A novel methodology for characterizing the morphology distribution of filler agglomerates in elastomer composites is presented based on laboratory-sourced X-ray tomography. Various feature extraction methods (via, e.g., image-processing filters, segmentation) and selection tools (Spearman's rank correlation coefficient) combined with K-means unsupervised clustering algorithm were developed for identifying the distinct morphological classes in model materials (carbon-filled ethylene propylene diene monomer rubber). The interest of this methodology was demonstrated by precisely differentiating the materials compounded with different processing parameters. For instance, in this example, thanks to this analysis, it was found that introducing the filler before the elastomer in internal mixer tends to favor more structured agglomerates.

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