Scientific paper ID 1239 : 2015/3

Nikolay Tontchev

This generalized research is devoted to numerical approaches to identify effective solutions in the field of metallurgy. Approaches to obtain the optimal combination of chemical composition and heat treatment to achieve certain properties are of fundamental importance for the realization of an effective project. They are at the basis to design or improve new alloys and the associated with them costs.

Effective solutions for rational alloying alloys are able to increase the strength / to reduce the weight while maintaining or reducing the cost of the alloy. The determination of these solutions, however, goes through numerical methods, algorithms and procedures that do not depend on the used databases.

The research of the genome of the material in this generalization of publications, relies entirely on statistical processing and it is aimed at creating opportunities for predicting the mechanical parameters as a function of the chemical composition and the heat treatment parameters taking into account the relevant boundary conditions.

Сплави Моделиране Оптимизация Nikolay Tontchev


[1] Tontchev N. Materials science, Effective solutions and technological variants, Academic Publishing LAMBERT, 2014.

[2] Tonchev N., P. Koprinkova-HristovaPopova, About the Possibility to Predict the Properties of Ductile Steels in Economical Alloying, Almanac of the "National Security and Defence’, 2012, pp. 222-230.

[3] Koprinkova-Hristova P., N.Tontchev, Popova, S., Two approaches to multi-criteria optimization of steel alloys for crankshafts production, Int. J. Reasoning – based Intelligent Systems, Vol. 5, № 2, 2013, pp. 96 – 103.

[4] Tontchev N., Z. Cekerevac Approach and Application in Multicriteria Decision Support in the Field of Materials Science. MEST Journal, (MEST) 2, no. 1 (01 2014): 18-29.

[5] Tontchev N, N. Hristov Method for solving multiple criteria decision making (mcdm) problems in building-up by welding area, VIII International Congress Machines, technologies, materials, 2011, vol. 1, 2011, pp.98-102.

[6] 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.

[7] Tonchev N. About the Decision on Optimal Choice of Materials and Technology for Processing a Given Class of Tool Steel, Operating in Hot Environment, Akademic journal Mechanics Transport Communication, issue 3, 2011, article No 623, University of Transport, 20119.

[8] Tontchev N., Y. Kalev, Determing Influence of Alloying Elements on Properties of Alloys by Robust Experiment, MEST Journal, July 2013, pp. 31 – 39.

[9] Tontchev N., Y. Kalev, Robust Bi-criteria Approach to Optimize the Composition and Properties of Alloy Steels, Godishnik VTU, Tom 4., 2013.
( [9] Tontchev N., Y. Kalev, Robust Bi-criteria Approach to Optimize the Composition and Properties of Alloy Steels, Годишник ВТУ, Том 4., 2013. )

[10] Tontchev N., N. Hristov, Numerical Procedure to Determine the Optimal Composition of the Steel a Small Volume Database with the Same Treatment, International virtual journal for science, technics and innovations for the industry, Machines, Technologies, Materials, VII, Issues 11, pp. 54 – 57, 2013.

[11] Tontchev N., S. Popov, P. Koprinkova-Hristova, S. Popova, Y. Lukarski. (2011). Comparative study on intelligent and classical modeling and composition optimization of steel alloys, Journal of Materials Sciences and Engineering with Advanced Technology, 4(1), 69-91.

[12] Tontchev N., M. Ivanov, Modeling and Optimization of the Composition of Iron – Based Alloys By Approximation with Neural Models and Genetic Optimization Algorithm, FBIM Transactions, 15 01, 2(1), pp. 1-12.

[13] Koprinkova-Hristova P., N.Tontchev, Echo State Networks for Multi-dimensional Data Clustering, Artificial Neural Networks and Machine Learning–ICANN 2012, pp. 571-578.




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