Modeling of mechanical and essential properties of nanocomposites

Predictive modeling and optimization of the desired mechanical performance and other properties of nanocomposites are possible using various conventional experimental designs, regression modeling, optimization methods, and other modern machine learning modeling techniques. Modeling of material properties helps to minimize the number of experimental trials performed by varying the input factors/process parameters. The quantitative analysis of the statistical factors and impacts on the mechanical performance and physical properties of the materials need to be analyzed. Moreover, the generation of predictive model equations and subsequent validation of the prediction accuracy, adequacy, and suitability of the derived models are desirable. This chapter presents a discussion on the widely used analytical, predictive modeling, and experimental design optimization approaches for the properties of polymeric nanocomposites and highlights examples of the modeling involving the effect of the numerous input parameters, including volume/weight fraction of nano-reinforcements/fillers, etc. on the mechanical properties of polymer nanocomposites. A classification of different modeling techniques and design tools based on size scale are given with their potentials. Special emphasis is given to discussions on the numerical modeling and optimization processes like the molecular dynamics modeling, finite element modeling, etc. and their different approaches. The chapter closes with a brief discussion on the future mechanisms that should be explored for improving the properties and performance of polymeric nanocomposites through the advances in the modeling and simulation.