Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artificial neural networks

Journal of Materials Processing Technology

Volumn 200, Issue 1-3, 2008, Pages 146-152

  Eyercioglu, Omer ^a   Kanca, Erdogan ^b   Pala, Murat ^b   Ozbay, Erdogan ^b  

^a GAZIANTEP UNIVERSITY   (Turkey)

^b KILIS 7 ARALIK UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Austenite transformation; Fe based shape memory alloys; Martensitic transformation

Indexed keywords

AUSTENITE; LEARNING ALGORITHMS; NEURAL NETWORKS; PARAMETER ESTIMATION; SHAPE MEMORY EFFECT;

AUSTENITE TRANSFORMATION; MARTENSITIC TRANSFORMATION;

MARTENSITE;

EID: 39949085420     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmatprotec.2007.09.085     Document Type: Article

References (35)

1. Altinkok N., and Koker R. Neural network approach to prediction of bending strength and hardening behavior of particulate reinforced (Al-Si-Mg)-aluminum matrix composites. Mater. Design 25 (2004) 595-602
2. Andrade M.S., Osthues R.M., and Arruda G.J. The influence of thermal cycling on the transition temperatures of a Fe-Mn-Si shape memory alloy. Mater. Sci. Eng. A 273-275 (1999) 512-516
3. Arruda G.J., Buono V.T.L., and Andrade M.S. The influence of deformation on the microstructure and transformation temperatures of Fe-Mn-Si-Cr-Ni shape memory alloys. Mater. Sci. Eng. A 273-275 (1999) 528-532
4. Bergeon N., Guenin G., and Esnouf C. Microstructural analysis of the stress-induced a martensite in a Fe-Mn-Si-Cr-Ni shape memory alloy part II: transformation reversibility. Mater. Sci. Eng. A 242 (1998) 87-95
5. Bliznuk V.V., Gavriljuk V.G., Shanina B.D., Konchits A.A., and Kolesnik S.P. Effect of nitrogen and carbon on electron exchange and shape memory in a Fe-Mn-Si base shape memory alloy. Acta Mater. 51 (2003) 6095-6103
6. Cotes S., Guillermet A.F., and Sade M. Phase stability and fcc/hcp martensitic transformation in Fe-Mn-Si alloys part I. Experimental study and systematics of the M and A temperatures. J. Alloys Compd. 278 (1998) 231-238
7. A.E. Demirbaş, 1998. Modeling of seismically excited structures using ANN, Master Thesis, METU, 23.
8. Emami S.M.R., Harada T., and Iwao Y. Prediction of peak horizontal acceleration using an artificial neural network model. J. Struct. Mech./Earthquake Eng., JSCE 549 37 (1996) 19-26
9. Forsberg A., and Agren J. Thermodynamics, phase equilibrium and martensitic transformation in Fe-Mn-Si alloys. Mat. Res. Soc. Sym. Proc. (1992) 289-295
10. Holyboroda I.G., Rusinko K., and Tanaka K. Description of an Fe-based shape memory alloy thermo mechanical behavior in terms of the synthetic model. Comp. Mater. Sci. 13 (1999) 218-226
11. Jost N. Thermal fatigue of Fe-Ni-Co-Ti shape-memory-alloys. Mater. Sci. Eng. A 273-275 (1999) 649-653
12. Kanada T., and Enokizono M. Fe-based magnetic shape memory alloy-sheet specimen. Magn. Magn. Mater. 196-197 (1999) 349-350
13. Kokorina V.V., and Wuttigb M. Magnetostriction in ferromagnetic shape memory alloys. Magn. Magn. Mater. 234 (2001) 25-30
14. Kubo H., Nakamurab K., Farjami S., and Maruyamad T. Characterization of Fe-Mn-Si-Cr shape memory alloys containing VN precipitates. Mat. Sci. Eng. A 378 (2004) 343-348
15. Li H., Dunne D., and Kennon N. Factors influencing shape memory effect and phase transformation behavior of Fe-Mn-Si based shape memory alloys. Mat. Sci. Eng. A 273-275 (1999) 517-523
16. Liang Y., Sutou Y., Wada T., Lee C.C., Taya M., and Mori T. Magnetic field-induced reversible actuation using ferromagnetic shape memory alloys. Scripta Mater. 48 (2003) 1415-1419
17. Maji B.C., and Krishnan M. The effect of microstructure on the shape recovery of a Fe-Mn-Si-Cr-Ni stainless steel shape memory alloy. Scripta Mater. 48 (2003) 71-77
18. MATLAB V 7.4 (2007) 3 Apple Hill Drive Natick MA 01760-2098 US.
19. Matsumura O., Sumi T., Tamura N., Sakao K., Furukawa T., and Otsuka H. Pseudoelasticity in an Fe-28Mn-6Si-5Cr shape memory alloy. Mat. Sci. Eng. A 279 (2000) 201-206
20. Nishimura F., and Tanaka K. Phenomenological analysis of thermo mechanical training in an Fe-based shape memory alloy. Comput. Mater. Sci. 12 (1998) 26-38
21. Nishimura F., Watanabe N., and Tanaka K. Back stress and shape recoverability during reverse transformation in an Fe-based shape memory alloy. Mat. Sci. Eng. A 247 (1998) 275-284
22. Nishimura F., Watanabe N., and Tanaka K. Evolution of martensite start condition in general thermo mechanical loads of Fe-based shape memory alloy. Int. J. Mech. Sci. 42 (2000) 347-365
23. Nishimura F., Watanabe N., Watanabe T., and Tanaka K. Transformation conditions in an Fe-based shape memory alloy under tensile-torsional loads: martensite start surface and austenite start finish planes. Mater. Sci. Eng. A 264 (1999) 232-244
24. Perzyk M., and Kochanski A.W. Prediction of ductile cast iron quality by artificial neural networks. J. Mater. Process. Technol. 109 (2001) 305-307
25. Todaka T., Szpryngacz M., and Enokizono M. Transformation behavior of Fe-Cr-Co-Ni-Si-Mn ferromagnetic shape memory ribbon. Int. J. Appl. Elec. Mech. 19 (2004) 149-152
26. Wang Z., and Zhu J. Effect of phase transformation on cavitations erosion resistance of some ferrous alloys. Mater. Sci. Eng. A 358 (2003) 273-278
27. Wang Z., and Zhu J. Cavitation erosion of Fe-Mn-Si-Cr shape memory alloys. Wear 256 (2004) 66-72
28. Wen Y., Li N., and Tu M. Effect of quenching temperature on recovery stress of Fe-18Mn-5Si-8Cr-4Ni. Scripta Mater. 44 (2001) 113-116
29. Wen Y.H., Yan M., and Li N. Effects of carbon addition and aging on the shape memory effect of Fe-Mn-Si-Cr-Ni alloys. Scripta Mater. 50 (2004) 441-444
30. Wu X., and Hsu T.Y. Effect of the Neel temperature, TN, on martensitic transformation in Fe-Mn-Si-based shape memory alloys. Mater. Charac. 45 (2000) 137-142
31. Wu X., and Lim S.Y. Prediction maximum scour depth at the spur dikes with adaptive neural networks. Neural Networks and Combinatorial Optimization in Civil and Structural Engineering (1993), Civil-Comp Press, Edinburgh pp. 61-66
32. Xuejun J., Jihua Z., and Hsu T.Y. Thermodynamic calculation of stacking fault energy in Fe-Mn-Si shape memory alloys. Mater. Design 21 (2000) 537-539
33. Yagawa G., and Okuda H. Neural Networks in Computational Mechanics. Arch. Comp. Method Eng. 3 (1996) 435-512
34. Yao R.B., Tang C.X., and Sun G.X. Predicting grey cast iron properties with artificial neural networks. AFS Trans. 104 (1996) 635-642
35. Zhang Y.S., Lu X., Tian X., and Qin Z. Compositional dependence of the Neel transition, structural stability, magnetic properties and electrical resistivity in Fe-Mn-Al-Cr-Si alloys. Mater. Sci. Eng. A 334 (2002) 19-27