Abstract
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.
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López-de-Uralde, J. et al. (2010). Automatic Morphological Categorisation of Carbon Black Nano-aggregates. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15251-1_15
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DOI: https://doi.org/10.1007/978-3-642-15251-1_15
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