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<p>Incorporating Cu and Zn into Mg as a biomaterial offers a unique opportunity to exploit their antibacterial performance and biodegradability. The main challenge in this area is understanding the ratio and effects of these elements. To achieve this, the present work, based on two separate studies, aims to develop a regression model and apply machine learning (ML) to predict the wear behaviors using the effects of Cu and Zn elements doped into Mg matrix at low ratios on wear and micro and nanostructure properties (Grain size, density, hardness, Crystallite Size, microstrain, dislocation density). The wear behavior of the samples was investigated under 5–20&#xa0;N loads at a constant sliding speed of 42&#xa0;mm/s. Auto Sklearn library was used to generate training models that accurately predict the wear loss, friction coefficient, and specific wear rate values. The model showed satisfactory explanatory power and reliability in predicting the volume loss target. It also exhibited remarkable capability in predicting the friction coefficient and specific wear rate targets. The results of sample wear tests (MgZn2 under 15&#xa0;N) conducted to generate data not included in the dataset showed a high degree of agreement with the ML results. Sensitivity analyses confirmed that Load, Environment, Hardness, and Grain Size are the most influential factors in predicting wear behavior, further validating the model’s reliability and interpretability.</p>

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The regression analysis of dry - wet wear outcomes and materials properties of biodegradable MgCu and MgZn, made by P/M, using machine learning models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月24日

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  • Rukiye Tekin Ünver,
  • Cihan Bayraktar,
  • Bilge Demir

摘要

Incorporating Cu and Zn into Mg as a biomaterial offers a unique opportunity to exploit their antibacterial performance and biodegradability. The main challenge in this area is understanding the ratio and effects of these elements. To achieve this, the present work, based on two separate studies, aims to develop a regression model and apply machine learning (ML) to predict the wear behaviors using the effects of Cu and Zn elements doped into Mg matrix at low ratios on wear and micro and nanostructure properties (Grain size, density, hardness, Crystallite Size, microstrain, dislocation density). The wear behavior of the samples was investigated under 5–20 N loads at a constant sliding speed of 42 mm/s. Auto Sklearn library was used to generate training models that accurately predict the wear loss, friction coefficient, and specific wear rate values. The model showed satisfactory explanatory power and reliability in predicting the volume loss target. It also exhibited remarkable capability in predicting the friction coefficient and specific wear rate targets. The results of sample wear tests (MgZn2 under 15 N) conducted to generate data not included in the dataset showed a high degree of agreement with the ML results. Sensitivity analyses confirmed that Load, Environment, Hardness, and Grain Size are the most influential factors in predicting wear behavior, further validating the model’s reliability and interpretability.