| description abstract | Magnesium (Mg) and its alloys provide a desirable mixture of characteristics, including minimal density and an excellent strength/weight ratio. Nevertheless, these materials have limitations in relation to their thermal conductivity, wear and corrosion resistance, among various other attributes. The limits described above place restrictions on the use of these alloys in various applications. Currently, various methods are being employed to efficiently address and alleviate those limitations through the utilization of composite materials. The incorporation of micro/nanosized elements has been utilized to elevate the properties of Mg. Various methods are utilized to provide a homogeneous dispersal of reinforcement throughout the matrix, resulting in the production of magnesium metal matrix composites (MgMMCs). The use of MgMMCs has experienced a notable rise across many sectors such as aerospace, defense, automotive, and biomedical. This may be attributed to their exceptional attributes, which consist of enhanced specific strength, reduced weight, and congruence with biological systems. The current study objective is to perform an exhaustive examination of the different reinforcements employed in the fabrication of MgMMCs and their impact on mechanical and tribological characteristics. Furthermore, the study presented in this paper showcases the development of prediction models for the wear properties of MgMMCs through the utilization of diverse machine learning approaches. | |
