Carbon blacks are widely used as filler in industrial products to modify their mechanical, electrical and optical properties. For rubber products, they are the subject of a standard classification system relative to their surface area, particle size and structure. The electron microscope remains the most accurate means of measuring these characteristics on condition that boundaries of aggregates and particles are correctly detected. In this paper, we propose an image processing chain allowing subsequent characterization for automatic grading of the carbon black aggregates. Based on literature review, 31 features are extracted from TEM images to obtain reliable information on the particle size, the shape and microstructure of the carbon black aggregates. Then, they are used for training several classifiers to compare their results for automatic grading. To obtain better results, we suggest to use a cluster identification of aggregates in place of the individual characterization of aggregates.

Date Published: 30 April 2015
PDF: 6 pages
Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340G (30 April 2015); doi: 10.1117/12.2182866

Published in SPIE Proceedings Vol. 9534:
Twelfth International Conference on Quality Control by Artificial Vision 2015
Fabrice Meriaudeau; Olivier Aubreton, Editor(s)