The effects of nano-additives on the mechanical, impact, vibration, and buckling/post-buckling properties of composites: A review

• Main Authors: Shan L., Tan C.Y., Shen X., Ramesh S., Zarei M.S., Kolahchi R., Hajmohammad M.H.
• Other Authors: 57219360208
• Format: Article
• Published: Elsevier Editora Ltda 2024
• Subjects: Graphene; Mechanical properties; MWCNTs; Nanocomposite; Nanoparticle; Thermal behavior; Additives; Buckling; Chemical bonds; Glass ceramics; Machine learning; Military applications; Multiwalled carbon nanotubes (MWCN); Nanocomposites; Nanoparticles; Tensile strength; Thermal conductivity; Vibrations (mechanical); Automotives; Fatigue strength; Impact buckling; Impact vibration; Mechanical impacts; Multi-walled-carbon-nanotubes; Nano additives; Postbuckling; Properties of composites; Thermal behaviours
• Institution: Universiti Tenaga Nasional

This study presents a review of the effect of nano-additives in improving the mechanical properties of composites. Nano-additives added to composites, also termed nanocomposites, have promising applications in aerospace, medical, biomedical, automotive, and military. The nanoparticles alter either the surface, bulk, or both, depending upon the process, and dramatically change the thermal conductivity, tensile strength, flexural strength, fatigue strength, impact resistance, vibration resistance, buckling, post-buckling, nanoparticles surface modification, and application of machine learning as well as optimization methods in nanocomposite materials. Such transformations in composite materials are extensively studied by researchers and positive implications are successfully deployed in various applications. Interestingly, the recent findings revealed that the weak chemical bonding between the fiber and matrix phase is the main reason for delamination, however, by the addition of nanoparticles, the chances of delamination are reduced even under excessive loading. Graphene and multi-walled carbon nanotubes (MWCNTs) are the most excessively reported nanomaterials for enhancing the vibration behavior and energy absorption capacity, as well as decreasing the adverse effects due to porosity within the composite structure. Also, machine learning techniques showed to be a promising way to further improve the mechanical properties while reducing the total cost of the fabrication process by predicting and providing optimum fabrication characteristics with acceptable accuracy compared to realistic conditions. � 2023 The Author(s)