Standard

Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods. / Gafarov, M.; Okishev, K.; Makovetskiy, A. и др.
в: Steel in Translation, Том 53, № 11, 01.11.2023, стр. 1120-1129.

Результаты исследований: Вклад в журнал › Статья › Рецензирование

Harvard

Gafarov, M, Okishev, K, Makovetskiy, A, Pavlova, K & Gafarova, E 2023, 'Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods', Steel in Translation, Том. 53, № 11, стр. 1120-1129. https://doi.org/10.3103/S0967091223110104

APA

Gafarov, M., Okishev, K., Makovetskiy, A., Pavlova, K., & Gafarova, E. (2023). Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods. Steel in Translation, 53(11), 1120-1129. https://doi.org/10.3103/S0967091223110104

Vancouver

Gafarov M, Okishev K, Makovetskiy A, Pavlova K, Gafarova E. Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods. Steel in Translation. 2023 нояб. 1;53(11):1120-1129. doi: 10.3103/S0967091223110104

Author

Gafarov, M. ; Okishev, K. ; Makovetskiy, A. и др. / Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods. в: Steel in Translation. 2023 ; Том 53, № 11. стр. 1120-1129.

BibTeX

@article{796f2bd611924330b4e132e1508e5123,
title = "Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods",
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.",
author = "M. Gafarov and K. Okishev and A. Makovetskiy and K. Pavlova and E. Gafarova",
note = "This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.",
year = "2023",
month = nov,
day = "1",
doi = "10.3103/S0967091223110104",
language = "English",
volume = "53",
pages = "1120--1129",
journal = "Steel in Translation",
issn = "0967-0912",
publisher = "Allerton Press Inc.",
number = "11",

}

RIS

TY - JOUR

T1 - Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods

AU - Gafarov, M.

AU - Okishev, K.

AU - Makovetskiy, A.

AU - Pavlova, K.

AU - Gafarova, E.

N1 - This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

PY - 2023/11/1

Y1 - 2023/11/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85186548573

U2 - 10.3103/S0967091223110104

DO - 10.3103/S0967091223110104

M3 - Article

VL - 53

SP - 1120

EP - 1129

JO - Steel in Translation

JF - Steel in Translation

SN - 0967-0912

IS - 11

ER -

ID: 53805191