An improved dataset augmentation approach for deep learning-based XCT images segmentation in layered composite fabric

Abstract

Accurate 3D representations of multiphase layered composite fabric, including distinguish and label the pores, matrices, warp and weft yarns, can assist in understanding and ultimately improving composite material design performance. In this work, a new data enhancement algorithm is proposed to generate realistic-looking artificial learning datasets for expanding the datasets. Then, we demonstrate how supervised, the Swin Transformer approach can help to realize accurate segmentation of low-resolution and poor-contrast fabric datasets by populating databases with real and artificial learning datasets. Most of the pixels were correctly identified and the boundaries of the yarn could be identified clearly, except for some spots and a few merged boundaries. The work presented here provides an effective tool that can be widely used in semantic segmentation for various composite materials (woven composites, fiber-reinforced composites, short-fiber materials, etc.), which enables higher-precision reconstruction of fiber-composites and significant reduction in dataset processing time, and provides a route to generating artificial learning dataset.