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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Artificial Intelligence

Background:

  • AZ31 magnesium alloys are lightweight but have limitations in wear resistance.
  • Titanium Carbide (TiC) particles can enhance magnesium matrix composites.
  • Predictive modeling for composite wear is crucial for material design.

Purpose of the Study:

  • To fabricate AZ31/TiC composites using Friction Stir Processing (FSP).
  • To evaluate the wear behavior of these composites under various conditions.
  • To develop and validate machine learning models for predicting wear performance.

Main Methods:

  • Friction Stir Processing (FSP) for composite fabrication.
  • Wear testing under controlled loads and sliding speeds.
  • Development and optimization of five machine learning algorithms for wear prediction.

Main Results:

  • TiC reinforcement significantly improved microstructural and mechanical properties, increasing hardness.
  • The AZ31/15 vol% TiC composite showed a refined grain structure (8 μm vs. 60 μm).
  • Gradient boost machine learning model achieved high predictive accuracy (R² = 0.9987) for wear performance.

Conclusions:

  • FSP is effective for creating AZ31/TiC composites with enhanced properties.
  • Machine learning models, particularly Gradient boost, can accurately predict composite wear.
  • An integrated experimental-ML approach offers a powerful tool for data-driven material design.