Neural Network Based Determination of the Degree of Fiber Mixing in Hybrid Yarns and Composites

Abstract:

A deep understanding on the intermixing of components in hybrid yarn or composite structures is decisive in order to develop hybrid structures with desired properties. This paper presents the development of a versatile procedure for the determination of the degree of fiber mixing in yarns and composites based on microscopy images auto-segmented by a neural network. The procedure is based on the quantification of blend irregularity values and blend homogeneity. For this purpose, functions of spatial point patterns analysis have been used to investigate the blend uniformity of yarn and composite cross sectional areas. The results show that the trained neural network model for segmentation of images has an accuracy of 92 %, indicating that the method is capable of accurately assessing the location of fibers in hybrid struc-tures. The results of the spatial point patterns analysis reveals a correlation between the blend value and the properties of yarns and composites. The proposed method provides a fast and reliable way to evaluate the hybrid structures, which could be used as a tool for quality control and process optimization.

• Periodical: Key Engineering Materials (Volume 978)
• Pages: 149-153
• DOI: https://doi.org/10.4028/p-x2kEu5
• Online since: March 2024
• Authors: Matthias Overberg*, Alexander Dams, Anwar Abdkader, Chokri Cherif
• Keywords: Dispersion, Hybrid Yarn, Image Segmentation, Intermixing, Neural Network

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