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Steel in Translation

Erschienen in:

01.11.2023

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

verfasst von: M. F. Gafarov, K. Yu. Okishev, A. N. Makovetskiy, K. P. Pavlova, E. A. Gafarova

Erschienen in: Steel in Translation | Ausgabe 11/2023

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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.

Vorheriger Artikel Analysis of Thermal Processes in Steels during Laser Heating

Nächster Artikel Methodology of Intensive Energy Saving in Ferrous Metallurgy. Experience and Results of Use

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Titel: Construction of Models for Predicting the Microstructure of Steels after Heat Treatment Using Machine Learning Methods
verfasst von: M. F. Gafarov
K. Yu. Okishev
A. N. Makovetskiy
K. P. Pavlova
E. A. Gafarova
Publikationsdatum: 01.11.2023
Verlag: Pleiades Publishing
Erschienen in: Steel in Translation / Ausgabe 11/2023
Print ISSN: 0967-0912
Elektronische ISSN: 1935-0988
DOI: https://doi.org/10.3103/S0967091223110104

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