Selective laser sintering (SLS) is a bed fusion additive manufacturing technology that facilitates rapid, versatile, intricate, and cost-effective prototype production across various applications. It supports a wide array of thermoplastics, such as polyamides, ABS, polycarbonates, and nylons. However, manufacturing plastic components using SLS poses significant challenges due to issues like low strength, dimensional inaccuracies, and rough surface finishes. The operational principle of SLS involves utilizing a high-power-density laser to fuse polymer or metallic powder surfaces. This paper presents a comprehensive analysis of the SLS process, emphasizing the impact of different processing variables on material properties and the quality of fabricated parts. Additionally, the study explores the application of machine learning (ML) techniques—supervised, unsupervised, and reinforcement learning—in optimizing processes, detecting defects, and ensuring quality control within SLS. The review addresses key challenges associated with integrating ML in SLS, including data availability, model interpretability, and leveraging domain knowledge. It underscores the potential benefits of coupling ML with in situ monitoring systems and closed-loop control strategies to enable real-time adjustments and defect mitigation during manufacturing. Finally, the review outlines future research directions, advocating for collaborative efforts among researchers, industry professionals, and domain experts to unlock ML’s full potential in SLS. This review provides valuable insights and guidance for researchers in regard to 3D printing, highlighting advanced techniques and charting the course for future investigations.

To improve the AlCoCrFeNi high entropy alloys’ (HEAs’) toughness, it was coated with different amounts of Cu then fabricated by the powder metallurgy technique. Mechanical alloying of equiatomic AlCoCrFeNi HEAs for 25 h preceded the coating process. The established powder samples were sintered at different temperatures in a vacuum furnace. The HEAs samples sintered at 950 °C exhibit the highest relative density. The AlCoCrFeNi HEAs model sample was not successfully produced by the applied method due to the low melting point of aluminum. The Al element’s problem disappeared due to encapsulating it with a copper layer during the coating process. Because the atomic radius of the copper metal (0.1278 nm) is less than the atomic radius of the aluminum metal (0.1431 nm) and nearly equal to the rest of the other elements (Co, Cr, Fe, and Ni), the crystal size powder and fabricated samples decreased by increasing the content of the Cu wt%. On the other hand, the lattice strain increased. The microstructure revealed that the complete diffusion between the different elements to form high entropy alloy material was not achieved. A dramatic decrease in the produced samples’ hardness was observed where it decreased from 403 HV at 5 wt% Cu to 191 HV at 20 wt% Cu. On the contrary, the compressive strength increased from 400.034 MPa at 5 wt% Cu to 599.527 MPa at 15 wt% Cu with a 49.86% increment. This increment in the compressive strength may be due to precipitating the copper metal on the particles’ surface in the nano-size, reducing the dislocations’ motion, increasing the stiffness of produced materials. The formability and toughness of the fabricated materials improved by increasing the copper’s content. The thermal expansion has increased gradually by increasing the Cu wt%.

In this study, we enhanced the adhesion of graphene nanosheets to achieve homogeneous dispersion, consequently improving the electrical and thermal conductivity, coefficient of thermal expansion, and corrosion resistance with an aluminum matrix containing up to 1.5 wt. % graphene. First, 2.5 wt. % Al₂O₃ and varying ratios of graphene up to 1.5 wt. % were coated with 5 wt. % silver nanoparticles to metalize their surfaces. Predetermined portions of coated alumina and graphene were mixed with Al/10 wt. % Cu powder for 45 h. Mixed samples were compacted under 600 MPa and sintered at 565 °C in a vacuum furnace for 60 min with a low heating rate of 2 °C/min. The strengthening effect of the added materials on the density, microstructure, electrical and thermal conductivities, thermal expansion, and corrosion behavior of aluminum were investigated. Excellent adhesion and homogeneous dispersion of the investigated reinforcements were achieved. Three phenomena were observed: (1) an improvement in the densification, electrical and thermal conductivity, thermal expansion, and corrosion rate by adding 10 wt. % Cu to the aluminum matrix; (2) deterioration of the properties of Al/10 wt. % Cu with the addition of 2.5 wt. % alumina nanoparticles; and (3) improved properties with the addition of graphene nanosheets up to 1 wt. % and a decrease in property values beyond 1.5 wt. % graphene content due to the formation of agglomerations and pores in the metal matrix.

In this research, 10, 15, 20, and 25 wt.% of WC-TiC-Co particles were coated with a nano-particles layer of copper by the electro-less deposition technique. All the composites were compacted at 900 MPa, and then sintered at 1000 and 1100℃ for 120 min in a hydrogen atmosphere furnace. Nano-composites, which sintered at 1100℃ exhibit relative density higher than those sintered at 1000℃. To investigate the crystal structure and chemical composition of the WC-TiC-Co powders, XRD was used. SEM and EDAX analysis for the sintered samples were performed to investigate the microstructure and morphology of the composites. The relative density, hardness, electrical conductivity and yield stress based on the results of the hardness values were studied. The results revealed that the relative density, hardness, and the yield stress based on the results of the Vickers hardness were improved by increasing the WC-TiC-Co content. On the other hand, the electrical conductivity was decreased by increasing the WC-TiC-Co content.