Fashion and Textiles最新文献

This study explores the development of continuous textile structures utilizing Thermoplastic Polyurethane (TPU) through advanced 3D printing techniques—namely, Material Extrusion (MEX) and Conveyor Material Extrusion (CMEX). The research aims to address limitations in conventional textile manufacturing by introducing digitally fabricated textile architectures that offer design flexibility, production efficiency, and sustainability. Various TPU filaments were comparatively evaluated to determine optimal material properties, including thermal stability, tensile strength, and dyeability. Three textile geometries—plain weave, flat knit, and spacer fabric—were modeled and fabricated under optimized printing conditions. By adjusting parameters such as nozzle angle, line thickness, and inter-yarn gap, the study achieved structurally stable and visually consistent textile forms. CMEX printing, which utilizes an inclined nozzle and continuous conveyor bed, demonstrated superior capability for producing uninterrupted textile lengths, overcoming the spatial constraints of traditional MEX platforms. Surface morphology analyses and mechanical tests revealed that spacer fabrics exhibited the highest tensile and tear strengths, while flat knit structures maintained superior flexibility. Dyeing experiments confirmed that disperse dyes produced the most stable coloration, with good wash and moderate dry-cleaning fastness. This research contributes to the growing body of literature on additive manufacturing in textiles by offering practical insights into the feasibility of TPU-based printed textiles for apparel use. The findings highlight the potential of CMEX 3D printing to revolutionize textile production, enabling sustainable, on-demand, and customizable garment manufacturing.

This study investigates the influence of spinning process parameters on microfiber release from polyester fabrics, identifying yarn hairiness as the dominant controlling factor. Experimental results demonstrate an extremely high correlation (R² = 0.997) between 3 mm hairiness and microfiber release, confirming protruding fibers as the primary shedding source. Among the parameters examined, spinning method exhibits greater influence than twist factor, with Siro compact spinning consistently delivering optimal performance by achieving minimal hairiness and microfiber release while maintaining high breaking strength. The optimal combination was determined as Siro compact spinning with a twist factor of 400. The research establishes that spinning method's effect on microfiber release is primarily indirect, mediated through its determination of yarn hairiness. These findings provide a scientific basis for source reduction of microplastic pollution in textiles, demonstrating that proper selection of spinning technologies and process parameters can significantly reduce microfiber shedding at the manufacturing stage. The study offers practical solutions for developing more sustainable textiles and advancing green manufacturing practices in the textile industry, contributing to environmental protection and circular economy objectives.

This randomized cross-over study investigates the impacts of different types of legwear on postural stability and lower limb biomechanics during single-leg drop landing (SDL). Eleven active healthy males (age: 26.0 ± 3.1 years; height: 170.2 ± 6.5 cm; weight: 67.6 ± 8.1 kg) have participated in this study and randomly received three different types of commercially available legwear for testing. Thirty reflective markers were adhered overlying bony landmarks the lower limbs. An eight-camera motion capture system with two force plates were synchronized and kinematic and kinetic data, respectively, at a sampling frequency of 100 Hz. The participants performed barefoot, single-leg drop landings from a 40-cm platform onto the force plate with the instruction to remain stationary for 5 s. Dependent variables included: time to the maximum vertical ground reaction force, time to stabilization, center of pressure displacement, and dynamic postural stability index and joint motion in all three planes. One-way repeated measures ANOVA were used to determine if there were differences between the three conditions with statistical significance accepted. The results demonstrated that the type of legwear significantly influenced landing biomechanics. Notably, the ankle-length compression tights were significantly associated with an improved dynamic postural stability index and a reduction in peak frontal plane moments at the knee and hip compared to the control condition. These findings suggest that ankle-length compression legwear may enhance landing stability by improving frontal plane control during SDLs. This indicates a potential for such garments to reduce injury risk in sports that involve similar landing demands, highlighting their practical significance for athletes and researchers in sports science and medicine.

This study introduces systematic fit adjustment frameworks to improve garment fit for diverse body types, and describes the development of a body type optimized pattern library for Made-to-Measure (MTM) apparel systems. The frameworks were designed to standardize adjustments by addressing structural and detailed fit imbalances. Unlike traditional methods reliant on empirical judgment, this approach identifies key fit imbalance factors-proportion, drop, and lateral posture- through expert evaluations of a block pattern on 126 virtual 3D fit models (size 100; chest circumference 98.5–101.4 cm). Based on these evaluations, a total of 48 optimized patterns were generated. These are organized in a three-dimensional pattern matrix combining: three proportion types (P, R, T), four drop types (Y, A, B, BB), and four lateral posture types (NN, NS, SN, SS), thereby permitting precise morphological customization. Expert evaluations using 11 criteria demonstrated that optimized patterns significantly outperformed the block pattern (p < 0.001) in ease allowance, balance, and seam alignment. Subsequent real-fit validation with muslin prototypes confirmed improvements in garment balance and reductions in drag lines, gaps, and hikes, verifying the effectiveness of virtual modifications. The introduced pattern library and frameworks offer a structured foundation for integrating modular pattern systems into digital MTM platforms, facilitating scalable automation and advancing mass customization with significantly improved fit.

Ag-coated conductive yarns are highly attractive for electronic textiles (E-textiles) such as electrodes and wiring. Nevertheless, their applications are limited by poor surface durability under mechanical stress (e.g., stretching, bending, or rubbing), which compromises the performance. To address this, conductive composite threads were fabricated with polyester yarn (P) and Ag-coated polyamide yarn (AP) with varying fineness (AP70, AP100, AP140, and AP200) under different twisting configurations. In Group #1, with various AP fineness, the threads exhibited improved elongation and enhanced electro-mechanical stability, with 2P-AP140 showing the most balanced properties. In Group #2, with various twisting configurations, while twisting structures were varied. The results indicated that hybrid twisting combining dissimilar fibers (P and AP) led to improved fiber alignment, enhanced modulus, and reduced electrical resistance degradation during repeated stretch-recovery. Among all samples, 2P-AP140 demonstrated the most favorable performance, achieving elongation (33.53 ± 1.47%), and excellent resistance stability (ΔR/R₀ = 4.46 after 1 cycle and 7.61 after the first and 100 cycles, respectively). These outcomes are attributed to optimized filament packing, enhanced inter-filament alignment, and reduced surface roughness. Taken together, these results indicate that conductive yarn fineness and twist configuration are critical structural parameters that determine the mechanical resilience and electrical reliability of composite threads in smart textile applications. Among the fabricated samples, 2P-AP140 demonstrated the most favorable balance of flexibility and stability, making it a strong candidate for wearable technologies such as biometric sensors and flexible interconnects.

This study explores the construction and ambivalence of cultural identity within the New Chinese Style by analyzing fashion content on Chinese social media platforms RedNote and Bilibili. Utilizing constructivist grounded theory and postcolonial theory, the research examines how users express their cultural identity through New Chinese Style fashion. The findings highlight several key aspects: some creators and users exhibit a skewed understanding of New Chinese Style, resulting in self-orientalism; others seek to reclaim cultural identity by challenging stereotypical portrayals; some advocate for transcending traditional boundaries to promote cultural exchange; while others engage with New Chinese Style as a means of gaining attention and benefits on social media. These insights suggest that New Chinese Style is not merely a fashion trend but reflects ongoing negotiations of Chinese cultural subjectivity, acknowledging both its challenges and efforts to preserve it. Although New Chinese Style may not fully escape self-orientalism, it represents continuous efforts within Chinese fashion to decolonize.

Appropriate colour combinations improved aesthetic design quality and provided a comfortable and pleasant visual experience. However, applying current colour harmony models to clothing colour matching raised doubts about whether the existing theory needed to be refined, requiring further clarification with modern aesthetic perspectives. The study conducted a psychophysical experiment to investigate modern people's perceptions of colour harmony in clothing colour combinations. The results indicated that modern perceptions of colour harmony in clothing differed significantly from previous theories. For applications, two colour harmony models were established with unified fashion datasets for evaluating clothes matching based on colour harmony rules and observers' perceptions. The result showed that the rule-based model could accurately predict all items following nine colour harmony theories, and the perception-based colour harmony evaluation models aligned with contemporary aesthetic preferences were developed with a semi-supervised learning approach. The models, including support vector machines and custom convolutional neural networks, could predict colour harmony perception for the input images with high performance, and the model based on the generative adversarial network could provide colour recommendations for colour matching. The machine learning and deep learning model proposed in the study could be used for aesthetic judgment to generate clothing colour recommendations and provide valid design suggestions for clothing colour matching in the fashion and clothing industry.

A tech pack is a set of documents that guides the production of a fashion product and includes all the necessary details to create the garment as designed. It helps to communicate between designers and manufacturers by specifying the detailed requirements for actual garment production. The motivation for this study originates from the resemblance between the flat sketch included in the tech pack and the final garment design. We propose DiffuseDesigner, a sketch-based controllable clothing image generation method, which is trained using pseudo flat sketches and prompts describing the desired output design. In light of this, we collected a large-scale fashion image dataset composed of multiple categories of clothing from three shopping websites. An edge detection algorithm was applied for each image to generate a pseudo flat sketch, and textual prompt information was extracted using a CLIP. Then, we constructed a triplet consisting of the pseudo flat sketch, textual prompt, and original image, and fine-tuned ControlNet to synthesize clothing images based on a pseudo flat sketch while leveraging the prompt information to guide the generation process. We conducted extensive experiments for performance evaluation based on the prompt the user wishes to generate, and quantitatively verified the effectiveness of clothing generation using five evaluation metrics. Finally, through variations in the prompt and pseudo flat sketch, we visually confirmed that the trained model was well-controlled in the direction desired by the designer.

In the modern fashion industry, 3D printing technologies are increasingly combined with traditional textile fabrics to create innovative 3D garments. Although this integration may not entirely replace traditional clothing manufacturing techniques, it presents opportunities for innovative applications in garment production. In terms of the performance of 3D printed composite fabrics (3D-PCF), adhesion between the 3D printing polymer and the substrate fabric is crucial. However, research on how fabric surface properties influence the adhesion between the substrate fabric and 3D filaments remains limited. Therefore, this study investigates the effects of alkaline surface modifications on the peel strength of substrate fabrics bonded with 3D printing filaments. Alkaline treatment reduces fabric weight, thickness, and tensile strength. Moreover, as both alkaline treatment time and temperature increased, the 3D-PCF peel strength decreased by 30–50%. This is attributed to the reduced fiber diameter and smaller adhesive surface area. Conversely, at NaOH concentrations of 4% and 6%, the increased surface roughness improved peel strength, reaching 15.6 N and 15.9 N, respectively. However, at 8%, surface etching reduced peel strength to 3.7 N. These results demonstrate that optimal alkaline treatment improves surface roughness and the peel strength, while excessive treatment diminishes the adhesive surface area and adhesion strength. Excessive treatment also significantly alters bulk fabric properties, further reducing adhesion. Controlling alkaline concentration to limit bulk changes while increasing surface roughness is an effective strategy to improve 3D-PCF adhesion. Enhanced peel strength through surface modification is expected to prolong the lifespan of 3D-PCF, especially in high-performance applications.