Influence of the Mg content on the microstructure and mechanical properties of Al-xMg-2.0Si-0.6Mn alloy

Yan, Xuanjie; Sui, Yudong; Zhou, Hao; Sun, Wei; Jiang, Yehua; Wang, Qudong

doi:10.1016/j.jmrt.2023.02.046

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Изучено влияние содержания Mg на микроструктуру и механические свойства сплава Al-xMg-2.0Si-0.6Mn [38]. Микроструктуру, фазовый состав и морфологию излома сплава определяли методами сканирующей электронной микроскопии, просвечивающей электронной микроскопии и рентгеноструктурного анализа.…”

Section: рисунок 6 схема экспериментальной установки для исследования...unclassified

Processing of low-quality manganese-containing raw materials to obtain pellets for production ferromanganese alloys. Review

Tastanova,

Kuldeyev,

Temirova

et al. 2023

EJSU

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Substandard ores and technogenic wastes can be considered as an additional source of raw materials due to the limited resources of mineral raw materials with a high content of manganese. The article presents an overview of studies intended to process low-quality manganese-containing raw materials by beneficiation of them with the production of manganese concentrates, agglomeration of manganese fines with the formation of pellets, and smelting of agglomerates into ferromanganese alloys. The main raw material sources of manganese are characterized. Manganese ores are divided into three main typesoxide, siliceous and carbonate ones. The method to beneficiate the raw material is chosen depending on the raw material type. The application areas of manganese alloys are described -they are used in the form of ferromanganese and ferrosilicomanganese to produce steel and cast iron in ferrous metallurgy; as an alloying metal in bronze, brass, most aluminum and magnesium alloys in non-ferrous metallurgy. The beneficiation processes for low-quality manganese-containing raw materials -substandard ores and technogenic wastes are considered. A promising direction in the beneficiation of ferromanganese ores is magnetic separation. The extraction of manganese from technogenic sources -ore fines, sludge tailings of processing plants is of great ecological and practical importance. Methods for agglomeration of manganese fines with the production of pellets, briquettes, the influence of the content of acidic and basic oxides, the composition of binder materials on the properties of pellets are described. Data on the influence of various factors on the manganese ferroalloy smelting process, the reduction of manganese ore pellets, and the phase transformations occurring during the process are specified.

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Section: рисунок 6 схема экспериментальной установки для исследования...unclassified

Processing of low-quality manganese-containing raw materials to obtain pellets for production ferromanganese alloys. Review

Tastanova,

Kuldeyev,

Temirova

et al. 2023

EJSU

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“…Mechanical properties are the key indicators of steel quality, and accurate prediction of the mechanical properties of products is of great significance in controlling product quality and optimizing product processes [2]. Since the production of hot rolled steel plate is a physical metallurgical process with a complex mechanism of action and a high degree of coupling of various factors, the prediction of mechanical properties is a complex, multidimensional, and nonlinear problem [3]. The traditional mechanism model and regression model are difficult to achieve accurate and reliable prediction, and they cannot adapt to the needs of actual industrial production.…”

Section: Introduction *mentioning

confidence: 99%

Yield Stress and Elongation Prediction and Process Parameter Optimization of Two C-Mn Steel Based on XGBoost and Non-dominated Sorting Genetic Algorithm-IIization of C-Mn steel based on XGBoost algorithm

QIN,

ZHAO,

JIN

et al. 2024

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Mechanical properties are the key guidance for controlling the product quality of steel and optimizing its process. In this work, four machine learning (ML) algorithms (XGBoost, SVR, BP, and RBF) are used to establish a correlation model between chemical composition, process parameters, and mechanical properties for performance optimization. The ML model showing high prediction accuracy is selected to analyze the importance of the model parameters. The XGBoost model has the highest prediction accuracy, with both yield strength (YS) and elongation-to-failure (EL) achieving a prediction accuracy of over 0.9 on the test set. Subsequently, the (YS) and (EL) of typical steel grades SPHC and Q235B are optimized by combining the high-precision XGBoost model and NSGA-II multi-objective optimization strategy. A comprehensive mechanical performance evaluation index (CP and EV) was proposed to screen the optimal Pareto frontiers, and two types of steel with excellent performance were successfully selected.

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Development of a Near Eutectic Al-Mg2Si-Mg Alloy with High Strength and Toughness

Wu,

Zhao,

et al. 2024

J. of Materi Eng and Perform

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Influence of the Mg content on the microstructure and mechanical properties of Al-xMg-2.0Si-0.6Mn alloy

Cited by 6 publications

References 30 publications

Processing of low-quality manganese-containing raw materials to obtain pellets for production ferromanganese alloys. Review

Processing of low-quality manganese-containing raw materials to obtain pellets for production ferromanganese alloys. Review

Yield Stress and Elongation Prediction and Process Parameter Optimization of Two C-Mn Steel Based on XGBoost and Non-dominated Sorting Genetic Algorithm-IIization of C-Mn steel based on XGBoost algorithm

Development of a Near Eutectic Al-Mg2Si-Mg Alloy with High Strength and Toughness

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