Sm-Co ALLOYS COERCIVITY PREDICTION USING STACKING HETEROGENEOUS ENSEMBLE MODEL

Andrii Trostianchyn,Zoia Duriagina,Volodymyr Kulyk,Ivan Izonin,Olena Pavliuk,Roman Tkachenko

doi:10.36547/ams.27.4.1173

Andrii Trostianchyn, Zoia Duriagina + Show 4 more

Open Access

https://doi.org/10.36547/ams.27.4.1173

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Abstract

The use of machine learning tools in modern materials science can significantly reduce the duration and cost of developing new materials and improving the properties of existing ones. This is especially true in studying expensive and strategic importance materials like alloys of rare earth metals, which are used to manufacture high-energy permanent magnets. At the same time, single machine learning algorithms do not always provide the accuracy required to solve a particular applied task. Therefore, the current paper aimed to develop an ensemble model for predicting the magnetic properties of Sm-Co system alloys with high accuracy. Based on literature data, we have collected the dataset of the relationship between phase composition, sample state, crystallographic orientation, microstructure, and magnetic properties. We have compared different machine learning algorithms. A stacking ensemble model was designed based on high-precision machine learning algorithms: Neural Networks, AdaBoost, Gradient Boosting, and Random Forest algorithm. The proposed ensemble scheme showed a significant increase in the accuracy for predicting the magnetic properties of Sm-Co alloys on the example of coercivity.

Highlights

Due to their high magnetic properties at both room and elevated temperatures, permanent magnets based on Sm-Co system alloys are widely used in aerospace technology, various sensors, wind turbines, hybrid electric vehicles, etc., due to their high magnetic properties at both room and elevated temperatures [1,2]
This paper aims to develop an ensemble model based on a set of heterogeneous machine learning (ML)-based regressors to solve the problem of Sm-Co alloy's magnetic properties prediction
In this article, based on the collected data set, an experimental comparison of eight existing machine learning methods was performed when solving the problem of predicting the coercivity of Sm-Co alloys

Summary

Introduction

Due to their high magnetic properties at both room and elevated temperatures, permanent magnets based on Sm-Co system alloys are widely used in aerospace technology, various sensors, wind turbines, hybrid electric vehicles, etc., due to their high magnetic properties at both room and elevated temperatures [1,2]. The experimental verification of the influence of these parameters on the magnetic properties is highly timeconsuming and resource-intensive This process can be significantly simplified, shortened, and reduced in price by using machine learning (ML), artificial intelligence, or neural network modeling for preliminary prediction of magnetic properties and subsequent experimental confirmation [7-9]. The authors [14] showed the possibility of ML tools application to create new soft magnetic materials In this case, the experimental data on the influence of chemical composition, modes of thermal treatment, and grain size on magnetic properties were used to predict saturation magnetization, coercivity, and magnetostriction of different alloys using a random forest model.

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