Using Data Science to Evaluate Nano-Reinforced Epoxy Surfaces

Toughened composites reinforced with nanofillers show improved mechanical performance such as increased abrasion resistance, fracture toughness, and fracture energy. The degree of these improvements is influenced by the degree of dispersion of the nanofillers which can be analyzed using force microscopy (AFM), a technique that allows for mapping the local height and elastic modulus of a surface. However, current AFM apparatuses can only measure a narrow range of moduli according to the type of tip, which complicates the full-field measurement of moduli in nanocomposites with nanosilica (~72 GPa) embedded in epoxy (0.1 – 5 GPa). Moreover, height mapping can only visualize filler particles exposed at the surface. These limitations make it challenging to determine the 3D location of nanoparticles near the surface of a composite. To overcome these limitations of conventional AFM, we used a combination of data science, micromechanics, and experimental data from AFM to locate the centroidal position of nanosilica (NS) particles relative to the surrounding epoxy surface. Using finite element simulations, a theoretical dataset of modulus values as a function of particle position relative to the epoxy surface was created as a training set. Bayesian optimization determines the “best” particle position that results in minimum error between simulated and experimental modulus contours. The algorithm returns the 3D position of the fully or partially embedded NS particle relative to the epoxy surface. The algorithm has shown the ability to partially produce simulated modulus contours that resemble the experimental modulus contours.