In the current study, the effect of different B4C additions (0, 2.5, 5, and 10 wt%) on the microstructural, solidification behavior, mechanical, and tribological properties of Al-20%Mg2Si composite were studied by means of scanning electron microscopy (SEM) equipped with energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), Vickers hardness, tensile, and dry sliding wear tests. The cooling curve thermal analysis (CCTA) approach was utilized to monitor the influence of B4C particles on the solidification behavior of Al-20%Mg2Si composite. The results revealed that the addition of B4C particles up to 10 wt% reduced the nucleation temperature (TN) and growth temperature (TG) of the primary Mg2Si phase. Moreover, the proper amount of B4C added to Al-20%Mg2Si composite has a significant effect on the microstructural alteration, mechanical, and tribological properties of the composite. The mean size of primary Mg2Si in Al-Mg2Si composite was 47 μm, in which with the addition of 5 wt% B4C, the particle size decreased to 33 μm. The highest UTS (217 MPa) and El% (7%) was achieved in Al-20%Mg2Si-5%B4C hybrid composite. The cast Al-20%Mg2Si composite revealed the brittle mode of fracture with some cleavage characterization, in which with the addition of 5%B4C, the fracture mode altered to a more ductile fracture. The wear results revealed that the Al-20%Mg2Si-5%B4C hybrid composite has the highest wear resistance with the lowest wear rate (0.46 mm3/Km) and friction coefficient (µ = 0.52) under 20 N applied load compared to other fabricated composites with mild abrasion as the governed wear mechanism.

In current study; the effect of various Gadolinium (Gd) additions on the microstructure and sliding wear behaviour of Al-15%Mg2Si composite before and after the hot extrusion process was examined. Optical microscopy (OM), scanning electron microscopy (SEM) equipped with EDX facility and X-ray diffraction (XRD) were used to characterize the microstructure. The results showed that with addition of 1.0 wt.% Gd to Al-15%Mg2Si composite, the primary Mg2Si particles size reduced from 44 µm to 23 µm and its morphology altered from dendritic to polygonal shape. Further refinement of primary Mg2Si particles was achieved after conducting hot extrusion which resulted in a decrease in its size to 19 µm with a transfer to near-spherical morphology. The Vickers hardness value increased from 55.6 HV in the as-cast and unmodified composite to 72.9 HV in the extruded 1.0% Gd modified composite. The wear test results revealed that composites treated with Gd possess higher wear resistance in comparison with those of without Gd. The highest wear resistance obtained with the lowest wear rates of 0.19 mm3/km and 0.14 mm3/km in the Al-15%Mg2Si-1.0% Gd before and after the hot extrusion, respectively. The high wear resistance of extruded Gd-modified Al-15%Mg2Si composite is due to the refinement of primary Mg2Si particles with uniform distribution in the composite matrix along with fragmentation of Gd intermetallic compounds.

Fused Deposition Modelling (FDM) is used widely in additive manufacturing technology. The research community is becoming increasingly concerned about FDM products' surface quality and dimensional accuracy. The design of the experimental methodology is based on the analysis of variance (ANOVA) of fractional factorial design. This analysis was used to study the effect of layer thickness, extrusion temperature, speed of deposition, and fill density on dimensional accuracy and surface roughness of the model developed by FDM. Polylactic Acid (PLA) material has been selected as an article-forming material. The results showed that layer thickness has an influential factor in determining surface roughness. Fill density (X and Z dimensions), layer thickness (Y dimension), and speed of deposition (Z dimension) are significant factors affecting dimensional accuracy. The influence of curvature on surface roughness was presented and discussed; however, , the minimum optimization point was not attained, Therefore, further experiments were required to reach it. In terms of optimizing dimensional accuracy, it was observed that lower levels of every factor except for fill density led to greater accuracy in the dimensions along X, Y, and Z. Consequently, fill density was optimized to enhance dimensional accuracy.