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The electromagnetic shielding effectiveness (SE) of various weft-knitted fabrics made of hybrid yarns is investigated by considering the anisotropy of the structures, which has not been analyzed in previous studies. The anechoic chamber with aperture method at different polarizations of electromagnetic waves within the frequency range of 30 MHz and 9.93 GHz is used to determine the SE of knitted fabrics manufactured on a circular weft knitting machine from siro-spun and siro core-spun yarns without and with a metal core. The results show that SE depends on the orientation of the fibers within the structure regarding the direction of the electrical field in addition to parameters such as metal content, loop length and frequency; these results can be used to outline the basic points in determining a knit structure with desired SE.

Geometrical modelling for tubular braids of different structures is studied and a simple versatile three-dimensional model is proposed after considering the crimp of the braiding yarn together with the tubular curvature of the tubular braid structure. The proposed model is versatile and suitable not only for different braid structures, but also, with the changes in the structural parameters such as braid angle, number of yarns in a set, yarn and mandrel diameter the model is still applicable. Application and 3D drawings of the model for diamond, regular and triaxial braids are given with the aid of Visual Basic and 3DSMax Studio.

Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions.

The objective of this study is to enhance the out-of-plane tensile and compressive performances of foam core sandwich composite via structural core modifications considering the ease of application and time consumption. The performances of single core perforated, single core stitched, divided core perforated, and divided core stitched sandwich composites are compared with each other and reference plain foam core sandwich composites. Results indicate that “perforated and stitched core” sandwich composites have superior strength, and in terms of performance modification, dividing the core is found very efficient for plain (non-perforated and non-stitched) core sandwich composites.