Date of Award
Master of Science
The morphology of fibers (e.g. spatial uniformity, orientation, and length) plays a decisive role in determining the material properties or fabrication quality of fiber-reinforced nanocomposites. Hence, determining the morphology becomes a very critical issue in the field of nanocomposite quality control. The conventional way of quality inspection is to take the scanning electron microscopic (SEM) images of the cross-section of composite material and do the visual checking of these SEM images to evaluate the nanofiber alignment and length distribution. But this type of inspection is often subjective, inaccurate and time consuming. Moreover, the extremely small size of nanofibers makes the quality control evaluation process very tedious. This research attempts to fill this gap and presents an image based automatic method to leverage the morphology analysis of fibers embedded into the hosting material. The proposed method consists of two broad steps, namely fiber segmentation and quantitative analysis of segmented fibers. One particular challenge in fiber segmentation step is to segment the overlapped fibers from the SEM image, especially in a very high density fiber environment. To handle this issue, we develop four methods, namely, the simple Hough Transform, partitioning Hough Transform, gradient based Hough Transform, and density based clustering (DBSCAN) approach, to automatically identify the fibers from the SEM images. In the second step we extract the fiber morphology based on the segmented fibers and perform the quantitative analysis. We compare the fiber extraction result among the methods. The methods can extract up to 97% fibers from the SEM image in the very high fiber density environment, which expedite to acquire a reliable characterization of fiber morphology. The performance of these methods are thoroughly evaluated and compared through simulation studies and real case studies.
Received from ProQuest
Md Fashiar Rahman
Rahman, Md Fashiar, "Extraction Of Fiber Morphology From Sem Images For Quality Control Of Fiber Reinforced Composites Manufacturing" (2018). Open Access Theses & Dissertations. 149.