Regression Analysis of Dry - Wet Wear outcomes and materials properties of Biodegradable MgCu and MgZn, made by P/M, using Machine Learning Models
Yazarlar (3)
Dr. Öğr. Üyesi Rukiye TEKİN ÜNVER
Karabük Üniversitesi, Türkiye
Dr. Öğr. Üyesi Cihan BAYRAKTAR
Karabük Üniversitesi, Türkiye
Prof. Dr. Bilge DEMİR
Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
|
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Applied Physics A Materials Science and Processing |
| Dergi ISSN | 0947-8396 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2025 |
| Cilt No | 131 |
| Sayı | 4 |
| DOI Numarası | 10.1007/s00339-025-08452-8 |
| Makale Linki | https://link.springer.com/journal/339 |
| Özet |
| 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. |
| Anahtar Kelimeler |
| Powder metallurgy | Wear test | Biodegradable | Regression analysis | Machine learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 3 |
| SCOPUS | 1 |
| Google Scholar | 4 |