In carbon black reinforced rubbers, the shape of the carbon black aggregates has a very signiﬁcant inﬂuence on the ﬁnal properties of the material. Accurately classifying these particles by shape has proven to be difﬁcult, but the results of the classiﬁcation would allow to model the ﬁnal mechanical properties of the material. In this work, 21 features are measured from 7714 isolated ﬁller images obtained from TEM images and used for the classiﬁcation. Support vector machines and artiﬁcial neural network techniques are used to classify the aggregates using a methodology to tune the algorithm parameters to improve the performance of the models. Also, genetic algorithms are applied to make a feature selection in order to get most robust and accurate models. It is demonstrated that the combination of genetic algorithms with support vector machines and artiﬁcial neural network improves the classiﬁcation results and minimizes the complexity of the resulting model. Graphical abstract Keywords Reinforced rubber blends · Digital image analysis · Aggregate shape classiﬁcation · Artiﬁcial neural networks · Support vector machines · Genetic algorithms 1 Introduction Communicated by V. Loia. Rubber blends are often reinforced using carbon black (CB), which consists of carbon-based spherical nanoparticles fused Extended author
Soft Computing – Springer Journals
Published: May 31, 2018
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