Designing expert system for in situ toughened Si3N4 based on adaptive neural fuzzy inference system and genetic algorithms

Materials and Design

Volumn 30, Issue 2, 2009, Pages 256-259

  Zeng, Qingfeng ^a,b   Zhang, Litong ^a   Xu, Yongdong ^a   Cheng, Laifei ^a   Yan, Xiutian ^b   Zu, Jiakui ^a   Dai, Guanzhong ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

A. Engineering ceramics; H. Selection for material properties; H. Selection of material processes

Indexed keywords

A. ENGINEERING CERAMICS; H. SELECTION FOR MATERIAL PROPERTIES; H. SELECTION OF MATERIAL PROCESSES; IN-SITU;

COMPUTER NETWORKS; FUZZY INFERENCE; FUZZY SYSTEMS; MANAGEMENT INFORMATION SYSTEMS; SILICON;

ADMINISTRATIVE DATA PROCESSING;

EID: 51249110845     PISSN: 02641275     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.matdes.2008.05.008     Document Type: Article

References (22)

1. Lange F.F. Fracture toughness of Si3N4 as a function of the initial α-phase content. J Am Ceram Soc 62 (1979) 428-430
2. Drew P., and Lewis M.H. Microstructures of silicon nitride ceramics during hot-pressing transformations. J Mater Sci 9 (1974) 261-269
3. Pyzik A.J., and Carroll D.F. Technology of self-reinforced silicon nitride. Annu Rev Mater Sci 24 (1994) 189-214
4. 4. Ceram Eng Sci Proc 10 (1989) 807-816
5. 3 additives. Composites 30A (1999) 945-950
6. Kawashima K., Okamoto H., Yamamoto H., and Kitamura A. Grain size dependence of the fracture toughness of silicon nitride ceramics. J Ceram Soc Jpn 99 (1991) 320-323
7. Padture N.P., and Lawn B.R. Toughness properties of a silicon carbide with an in situ induced heterogeneous grain structure. J Am Ceram Soc 77 (1994) 2518-2522
8. 4, PhD thesis. Xi'an, China: Northwestern Polytechnical University; 1996.
9. 3. J Northwest Polytech Univ 15 (1997) 494-496
10. 4. Mater Des 23 (2002) 287-290
11. Holland J.H. Adaptation in natural and artificial systems (1975), University of Michigan Press, Ann Arbor
12. Goldberg D.E. Genetic algorithms in search, optimization, and machine learning (1989), Addison-Welsley, Massachusetts
13. Fraile-Ardanuy J., and Zufiria P.J. Design and comparison of adaptive power system stabilizers based on neural fuzzy networks and genetic algorithms. Neurocomputing 70 (2007) 2902-2912
14. Lei Y., He Z., Zi Y., and Hu Q. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mech Syst Signal Pr 21 (2007) 2280-2294
15. Yeh F.H., Lu Y.H., Li C.L., and Wu M.T. Application of ANFIS for inverse prediction of hole profile in the square hole bore-expanding process. J Mater Proc Tech 173 (2006) 136-144
16. Gao D.M., and Chen J. ANFIS for high-pressure waterjet cleaning prediction. Surf Coat Tech 201 (2006) 1629-1634
17. Aguilar L., Melin P., and Castillo O. Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach. Appl Soft Comput 3 (2003) 209-219
18. Zadeh L.A. Fuzzy sets. Inform Control 8 (1965) 338-353
19. Xing L.N., Chen Y.W., and Cai H.P. An intelligent genetic algorithm designed for global optimization of multi-minima functions. Appl Math Comput 178 (2006) 355-371
20. McCall J. Genetic algorithms for modelling and optimization. J Comput Appl Math 184 (2005) 205-222
21. Kulkarni A.J., Krishnamurthy K., Deshmukhb S.P., and Mishra R.S. Microstructural optimization of alloys using a genetic algorithm. Mater Sci Eng 372A (2004) 213-220
22. 4. Chin J Inorganic Mater 17 (2002) 105-110