Review of micromechanical homogenization models and comparison with experimental data

A significant challenge in the development of nanocomposites is the frequent discrepancy between theoretically predicted elastic properties and experimental results, which hinders their reliable design. This review explores manufacturing methods and predictive models for evaluating the elastic properties of nanocomposites. The purpose is to assess under which condition analytical models for nanocomposites effectively predict the elastic properties. We aim to identify best practices in both traditional and additive manufacturing to obtain better materials and to determine when analytical methods can be reliably used. By analyzing several studies, we assessed manufacturing processes and evaluated analytical models like the Mori-Tanaka and Eshelby methods against experimental data. We focused on how factors such as filler dispersion quality, functionalization, and manufacturing conditions influence the stiffness of nanocomposites, particularly Young’s modulus. Achieving the mechanical properties predicted by analytical models requires effective filler dispersion and functionalization. Techniques such as sonication and chemical modification enhance material properties by reducing nanoparticle aggregation and improving load transfer between filler and matrix. Analytical models closely predicted the outcomes when these best practices were followed, while deviations occurred when fillers were poorly dispersed or when manufacturing introduced voids. Employing best manufacturing practices is essential for optimizing the mechanical properties of nanocomposites and ensuring the reliability of analytical models. Understanding the limitations and applicability of these models allows for effective prediction and enhancement of elastic properties, which is vital for advanced applications in industries like automotive and aerospace.