Evaluation of stir cast AlSiC metal matrix composite by energy-dispersive spectroscopy and study of influences of milling parameters by particle swarm optimization

Aluminum silicon carbide (AlSiC) composite, weight-sensitive characteristic material having wide applications in electric vehicles and semiconductor industries usually made by stir casting of aluminum metal with SiC (7.5%) and Mg (2.5%) powders. The microstructure study followed by machining parameters optimization was done by particle swarm optimization (PSO) on the fabricated AlSiC composite. Initially, energy-dispersive spectroscopy (EDS) in combination with scanning electron microscopy (SEM) provided the investigation of near-surface elements and their quality of spread at various positions in order to develop a map of the AlSiC composite. From the results of EDS, the AlSiC composite is verified with Al—46.23%, Si—5%, Mg—3.3% (wt %). Finally, optimization study on machining parameters states that the feed rate influences reduction of both the surface roughness (SR) and machining time (MT) while the depth of cut increases the material removal rate (MRR) and spindle speed is causing a rise in temperature. This work delivers a mathematical model for each output response with its interactions. Significant process parameters for machining were identified, validated, and proposed.