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    Tytuł:
    The character of the structure formation of model alloys of the Fe-Cr-(Zr, Zr-B) system synthesized by powder metallurgy
    Autorzy:
    Duriagina, Z. A.
    Romanyshyn, M. R.
    Kulyk, V. V.
    Kovbasiuk, T. M.
    Trostianchyn, A. M.
    Lemishka, I. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/366907.pdf
    Data publikacji:
    2020
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    model alloys
    phase diagram
    microstructure
    microhardness
    XRD analysis
    stop modelowy
    diagram fazowy
    mikrostruktura
    mikrotwardość
    analiza XRD
    Opis:
    Purpose: The purpose of the work is to synthesize and investigate the character of structure formation, phase composition and properties of model alloys Fe75Cr25, Fe70Cr25Zr5, and Fe69Cr25Zr5B1. Design/methodology/approach: Model alloys are created using traditional powder metallurgy approaches. The sintering process was carried out in an electric arc furnace with a tungsten cathode in a purified argon atmosphere under a pressure of 6·104 Pa on a water cooled copper anode. Annealing of sintered alloys was carried out at a temperature of 800°C for 3 h in an electrocorundum tube. The XRD analysis was performed on diffractometers DRON-3.0M and DRON-4.0M. Microstructure study and phase identification were performed on a REMMA-102-02 scanning electron microscope. The microhardness was measured on a PMT-3M microhardness meter. Findings: When alloying a model alloy of the Fe-Cr system with zirconium in an amount of up to 5%, it is possible to obtain a microstructure of a composite type consisting of a mechanical mixture of a basic Fe2(Cr) solid solution, solid solutions based on Laves phases and dispersive precipitates of these phases of Fe2Zr and FeCrZr compositions. In alloys of such systems or in coatings formed based on such systems, an increase in hardness and wear resistance and creep resistance at a temperature about 800°C will be reached. Research limitations/implications: The obtained results were verified during laser doping with powder mixtures of appropriate composition on stainless steels of ferrite and ferrite-martensitic classes. Practical implications: The character of the structure formation of model alloys and the determined phase transformations in the Fe-Cr, Fe-Cr-Zr, and Fe-Cr-B-Zr systems can be used to improve the chemical composition of alloying plasters during the formation of ferrite and ferrite-martensitic stainless steel coatings. Originality/value: The model alloys were synthesized and their phase composition and microstructure were studied; also, their microhardness was measured. The influence of the chemical composition of the studied materials on the character of structure formation and their properties was analysed.
    Źródło:
    Journal of Achievements in Materials and Manufacturing Engineering; 2020, 100, 2; 49-57
    1734-8412
    Pojawia się w:
    Journal of Achievements in Materials and Manufacturing Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task
    Autorzy:
    Trostianchyn, A.M.
    Izonin, I.V.
    Duriagina, Z.A.
    Tkachenko, R.O.
    Kulyk, V.V.
    Havrysh, B.M.
    Powiązania:
    https://bibliotekanauki.pl/articles/24200577.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    Sm-Co alloy
    ensemble learning
    gradient boosting
    prediction accuracy
    Stop Sm-Co
    uczenie zespołowe
    dokładność przewidywania
    Opis:
    Purpose: This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.
    Źródło:
    Archives of Materials Science and Engineering; 2022, 116, 2; 71--80
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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