Fiber Break Segmentation in Composite Materials

Composite materials have many applications and are used in buildings, bridges, and vehicles. We wish to understand how damage propagates in these materials. Fiber breaks form within composite materials when tensile loads are applied to them. Segmentations of these fiber breaks are often needed to study the damage propagation within the material, but such segmentations can be hard to obtain in some materials. In this study, we utilize interactive machine learning to create a pipeline for fiber break segmentation in synchrotron radiation computed tomography images. This method does not provide a single model for fiber break segmentation, but a framework that can be used to obtain new models in new scenarios. We use the open-source IML software RootPainter3D [Smith et al., 2021] and adapt it for composite materials. We propose to diminish the cold start problem by including preliminary segmentations and present an algorithm to obtain preliminary segmentations of fiber breaks. Our method was successful and allowed us to obtain fiber break segmentations with a Dice score of up to 0.95 during the interactive annotation procedure. We perform user tests of the pipeline and get a usability SUS score of 0.85. Current models fail to predict fiber break development accurately. We study the hypothesis that the microstructure of the material influences the development, by using the SRCT in-situ images and the labels obtained with IML to predict fiber breakage at later time steps. We did not find conclusive evidence that microstructure influences fiber break formation.