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Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task

Trostianchyn A.M., Izonin I.V., Duriagina Z.A., Tkachenko R.O., Kulyk V.V., Havrysh B.M.

Archives of Materials Science and Engineering

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2022

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Vol. 116, nr 2

71--80

EN

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.

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The character of the structure formation of model alloys of the Fe-Cr-(Zr, Zr-B) system synthesized by powder metallurgy

Duriagina Z. A., Romanyshyn M. R., Kulyk V. V., Kovbasiuk T. M., Trostianchyn A. M., Lemishka I. A.

Journal of Achievements in Materials and Manufacturing Engineering

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2020

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Vol. 100, nr 2

49--57

EN

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.

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Determination of the best microstructure and titanium alloy powders properties using neural network

Duriagina Z. A., Tkachenko R. O., Trostianchyn A. M., Lemishka I. A., Kovalchuk A. M., Kulyk V. V., Kovbasyuk T. M.

Journal of Achievements in Materials and Manufacturing Engineering

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2018

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Vol. 87, nr 1

25--31

EN

Purpose: Create a software product using a probabilistic neural network (PNN) and database based on experimental research of titanium alloys to definition of the best microstructure and properties of aerospace components. Design/methodology/approach: The database creation process for artificial neural network training was preceded by the investigation of the granulometric composition of the titanium powder alloys, study of microstructure, phase composition and evaluation of micromechanical properties of these alloys by the method of material plasticity estimation in the conditions of hard pyramidal indenters application. A granulometric analysis was done using a special complex of materials science for the images analysis ImageJ. Metallographic investigations of the powder structure morphology were carried out on the scanning electron microscope EVO 40XVP. Specimens for micromechanical testing were obtained by overlaying the titanium alloy powders on the substrate made of the material close to chemical composition. Substrates were prepared by pressing the powder mixture under the load of 400 MPa and following sintering at 1300°C for 1 hour. Overlaying was performed by an electron gun ELA-6 (beam current – 16 mA). Findings: According to the modelling results, a threshold of the PNN accuracy was established to be over 80%. A high level of experimental data reproduction allows us a full or partial replacement of a number of experimental investigations by neural network modelling, noticeably decreasing, in this case, the cost of the material creation possessing the preset properties with preserved quality. It is expected that this software can be used for solving other problems in materials science too. Research limitations/implications: The accuracy of the PNN algorithm depends on the number of input parameters obtained experimentally and forms a database for the training of the system. For our case, the amount of input data is limited. Practical implications: Previously trained system based on the PNN algorithm will reduce the number of experiments that significantly reduce costs and time to study. Originality/value: A software product on the basis of the PNN network was developed. The training sample was built based on a series of laboratory studies granulometric composition of the titanium powder alloys, study of microstructure, phase composition and evaluation of micromechanical properties of powder materials. The proposed approach allows us to determine the best properties of the investigated material for the design of aerospace components.

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Alloys selection based on the supervised learning technique for design of biocompatible medical materials

Tepla T. L., Izonin I. V., Duriagina Z. A., Tkachenko R. O., Trostianchyn A. M., Lemishka I. A., Kulyk V. V., Kovbasyuk T. M.

Archives of Materials Science and Engineering

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2018

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Vol. 93, nr 1

32--40

EN

Purpose: The main aim of this paper is development, software implementation and use of the alloys selection method for the design of biocompatible materials in medical production. It is based on the use of Ito decomposition and Logistic Regression. Design/methodology/approach: The technology of machine learning is used to solve the task. The developed classification method is based on the application of multiclass Logistic Regression. In order to reduce the probability of incorrect alloy identification, expansion of the input characteristics based on the Ito decomposition of the second order has been made. On the one hand, it increased the dimension of the input features space, and as a result, it increased the time for training procedure, but on the other, it increased the solution accuracy of the alloys selection task. The accuracy evaluation of the method was carried out using different criteria. In particular, the method accuracy was estimated based on the ratio of correctly classified titanium alloys samples to the test sample dimension. This measure was used to assess the classification accuracy in the training and test modes. For a more detailed analysis of the classification method results, two additional measures were further used: Precision and Recall. Their calculation was based on the constructed confusion matrix. This made it possible to assess the ability of the developed method to find the instances of each individual alloy as a whole, as well as the ability to distinguish instances of one class from representatives on the other. The combination of these indicators allowed to evaluate the classification task accuracy in the conditions of the imbalance dataset for each class of the investigated material separately. Findings: The simulation results confirmed the effectiveness of the use of machine learning tools to solve this task. High indicators of the method’s accuracy based on the experimental results were established. In particular, the overall accuracy of the method is 96.875%, and the average values of Precision and Recall for all four classes are 94% and 98% respectively. Expansion of each vector's features from the training dataset by using Ito decomposition increased the method accuracy by more than 33% compared to the basic Logistic Regression. Research limitations/implications: The Logistic Regression's training procedure, as well as the increase of the space size of the investigated alloy's input features by using Ito decomposition, imposes a number of limitations on the application of the method in tasks that depend on the duration of the work. Practical implications: The proposed machine learning approach foralloys selection allows reducing the time, material and human resources needed to investigate the titanium alloys properties. The developed method increases the accuracy of the selection alloys task compared to the four known methods, an average of 14.5%. It can be used to select materials with appropriate properties for the design of biocompatible medical products. Originality/value: A method and software product for the titanium alloys classification task using a supervised learning technique has been developed. For this aim, the method of Logistic Regression with expanding inputs based on the second-order Ito decomposition is used.