Development of a database system for operational use in the selection of titanium alloys

Abstract

The selection of titanium alloys has become a complex decision-making task due to the growing number of creation and utilization for titanium alloys, with each having its own characteristics, advantages, and limitations. In choosing the most appropriate titanium alloys, it is very essential to offer a reasonable and intelligent service for technical engineers. One possible solution of this problem is to develop a database system (DS) to help retrieve rational proposals from different databases and information sources and analyze them to provide useful and explicit information. For this purpose, a design strategy of the fuzzy set theory is proposed, and a distributed database system is developed. Through ranking of the candidate titanium alloys, the most suitable material is determined. It is found that the selection results are in good agreement with the practical situation.

Keywords

titanium alloys / database system / fuzzy set theory / evaluation

References

[1] Ljungberg L.Y. Materials selection and design for structural polymers. Mater. Des., 2003, 24(5): 383.

[2] Fayazbakhsh K., Abedian A., Manshadi B.D., et al. Introducing a novel method for materials selection in mechanical design using Z-transformation in statistics for normalization of material properties. Mater. Des., 2009, 30(10): 4396.

[3] Khabbaz R.S., Manshadi B.D., Abedian A., et al. A simplified fuzzy logic approach for materials selection in mechanical engineering design. Mater. Des., 2009, 30(3): 687.

[4] Jee D.H., Kang K.J. A method for optimal material selection aided with decision making theory. Mater. Des., 2000, 21(3): 199.

[5] Liao T.W. A fuzzy multicriteria decision-making method for material selection. J. Manuf. Syst., 1996, 15(1): 1.

[6] Zucker J., Demaid A. Object-oriented representation of qualitative engineering properties. Comput. Aided Des., 1994, 26(10): 722.

[7] Jiun M., Wang J., Chang T.C. Tool steel materials selection under fuzzy environment. Fuzzy Sets Syst., 1995, 72(3): 263.

[8] Singer D. Fuzzy algorithms for planning the maintenance of large systems with insufficient resources. Int. J. Syst. Sci., 1990, 21(11): 2199.

[9] Hashmi K., Graham I.D., Mills B. Data selection for turning carbon steel using a fuzzy logic approach. J. Mater. Process Technol., 2003, 135(1): 44.

[10] Zadeh L.A. Fuzzy sets. Inf. Control, 1965, 8(3): 338.

[11] Zhou C.C., Yin G.F., Hu X.B. Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Mater. Des., 2009, 30(4): 1209.

[12] Matsuoka H., Nishiwaki Y., Ryjoy A., et al. An evaluation method on the integrated safeguards based on fuzzy theory. Inf. Sci., 2002, 142(1–4): 131.

[13] Ross T.J. Fuzzy Logic with Engineering Applications, 2004 Chichester, John Wiley Press

[14] You M.L., Chyu Y.W. The development of fuzzy comprehension evaluation toolbox and apply in students’ reaction on teacher’s instruction. J. Quant. Manage., 2006, 3(1): 61.

[15] Zhou Y.G., Zeng W.D., Yu H.Q. A new high-temperature deformation strengthening and toughening process for titanium alloys. Mater. Sci. Eng A, 1996, 221(1–2): 58.

[16] Mamdani E.H., Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Hum. Comput. Stud., 1975, 7(1): 1.

[17] Sandström R., Grahn B. The assessment and evaluation of property data for materials selection purposes. Mater. Des., 1986, 7(4): 198.

[18] van Kesteren I.E.H. Product designers’ information needs in materials selection. Mater. Des., 2008, 29(1): 133.

[19] Zeng W.D., Zhou Y.G. The influence of microstructure on dwell sensitive fatigue in Ti-6.5Al-3.5Mo-1.5Zr-0.3Si alloy. Mater. Sci. Eng. A, 2000, 290(1–2): 33.