Journal of Textile Science & Engineering

Department of Textile and Technology, Institute of Production Science, Karlsruhe, Germany

Publications

• Mini Review   
  Textile Fabrics in an Optical Coherence Tomography Image Dataset
  Author(s): Kadir Ozlem*
  
  Since successful sorting of various materials is necessary for high-quality recycling, classification of material types is essential in the recycling industry. Wool, cotton, and polyester are the most frequently used fiber materials in textiles. It is essential to quickly and accurately identify and sort various fiber types when recycling fabrics. The burn test, followed by a microscopic examination, is the standard method for determining the type of fabric fiber material. Because it involves cutting, burning, and examining the fabric's yarn, this traditional method is time-consuming, destructive, and slow. With the help of deep learning and optical coherence tomography (OCT), we show that the identification procedure can be carried out in a nondestructive manner. A deep neural network is trained on the OCT image scans of fabrics made of wool, cotton, and polyester.. Read More»
  DOI: 10.37421/2165-8064.2022.12.498

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