Skip to main content
SpringerLink
Log in

Predicting Alloy Strength by Neural Network Modeling: Al–Ca–Mn Alloys

  • Published:
Russian Engineering Research Aims and scope

Abstract

A neural network may be used to predict alloy strength with high accuracy in a minimum of iterations. The corresponding organization of the network structure and selection of the optimal training algorithm is considered for the example of Al–Ca–Mn alloys.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

REFERENCES

  1. Beletskii, V.M. and Krivov, G.A., Alyuminievye splavy (sostav, svoistva, tekhnologiya, primenenie): Spravochnik (Aluminum Alloys (Composition, Properties, Technology, Application): Handbook), Fridlyander, I.N, Ed., Kiev: Komintekh, 2005.

  2. Shemetev, G.F., Alyuminievye splavy: Sostavy, svoistva, primenenie: Uchebnoe posobie po kursu “Proizvodstvo otlivok iz splavov tsvetnykh metallov” (Aluminum Alloys: Compositions, Properties, Application: Manual on the Course “Production of Non-Ferrous Metal Alloy Castings”), St. Petersburg, 2012, part 1.

  3. Mutylina, I.N., Materialovedenie. Tsvetnye metally i splavy na ikh osnove:Uchebno-metodicheskii kompleks (Materials Science. Non-Ferrous Metals and Alloys on Their Basis: Educational and Methodical Complex), Moscow: Prospekt, 2019.

  4. Lemzikov, A.V., Kundas, S.P., Kutsylo, V.V., and Ivanov, D.G., Artificial neural networks implementation for prediction of characteristic points of phase transformations in steels with a wide range of chemical composition, Informatika, 2007, no. 1 (13), pp. 89–97.

  5. Lemzikov, A.V. and Kundas, S.P., Training artificial neural networks for predicting properties of steels, Informatika, 2009, no. 4 (24), pp. 101–111.

  6. Nurgayanova, O.S. and Yusupova, N.I., Training a neural network to predict the properties of nickel alloys with a genetic algorithm, Tr. Inst. Sist. Anal. Ross. Akad. Nauk, 2019, vol. 69, no. 4, pp. 22–28.

    Google Scholar 

  7. Bol’shakov, A.E. and Lovushkin, M.Yu., Prognosis compositions aluminum alloys according to their properties, Mashinostr. Bezopasn. Zhiznedeyat., 2011, no. 2 (9), pp. 37–43.

  8. Nurgayanova, O.S., Application of artificial neural networks in the tasks of classification of multicomponent alloys, Proc. 6th All-Russian Sci. Conf. “Information Technologies for Intelligent Decision Making Support”, 2018, vol. 3.

  9. Tukhvatulin, I.Kh. and Lankin, Yu.P., Prediction of metal alloy properties using neural networks, Trudy II Vserossiiskoi nauchno-tekhnicheskoi konferentsii “Neiroinformatika-2000” (Nauchnaya sessiya MIFI-2000) (Proc. II All-Russian Sci.-Tech. Conf. “Neuroinformatics-2000” (Sci. Session of MEPhI-2000)), Moscow: Mosk. Inzh.-Fiz. Inst., 2000, part 2, pp. 119–125.

  10. Tukhvatulin, I.Kh., Kolokol’tsev, V.M., Dolgopolo-va, L.B., and Lankin, Yu.P., Metallurgical neural network expert system for assessing the properties of alloys and issuing practical recommendations, Litein. Proizvod., 2000, no. 3, pp. 6–8.

  11. Lankin, Yu.P., Kolokol’tsev, V.M., Tukhvatulin, I.Kh., and Dolgopolova, L.B., Using neural network methods to create new alloys, Izv. Vyssh. Uchebn. Zaved., Chern. Metall., 2000, no. 11, pp. 44–48.

  12. Nurgayanova, O.S. and Yusupova, N.I., Training a neural network to predict the properties of nickel alloys with a genetic algorithm, Tr. Inst. Sist. Anal. Ross. Akad. Nauk, 2019, vol. 69, no. 4, pp. 22–28.

    Google Scholar 

  13. Yusupova, N.I., Bogdanova, D.R., and Boiko, M.V., Obrabotka slabostrukturirovannoi informatsii na osnove metodov iskusstvennogo intellekta: Monografiya (Processing of Weakly Structured Information Based on Artificial Intelligence Methods: Monograph), Moscow: Innovats. Mashinostr., 2016.

  14. Kharitonova, L.P., On the issue of neural network modeling to assess the energy consumption of large arrays of consumers (buildings), Mezhdunar. Nauch.-Issled. Zh., 2016, no. 8 (50), pp. 155–158. https://doi.org/10.18454/IRJ.2016.50.176

  15. Pradhan, R.P. and Kumar, R., Forecasting exchange rate in India: An application of artificial neural network model, J. Math. Res., 2010, vol. 2, no. 4, pp. 111–117.

    Article  MATH  Google Scholar 

  16. Bol’shakov, V.I., Dubrov, Yu.I., Tkachenko, A.N., and and Tkachenko, V.A., The task of identifying the qualitative characteristics of materials based on expert systems, Visn. Pridniprovsk. Derzh. Akad. Budiv. Arkh., 2010, no. 1, pp. 46–49.

  17. Tukhvatulin, I.Kh. and Lankin, Yu.P., Prediction of metal alloy properties using neural networks, Trudy II  Vserossiiskoi nauchno-tekhnicheskoi konferentsii “Neiroinformatika-2000” (Nauchnaya sessiya MIFI-2000) (Proc. II All-Russian Sci.-Tech. Conf. “Neuroinformatics-2000” (Sci. Session of MEPhI-2000)), Moscow: Mosk. Inzh.-Fiz. Inst., 2000, part 2, pp. 119–125.

  18. Klochkova, K.V., Petrovich, S.V., and Abramova, V.V., Training a neural network using a genetic algorithm to predict the properties of cast irons with vermicular graphite, Grani Nauki, 2013, vol. 1, no. 2, pp. 104–107.

    Google Scholar 

  19. Kruglov, V.V. and Borisov, V.V., Iskusstvennye neironnye seti. Teoriya i praktika (Artificial Neural Networks. Theory and Practice), Moscow: Goryachaya Liniya–Telekom, 2002.

  20. Rostovtsev, V.S., Iskusstvennye neironnye seti: Uchebnik (Artificial Neural Networks: Textbook), St. Petersburg: Lan’, 2019. ISBN 978-5-8114-3768-9. https://e.lanbook.com/book/122180. Accessed August 24, 2020.

  21. Doroshenko, V.V., Processability of multicomponent aluminum-calcium alloys in casting and die machining, Cand. Sci. (Eng.) Dissertation, Moscow, 2019.

  22. Letyagin, N.V., Effect of additional alloying (La, Cu, Mn) on structure, processability and mechanical properties of composite alumina-calcium alloys, Cand. Sci. (Eng.) Dissertation, Moscow, 2021.

Download references

Author information

Authors and Affiliations

  1. Naberezhnye Chelny Institute, Kazan (Volga Region) Federal University, Naberezhnye Chelny, Russia

    L. A. Simonova

  2. STANKIN Moscow State Technical University, Moscow, Russia

    K. V. Klochkova & I. S. Bredinin

Authors
  1. L. A. Simonova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. V. Klochkova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. S. Bredinin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. A. Simonova.

Additional information

Translated by B. Gilbert

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Simonova, L.A., Klochkova, K.V. & Bredinin, I.S. Predicting Alloy Strength by Neural Network Modeling: Al–Ca–Mn Alloys. Russ. Engin. Res. 43, 329–333 (2023). https://doi.org/10.3103/S1068798X23040305

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1068798X23040305

Keywords:

Search

Navigation