Multi-objective parametric optimization on the EDM machining of hybrid SiCp/Grp/aluminum nanocomposites using Non-dominating Sorting Genetic Algorithm (NSGA-II): Fabrication and microstructural characterizations

Abstract

AbstractIn this study, different input parameters for electric discharge machining (EDM) are examined in order to revise the distinctiveness of EDM for machining aluminum-based hybrid metal matrix composites (MMCs). The versatility of hybrid aluminum MMCs makes them very popular and sought after in the automotive, aerospace, marine, and space industries. In this article, an optimized process parameter setting for hybrid MCCs machining with an EDM machine is determined that have silicon carbide (SiCp) and graphite (Grp) particles added as reinforcement materials in varying amounts (Al–0.7Fe–0.6Si–0.375Cr–0.25Zn/10 wt%SiC/3 wt%Gr–MMC, Al–0.7Fe–0.6Si–0.375Cr–0.25Zn/15 wt%SiC/5 wt%Gr–MMC, and Al–0.7Fe–0.6Si–0.373Cr–0.25Zn/20 wt%SiC/8 wt%Gr–MMC). The stir casting method was used to prepare these hybrid aluminum MMCs (3 samples). A study of surface roughness (SR) and material removal rate (MRR) was conducted to examine the effects of dominant parameters. An experiment is planned using a central composite rotatable design (CCRD) of response surface methodology (RSM). It is possible to predict MRR and SR with 95% degree of accuracy by utilizing the quadratic model. Non-dominating Sorting Genetic Algorithm-II was employed to solve “mathematical models” for multi-objective optimization of output response characteristics. The scanning electron microscope (SEM) images of the tool and workpiece materials show that the recast layer has been formed on the tool face and the surface of the machined work-piece. Based on the results, it was determined that an optimal value of MRR (2.97 g·min−1) was obtained at 90 µs, 30 µs, 7.0 V, and 14 A asPon,Poff, gap voltage, and peak current, respectively. As a result of the findings, the SR is reciprocally proportional toPon, and the SR is commensurate withPoff. It was determined that the optimal value of SR (2.41 µm) could be attained at 30 µs, 52 µs, 6.0 V, and 12 A as thePon,Poff, gap voltage, and peak current, respectively. For an optimal set of response variables,Poncan be specified as 30 µs,Poffas 30 µs, gap voltage as 6 V, and peak current as 14 A as process parameters for MRR and SR. The SEM images of the tool material and the workpiece material clearly demonstrate a recast layer formed on the tool face and the machined surface of the workpiece. The optical microscopy analysis reveals a uniform distribution of SiCpand Grpparticles in the Al–0.7Fe–0.6Si–0.375Cr–0.25Zn matrix. In addition to recast layers and machined surfaces, EDS analysis reveals the deposition of tool material on the surface of the workpiece. The composites fabricated may replace materials in many of these applications where “friction” is a significant factor.