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This paper investigated Al-based hybrid metal matrix composites (HMMCs) comprised of Al7075-(T6) base metal matrix doped with SiC+MoS 2 reinforced particles for high-strength, lightweight, and wear-resistant applications. Taguchi’s design of L 9 orthogonal array (OA) with three process variables having different levels (such as SiC particle (2.5, 3.5, and 4.5 wt.%), MoS 2 particle (1.5, 2.5, and 3.5 wt.%), and stirring speed (580, 600, and 620 rpm)) has been selected for fabrication of Al-based HMMCs via an advanced vacuum-assisted stir casting route. The controlled porosity level and dry sliding wear behavior (i.e. wear loss) of fabricated hybrid composites at an ambient temperature of 35°C have been conducted. Besides, metallographic evaluation was inspected through optical microscopy, high-resolution FESEM, and SEM-EDS techniques. Furthermore, experimental outcomes obtained from Taguchi’s design of experimentation were optimized by analysis of variance (ANOVA); and also validated through an artificial neural network (ANN) modeling with a network topology of 3-10-2. The results concluded that a homogeneous dispersion of reinforced particles over the base metal matrix (with controlled both abrasive and adhesive wear mechanisms of worn surfaces). Moreover, a significant reduction in porosity level (up to 29.41%) and wear loss (up to 4.94%) of the proposed hybrid specimen (S 4 ) as compared with an unreinforced specimen (S 0 ). In addition, the predicted values obtained from the ANN model were confirmed in close proximity to Taguchi’s experimental outcomes (with less percentage error), which in turn certified the authentication of the constructed ANN model for the Al-based HMMCs.
Singh et al. (Mon,) studied this question.