COMBINED TECHNIQUES AND RELEVANT IMAGE PROCESSING FOR QUANTITATIVE STATISTICAL CHARACTERIZATION OF INCLUSIONS IN ELASTOMERS

Abstract

The properties of elastomeric materials are strongly influenced by the inclusions resulting from the ingredients and the elaboration process. A methodology is proposed to differentiate the inclusions harmful for fatigue (larger than a few micrometers) in elastomers according to their chemical nature, and to characterize them quantitatively with sufficient statistics. Three techniques are used and compared: digital optical microscopy (OM), scanning electron microscopy (SEM) associated with energy dispersive X-ray spectroscopy, and X-ray micro-computed tomography (μ-CT). Six materials are used to challenge the methodology. In addition to the usual metal oxides and carbon black agglomerates, three atypical types of inclusions are highlighted, generating specific detection difficulties. A relevant image analysis procedure is developed to automatically detect the inclusions from the acquired images, more objectively and accurately than with the classical thresholding methods. The morphology and the spatial distribution of the different inclusions populations are then determined. μ-CT is the most comprehensive and accurate method for classification and statistical characterization of inclusions. Furthermore, relevant data on the size distribution of inclusions can be obtained using backscattered electrons (SEM-BSE) or digital OM. SEM-BSE provides more accurate results than digital OM.