Methodology to classify the shape of reinforcement fillers: optimization, evaluation, comparison, and selection of models

This work applies statistical analysis, and classical and advanced machine learning algorithms to classify 7714 aggregates into four categories according to their shape. The aggregates under study are obtained from several grades of carbon black: Vulcan XC 605, Vulcan XC 72, CSX 691, Printex 25, N990, and N762. The classification of the shape is of great significance in order to explain and predict the end-use properties of the composite materials, like mechanical properties. The proposed approach combines transmission electron microscopy and automated image analysis to obtain the dataset of the morphological features that defines the shape of the aggregate, and statistical analysis and machine learning techniques to create the classification models using feature transformation and reduction, parameter tuning, and validation methods in order to achieve robust classification models. The best result is obtained from a classification tree based on evolutionary algorithms with a principal component analysis-based feature reduction that reports an acceptable accuracy, thereby validating both the final chosen model and the methodology.