.
2. Balazinski, M., Bellerose, M., Czogala, E., 1994. Application of fuzzy logic techniques to the selection of cutting parameters in machining processes. Fuzzy Sets and Systems 61, 301-317.
3. Chiu, S.L., 1994. Fuzzy model identification based on cluster estimation. Journal on International Fuzzy Systems 2, 267-278.
4. Demirli, K., Cheng, S.X., Muthukumaran, P., 2003. Subtractive clustering based on modelling of job sequencing with parametric search. Fuzzy Sets and Systems 137, 235-270.
5. Du, R., Elbestawi, M.A., Wu, S.M., 1995. Automated monitoring of manufacturing processes, Part 1 and Part 2. Journal of Engineering for Industry 117, 121-141.
6. El Gomayel, J.I., Bregger, K.D., 1998. On-line tool. Transactions of ASME. Journal of Engineering for Industry 108 (1), 4447.
7. Lee, Y., 1991. Monitoring and planning for open architecture manufacturing of precision machining using acoustic emission. Ph.D. Dissertation, Mechanical Engineering Department, University of California at Berkeley.
8. Moriwaki, T., 1983. Application of acoustic measurements to sensing of wear and breakage of cutting tool. Bulletin of the Japan Society of Precision Engineering 17, 3.
9. Mucientes, M., Vidal, J.C., Bugarn, A., Lama, M., 2009. Processing time estimations by variable structure TSK rules learned through genetic programming. Soft Computing 13 (5), 497-509.
10. Nandi, A.K., 2006. TSK-type FLC using a combined LR and GA: surface roughness prediction in ultraprecision turning. Journal of Materials Processing Technology 178 (1-3), 200-210.
11. Nandi, A.K., Davim, J.P., 2009. A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules. Mechatronics 19 (2), 218-232.
12. Ren, Q., Balazinski, M., Baron, L., Jemielniak, K., 2008. Tool condition monitoring using the TSK fuzzy approach based on subtractive clustering method. Lecture Notes in Computer Science, 5027 LNAI, New Frontiers in Applied Artificial Intelligence-21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems.
13. Sugeno, M., Kang, G., 1988. Structure identification of fuzzy model. Fuzzy Sets and Systems 28 (1), 15-33
14. Takagi, T., Sugeno, M., 1985. Fuzzy identification of systems and its applications to modelling and control. IEEE Transaction on Systems, Man, and Cybernetics 15 (1), 116-132.
15. Zadeh, L.A., 1968. Fuzzy algorithm. Information and Control 12, 94-102.
16. Zadeh, L.A., 1973. Outline of a new approach tothe analysis of complex systems and decision processes. IEEE Transactionon Systems,Man,andCybernetics3, 28-44.