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Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods

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Steel in Translation Aims and scope

Abstract

Process of building machine learning models to predict microstructures of pipe steels after continuous cooling involves the collection and preparation of data, the source of which is thermokinetic diagrams of supercooled austenite decomposition. Statistics of intermediate and final data, as well as algorithms for their transformation are given. Evaluations of machine learning models for selected microstructures are considered. A method for generating data under small sample conditions and introducing an evaluative feature of grain size are proposed. Models were validated and the significance of features was interpreted. The practical use of models for constructing thermokinetic diagrams of austenite decomposition and analysis of modeling results is shown.

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. South Ural State University (National Research University), 454091, Chelyabinsk, Chelyabinsk oblast, Russia

    M. F. Gafarov & K. P. Pavlova

  2. PJSC “TMK” (Chelyabinsk Pipe Plant), 454129, Chelyabinsk, Chelyabinsk oblast, Russia

    M. F. Gafarov & A. N. Makovetskiy

  3. Ural Federal University named after the first President of Russia B.N. Yeltsin, 620002, Yekaterinburg, Sverdlovsk oblast, Russia

    K. Yu. Okishev

  4. South Ural State Humanitarian Pedagogical University, 454080, Chelyabinsk, Chelyabinsk oblast, Russia

    E. A. Gafarova

Authors
  1. M. F. Gafarov
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  2. K. Yu. Okishev
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  3. A. N. Makovetskiy
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  4. K. P. Pavlova
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  5. E. A. Gafarova
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Correspondence to M. F. Gafarov.

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Translated by V. Selikhanovich

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Gafarov, M.F., Okishev, K.Y., Makovetskiy, A.N. et al. Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods. Steel Transl. 53, 1120–1129 (2023). https://doi.org/10.3103/S0967091223110104

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