Wear Behavior Analysis and Gated Recurrent Unit Neural Network Prediction of Coefficient of Friction in Al10Cu-B<sub>4</sub>C Composites

Wear Behavior Analysis and Gated Recurrent Unit Neural Network Prediction of Coefficient of Friction in Al10Cu-B<sub>4</sub>C Composites

Aluminum-based metal matrix composites reinforced with B<sub>4</sub>C are advanced materials recognized for their exceptional combination of lightweight properties, high hardness, and superior wear resistance. These characteristics make them perfectly suited for applications demanding ex...

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Bibliographic Details
Main Authors: Mihail Kolev, Ludmil Drenchev, Veselin Petkov, Rositza Dimitrova, Krasimir Kolev, Boris Yanachkov
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Lubricants
Subjects:
Al10Cu-B<sub>4</sub>C composites
metal matrix composites
wear characteristics
dry-sliding friction
gated recurrent unit
neural network
Online Access:https://www.mdpi.com/2075-4442/13/1/6
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