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, among other fiber materials. The ability of the developed deep learning models to classify various types of fabric fiber materials is demonstrated by the results that we provide. OCT imaging and deep learning, according to our findings, enable the nondestructive identification of various fiber material types with high recall and precision. This novel method can be used automatically in recycling plants to sort wool, cotton, and polyester fabrics because OCT and deep learning can classify the material type.
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Journal of Textile Science & Engineering received 1008 citations as per Google Scholar report
