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Application of Machine Learning Algorithms With and Without Principal Component Analysis for the Design of New Multiphase High Entropy Alloys

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Abstract

The design of high entropy alloys (HEAs) can be accelerated using machine learning (ML) algorithms. In the current study, the design parameter’s effect on the algorithm prediction was established using Shapley additive explanation (SHAP) values. The higher dimension problem is reduced to lower dimension using kernel principal component analysis (KPCA). Testing accuracy of more than 85 pct was obtained for the support vector machine (SVM) with KPCA. Experimental data comparison confirms the improvement of accuracy for the decision tree (DT) and random forest (RF) after applying KPCA.

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Notes

  1. THERMO-CALC is a trademark of Thermo-Calc software

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One of the authors (ASB) acknowledges Ujjawal Kumar Jaiswal and Yegi Vamsi Krishna for their help with the algorithm development and for useful discussions. RMR acknowledges Professor G. Phanikumar (Department of Metallurgical and Materials Engineering, IIT, Madras) for providing research facilities and for useful discussions and Naishalkumar Shah for helpful communications.

On behalf of all of the authors, the corresponding author states that there is no conflict of interest.

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  1. Department of Fuel, Minerals and Metallurgical Engineering, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, Jharkhand, 826004, India

    Amit Singh Bundela & M. R. Rahul

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  1. Amit Singh Bundela
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  2. M. R. Rahul
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Correspondence to M. R. Rahul.

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Bundela, A.S., Rahul, M.R. Application of Machine Learning Algorithms With and Without Principal Component Analysis for the Design of New Multiphase High Entropy Alloys. Metall Mater Trans A 53, 3512–3519 (2022). https://doi.org/10.1007/s11661-022-06764-5

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