Artificial neural networks for predicting mechanical properties of Al2219-B₄C-Gr composites with multireinforcements

Abstract

Artificial neural networks (ANNs) have gained prominence as a reliable model for clustering, grouping, and analysis in various domains. In recent times, machine learning (ML) models such as ANNs have proved to be on par with traditional regression and statistical models in terms of performance and usability. This study focuses on the fabrication of multicomponents-reinforced composites (Boron carbide (B4C) and Graphite (Gr)) using the stir casting technique. The addition of Magnesium to the melt enhances the wettability of B4C and Gr particles within the matrix. The microstructure and mechanical properties of the resulting Al-Mg-metal matrix composites (MMCs) are analyzed. Scanning electron micrographs reveal that B4C and Gr particles were uniformly dispersed in the matrix. X-Ray diffraction analysis confirmed the dispersion of the strengthening. The mechanical properties, including hardness, tensile, compressive, and impact strength, increased with the increase in B4C and Gr wt.%. As the percentage of B4C and Gr reinforcement wt.% increased, the load on the matrix reduced and its load-bearing capacity improved. The strain field generation rate also increased with an increase in B4C and Gr in the matrix, resulting in enhanced mechanical properties. The ANN analysis further confirmed that B4C was the more significant contributor to the mechanical properties of the composites.