Leakage-Conscious Machine Learning and MCDM Analysis of Y/Bi-Substituted Bi-2212 Ceramics

Nuri Alper Metin; Gülnur Kurtul

doi:10.21541/apjess.1977752

Leakage-Conscious Machine Learning and MCDM Analysis of Y/Bi-Substituted Bi-2212 Ceramics

Abstract

Y/Bi substitution in Bi-2212 ceramics produces a peaked mechanical response within a narrow composition window. This materials informatics study applies a compact, leakage-conscious machine-learning protocol to the published indentation dataset to recover and explain that compositional trend. The modelling table comprises 35 load-level rows drawn from seven ceramic compositions across five Vickers loads, with sample-level EDX descriptors assigned to the five load rows of each composition; validation followed leave-one-sample-out splitting at the composition level. Five descriptors were used: x, F, xF, Cu/Bi and Y/Bi. Ridge regression and Extra Trees regression were evaluated with fixed, a priori parameters. For Vickers hardness, Extra Trees gave MAE = 0.0155 GPa, RMSE = 0.0189 GPa and R² = 0.8923 under composition-held-out validation. The same compact protocol recovered the measured trend for stiffness-related descriptors; fracture toughness proved harder to capture. TreeSHAP analysis of the Extra Trees hardness model identified substitution-related descriptors as the dominant explanatory variables: the EDX-derived Y/Bi ratio and nominal x carried the largest mean contributions, followed by Cu/Bi, the load-coupled xF term and the applied load. A TOPSIS ranking of the seven compositions over the five indentation-derived descriptors placed Y-2 first, consistent with both the measured trend and the SHAP-based explanation. The contribution is methodological: a leakage-conscious validation protocol, a compact and auditable descriptor set, an explainable-AI interpretation of the fitted model, and an independent decision-analysis check are added to the original experimental measurements.

Keywords

Supporting Institution

The authors declared that this study has received no financial support.

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Details

Primary Language

English

Subjects

Machine Learning (Other)

Journal Section

Research Article

Authors

Nuri Alper Metin ^*
0000-0002-9962-917X
Türkiye

Gülnur Kurtul This is me
0000-0002-4497-6780
Türkiye

Publication Date

October 1, 2026

Submission Date

July 3, 2026

Acceptance Date

August 29, 2026

Published in Issue

Year 2026 Volume: 14 Number: 3

DOI

https://doi.org/10.21541/apjess.1977752

IZ

https://izlik.org/JA88BJ73BB

Cite

RIS / Bibtex

APA

Metin, N. A., & Kurtul, G. (2026). Leakage-Conscious Machine Learning and MCDM Analysis of Y/Bi-Substituted Bi-2212 Ceramics. Academic Platform Journal of Engineering and Smart Systems, 14(3), 165-177. https://doi.org/10.21541/apjess.1977752

AMA

1.Metin NA, Kurtul G. Leakage-Conscious Machine Learning and MCDM Analysis of Y/Bi-Substituted Bi-2212 Ceramics. APJESS. 2026;14(3):165-177. doi:10.21541/apjess.1977752

Chicago

Metin, Nuri Alper, and Gülnur Kurtul. 2026. “Leakage-Conscious Machine Learning and MCDM Analysis of Y Bi-Substituted Bi-2212 Ceramics”. Academic Platform Journal of Engineering and Smart Systems 14 (3): 165-77. https://doi.org/10.21541/apjess.1977752.

EndNote

Metin NA, Kurtul G (October 1, 2026) Leakage-Conscious Machine Learning and MCDM Analysis of Y/Bi-Substituted Bi-2212 Ceramics. Academic Platform Journal of Engineering and Smart Systems 14 3 165–177.

IEEE

[1]N. A. Metin and G. Kurtul, “Leakage-Conscious Machine Learning and MCDM Analysis of Y/Bi-Substituted Bi-2212 Ceramics”, APJESS, vol. 14, no. 3, pp. 165–177, Oct. 2026, doi: 10.21541/apjess.1977752.

ISNAD

Metin, Nuri Alper - Kurtul, Gülnur. “Leakage-Conscious Machine Learning and MCDM Analysis of Y Bi-Substituted Bi-2212 Ceramics”. Academic Platform Journal of Engineering and Smart Systems 14/3 (October 1, 2026): 165-177. https://doi.org/10.21541/apjess.1977752.

JAMA

1.Metin NA, Kurtul G. Leakage-Conscious Machine Learning and MCDM Analysis of Y/Bi-Substituted Bi-2212 Ceramics. APJESS. 2026;14:165–177.

MLA

Metin, Nuri Alper, and Gülnur Kurtul. “Leakage-Conscious Machine Learning and MCDM Analysis of Y Bi-Substituted Bi-2212 Ceramics”. Academic Platform Journal of Engineering and Smart Systems, vol. 14, no. 3, Oct. 2026, pp. 165-77, doi:10.21541/apjess.1977752.

Vancouver

1.Nuri Alper Metin, Gülnur Kurtul. Leakage-Conscious Machine Learning and MCDM Analysis of Y/Bi-Substituted Bi-2212 Ceramics. APJESS. 2026 Oct. 1;14(3):165-77. doi:10.21541/apjess.1977752