Machine learning models based wear performance prediction of AZ31/TiC composites

TL;DR

This paper uses machine learning to predict the wear performance of magnesium composites reinforced with TiC particles, showing high accuracy in predictions.

Contribution

The novel use of machine learning algorithms to model and predict wear performance in AZ31/TiC composites with high accuracy.

Findings

AZ31/15 vol% TiC composite showed a refined grain structure (~8 μm) and increased hardness (116 HV).

Gradient boost ML algorithm achieved high predictive accuracy (R² = 0.9987) for wear performance.

The study established a benchmark for AI-driven modeling in magnesium-based composites.

Abstract

This study presents the fabrication of AZ31 magnesium matrix composites reinforced with 5, 10 and 15 vol% TiC particles using the Friction Stir Processing (FSP) technique and evaluates their wear behavior under varying loads (10–50 N) and sliding speeds (75–225 mm/s). The incorporation of TiC significantly enhanced the microstructural and mechanical properties of the composites. In particular, the AZ31/15 vol% TiC composite exhibited a refined grain structure with an average grain size of ~ 8 μm, compared to ~ 60 μm in the unreinforced AZ31 alloy. The same composite also demonstrated a substantial increase in hardness from 62 HV (base alloy) to 116 HV, highlighting the effectiveness of TiC reinforcement in improving strength. A key innovation of this work is the application of five machine learning (ML) algorithms, trained on experimental data using input features such as load, sliding speed and reinforcement content, to model and predict wear performance. After rigorous hyperparameter optimization, the Gradient boost algorithm achieved the highest predictive accuracy (R² = 0.9987), with errors falling within the range of experimental uncertainty. The study further includes residual analysis and computational efficiency assessment, supporting interpretable and robust AI-driven modeling. This integrated experimental-ML approach establishes a new benchmark for predictive modeling and data-driven material design in magnesium-based metal matrix composites.

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