Data on the coefficient of friction and its prediction by a machine learning model as a function of time for open-cell AlSi10Mg-Al2O3 composites with different porosity tested by pin-on-disk method
Author:
Funder
Bulgarian National Science Fund
European Regional Development Fund
Publisher
Elsevier BV
Subject
Multidisciplinary
Reference6 articles.
1. Production and tribological characterization of advanced open-cell AlSi10Mg-Al2O3 composites;Kolev;Metals,2023
2. Casting protocols for the production of open cell aluminum foams by the replication technique and the effect on porosity;Elizondo Luna;J. Vis. Exp.,2014
3. Optimization of wear behaviour on AA6061/Al2O3/SiC metal matrix composite using squeeze casting technique – statistical analysis;Natrayan;Mater. Today,2020
4. G02 Committee Test Method for Wear Testing with a Pin-on-Disk Apparatus,2017
5. XGBoost: a scalable tree boosting system;Chen,2016
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1. An examination of the flow of Magnetohydrodynamic viscous fluid and heat transfer between penetrable disks using the Variation Iteration Method and Finite Element Method;International Journal of Thermofluids;2024-02
2. Open-Cell AlSn6Cu-SiC Composites: Fabrication, Dry-Sliding Wear Behavior, and Machine Learning Methods for Wear Prediction;Materials;2023-09-14
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