Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models

İsmail Enes Parlak; Cantekin Kaykılarlı; Taha Yasin Eken

doi:10.38088/jise.1769011

Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models

Abstract

The growth of industrial development has increased attention to sustainability and efficiency, resulting in greater research advances toward improved performance of materials. The tribological behavior of materials, specifically friction and wear, is one of the most primary topics of interest in material performance advancement. This work provides an advanced machine learning regression-based model for the quantitative prediction of the Coefficient of Friction (CoF) and wear rate of the Ti6Al4V alloy. The novel approach employs an extensive pipeline of advanced feature engineering to inform an ensemble model based on a dataset compiled from the literature. The optimized Gradient Boosting Regressor achieved F1 results in excess of 95% accuracy on an unseen data set (R2 = 0.944; RMSE = 0.020) for CoF predictions, and a stacking regressor/model markedly improved wear rate predictions (R2 = 0.730) compared to baseline models and the CoF predictions for clarification of real-time engineering applications. The ensemble regression model is designed to provide high-fidelity, quantitative benchmarks for Ti6Al4V, which can be used as critical tools for materials design and optimization. The methodology confirmed the models' important physical relevance through feature-importance analysis: Hardness × Load for the CoF models, and Sliding Distance for wear rate.

Keywords

References

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Details

Primary Language

English

Subjects

Modelling and Simulation , Metals and Alloy Materials

Journal Section

Research Article

Authors

İsmail Enes Parlak
0000-0002-0694-9220
Türkiye

Cantekin Kaykılarlı
0000-0002-2380-3258
Türkiye

Taha Yasin Eken ^*
0000-0001-6693-8091
Türkiye

Publication Date

April 11, 2026

Submission Date

August 22, 2025

Acceptance Date

November 16, 2025

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.38088/jise.1769011

IZ

https://izlik.org/JA49KS99UL

Cite

RIS / Bibtex

APA

Parlak, İ. E., Kaykılarlı, C., & Eken, T. Y. (2026). Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models. Journal of Innovative Science and Engineering, 10(1), 46-64. https://doi.org/10.38088/jise.1769011

AMA

1.Parlak İE, Kaykılarlı C, Eken TY. Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models. JISE. 2026;10(1):46-64. doi:10.38088/jise.1769011

Chicago

Parlak, İsmail Enes, Cantekin Kaykılarlı, and Taha Yasin Eken. 2026. “Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression With Feature Engineering and Ensemble Models”. Journal of Innovative Science and Engineering 10 (1): 46-64. https://doi.org/10.38088/jise.1769011.

EndNote

Parlak İE, Kaykılarlı C, Eken TY (April 1, 2026) Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models. Journal of Innovative Science and Engineering 10 1 46–64.

IEEE

[1]İ. E. Parlak, C. Kaykılarlı, and T. Y. Eken, “Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models”, JISE, vol. 10, no. 1, pp. 46–64, Apr. 2026, doi: 10.38088/jise.1769011.

ISNAD

Parlak, İsmail Enes - Kaykılarlı, Cantekin - Eken, Taha Yasin. “Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression With Feature Engineering and Ensemble Models”. Journal of Innovative Science and Engineering 10/1 (April 1, 2026): 46-64. https://doi.org/10.38088/jise.1769011.

JAMA

1.Parlak İE, Kaykılarlı C, Eken TY. Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models. JISE. 2026;10:46–64.

MLA

Parlak, İsmail Enes, et al. “Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression With Feature Engineering and Ensemble Models”. Journal of Innovative Science and Engineering, vol. 10, no. 1, Apr. 2026, pp. 46-64, doi:10.38088/jise.1769011.

Vancouver

1.İsmail Enes Parlak, Cantekin Kaykılarlı, Taha Yasin Eken. Quantitative Prediction of Ti6Al4V Tribological Behavior Using Advanced Machine Learning Regression with Feature Engineering and Ensemble Models. JISE. 2026 Apr. 1;10(1):46-64. doi:10.38088/jise.1769011