In-situ quantitative tracking of micro-crack evolution behavior inside CMCs under load at high temperature: A deep learning method

Abstract

Micro-cracks play an extremely significant role in the failure of ceramic matrix composites (CMCs). In-situ quantitative tracking of micro-crack evolution behavior remains a great challenge. In this study, a deep learning micro-crack segmentation method based on the generative adversarial network was developed to quantitatively characterize the micro-crack evolution behavior of CMCs under tensile load at high temperature with in-situ X-ray computed micro-tomography (μCT). This method realizes a precise and robust segmentation of the micro-cracks in μCT images with low gray contrast and image quality caused by noise and artifacts. The crack parameters including crack opening area, crack opening displacement and crack volume of each micro-crack were obtained. For the most micro-cracks, the values of these parameters exhibited no obvious increase during the loading process. Noticeable increasing of crack parameters occurred in some large micro-cracks at high load levels. The evolution of each micro-crack was also tracked to further identify the critical damages dominating the eventual failure. The four largest cracks exhibiting a more dramatic volume evolution were captured as the main cracks. These main cracks all originated from the preexisting micro-cracks and were usually formed by the coalescence of adjacent small micro-cracks with increasing tensile loads. The ultimate fracture was demonstrated to take place near these main cracks.