Fabric hand properties significantly influence consumer satisfaction and product quality in the textile industry. This study investigates the application of the Fabric Touch Tester (FTT) and Fabric Big Data (FBD) platform for digitising and tracing fabric hand properties during wool textile manufacturing. The research builds on prior studies, confirming that FTT effectively quantifies hand properties during manufacturing, while the FBD platform enables real-time visualisation and networked access to production data. Results reveal that this approach allows fabric properties during manufacturing to be well monitored and enable manufacturers to consider whether redundant steps could be eliminated to enhance resource efficiency. Additionally, this study demonstrates how integrating digital tools into production workflows aligns with ESG and ESPR goals by reducing waste and optimising resource use. These findings offer practical guidance for advancing sustainable textile manufacturing, laying the foundation for more intelligent and transparent production systems.

Warmth is a key consideration for consumers when choosing products. Existing research has mostly focused on testing the warmth performance of down products in a horizontal state, neglecting the nonhorizontal state during actual wearing. This study investigates the thermal insulation performance of down waddings under different placement conditions and their interrelationships. Four types of stitching spacing and five types of unit filling amounts are determined through market research, and 20 pieces of down waddings are made. Thermal resistance experiments are conducted in both horizontal placement and 24-hour suspension states. The experimental results show that under 24-hour suspension, the thermal resistance value of down waddings generally decreases; Under the same stitching spacing conditions, the unit filling amount corresponding to the maximum thermal resistance value in the 24-hour suspension state has decreased; There is a significant difference in thermal resistance values between the two placement states, with a Pearson correlation coefficient of 0.939, indicating a strong positive correlation; A mathematical regression model y = 0.825x + 0.033 is established through SPSS analysis to describe the relationship between the thermal insulation of down wadding in two different placement conditions. The findings of this study provide an important theoretical basis and practical guidance for further research, design, and production of down products.

This work realises the total parametric design of the insole through the topological structural design of the lattice units, which helps to meet the pressure requirements of different locations and increase the comfort and personalisation of insoles. Prior research has primarily concentrated on creating planar porous structures and basic geometric insole structures; intricate three-dimensional lattice structure optimisation has been systematically neglected. To close this gap, the research examines three common porous lattice structural units for analysis: equilateral triangular, square, and hexagonal units. It does this by using 3D printing technology to produce customised insoles. In addition, variance analysis is carried out, and the orthogonal experimental design method is used to examine the significant impact of structural design factors on the compressive performance of the porous lattice structure. The lattice’s structural neutral size, unit size, and rod diameter are chosen to influence the elastic modulus. With a 22% reduction in maximum plantar pressure and an 18% reduction in average pressure compared to the uniform solid structure, research reveals a considerable improvement in plantar pressure distribution with the lattice insole structure created in this study. In the meantime, the porous lattice structure’s overall weight is 15% less than that of the solid structure, which successfully reduces the insole’s burden while still fulfilling the standards for mechanical performance. This study offers a fresh technological perspective on creating customised, comfortable insoles.

To increase the protective capacity of yarns while maintaining human wearing comfort, pure UHMWPE filament and Spandex were combined to fabricate an elastic high-performance covered yarn. The various wrapping process parameters were adjusted to prepare this single-lay-covered yarn with a perfect wrapping status, which also can achieve the balance of strength and elasticity. It was discovered that the wrapping process seriously affected the covered yarn’s performance, and the new single-lay covered yarn showed desirable high strength and super elasticity. The strength of covered yarns shows three types of trend with the twist added, including first steep (300 t/m ∼ 400 t/m), then gentle (500 t/m ∼ 600 t/m) and last drastic (700 t/m ∼ 800 t/m). The covered yarn twisted at 700 t/m shows the optimal coverage morphology, excellent elasticity, and high strength. Finally, an elastic cut-resistant fabric is practically knitted by this fabricated elastic cut-resistant composite yarn, and it presents good flexibility and protection with Level 2 cut resistance under standard testing. The fabricated elastic high-performance covered yarn is an ideal material for producing highly elastic protective textiles and is applied to manufacture high-quality flexible protective equipment.

To solve the problems of wheelchair users’ shoes and boots in the aspects of convenience, warmth, and comfort in low-temperature environments and better meet their physiological and psychological needs, this paper proposes a structural design scheme of easy-to-wear and take-off warm boots based on a new type of warm wadding. At the same time, the fuzzy mathematics comprehensive evaluation method was used to select a new kind of flocculant with excellent thermal and wet comfort polyester fiber flocculant as the filling material for the follow-up warm boots. This paper has guiding significance and application value to the development and application of wheelchair users to put on and take off warm boots.

To prepare wearable personal thermal management fabric, conductive yarn was synthesised by in-situ polymerisation of polyaniline (PANI) and electroless silver plated nanoparticles (AgNPs) using acrylic (PAN) yarn as substrate. Subsequently, AgNPs/PANI/PAN conductive yarns of different structures are woven. SEM, XRD and FT-IR characterised the structure and properties of AgNPs/PANI/PAN yarns. The resistance and temperature of fabric at different voltages were measured by four probe testers and a thermal infrared imager. The results show that when AgNO3 concentration is 10 g/L, the resistance of AgNPs/PANI/PAN conductive yarn is 0.81 Ω/cm. At the same warp and weft density, the resistance of the satin fabric is 0.272 Ω/sq, and the resistance of the plain fabric is 0.404 Ω/sq. When the voltage is 0.8 V, the equilibrium temperature of the satin fabric reaches 77.6 ◦C, and that of the plain fabric reaches 63.4 ◦C. With the increase of applied voltage, the heat loss of satin fabric decreases during the heating process.

Fabric flammability is influenced by factors such as the composition of the fibre material, the manufacturing process and additional chemical treatments that are applied. These variables make it difficult to predict the nature and intensity of a clothing fire and the risk of injury associated with flame exposure. The severity of an injury will depend on the duration of the exposure and the temperature of the flames. This investigation aimed to identify the flaming characteristics of multi-layered fabric samples, which have not been reported extensively in the literature. Ninety fabric samples were used in this study, including cotton, wool, polyester, nylon and silk. The tests were conducted inside a controlled laboratory environment. The results revealed that fabric flame temperatures and burn times for wool, polyester, nylon and silk decreased significantly with adding one and two fabric layers. This appears to represent a “protective factor” against burn injury. However, the Cotton samples exhibited the opposite. The maximum flame temperature and combustion times were increased when additional layers were applied. Therefore, it is necessary to perform flammability tests that include multiple layers of fabric material when assessing the potential for fire injury in clothing.

As a visual symbol of information, brand identity influences consumers’ perception of brand image and their purchase intention. However, few literatures have examined the influence of brand identity color on consumers’ purchase intention. The present study takes the green brand logo of clothing as a stimulus material to explore the green symbol of its color and the influence of consumers’ green consumption intention. The results show that (1) the color of clothing brand logo has a significant impact on consumers’ green consumption intention. Consumers have the strongest purchase intention aroused by green clothing brand logos, and the weakest purchase intention aroused by red and purple clothing brand logos. (2) Consumers have the strongest purchase intention provoked by dark-colored clothing brand logos, and the weakest purchase intention provoked by light-colored clothing brand logos; (3) Green purchase intention is highly correlated with the symbol of clothing brand logo color.

Supportive waist protection is the most used among various functional waist protection products. It achieves its supportive effect primarily through elastic-banded supportive strips, with their shape, number, and material influencing the level of support. This study assesses the subjective and objective pressure comfort of waist protection with different support strip designs, utilizing a combination of subjective pressure numerical representation and the Novel-appliance pressure testing system. The findings indicate that pressure from supportive waist protection is primarily concentrated in the posterior and lateral lumbar regions, with the lateral region experiencing greater pressure than the anterior and posterior centre regions. Moreover, pressure along the abdominal circumference line generally exceeds that along the waist circumference line. The comfortable pressure value in the posterior waist region exceeds that in the lateral and frontal regions. Finally, varying shapes, numbers, and materials of support strips result in different pressure intensities in the posterior waist region, with 4 strips of bionic material offering the best waist comfort and 4 strips of steel plate providing the least comfort.

Analysing human body shapes requires a substantial amount of anthropometric data. However, traditional manual measurement methods are often inefficient, while 3D scanning devices are expensive and inconvenient. To address these challenges, this study presents a method based on the U2-Net neural network model for extracting human body contours in complex backgrounds, feature point extraction, and circumference fitting analysis. Using data enhancement techniques, we utilized a dataset comprising 2 560 frontal and lateral images of individuals against various backgrounds, which was augmented to 42 800 images. Subsequently, a deep learning network model was trained to accurately fit chest, waist, and hip circumference measurements. Finally, 20 samples were selected for validation, and the predicted values were compared with manually measured values, showing that the errors fall within an acceptable range. The effectiveness and accuracy of this method have been validated, providing a practical solution for anthropometric data collection and body shape research in remote areas.