Metal matrix composites (MMCs) are the new-generation advanced materials that have excellent mechanical properties, such as high specific strength, strong hardness, and strong resistance to wear and corrosion. All these qualities make MMCs suitable material in the manufacture of automobiles and aircraft. The machining of these materials is still difficult due to the abrasive nature of the reinforced particles and hardness of MMCs. The conventional machining of MMCs results in high tool wear and slow removal of materials, thereby increasing the overall machining cost. The nonconventional machining of these materials, on the contrary, ensures much better performance. This paper reviews various research works on the development of MMCs and the subsequent hybrid composites and evaluates their performances. Further, it discusses the influence of the process parameters of conventional and nonconventional machining on the performance of MMCs. At the end, it identifies the research gaps and future scopes for further investigations in this field.

Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) technique for developing complex features and geometries within the shortest possible time as per customer needs. Nowadays, the customization of biomedical parts is becoming possible due to the increasing accuracy and enhanced ability of FDM machines to control the process parameters. The control on surface quality of parts produced by FDM process is of prime concern for the researchers, which are induced due to the stair steps on sloping surfaces, and needs addressing. In addition, to meet customer demands rapidly, the FDM part build time must be reduced without much compromising the strength of the parts necessary for specific applications. In the same context, this paper presents the effect and control of FDM process parameters, i.e., layer thickness, raster angle, infill density and internal structure, on surface roughness, build time and compressive strength of developed biomedical implant parts. The face-centered central composite design is employed to consummate the experimental trials, and experimental data are used to establish an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the surface roughness, build time and compressive strength with respect to changes in FDM process parameters. At the end, whale optimization algorithm (WOA) has been applied to minimize surface roughness, minimize build time and maximize compressive strength simultaneously. Then, the optimal solutions obtained from WOA methodology have been compared with ANFIS predicted results. The results reveal that ANFIS-WOA methodology provides optimal combination of FDM process parameters accurately.