Scientific paper ID 1081 : 2014/3
BI-CRITERIA OPTIMIZATION APPROACH APPLIED IN METALLURGY TO SEARCH AN INTERVAL OF PARAMETERS TO SATISFY A PRIORI DEFINED REQUIREMENTS FOR THE VARIABLES

Nikolay Tonchev, Emil Ivanov

This paper presents an approach and validated techniques for the compromise solutions to meet any specific ments on the properties of the alloys. For examples of specific properties when studied titanium alloys indicating the possible ranges of alloying elements. Conceptually, this approach is applicable to the design and optimization of random alloys, since in practice it is independent of database. The approach extends our previous studies and covers additional objectives.


проектиране състава на сплави титанови сплави якостни и пластични свойства.metallurgicaical design titanium alloys tensile properties composition-processing-property correlationNikolay Tonchev Emil Ivanov

BIBLIOGRAPHY

[1] Tontchev N. Materials Science, Effective solutions and Technological variants, 2014/3/3, LAMBERT Academic Publishing

[2] Campbell C.E., G.B. Olson, “System design of high performance stainless steels I, Conceptual and computational design”, Journal of Computer-Aided Materials Design 2001, 7:145-17

[3] Vitos L., P.A. Korzhavyi and B. Johansson, "Stainless steel optimization from quantum mechanical calculations", Nature Materials 2003, 2:25-28

[4] Maier M.F., K. Stowe and S. Sieg, "Combinatorial and high-throughput materials science", Angewandte Chemie-International Edition 2007, 46:6016-6067

[5] Fischer C.C, K.J. Tibbetts, D. Morgan and G. Cedar, "Predicting crystal structure by merging data mining with quantum mechanics", Nature Materials 2006, 5:641-646

[6] Borst R. de, “Challenges in computational materials science: Multiple scales, multi-physics and evolving discontinuities”, Computational Materials Science 2008, 43:1-15

[7] Tontchev N.., L. Kirilov , Two Approaches for Solving Multiple Criteria Decision Making (MCDM) Problems with an Illustrative Example, PROBLEMS OF ENGINEERING, CYBERNETICS AND ROBOTICS, 58, 2007, 53-63 2007

[8] Fujii, MacKay, D. J. C. and Bhadeshia, H. K. D. H., "Bayesian neural Network Analysis of Fatigue Crack Growth Rate in Nickel-Base Superalloys”, ISIJ International, Vol. 36, 1996, pp. 1373-1382.

[9] Jones, J. , MacKay, D. J. C., "Neural Network Modeling of the Mechanical Properties of Nickel Base Superalloys", 8th Int. Symposium on Superalloys, Seven Springs, PA, eds. R. D. Kissinger et al., published by TMS, 1996, pp. 417-424.

[10] Vuchkov, I., & Stoyanov, S. (1980). Mathematical modeling and optimization of process units. Sofia: Engineering, (In Bulgarian).

[11] Ilyin AA Titanium alloys. The composition, structure and properties. Handbook / A.A.Ilin, B.A.Kolachev, I.S.Polkin - M.: VILS-MATI, 2009, 520 pp. (in Russian)

[12] Malinov, S, Sha, McKeown, J.J.: Modelling and Correlation between Processing Parameters and Properties of Titanium Alloys using Artificial Neural Network. Computational Material Science 21, 375- -394 (2001)

 

 

 

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