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Bearing Aluminum-Based Alloys: Microstructure, Mechanical Characterizations, and Experiment-Based Modeling Approach.

Ahmed O Mosleh1, Elena G Kotova2, Anton D Kotov3

  • 1Mechanical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt.

Materials (Basel, Switzerland)
|December 11, 2022
PubMed
Summary

This study developed an artificial neural network (ANN) to predict mechanical properties of aluminum-based alloys for engine bearings. The ANN accurately models alloy composition for optimal tribological and mechanical performance.

Keywords:
aluminum alloysanti-friction materialsmaterial designmechanical propertiesmicrostructureneural network

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computational Materials Science

Background:

  • Engine bearing alloys require high mechanical and tribological properties due to start/stop systems and load variations.
  • Al-rich anti-friction alloys can be enhanced by incorporating additional elements to improve performance.

Purpose of the Study:

  • To develop an artificial neural network (ANN) for accurate modeling and prediction of mechanical properties in aluminum-based bearing alloys.
  • To optimize the chemical composition of these alloys for superior mechanical characteristics.
  • To investigate the influence of soft and solid phases on alloy mechanical properties.

Main Methods:

  • Casting and annealing of 198 unique aluminum-based alloys with varying compositions (Sn, Pb, Cu, Mg, Zn, Si, Ni, Bi, Ti, Mn, Fe, Al).
  • Microstructural analysis to identify soft (Sn-rich, Sn-Pb, Pb-Sn) and solid (Al2Cu) phases.
  • Testing of mechanical properties: ultimate tensile strength (σu), Brinell hardness (HB), and elongation to failure (δ).
  • Construction and optimization of an ANN architecture with two hidden layers and 20 neurons each.

Main Results:

  • The optimal ANN achieved high prediction accuracy: R=0.94, RMSE=3.5, AARE=1.0%.
  • Microstructural analysis confirmed the presence of beneficial soft and solid phases.
  • The developed alloys exhibited excellent tribological characteristics and met mechanical property requirements, with maximum values: σu = 197 ± 7 MPa, HB = 77 ± 4, and δ = 20.3 ± 1.0%.
  • Model validation demonstrated accurate prediction of new alloy characteristics (R ≥ 0.97, RMSE = 1-2.65, AARE < 10%).

Conclusions:

  • The ANN model effectively predicts mechanical properties, enabling optimization of aluminum-based bearing alloy compositions.
  • The study highlights the critical role of soft (Sn, Pb) and solid (Cu) phases in achieving desired mechanical performance.
  • This approach facilitates the development of advanced anti-friction materials for demanding engine applications.