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Journals / International Journal of Pure and Applied Sciences / 2026 / Volume: 12 Issue: 1

Deep Learning-Based Prediction of Tribological Properties in Mg-Cu and Mg-Zn Alloys for Biomedical Applications

Rukiye Tekin Ünver(KARABUK UNIVERSITY, ESKİPAZAR VOCATIONAL SCHOOL)0000-0001-8265-2018
Cihan Bayraktar(KARABUK UNIVERSITY, ESKİPAZAR VOCATIONAL SCHOOL, DEPARTMENT OF COMPUTER TECHNOLOGIES, INFORMATION SECURITY TECHNOLOGY PR.)0000-0003-4321-5485
Bilge Demir(KARABUK UNIVERSITY, FACULTY OF ENGINEERING)0000-0002-3617-9749
Kürşat Mustafa Karaoğlan(KARABUK UNIVERSITY)0000-0001-9830-7622
Pages
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Publication Date
2026-06-22
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Sensitivity analysis results indicated that load, environment, hardness, and grain size are the most critical factors determining wear behaviour.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Incorporating Cu and Zn at low concentrations into a magnesium matrix provides antibacterial properties and biodegradability advantages for biomedical applications.
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Key Findings & Data:Experimental data were obtained from wear tests conducted under 5–20 N loads and a sliding distance of 250 m in dry and simulated body fluid environments.
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Conclusion & Impact:Sensitivity analysis results indicated that load, environment, hardness, and grain size are the most critical factors determining wear behaviour.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Biodegredable Alloys · Deep Learning · Powder Metallurgy

Abstract

Incorporating Cu and Zn at low concentrations into a magnesium matrix provides antibacterial properties and biodegradability advantages for biomedical applications. However, the complex effects of these alloying additions on microstructural and tribological behaviours and their optimal ratios have not yet been fully elucidated. This study developed a deep learning-based regression model to predict the wear properties of Mg-Cu and Mg-Zn alloys. Experimental data were obtained from wear tests conducted under 5–20 N loads and a sliding distance of 250 m in dry and simulated body fluid environments. Microstructural parameters, including grain size, density, hardness, crystallite size, microstrain, and dislocation density, were used as model inputs. In contrast, volume loss, coefficient of friction, and specific wear rate were used as output variables. The developed Deep Multilayer Perceptron model with optimised hyperparameters demonstrated high predictive performance on both the training and test datasets, preventing overfitting. Model evaluation yielded R² values of 0.9912 for volume loss, 0.9935 for coefficient of friction, and 0.9545 for specific wear rate, with RMSE values of 0.58, 0.011, and 0.61, respectively, confirming model reliability. Error analyses proved a narrow distribution and high correlation between predicted and experimental data. Sensitivity analysis results indicated that load, environment, hardness, and grain size are the most critical factors determining wear behaviour. This work presents a machine learning framework for tribological prediction in biodegradable Mg alloys, which may contribute to alloy design and optimisation of ortho-paedic implant materials.

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Cite This Article

Ünver, R. T., Bayraktar, C., Demir, B., Karaoğlan, K. M. (2026). Deep Learning-Based Prediction of Tribological Properties in Mg-Cu and Mg-Zn Alloys for Biomedical Applications. International Journal of Pure and Applied Sciences, Volume: 12 Issue: 1. https://doi.org/10.29132/ijpas.1811247

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