Prediction of wear performance of ZK60 / CeO2 composites using machine learning models
Author:
Publisher
Elsevier BV
Subject
Surfaces, Coatings and Films,Surfaces and Interfaces,Mechanical Engineering,Mechanics of Materials
Reference108 articles.
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1. The Prediction of Wear Depth Based on Machine Learning Algorithms;Lubricants;2024-01-26
2. Experimental Investigation of the Influence of Various Wear Parameters on the Tribological Characteristics of AZ91 Hybrid Composites and Their Machine Learning Modeling;Journal of Tribology;2024-01-22
3. Machine learning enabled prediction of tribological properties of Cu-TiC-GNP nanocomposites synthesized by electric resistance sintering: A comparison with RSM;Journal of Materials Research and Technology;2024-01
4. Designing high elastic modulus magnesium-based composite materials via machine learning approach;Materials Today Communications;2023-12
5. Microstructural, mechanical, tribological, and corrosion behavior of ultrafine bio-degradable Mg/CeO2 nanocomposites: Machine learning-based modeling and experiment;Tribology International;2023-12
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