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For over a century, traditional Japanese cotton crepe fabrics have been popular for men’s underwear in the humid summer. Now, consumer demand is for crepe fabrics that are more attractive, reflecting a shift in use from underwear to women’s dresses. The purpose of this paper is to clarify how the structures of the crepe and its constituent yarns affect the physical properties, handle and silhouette formability of crepe fabrics for dresses.

Three plain-weave gray fabrics were finished by four different processes to change their crepe structures. The mechanical and surface properties of the fabrics were measured using the Kawabata evaluation system for fabrics. The primary hand values and silhouette formability of the fabrics were calculated using conversion equations based on the physical properties. The handle of the crepe fabrics and the aesthetic appearance of flared collars made of them were assessed by female students using the semantic differential method.

Comparing the fabrics made from the same gray fabric, the piqué crepe fabrics showed larger Hari (anti-drape) and Shari (crispness) than the others. The subjective hand value of softness was closely related to fabric thickness. The assessors preferred the fine piqué crepe fabrics over the wide piqué fabrics regarding both the tactile feeling of the fabrics and the aesthetic appearance of the flared collars. The attractiveness of the flared collars was dominated by the shear stiffness of the fabrics.

The fine piqué crepe fabric made from fine yarns produced a more preferable handle. The fine piqué fabric made from thicker yarns produced flared collars with silhouettes that are more attractive. This indicates that the fine piqué structure is a positive feature that makes the fabric suitable for various types of dresses.

The study aimed to clarify differences in fabric hand perceptions among Japanese and Chinese participants and implement online shopping strategies that enable consumers to easily recognize fabric texture.

Forty (20 Japanese and 20 Chinese) participants knowledgeable about clothing and fabric were recruited. Participants evaluated fabric by sight and touch in a visuotactile experiment (VTE). The stimulus material comprised 39 fabric samples representing a broad range of fabric attributes (7 fibers, 5 weaving/knitting techniques and 3 yarn thicknesses and density). A Mann–Whitney U test and a factor analysis were conducted to determine differences in responses for the different fabric variables.

The fabric hand perceptions factors were similar between both groups. Japanese participants showed a stronger preference for fabrics that felt wet. Japanese participants’ fabric hand perceptions had a 3-factor structure, while Chinese participants had a 2-factor structure. Chinese participants regarded “crisp” as perceptually and linguistically equivalent to “stretchy.”

The study’s findings suggest that Chinese people have stronger preferences in fabrics than Japanese people do. Japanese people evaluate fabric hand in a more nuanced manner than Chinese individuals, including discerning different fabric attributes, such as fiber and yarn thickness and density. Thus, nationality may influence fabric hand perceptions more than fabric knowledge does. Specifically, in evaluating “crispness,” the results required further analysis because differences in nationality may have affected evaluations regarding perception and linguistic perspectives. The findings provide design guidelines for implementing online shopping strategies adapted to each participant group.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

Crystallization is the process widely used for components separation and solids purification. The systems for crystallization process evaluation applied so far, involve numerous non-invasive tomographic measurement techniques which suffers from some reported problems. The purpose of this paper is to show the abilities of three-dimensional Electrical Capacitance Tomography (3D ECT) in the context of non-invasive and non-intrusive visualization of crystallization processes. Multiple aspects and problems of ECT imaging, as well as the computer model design to work with the high relative permittivity liquids, have been pointed out.

To design the most efficient (from a mechanical and electrical point of view) 3D ECT sensor structure, the high-precise impedance meter was applied. The three types of sensor were designed, built, and tested. To meet the new concept requirements, the dedicated ECT device has been constructed.

It has been shown that the ECT technique can be applied to the diagnosis of crystallization. The crystals distribution can be identified using this technique. The achieved measurement resolution allows detecting the localization of crystals. The usage of stabilized electrodes improves the sensitivity of the sensor and provides the images better suitable for further analysis.

The dedicated 3D ECT sensor construction has been proposed to increase its sensitivity in the border area, where the crystals grow. Regarding this feature, some new algorithms for the potential field distribution and the sensitivity matrix calculation have been developed. The adaptation of the iterative 3D image reconstruction process has also been described.