Journal of Textile Science & Engineering

Potential applications in human computer interaction, object detection, and human motion recognition, soft pressure sensors have garnered a lot of attention over the past ten years. However, the complicated and time-consuming steps limit their mass production and availability to end users. A significant obstacle is also the scalability of the working range for various applications. As a result, this work proposes a fast, labour-free, and scalable manufacturing method for capacitive-based soft pressure sensors with a wide working range and high sensitivity. By altering production parameters in accordance with specific application requirements, the novel manufacturing technique makes it possible to alter the properties of sensors. The dielectric layers of the proposed sensor are made of thermoplastic polyurethane (TPU) sheets, and the electrode is made of conductive knit fabric. In order to capture low pressures of less than 1 kPa, the novel method makes it possible to create scalable air gaps between dielectric layers and electrodes. The working range of sensors up to 1000 kPa is also increased when multi-layer TPU sheets are used. The proposed technology is successfully used to create a variety of sensor mats for a variety of uses, including interactive gaming mats for children and improved gesture and shape recognition.

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.