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Automatic Image Segmentation of CT Data from the Low Velocity Impact Tests of CFRP Composites



In this work, the role of image segmentation in the analysis of the micro-CT data for the low velocity damage assessment in carbon fiber reinforced polymer (CFRP) composites is discussed. A novel automatic image segmentation method based on the unsupervised learning approach and the Kullback–Leibler divergence is presented. The method has been successfully applied to identify and isolate impact damage in the CFRP composites subjected to the low velocity impact. The results show that the method enables not only an automatic image segmentation, but also delivers a statistics based rigorous damage threshold.


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