Volume 25, Issue 1

This study looks at two aspects of ancient Chinese genre painting to interpret the more realistic ancient shopping scenes in the painting of the peddler in spring scenery in the Ming Dynasty, namely, the Ming spring scene and the characters’ costume image features. Through the analysis of the detailed characteristics of the style, structure, pattern, and color of the figure costume in the painting of the peddler, a total of five sets of figure costume for an adult male and four children in the painting of the peddler in the spring scenery of the Ming Dynasty in China were digitally restored using the 3D virtual fitting technology, which clearly showed the characteristics of the figure costume of the peddler and children in the Ming Dynasty (1368–1644). The figure’s costume style in the painting of the salesman restored in the article is Beizi, closed crotch trousers, open crotch trousers, and abdominal circumference (a type of apron from the Song Dynasty. It is made to resemble a short apron and is wrapped around the abdomen). The study of the painting of peddler in the Ming dynasty in this work provides a reference for future study on ancient Chinese genre paintings of peddler and the restoration of the costumes of the figures in the painting.

In order to realize the design of the warp knitted upper based on the three-dimensional shoe model, this study investigates the texture mapping technique based on angle-preserving and area-regularizing as-rigid-as-possible (ARAP) parameterization. The ARAP algorithm, based on both partial and global considerations, is utilized to parameterize the 3D mesh. Moreover, detailed methods for calculating 3D vertex adjustments and selecting seam lines during the unfolding process are provided. Additionally, discrete harmonic and parametric optimization unfolding algorithms are employed to enhance texture mapping quality. To reduce mesh distortion, the results of discrete harmonic parameterization are used as inputs for ARAP parameterization, making the mapping effect more natural and reducing the number of required optimizations. Experimental results demonstrate that the proposed method maintains a balanced distribution of angles and areas while minimizing conformal distortions, thereby achieving superior texture mapping.

The environmental issues associated with synthetic fibres have led to a significant growth in the use of natural fibres (NFs). Because of this environmental issue, the demand for NF is higher as compared to synthetic fibres. The purpose of the present study was to extract and characterize fibres from the Ethiopian Typha latifolia leaf plant. Manual, water-retting, and chemical extraction techniques were employed for the extraction of the fibre from the leaf of the Typha latifolia plant. The extracted fibre’s chemical content was evaluated, and its cellulose, hemicellulose, and lignin contents were almost similar to those of other NFs. The mean length, fineness, and tensile strength of fibre extracted by manual, water retting, and alkaline methods were determined, and its length was 502, 489, and 389 mm, and its fineness was 1.42 ± 0.4 Tex, 1.32 ± 0.2 Tex, and 1.12 ± 0.15 Tex, and its strength was 260.3, 308.5, and 192.8 MPa, respectively. The ranges for moisture content and regain were 8.4–10.8% and 8.7–11.4%, respectively, which is comparable to other natural plant fibres (5–17%). The Fourier transform-infrared curves of the extracted fibre revealed the presence of the functional groups for cellulose, hemicellulose, and lignin.

The aim of this study was to determine the frequency of body shapes of the studied group of women using different body shape classification methods. The objective was to investigate the fit of clothing for the average body shapes identified. Forty female participants from Slovenia, aged 20–30, were included in the analysis. Body shapes were classified using three methods: Visual assessment of body shape (VABS), female figure identification technique (FFIT), and body shape classification method (BSCM). For each classification method, ANOVA tests were performed to determine whether there were statistically significant differences in body measurements across the identified body shapes. A comparison of body shapes was carried out and also visualised by simulating 3D body shapes. The fit of clothing to body shapes was analysed with the help of virtual 3D prototyping. Four characteristic body shapes have been identified, namely triangle (pear, spoon), hourglass, rectangle and inverted triangle. The VABS method differs slightly from the other two (FFIT, BSCM), which give very similar results. When applying the FFIT and BSCM, the triangle body shape is the most representative, followed by the hourglass shape, the rectangle and the inverted triangle shape. Simulating the fit of the basic dress pattern design on 3D body shapes using the best match of dress size shows that the dress fits the hourglass shape best, while the fit for the other three shapes is inadequate. The latter indicates the need to make most of the garments for the triangle body shape and a corresponding number of garments for the hourglass and rectangle body shapes to ensure the correct fit of the garments.

In this study, the effect of melt-blown parameters on the properties of nonwovens manufactured from novel, commercially available biobased polybutylene succinate (BioPBS) was investigated. The research focused on the structure, surface, and mechanical properties. The parameters of processing, such as the air temperature (240 and 200°C) and die-to-collector distance (−40 cm), were evaluated in order to determine their impact on the prepared materials. Analyzing the effect of changes in the process parameters on individual fiber properties, it was noticed that the reduction of air temperature from 240 to 200°C results in an increase in the fiber diameter, which directly affects other physical properties such as apparent density and air permeability. In addition, the differences in the surface structure of nonwovens were determined. The investigation clearly shows that the obtained nonwovens can be characterized by various roughness sides with an insignificant impact on the friction and contact angle values. The mechanical tests suggest the influence of the supramolecular structure of PBS on the elongation and tenacity of the materials. The structure of the obtained nonwovens is different from the known characteristic nonwoven structure obtained by the melt-blown method from conventional polymers but can be useful in agricultural or horticultural applications.

Nowadays modern apparel industry encounters numerous challenges, including the dynamic nature of the market and the increasing customer demand for product variety. This drives fashion industries to diversify their product range, intensifying competition and leading to the production of a wide range of products in a single factory. Hence, implementing a systematic method and/or model to ascertain optimal product mix combinations within a garment factory becomes pivotal for informed decision-making. The current research aims to formulate a model to manage the available resources and improve overall operational efficiency through optimal product mix and production scheduling. The case study took place at CBA Garments Factory PLC in Ethiopia, which aimed to produce multiple garments but ultimately manufactured only one product at a time. The research employed quantitative research through analyzing data, general sewing data system, utilizing sequencing and scheduling approaches, and employing simple integer linear programming modeling tools. Based on the result, the newly developed optimized model enabled the case company to produce an average of four product types to fulfil multiple orders and increase resource utilization from 19.6% to an average of 73%.

This article aims to provide methods and a system to recognize the woven fabric patterns automatically. Two different approaches were designed to identify the woven patterns in this article: image analysis and the analytic hierarchy process (AHP). In this work, identifying the woven fabric patterns is based on the measurement of some characteristics and parameters that clearly describe and explain the contexture of the fabric. The methods used were essentially pattern recognition and image processing, which aim to numerically calculate four critical parameters which can give a clear idea of the texture and the patterns of fabrics: the continuity of diagonal lines, the section succession in the vertical direction, section succession in the horizontal direction, and isolated blocks. Then, we used the AHP to classify basic weave patterns into three classes representing the fundamental weaves: plain, twill, and satin. Finally, the statistical approach used to evaluate the efficiency of the proposed method concerning classification showed high correlation values between the predicted results and the experimental values. This study has practical implications in the textile industry, especially in the clothing sector. The manufacturers can use the results to automatically identify the woven fabric patterns without using the manual examination method.

To address the issues of friction, heat generation, and noise associated with traditional shuttle systems during flight, this article proposes a high-temperature superconducting (HTS) shuttle that stably levitates above a Halbach permanent magnet guideway (PMG) without energy consumption. The levitation force experienced by the superconductor is closely related to the dimensions of both the PMG and the superconductor. To ensure the stability of the shuttle’s levitation during the weaving process, an optimization method that considers both the levitation force and material usage is proposed. The ratio of the levitation force per unit length of the superconductor ( F Z ) to the PMG’s height ( h pm ) is defined as F h , with the optimal height of the PMG occurring when F h is maximized. Based on this, simulations were conducted to analyze the trend of levitation force changes with variations in guideway’s width, height, and superconductor material’s thickness. Finally, an experimental platform was built to verify the accuracy of the simulation results. The findings indicate that rational optimization of guideway dimensions can enhance the system’s levitation performance while optimizing material usage, with the optimized guideway providing significantly enhanced levitation force. This study offers theoretical and practical guidance for the design and application of HTS levitation guideways.

In the modern fashion industry, computer-aided systems are essential for planning, design, and marketing. Three-dimensional (3D) virtual simulation technology allows designers to style virtual models and create distinct looks. However, accurately simulating real fabrics requires more than just color, fiber type, mass, and thickness. This research aimed to evaluate the color uniformity and accuracy of 3D virtual simulations of knitted fabrics compared to real ones. Color uniformity ensures that cloth is observed as it is throughout the model until we observe it from different angles. Without this, the fabric would have a different color impression, which may impart our study towards different sides. Focusing on 80 knitted fabric samples, the study analyzed yarn types, stitch patterns, and colors. A seven-point Likert scale was used to assess the product quality through sensory evaluation of real and simulated garments. The results demonstrated that 3D simulations can accurately replicate the color characteristics of knitted fabrics, producing outcomes comparable to real ones. This study highlights the potential of 3D simulation as a valuable tool in the textile industry, improving efficiency and innovation in fabric design and manufacturing. The outcomes demonstrate that 3D virtual simulations can accurately replicate the color characteristics of knitted fabrics, producing outcomes that are comparable to those of actual knitted fabric. This study advances the use of digital tools in the fashion industry while encouraging efficiency and innovation in fabric creation.

Covid-19 pandemic, which has negatively affected all sectors and life, has also negatively influenced the clothing sector, which possesses a global supply chain. Within the clothing sector, which is a labor-intensive sector, mostly women workers are employed. Women workers’ issues, who already face work–life balance and occupational segregation problems in their work lives, have also been increased with the pandemic. In this context, this research aims to analyze the effects of Covid-19 pandemic on women workers in clothing sector. In accordance with the aim of the research, a survey is conducted on women workers, who are employed in Türkiye’s provinces, where clothing sector intensively operates. The obtained results are analyzed statistically and suggestions are made to the clothing enterprises on the basis of these results. According to the obtained results, Turkish women clothing workers, who participated in the survey, indicated that the increases that occurred in their physical and psychological problems are the most important issues that they faced during the Covid-19 pandemic. Besides, participants’ emotional behaviors, concerns, anxieties, hopelessness, sleeping issues, anger, and rages also increased.

Waste clothing, as a renewable resource, has received increasing attention. Due to high production costs, establishing an efficient supply chain for the recycling of waste clothing is a problem that brand owners need to solve. This study uses mathematical modeling methods to study the closed-loop supply chain models of original equipment manufacturers and cutting making trimming (CMT) in the recycling of waste clothing. By using the Stackelberg game method to analyze the model, the optimal decisions and profits of supply chain members under these two modes were obtained. Based on this, further comparative analysis was conducted between the two modes. Our research finds that brand owners choose to produce and sell recycled clothing not for profit maximization, but based on enhancing brand image or corporate social responsibility considerations. For the brand owner, adopting the CMT model is more advantageous, but this mode requires higher production technology for the recycled fabric supplier.

In order to improve the precision of clothing development of fast fashion brands, consumers’ sense of experience, and brand loyalty, a design method of clothing pattern is proposed by combining Kansei engineering theory and improved particle swarm optimization (IPSO)–back propagation neural network (BPNN) model. First, based on the theory of Kansei engineering, the perceptual image experiment of clothing patterns was designed, and the mean value of perceptual image evaluation of clothing patterns by young consumers was obtained through an online questionnaire survey. Second, based on the IPSO and the BPNN, the nonlinear correlation mapping model between the design elements of clothing pattern and consumers’ perceptual image is established. Finally, based on the calculation of target image weight by analytic hierarchy process (AHP) method and IPSO-BPNN model, the optimal combination of clothing pattern design elements under the requirement of multi-target image is output. Taking the paper-cut pattern of sweater shirt as an example, the feasibility of this research method is verified. The research not only helped the designer to design a costume pattern that meet the individual emotional needs of consumers, but also provided a clear design index and reference, and made the costume design process more targeted, precise, and intelligent.

This study presents a numerical model designed to simulate the transport properties of textronic structures produced by physical deposition from the gas phase. For the numerical implementation of the model, the method of fundamental solutions was used, which was implemented using the author’s iterative algorithm. Such an approach is rarely used due to the difficulty of obtaining accurate solutions with optimal computational cost. The developed algorithm ensures good convergence of solutions (δ k ≈ 5%) with acceptable computational time (t ≈ 180 s for 17 × 10 3 iterations). The software tool has options for calculating the electric field and density of current distributions for the given system power conditions. The ability to define a conductive path in three geometric dimensions makes it possible to study the effect of changes in the surface geometry on the resistance of the textronic structure. The accuracy of the model was verified by measuring the resistance of selected samples of simulated materials. The measurements confirmed the conclusions of the simulations about the clear dependence of the resistivity of the structure on the roughness of the conducting surface. The range of the largest changes in resistivity as a function of the surface roughness of the conducting path was also determined.

This study investigates the application of particle swarm optimization (PSO) algorithm in optimizing the force uniformity of personalized 3D-printed insoles, aiming to enhance the comfort and functionality of the insoles. Traditional insole designs often lead to uneven force distribution due to fixed lattice materials and structures, particularly in critical areas such as the forefoot and heel, which can result in health issues. This research proposes an optimization model that combines the PSO algorithm with a variable density algorithm, enabling dynamic adjustments to the support capabilities of different regions of the insole to achieve uniform force distribution. The results indicate that after optimization with the PSO algorithm, the force distribution of the insoles has significantly improved, with pressure peaks effectively dispersed in critical areas. Furthermore, this research validates the effectiveness of the optimization method through 3D printing technology, providing a theoretical foundation for the design and optimization of personalized 3D-printed insoles. It demonstrates that the combination of the PSO algorithm and variable density methods can effectively improve insole performance, showing promising application prospects.

Auxetic foams and lattices, characterized by a negative Poisson’s ratio, offer unique mechanical and thermal properties that make them promising candidates for next-generation sports and outdoor equipment. This study investigates the application of auxetic materials in the shoulder straps of trekking backpacks to improve both mechanical performance and thermal comfort. First, EVA foam samples were cut using water jet technology to create different auxetic geometries. After conducting tensile tests and digital image correlation analyses, the re-entrant geometry was identified as the most effective. Based on these findings, three types of shoulder straps were developed: a standard model and two auxetic models that differed in cutting pattern (one with undulating and one with linear edges) to evaluate their effects on deformation behavior. After further testing, the auxetic design with wavy edges proved to be superior and was integrated into a fully functional backpack prototype. Tests were then conducted on human subjects to determine whether the auxetic structure improved thermophysiological comfort and load distribution compared to a standard model made from the same materials. The study ultimately led to the development of a patented design for backpacks with auxetic shoulder straps and highlighted their potential to improve the user experience in outdoor applications.

Cashmere and wool fibers are important raw materials in the textile industry, but their similar morphological structures make accurate distinctions challenging. Image preprocessing methods will cause some damage to the fiber contours, resulting in the loss of feature information. The keypoint features that do not require image preprocessing are added to the library of morphological and texture features. At the same time, existing methods of feature selection often ignore the relation between features and the classifier. Therefore, we propose a novel feature selection method with support vector machine-recursive feature elimination (SVM-RFE). The SVM-RFE method recursively removes the features of the least contribution to SVM classification, ultimately generating the optimal feature set. Our approach achieves a recognition accuracy of 98.06%, which is 8.34% higher than the traditional two-feature method and 6.12% higher than the three-feature method, both without feature selection. Experimental results demonstrate that keypoint features effectively compensate for the information loss caused by image preprocessing, while the SVM-RFE feature selection method can select the optimal feature subset relevant to the classifier so as to accurately distinguish cashmere and wool fibers.

In recent years, electromagnetic weft insertion has been widely studied due to its excellent speed controllability and low energy consumption. However, the yarn tension in the process of electromagnetic weft insertion is affected by many factors. In order to improve the fabric quality, this study takes three typical yarns, which are wool yarn, cotton yarn, and nylon yarn, as examples to study the yarn tension in the process of electromagnetic weft insertion and explore the variation rules of yarn tension under different materials and different weft insertion speeds. When constructing the yarn tension model, the Levenberg–Marquardt algorithm in ORIGIN software is used for data fitting. The experiment shows that the yarn tensions of the three materials all have maximum values and change positively with the weft insertion speed. In addition, this study establishes a yarn tension model, whose R 2 value exceeds 0.95, indicating a good model fitting effect. And the relationships between the maximum value, average value, standard deviation of tension, and speed are iterated respectively, providing a theoretical basis for the establishment of electromagnetic weft insertion strategies and optimal control.

Laminates for the automotive industry are composite materials that consist of at least two layers, at least one of which has textile properties. Automotive laminates are most commonly found in vehicle interiors, including door panels, dashboards, headliners, and seats. As part of the research, the influence of three-dimensional warp knitted spacer fabrics on the functionality of laminates was investigated. The experimental part was designed in such a way that the laminates were divided into two groups. The first group comprised laminates consisting of PU foam and two layers of warp knitted fabric (Locknit), while the second group comprised laminates consisting of polyurethane foam and three-dimensional warp knitted spacer fabric. The polyurethane foam in both groups was open cell. All laminates investigated in this work were produced by flame lamination. In the experimental part, the influence of the three-dimensional warp knitted spacer fabric on the typical mechanical properties and the permeability properties of laminates for car seats was analyzed. The results show that laminates with a thickness of more than 7 mm and a proportion of the 3D warp knitted spacer fabric of 50% achieve the highest permeability, delamination force, and tensile and compressive strength.

Spinal cord injury (SCI) consists of partial or complete damage to the organ’s functions. Injuries can be traumatic or non-traumatic. New investigations have pointed out different paths in terms of spinal cord regeneration. Among these, the use of scaffolds has grown, structures created based on biomaterials and synthetic materials, aimed at remodelling the injured area, promoting tissue growth, regenerating damaged axons, and vascularizing the affected region. This work developed a fibrous scaffold using a vertical braider to produce Maypole structures from Polyamide 6 fibres, known for their strong mechanical properties, 3D architecture, and porosity, which support cell growth. The scaffold structures were evaluated based on porosity, mechanical strength, and dimensional stability under compression. Among the tested models, the T2/A8B40/E16B50 structure demonstrated superior performance, withstanding a tensile strength of 1,674 N, surpassing other samples. Its external layer of 16 yarns (0.50 mm) and internal layer of 0.40 mm yarns provided greater rigidity and load-bearing capacity. It also showed high elastic recovery (96.47%) after 10 compression cycles, maintaining excellent recovery despite its high load capacity. With 50.7% porosity and 49.3% coverage, the T2/A8B40/E16B50 scaffold balanced mechanical strength with permeability, making it the most promising candidate for SCI treatment and future implant testing.

Mechanical textile recycling has great advantages over other types such as chemical or thermomechanical recycling. The main drawback of this textile-to-textile recycling technology is that the length of the fibres is reduced over successive recycling processes. In most cases, due to the length reduction, it is necessary to blend the recovered fibres with virgin fibres to obtain high-quality yarns that can be reincorporated into the textile value chain. Despite the maturity of the technology, this problem has yet to be fully resolved, and it continues to present a major technological challenge. This study aims to determine the optimum shredding conditions to recycle denim-like fabrics with an edge-trim opener machine. It also examines the effect of oiling pre-treatment on the retention of fibre length of the recovered fibres and the performance of yarns produced with the recycled fibres. The best results are achieved by processing the material in three shredding cycles at feeding and opening speeds of 0.3 m/min and 947 rpm, respectively. The addition of lubricant, although not having a significant effect on the efficiency of the defibration process, led to an improvement in the quality of the recycled yarn.

Determination of abrasion resistance of textile fabrics is one of the important parameters describing textile durability, especially utility properties. Currently, used test methods for determination of abrasion resistance are based on organoleptic examination. The end point of the abrasion resistance test carried out for textile fabrics is described in the form of criteria in the standard. The identification of the end point requires manual observation of the sample’s surface. This process is labour and time-consuming and requires a lot of experience. The article presented a new instrumental method for assessing abrasion resistance. The method, based on image analysis techniques, allows identifying the end point of abrasion test without a constant examination of sample surface, without constantly examining the sample surface. The method presented for abrasion resistance assessment involves evaluation of brightness profiles taken from sample scans. The new method, subjected to precision experiments, has shown a good level of repeatability and reproducibility. The instrumental method, together with end point criteria for single thread, could support operators during breakage identification test significantly, shortening the test time and labour consumption.

The research presented in this article concerns the modeling of physical processes occurring in protective clothing that determine the ergonomics and thermal balance between its user and the work environment. In the first part, three-dimensional models of real composites based on cotton fabrics and aerogel were designed using the original method of Parylene C deposition in the chemical vapour deposition (CVD) process, which have potential application in thermal protective gloves. The models included geometric parameters of real textiles calculated based on high-resolution X-ray tomography. This technique also allowed for the accurate determination of the porosity of the tested materials and the inclusion of the exact air content in the models without the need to reproduce the complex geometry of fibers in the fabric and microgranules in the aerogel. The results of heat transfer simulations performed using the finite volume method, correlating with the results of the experiment verifying them using thermography, showed that the designed models allow for the prediction of heat transfer with high accuracy despite the use of a lot of simplifications in the geometry. The differences between the modeling results and the experiment range from 0.7 to 5.5% depending on the complexity of the model geometry.

To investigate the influence of materials on the surface morphology and compressive performance of honeycomb structural components prepared by stereo lithography apparatus (SLA) technology, three typical materials, namely Dutch State Mines (DSM) 8000, Somos ® Taurus, and FS 3400GF (Glass Bead Filled Nylon Powder), were selected as raw materials for printing honeycomb structure via SLA. FS 3400GF can withstand a maximum compression force of 14.1 kN in vertical compression, while it is 10.1 kN for DSM 8000 and 5.8 kN for Somos ® Taurus, indicating that FS 3400GF bears significantly higher vertical pressure. The maximum force bore by FS 3400GF under lateral compression is 0.47 kN, DSM 8000 is 0.56 kN, and Somos ® Taurus is 0.17 kN, suggesting that the lateral compression failure is highly likely to occur. The mechanical compression failure reveals the physical properties of honeycomb structures and provides an experimental support for expanding the potential functional application of honeycomb structures in additive manufacturing.

Integrating electronic components into smart textiles revolutionised the field, with snap fasteners often serving as electrical connectors. This study investigated the electrical and mechanical durability of sewn-on snap fasteners under cyclic fastening–unfastening and interruption of direct current (DC) flowing through them. The available literature indicates that snap fasteners can endure repeated mechanical stresses; however, there is a lack of data regarding their behaviour during the cyclic interruption of DC flow. This research fills this gap by analysing changes in electrical resistance and unfastening forces over 10,000 cycles of current interruption. Results indicate that larger snap fasteners offer greater mechanical resistance, although size does not significantly impact electrical durability. For example, the average force needed to release the snap fasteners after 10,000 cycles is reduced to 5 N for large snap fasteners and 2 N for the small ones. This article also discusses how the substrate fabric and the type of thread used to attach the snap fastener affect the electrical strength of the tested connector. The findings presented offer valuable insights for designing and selecting materials in future smart textile applications, thereby enhancing the robustness and functionality of wearable electronics.

Near-infrared (NIR) spectroscopy is essential for distinguishing cashmere from wool. It is fast and non-destructive. Both cashmere and wool contain keratin. Their NIR spectral images are very similar. This makes it hard to tell them apart. This article proposes a method to identify and classify two similar fibers. It uses NIR spectroscopy combined with chemometrics. The chemometrics model mainly uses sparse representation and an improved AdaBoost classifier. The sparse representation is used for feature extraction. It expands the distance between sample spectra. This strategy first standardizes the dataset. It then uses the K-singular value decomposition algorithm to learn a low-dimensional dictionary. Finally, it maps the spectra using the dictionary. This approach creates a low-dimensional positive–negative distribution of sample features. It aims to widen the gap for later classification. Also, due to the many cashmere and wool species, some intra-species gaps are larger than the inter-species ones. This has increased misclassification errors. This article uses AdaBoost to assign weights to samples of different species. It optimizes these weights with many decision tree (DT) classifiers. It then uses the sparrow optimization algorithm. It finds the best number and depth of DTs. In the comparison experiments of this article, the sparse representation can effectively amplify the gap between cashmere and wool than principal component analysis and independent component analysis, and the classification efficiency of AdaBoost is also higher than the classification efficiency of K-nearest neighbors, Random Forest, and other classifiers. The combination of these two algorithms can achieve a classification accuracy of 97.4%, which can effectively classify cashmere and wool fibers.

The widespread use οf detergents in the textile industry has raised cοncerns regarding their adverse effects οn the envirοnment and human health. This study prοpοses an innοvative apprοach tο address this issue by develοping a naturally sοurced surfactant. The surfactant was extracted frοm Mesembryanthemum crystallinum (GH) and Salsοla vermiculata (F) by maceratiοn under different time and temperature conditions. The results demοnstrated the high efficiency and detergent efficacy οf the extracts studied. GH, F exhibited the superiοr fοaming capacity reaching 48.51%, stability up to 60 min, and a wetting time up tο 5 min at 25°C. Mοreοver, GH and F detergency capacity is cοmparable tο that οf the reference prοduct. These findings suggest the pοtential applicatiοn οf GH and F as sustainable and envirοnmentally friendly cleaning agents in the field οf textile.

This article presents an experimental study on the influence of various parameters on sewing thread consumption. Four knitted samples, featuring different structures and thicknesses, were tested by sewing two- and three-layer using chain stitch type 401. This work allows for examining the effects of the sewing machine foot pressure height, surface mass, fabric thickness, and number of layers sewn on thread consumption. It was concluded that lower foot pressure results in a significant increase in the amount of thread used. In addition, accurately predicting thread consumption makes it easier to control the stock level of inventories, which is important for effective supply chain management. With optimized inventory, companies will save on storage expenses and minimize the downtime between operations, resulting in increased productivity. The combined application of neural nets and statistical techniques increases the accuracy of forecasts, which is essential for manufacturers in a highly competitive environment.

Smart clothing, as a cutting-edge artificial intelligence product integrating fashion and technology, demonstrates substantial market potential. However, smart clothing featuring traditional Chinese medicine (TCM) therapeutic functions has yet to achieve widespread adoption, with notably low usage frequency and short application cycles among users. To address this challenge, this study constructs a theoretical model of user behavior for TCM therapeutic smart clothing by integrating the Technology acceptance model, theory of planned behavior, and perceived risk (PR) theory. A multi-stage research design was adopted. A structured questionnaire based on established scales yielded 567 valid online responses (response rate: 97.7%), providing empirical support. Descriptive statistics and cluster analysis explored sample characteristics and group differences. Confirmatory factor analysis assessed measurement reliability and validity, while structural equation modeling tested hypothesized relationships. Additionally, hierarchical regression and simple slope analysis examined the moderating effect of user technology literacy (UTL). Results showed that perceived ease of use (PEOU) and perceived usefulness significantly enhanced user stickiness, which positively influenced usage behavior. PR negatively affected stickiness, and UTL moderated the link between PEOU and stickiness. Notably, perceived behavioral control and perceived cost did not show significant effects, suggesting users prioritize functional benefits and health outcomes over controllability and economic concerns. This study integrates theoretical and empirical analysis to quantify key behavioral factors, offering insights for product design, marketing, and user experience optimization in TCM therapeutic smart clothing.

Fur is the soft, thick hair that covers the skin of certain animals, and is often used in clothing, while the cut pile loop (faux fur) knitted fabrics are produced to simulate the natural fur. The production process of these fabrics was explained in detail, and the effects of fabric construction parameters (ground loop length ( L ), ground count ( G ), and pile yarn count ( F )) on the geometrical, thermal, and abrasion resistance properties were investigated. The faux fur knitted fabric samples were produced at three levels of ground loop length (3.3, 3.5, and 3.7 mm), two different ground count (100 and 150 den), and three levels of pile yarn’s count (200, 300, and 400 den). The Tien Yang circular knitting machine settings, feeders, cams and yarns’ arrangements were illustrated. It can be concluded from the evaluated results that the fabric construction parameters had significant effects on the faux fur fabric’s properties. By increasing the pile yarn count from 200 to 400 den; the fabric weight, thickness, thermal resistance, and weight loss due to abrasion were increased approximately 75, 78, 56, and 61%, respectively.

Two prototypes of a women’s skirt intended for use in the autumn season were designed and manufactured using innovative sewing machines, along with their programming. This article describes the innovative sewing machines used to sew two prototypes of a women’s skirt – specialist machines, semi-automatic, and automatic sewing machines. The main assumption during the sewing process of the designed skirt prototypes was to program the selected sewing machines necessary for their implementation. This article refers to Industry 4.0 through the automation of the production process, which has contributed to increased productivity. In addition, the impact of using modern sewing machines on the lead times of two models of women’s skirts is presented. This article describes the individual stages of programming the innovative machines used, such as an automatic machine for sewing small elements, a two-needle lockstitch with switchable needles, a clothing buttonhole machine, or a button sewing machine.

The interval distribution under different ease allowances directly influences the dressing effect. However, the indistinct 3D dressing process and unclear interval distribution pose severe challenges to practical applications. Inspired by real-time acquisition, the finite element model based on contact theory (FECM) was proposed. First, we constructed a biomechanical body geometric model consisting of the torso and soft tissues. Meanwhile, the garment pieces with different ease allowances were also constructed. Then, the FECM was constructed to simulate the interactions. The dressing period was applied to acquire dressing states. Finally, the displacements and interval distribution under different ease allowances were acquired. The results demonstrated the effectiveness of the FECM. During dressing, the displacements of garment pieces mainly occurred in the full contact and equilibrium phases. Dynamic changes in the shoulder gradually increase from small displacements, while the opposite occurs in the waist and hem area. Meanwhile, the interval distribution followed back > front > side seam. As the ease allowance increases, the interval distribution dynamic shifts from the back to the front piece. The reason is that the back muscle gradually replaced the chest convex points in the supporting garment pieces. The findings could enhance the dressing effect under different ease allowances.

Celebrity fashion lines represent the foray of notable individuals from various industries, such as entertainment, sports, academia, and even politics, into the world of fashion design and retail. The purpose of this study is to analyze the phenomenon of celebrity fashion and discuss its impact on contemporary mainstream fashion. This research employs methodologies including literature review, database analysis, concept mapping, and synthesis. Data were collected from academic databases such as Web of Science, Google Scholar, and Bing Scholar. A manual screening process was then applied to exclude materials that did not align with the predefined inclusion criteria. This study sheds light on the typical business models while analyzing the impact of the phenomenon on mainstream fashion. The study contends that the incidence of celebrity fashion lines has, to a large extent, bestowed a positive impact on the fashion landscape. Even though some cons exist, the pros far outweigh the cons. The phenomenon has impacted not only the design and production of fashion goods but also the marketing of goods in general. New marketing paradigms such as celebrity marketing and celebrity branding have emerged. Our findings have implications for scholars, industry stakeholders, and policymakers navigating the evolving fashion landscape.

As cultural heritage preservation and sustainable development concepts become increasingly integrated, China’s traditional textile dyeing protection and development gain heightened attention. This study focuses on tie-dyeing, a representative intangible cultural heritage (ICH) craft, to investigate the expectations of its current and potential consumers. The ultimate goal is to translate these expectations into actionable guidance that supports the long-term viability of the tie-dyeing industry. Through a combined methodology of grounded theory and the Kano model, the study initially identified demand indicators via grounded theory, yielding 4 dimensions and 26 demand items. The Kano model analysis was then applied to 239 valid responses. By combining the Better–Worse coefficient and quadrant analysis, the study established a classification system for consumer expectations and prioritized demand importance. The research indicates that the five primary consumer expectations for tie-dyed products are as follows: ease of maintenance, esthetic expression, natural materials, product diversity, and cultural symbolism. These elements represent core consumer demands and warrant primary consideration during product development. Based on these findings, the study presents targeted strategic recommendations for tie-dyeing enterprises regarding product development, market promotion, and brand building. This research contributes to the theoretical understanding of consumer expectations within the ICH domain while offering novel perspectives for promoting the sustainable inheritance and innovative development of tie-dyeing in contemporary markets.

The aim of this study was to determine the concentration of phthalic acid esters (PAEs) in firefighter protective clothing, made from two different material configurations varying in composition, used during live-fire enclosure simulations. Analysis involved six PAEs, with the total PAE concentration being dependent on the protective clothing material configuration and its associated composition. Before washing, a significantly higher Mconcentration of phthalates (548 µg g −1 ) was recorded in the membrane of material configuration A (composition: 50% meta-aramid, 25% para-aramid, and 25% polytetrafluoroethylene laminate) compared to the outer shell and the thermal barrier. In turn, material configuration B revealed a higher phthalate content (38 µg g −1 ) in the outer shell (58% para-aramid, 40% polybenzimidazole, and 2% antistatic fiber). The washing process after ten uses of the clothing showed a significant effect on reducing total phthalate content only for the membrane in material configuration ‎A, which showed a reduction in total phthalate concentration from 548 to 275 µg g −1 . Washing was more effective for material configuration A: 32 ± 23% for the outer shell, 48 ± 14% for the middle membrane, and 10 ± 10% for the thermal barrier.

Fast fashion, which is very popular amongst the young generation, is facing criticisms about ignoring and damaging the social and environmental aspects of sustainability recently. The aim of this research is to understand the behaviors and attitudes of Turkish Generation Z toward sustainability strategies of the fast fashion brands. Qualitative research method as an exploratory research model was used and in-depth interviews were conducted with 15 different participants. The main outcome of the research is that Gen Z is not finding fast fashion companies sincere in their sustainability approaches and although Gen Z cares about sustainability, they experience an attitude-behavior gap, which is explained by the lens of cognitive dissonance theory.

China’s textile industry faces critical transformation pressures transitioning from labor-intensive to technology-driven manufacturing. This study examines how digital tax administration systems influence innovation capabilities in textile enterprises, utilizing China’s Golden Tax Phase III project staggered implementation as a quasi-natural experiment. Analyzing 4,250 observations from Chinese listed textile companies (2008–2018) through difference-in-differences methodology, we investigate digital governance impacts on textile innovation outcomes. Results demonstrate that digital tax administration significantly enhances textile enterprise innovation capabilities, increasing invention patent applications by 13.4%. The mechanism analysis reveals that digitalized systems suppress earnings management behaviors, compelling textile manufacturers to redirect resources from financial manipulation toward substantive innovation investments in textile technologies and processes. Heterogeneity analysis shows pronounced effects for enterprises with high agency costs and advanced digital transformation levels. This research provides crucial evidence for understanding how digital governance frameworks can catalyze innovation transformation in traditional textile manufacturing, offering policy insights for promoting technology-driven development in China’s textile sector. The findings suggest that digital administrative reforms create positive externalities for textile industry innovation upgrading and international competitiveness enhancement.

One of the major issues in the textile industry’s supply chain logistics is transportation, specifically cost, efficiency, and delivery timeframe. This study investigates using the Ant Colony Optimization (ACO) algorithm as a promising method of transport optimization in textile industry logistics. The ACO model uses the same mechanisms as the ants; it considers using more than one distribution point, including many important factors such as distance and fuel. We will elaborate on the entire framework of ACO, with particular emphasis on updating the rules of pheromones and virtual ants’ decision-making process. This study illustrates the application of the ACO model in textile logistics, employing case studies and computer modeling, leading to more significant cost savings and improved service levels in the industry. In practice, the ACO theory improves the supply chain’s transportation efficiency, flexibility, and environmental sustainability, giving the textile industry a competitive edge in emerging markets.

To develop distinctive elastic fabrics, it is essential to produce new elastomeric yarns that differ from conventional elastane filaments. The elastomeric staple yarns examined in this study are composed of poly(ether-ester), or PEE, featuring a chemical structure with an 80/20 weight ratio of a rigid segment of poly (butylene terephthalate) (PBT) and a flexible segment of poly (oxytetramethylene). Initially, staple fibers were created from this elastomeric PEE along with a 25% PBT blend by weight, followed by the production of elastomeric yarns from pure elastomeric staples and blended staple fibers of cotton or poly (ethylene terephthalate) using ring and compact spinning techniques. This study provides a comprehensive overview of the manufacturing process for elastomeric yarns derived from these elastomeric staple fibers. The fundamental mechanical characteristics of the produced elastomeric yarns, such as tenacity, modulus, breaking extension, and elastic recovery, were evaluated and detailed in relation to yarn composition (blending ratio). The most significant feature of high elastic recovery (exceeding 90%) at 10–15% extension is anticipated to facilitate the creation of numerous new types of stretchy materials.

In order to reduce the development cost of open topping-on structured crochet fabrics and realize the diversified design and three-dimensional (3D) simulation of fabric, the weaving process and principle of this kind of fabric were deeply studied, and the lapping motion and yarn threading motion models of fabric structure were established. Combined with the structural characteristics of fabric, the geometric models of pillar stitch organization, weft lining organization, open topping-on organization, and fringed weft-insertion organization were established under ideal conditions. Through the measurement and analysis of the actual fabric, the number and position of the coil type value points are further determined. The shape transformation of the fringes is realized by combining the random geometric transformation algorithm. This work introduces the structural characteristics and process design method of a typical fabric, and explores the diversified combination of structures, and finally realizes the 3D simulation of fabric with the help of JavaScript and C# computer programming language. The results show that, through 3D simulation technology, we can quickly try different patterns, materials, and color combinations, so as to see the design effect intuitively. This way of virtual display and iterative modification can significantly improve the design efficiency, and greatly reduce the trial and error cost and time consumption, providing an efficient way to find the best design scheme.

This study aims to investigate the impact of calf compression sleeves (CCS) on the thermal and moisture comfort of individuals during indoor jogging, particularly focusing on sedentary populations. Methods: Mechanical and hygroscopic properties of various CCS were assessed using grey relation analysis to evaluate their thermal and moisture characteristics. Physiological indicators, including skin temperature and humidity, were measured during different stages of exercise in a controlled environment. The findings revealed significant differences ( p  < 0.05) in the thermal and moisture properties of CCS with varying thicknesses and densities. Participants wearing CCS experienced higher skin temperatures and sweating rates during exercise, indicating improved heat dissipation and moisture wicking capabilities. This study proposes a comprehensive evaluation strategy for CCS regarding thermal and moisture comfort, providing a basis for the functional design of sports equipment tailored to enhance comfort and performance for sedentary individuals engaging in indoor exercise.

This paper describes a method developed to improve the accuracy of thread consumption predictions for technical textile industrialists, particularly for products such as the automotive leatherette cover, by applying a neural network method. The approach relies on analysing a database regarding different fabrics, garment designs, and relevant production parameters to develop a suitable prediction model. The developed model using advanced supervised learning techniques and statistical methodologies, including multiple linear regression, captures the intricate relationships between many parameters to predict thread consumption accurately. The results show that the model without interaction achieves an R 2 of 0.76, while the model with linear regression including interactions achieves an R 2 of 0.91. In contrast, the use of neural networks achieves an R 2 of 0.94. This framework is advantageous for manufacturers, as it allows them to optimize the organization of their production schedules and other operations. By integrating predictive analytics, manufacturers can source and manage raw materials more accurately, avoiding excess or insufficient production output, while improving productivity. Meanwhile, reducing the waste of materials helps in cost-reduction strategies and improves the financial efficacy of the business. This practice becomes sustainable by addressing the environmental concerns due to overproduction, one of the modern issues. In addition, accurately predicting thread consumption makes it easier to control the stock level of inventories, which is important for effective supply chain management. With optimized inventory, companies will save on storage expenses and minimize the downtime between operations, resulting in increased productivity. The combined application of neural nets and statistical techniques increases the accuracy of forecasts, which is essential to manufacturers in a highly competitive environment.