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Engineering of Novel Fe-Based Bulk Metallic Glasses Using a Machine Learning-Based Approach

  • Research Article-Mechanical Engineering
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A Correction to this article was published on 21 September 2021

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Abstract

A broad range of potential chemical compositions makes difficult design of novel bulk metallic glasses (BMGs) without performing expensive experimentations. To overcome this problem, it is very important to establish predictive models based on artificial intelligence. In this work, a machine learning (ML) approach was proposed for predicting glass formation in numerous alloying compositions and designing novel glassy alloys. The results showed that our ML model accurately predicted the glass formation and critical thickness of MGs. As a case study, the ternary Fe–B–Co system was selected and effects of minor additions of Cr, Nb and Y with different atomic percentages were evaluated. It was found that the minor addition of Nb and Y leads to the significant improvement of glass-forming ability (GFA) in the Fe–B–Co system; however, a shift in the optimized alloying composition was occurred. The experimental results on selective alloying compositions also confirmed the capability of our ML model for designing novel Fe-based BMGs.

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Author information

Authors and Affiliations

  1. CAIC, DPU, Bangkok, Thailand

    Tzu-Chia Chen

  2. Universitas Bandar Lampung, Bandar Lampung, Indonesia

    Rajiman Rajiman

  3. Data Science and Computational Intelligence Research Group, Universitas Sumatera Utara, Medan, Indonesia

    Marischa Elveny

  4. Department of Energy, Universidad de la Costa, Barranquilla, Colombia

    John William Grimaldo Guerrero

  5. Department of Accounting and Finance, Landmark University, Omu-Aran, Nigeria

    Adedoyin Isola Lawal

  6. Sustainable Development Goal 17 (Partnership for the Goals) Research Cluster, Landmark University, Omu-Aran, Nigeria

    Adedoyin Isola Lawal

  7. Faculty of Engineering, Udayana University, Bali, Indonesia

    Ngakan Ketut Acwin Dwijendra

  8. Saveetha Institute of Medical and Technical Sciences, Chennai, India

    Aravindhan Surendar

  9. Sechenov First Moscow State Medical University, Moscow, Russia

    Svetlana Dmitrievna Danshina

  10. School of Mechanical Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Yu Zhu

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  1. Tzu-Chia Chen
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  2. Rajiman Rajiman
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  3. Marischa Elveny
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  8. Svetlana Dmitrievna Danshina
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  9. Yu Zhu
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Corresponding authors

Correspondence to Marischa Elveny or Yu Zhu.

Additional information

The original version of this article was revised: the affiliation of author, Dr. Svetlana Dmitrievna Danshina has been corrected.

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Chen, TC., Rajiman, R., Elveny, M. et al. Engineering of Novel Fe-Based Bulk Metallic Glasses Using a Machine Learning-Based Approach. Arab J Sci Eng 46, 12417–12425 (2021). https://doi.org/10.1007/s13369-021-05966-0

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  • DOI: https://doi.org/10.1007/s13369-021-05966-0

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