This review provides a comprehensive assessment by bringing together current research on textile-based material design and production techniques in the field of wearable antenna technologies under thematic headings. The relationship between the electromagnetic properties of conductive and dielectric textile materials used in wearable antennas and the production methods of these materials has been systematically investigated. The effects of different production techniques such as bonding, embroidery, weaving and printing on antenna performance have been comparatively analyzed in terms of frequency shift, gain, bandwidth, bending strength and SAR (Specific Absorption Rate) values. In addition, the contributions of textile-based substrates such as denim, neoprene, polyester and jute, which are widely used in the literature, to the electromagnetic performance have been detailed. In this context, the potential offered by wearable antennas in various application areas (health, communication, energy harvesting, defense etc.) has been revealed and suggestions for future research have been presented. The review aims to contribute to the literature by systematically framing the current knowledge in the field.
Garments should maintain thermal balance of the body under various environmental conditions. In order to provide comfort for wearer, it is necessary for clothing to transmit water vapor from the body to the environment as fast as possible. In this study, water vapor permeability of single jersey knitted fabrics produced from different materials and different yarn and fabric properties were predicted with both linear regression and neural network. Results showed that material, yarn count and fabric density had an important effect on the water vapor permeability of the fabrics. Both linear regression and neural network were appropriate for predicting water vapor permeability of single jersey fabrics, but predicting performance of neural network was better. While R2 values of linear regression models were lower than 0.80; R2 values of neural network models can reach 0.86.
Denim fabrics woven with different raw materials (Tencel, Linen, Cotton/Hemp, Soybean, Modal/Cellulose Acetate), especially considering the use of sustainable materials were investigated. As well as the effects of 3/1 twill used in conventional denim, the effect of zigzag twill and Bedford cord weaves on various physical properties of fabrics were investigated. For this purpose, the surface (surface roughness and friction coefficients), handling (bending rigidity, drapeability and crease recovery angle) and permeability (air permeability, thermal resistance and moisture management performance) properties were examined. From the results, it was found that the weave structure was the most decisive structural parameter on all the physical properties of fabrics examined. The raw material properties have affected on different physical properties of fabrics in different manners. It was anticipated that the experimental results might contribute to the selection of structural parameters in denim fabric designs depending on usage areas and the season.
The durability and effectiveness of antibacterial agents are a major factors for consumer use. In this study, copper(I)oxide and copper(II)oxide<5 & micro;m particles were used as antibacterial agents. These particles were applied to cotton fabrics with five different structures of polycarboxylic acid crosslinkers such as CA, DL-malic, fumaric, itaconic, BTCA and also ethylmetacrylate via knife-over coating method and antibacterial properties were imparted. The aim is to impart permanent antibacterial properties to cotton fabric by using copper(I)oxide and copper(II)oxide<5 & micro;m particles with six different structures of cross-linkers and to compare the aid of cross-linkers in terms of the particles antibacterial properties. The best result for gram-negative bacteria Klebsiella pneumonia (ATCC 70063) was obtained by using copper(I)oxide<5 & micro;m particles with CA crosslinker treated cotton fabric samples at inhibition zone of 31.53 mm as well as for gram-positive bacteria Staphylococcus aureus (ATCC 43300) the best result was obtained by using copper(I)oxide<5 & micro;m particles with CA crosslinker on treated cotton fabric samples at inhibition zone of 31.02 mm even after 20 repeated washing cycles.
Machine learning and data mining techniques provide businesses with cutting-edge data-driven decision-making capabilities. Their popularity is growing because they enable more accurate and consistent evaluation and prediction of current and future situations based on previous data. This study used machine learning methodologies to address three of the textile industry's most pressing concerns: lead time, cloth waste, and price. A multi-output regressor model in which three subjects are predicted simultaneously is also investigated, in addition to training individual models for each subject. XGBoost is the model with the best lead time prediction results, with an R2 of 0.86 and an MAE of 8.35. When all three subjects are predicted at the same time, XGBoost achieves R2 of 0.88 and MAE of 3.79. These findings indicate that, in addition to single models, multi-output models are also promising.
