Use of Classification Trees and Rule-Based Methods to Predict Shapes of Nano-Aggregates of Reinforcement Fillers

While manufacturing composite materials, reinforcement fillers inevitable collide with each other and subsequently they congregate to aggregates with different shapes. The shape of these nanoparticles aggregates are of great significance for the mechanical material properties and in consequence, knowing the percentage of aggregates of each shape within of a specific group of shapes can give an idea of the final properties of the material. This work classifies aggregates, a new dataset of 5713 carbon black aggregates gathered based on transmission electron microscopy image processing, using several classification trees and rule-based methods. Based on these methods several models are built, trained and tested to perform the classification. And then, the most reliable and accurate model to classify aggregates is selected, obtaining a testing accuracy of the 74.57% according to their shape.