Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing

Journal of Intelligent Manufacturing

Volumn 24, Issue 4, 2013, Pages 755-762

  Kovac, P ^a   Rodic, D ^a   Pucovsky, V ^a   Savkovic, B ^a   Gostimirovic, M ^a  

^a UNIVERSITY OF NOVI SAD   (Serbia)

Author keywords

Fuzzy logic; Regression analysis; Surface roughness

Indexed keywords

ARTIFICIAL INTELLIGENCE TOOLS; CONVENTIONAL APPROACH; DRY MACHINING; EMPIRICAL MODEL; FUZZY LOGIC MODELING; MACHINING PARAMETERS; NEW APPROACHES; SURFACE FINISHES;

ARTIFICIAL INTELLIGENCE; FUZZY LOGIC; REGRESSION ANALYSIS; STATISTICS;

SURFACE ROUGHNESS;

EID: 84880318213     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-012-0623-z     Document Type: Article

References (27)

1. Achiche S.; Balazinski M.; Baron L.; Jemielniak K. (2002) Tool wears monitoring using genetically-generated fuzzy knowledge bases. Engineering Applications of Artificial Intelligence 15: 303-314 (Pubitemid 35412476)
2. Alauddinel M.; Baradie M. A.; Hashmi M. S. J. (1997) Prediction of tool life in end milling by response surface methodology. Journal of Materials Processing Technology 71: 456-465 (Pubitemid 127379859)
3. Azouzi R.; Gullot M. (1997) On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion. International Journal of Machine Tools and Manufacturing 37(9): 1201-1217 (Pubitemid 127369745)
4. Balic J.; Korosec M. (2002) Intelligent tool path generation for milling of free surfaces using neural networks. International Journal of Machine Tools & Manufacture 42: 1171-1179 (Pubitemid 34965395)
5. Chen J. C.; Savage M. (2001) A fuzzy-net-based multilevel in-process surface roughness recognition system in milling operations. The International Journal of Advanced Manufacturing Technology 17: 670-676 (Pubitemid 32914763)
6. Choudhary A.; Harding J.; Tiwari M. (2009) Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing 20(5): 501-521
7. Grzenda, M.; Bustillo, A.; Zawistowski, P. (2010). A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-010-0478-0.
8. Hany F. (2003) Handwriting digit reorganization with fuzzy logic. Jurnal Teknik Elektro Indonesia 5: 84-87
9. Ho S. Y.; Lee K. C.; Chen S. S.; Ho S. J. (2002) Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. International Journal of Machine Tools and Manufacture 42(13): 1441-1446
10. Klir G. J.; Folger T. A. (1988) Fuzzy sets, uncertainty and information. Prentice-Hall of India Private Limited, New Delhi, p 10
11. Lajis M. A.; Mustafizul K. A. N.; Nurul A. K. M.; Hafiz L. G. T. (2008) Prediction of tool life in end milling of hardened steel AISI D2. European Journal of Scientific Research 21: 592-602
12. Luis Perez C. J. (2002) Surface roughness modeling considering uncertainty in measurements. International Journal of Production Research 40(10): 2245-2268 (Pubitemid 34811532)
13. Mamdani E.H.; Assilian S. (1975) An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(13): 1-13
14. Mendel J. (2001) Uncertain rule-based fuzzy inference systems: Introduction and new directions. Prentice-Hall, Upper Saddle River, NJ
15. Palanikumar K.; Muthukrishnan N.; Hariprasad K. S. (2008) Surface roughness parameters optimization in machining a356/sic/20p metal matrix composites by pcd tool using response surface methodology and desirability function. Machining Science and Technology 12: 529-545
16. Plemenos D.; Miaoulis G. (2008) Artificial intelligence techniques for computer graphics. Studies in Computational Intelligence 159: 175-199
17. Quintana G.; Garcia-Romeu M. L.; Ciurana J. (2009) Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing 22: 607-617
18. Rajasekaran, T.; Palanikumar, K.; Vinayagam, B. K.; (2011). Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool. Production Process. doi: 10.1007/s11740-011-0297.
19. Ross T. J. (1995) Fuzzy logic with engineering applications. McGraw-Hill, New York
20. Sharif U.; Harib K. (2006) A human-assisted knowledge extraction method for machining operations. Advanced Engineering Informatics 20: 335-350 (Pubitemid 44646900)
21. Sivarao, P. B. (2009). Mamdani fuzzy inference system modeling to predict surface roughness in laser machining. International Journal of Intelligent Information Technology Application, 2(1), 12-18.
22. Sivarao S.; Castillo, Taufik (2000) Machining quality predictions: comparative analysis of neural network and fuzzy logic. International Journal of Electrical & Computer Sciences IJECS 9: 451-456
23. Sokovowski A. (2004) On some aspects of fuzzy logic application in machine monitoring and diagnostics. Engineering Applications of Artificial Intelligence 17: 429-437
24. Sugeno M.; Kang G. T. (1988) Structure identification of fuzzy model. Fuzzy Sets and Systems 28: 15-33
25. Tsukamoto, Y. (1979). An approach to fuzzy reasoning method. In M. M. Gupta, R. K. Ragade, & R. R. Yager (Eds.), Advances in fuzzy set theory and applications (pp. 137-149). Amsterdam: Elservier
26. Wu, M.; Meyer, R. N. (1964). Cutting-tool temperature predicting equation by response surface methodology. Journal of Engineering for Industry; Trans. ASME, Series B 86, 150-156.
27. Zadeh L. A. (1975) The concept of a linguistic variable and its application to approximate reasoning. Part 1. Information Sciences 8: 199-249