Critical Factors Governing the Frictional Coefficient in Mg Alloys—Learn From Machine Learning
ABSTRACT Data‐driven methods are emerging as a promising approach in discovering the correlation between tribological properties, composition, and mechanical properties of engineering materials. In the present study, the capability of several ML models in predicting the coefficient of friction (COF)...
Saved in:
| Main Authors: | Negar Bagherieh, Moslem Noori, Dongyang Li, Meisam Nouri |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-05-01
|
| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.70140 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluating the influence of vibrational assistance friction stir processing on improving the wear and mechanical properties of hybrid composite AZ31 Mg alloy
by: Zhaoyang Zuo, et al.
Published: (2025-05-01) -
Transferability of Model-Based Static Coefficient of Friction
by: Jonathan Schanner, et al.
Published: (2024-10-01) -
Effect of Elastic Strain Energy on Dynamic Recrystallization During Friction Stir Welding of Dissimilar Al/Mg Alloys
by: Faliang He, et al.
Published: (2025-05-01) -
Impact of High Entropy Alloy Reinforcements in Friction Stir Processed Materials - A Detailed Review
by: Nouranga K. N., et al.
Published: (2025-09-01) -
A critical review on the performance and microstructural characteristics of materials fabricated through friction stir additive methods and deposition techniques
by: Yaknesh S, et al.
Published: (2024-11-01)