Nano-technology is the study of matter behaviour on atomic and molecular scale (i.e. nano-scale). In particular, carbon black is a nano-material generally used for the reinforcement of rubber compounds. Nevertheless, the exact reason behind its success in this concrete domain remains unknown. Characterisation of rubber nano-aggregates aims to answer this question. The morphology of the nano-aggregate takes an important part in the final result of the compound. Several approaches have been taken to classify them. In this paper we propose the first automatic machine-learning-based nano-aggregate morphology categorisation system. This method extracts several geometric features in order to train machine-learning classifiers, forming a constellation of expert knowledge that enables us to foresee the exact morphology of a nano-aggregate. Furthermore, we compare the obtained results and show that Decision Trees outperform the rest of the counterparts for morphology categorisation. © 2010 Springer-Verlag.
CITATION STYLE
López-de-Uralde, J., Ruiz, I., Santos, I., Zubillaga, A., Bringas, P. G., Okariz, A., & Guraya, T. (2010). Automatic morphological categorisation of carbon black nano-aggregates. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6262 LNCS, pp. 185–193). https://doi.org/10.1007/978-3-642-15251-1_15
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