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Sewing knits from fit to finish : proven methods for conventional machine and serger
\"This book teaches everything you need to know about sewing knit fabrics - from choosing patterns to laying out designs, to sewing and adding embellishments and closures\"-- Provided by publisher.
Book
Programming mechanics in knitted materials, stitch by stitch
Knitting turns yarn, a 1D material, into a 2D fabric that is flexible, durable, and can be patterned to adopt a wide range of 3D geometries. Like other mechanical metamaterials, the elasticity of knitted fabrics is an emergent property of the local stitch topology and pattern that cannot solely be attributed to the yarn itself. Thus, knitting can be viewed as an additive manufacturing technique that allows for stitch-by-stitch programming of elastic properties and has applications in many fields ranging from soft robotics and wearable electronics to engineered tissue and architected materials. However, predicting these mechanical properties based on the stitch type remains elusive. Here we untangle the relationship between changes in stitch topology and emergent elasticity in several types of knitted fabrics. We combine experiment and simulation to construct a constitutive model for the nonlinear bulk response of these fabrics. This model serves as a basis for composite fabrics with bespoke mechanical properties, which crucially do not depend on the constituent yarn.
Knitted fabrics are prized for their stretchability, breathability, and long-wearability in everyday life. This study combines experiments and simulations to present a micromechanical approach to understanding the origin of the anisotropic elasticity of four canonical patterns of knitted fabrics.
Journal Article
Assessing the Role of Yarn Placement in Plated Knit Strain Sensors: A Detailed Study of Their Electromechanical Properties and Applicability in Bending Cycle Monitoring
In this study, we explore how the strategic positioning of conductive yarns influences the performance of plated knit strain sensors fabricated using commercial knitting machines with both conductive and non-conductive yarns. Our study reveals that sensors with conductive yarns located at the rear, referred to as ‘purl plated sensors’, exhibit superior performance in comparison to those with conductive yarns at the front, or ‘knit plated sensors’. Specifically, purl plated sensors demonstrate a higher sensitivity, evidenced by a gauge factor ranging from 3 to 18, and a minimized strain delay, indicated by a 1% strain in their electromechanical response. To elucidate the mechanisms behind these observations, we developed an equivalent circuit model. This model examines the role of contact resistance within varying yarn configurations on the sensors’ sensitivity, highlighting the critical influence of contact resistance in conductive yarns subjected to wale-wise stretching on sensor responsiveness. Furthermore, our findings illustrate that the purl plated sensors benefit from the vertical movement of non-conductive yarns, which promotes enhanced contact between adjacent conductive yarns, thereby improving both the stability and sensitivity of the sensors. The practicality of these sensors is confirmed through bending cycle tests with an in situ monitoring system, showcasing the purl plated sensors’ exceptional reproducibility, with a standard deviation of 0.015 across 1000 cycles, and their superior sensitivity, making them ideal for wearable devices designed for real-time joint movement monitoring. This research highlights the critical importance of conductive yarn placement in sensor efficacy, providing valuable guidance for crafting advanced textile-based strain sensors.
Journal Article
Knit-Pix2Pix: An Enhanced Pix2Pix Network for Weft-Knitted Fabric Texture Generation
Texture mapping of weft-knitted fabrics plays a crucial role in virtual try-on and digital textile design due to its computational efficiency and real-time performance. However, traditional texture mapping techniques typically adapt pre-generated textures to deformed surfaces through geometric transformations. These methods overlook the complex variations in yarn length, thickness, and loop morphology during stretching, often resulting in visual distortions. To overcome these limitations, we propose Knit-Pix2Pix, a dedicated framework for generating realistic weft-knitted fabric textures directly from knitted unit mesh maps. These maps provide grid-based representations where each cell corresponds to a physical loop region, capturing its deformation state. Knit-Pix2Pix is an integrated architecture that combines a multi-scale feature extraction module, a grid-guided attention mechanism, and a multi-scale discriminator. Together, these components address the multi-scale and deformation-aware requirements of this task. To validate our approach, we constructed a dataset of over 2000 pairs of fabric stretching images and corresponding knitted unit mesh maps, with further testing using spring-mass fabric simulation. Experiments show that, compared with traditional texture mapping methods, SSIM increased by 21.8%, PSNR by 20.9%, and LPIPS decreased by 24.3%. This integrated approach provides a practical solution for meeting the requirements of digital textile design.
Journal Article
Knitting out the Touch Hunger: A Research Project to Design the Overcoming of Post-Pandemic Emotional Fear of Touching
The pandemic has altered human attitudes affecting common gestures: hugs, kisses, hands shaking, all the human behaviors related to touching have become dangerous, generating what scientists called “touch hunger”. If with touch we define ourselves as our form of being in the world we are in front of complex touch-related costs that make people today feel lost and distant. Starting from this premise, the research team in the knit design of ***affiliation*** has been working to relate scientific data and innovative design languages to help people in redefining and rediscover the human attitudes that connect us with others. Exploiting digital technologies, innovative materials, and tactile surfaces belonging to the world of knitwear and textiles, researchers designed an emotional and sensorial journey to guide participants in overcoming the fear of touching, finding new possible ways of being together.
Journal Article
An Improved Neural Network Model Based on DenseNet for Fabric Texture Recognition
In modern knitted garment production, accurate identification of fabric texture is crucial for enabling automation and ensuring consistent quality control. Traditional manual recognition methods not only demand considerable human effort but also suffer from inefficiencies and are prone to subjective errors. Although machine learning-based approaches have made notable advancements, they typically rely on manual feature extraction. This dependency is time-consuming and often limits recognition accuracy. To address these limitations, this paper introduces a novel model, called the Differentiated Leaning Weighted DenseNet (DLW-DenseNet), which builds upon the DenseNet architecture. Specifically, DLW-DenseNet introduces a learnable weight mechanism that utilizes channel attention to enhance the selection of relevant channels. The proposed mechanism reduces information redundancy and expands the feature search space of the model. To maintain the effectiveness of channel selection in the later stages of training, DLW-DenseNet incorportes a differentiated learning strategy. By assigning distinct learning rates to the learnable weights, the model ensures continuous and efficient channel selection throughout the training process, thus facilitating effective model pruning. Furthermore, in response to the absence of publicly available datasets for fabric texture recognition, we construct a new dataset named KF9 (knitted fabric). Compared to the fabric recognition network based on the improved ResNet, the recognition accuracy has increased by five percentage points, achieving a higher recognition rate. Experimental results demonstrate that DLW-DenseNet significantly outperforms other representative methods in terms of recognition accuracy on the KF9 dataset.
Journal Article
Structure design of unidirectional moisture transfer fabric with high wool content based on multiple differential effects
The natural functions of wool fiber, such as natural air permeability, dryness of skin contact, have advantages in the development of sports fabric. So far, the development of unidirectional moisture transfer fabric with high content wool is not yet mature. This paper novelly designed a ply yarn formed by different content of wool and nylon yarn and a double-sided knitted fabric with different wool content in the inner and outer layers based on unidirectional moisture transfer principles. The results showed that the difference in wool content between layers had a significant effect on the cumulative one-way moisture index, while small yarn linear density change had no significant effect on this index, but had a significant effect on the surface diffusion rate. The cumulative one-way moisture conductivity indexes of samples 1/9, 1/8, 1/6, 1/7 and 1/5 were all above 400, with the rating of 5, indicating excellent one-way moisture conductivity, so the one-way moisture conductivity of the fabric with high wool content is realized. The application of this technology will provide better moisture absorption and perspiration performance and wearing comfort for wool sportswear fabrics.
Journal Article
Structural influence of knitting patterns on mechanical, electrical and durability characteristics of conductive fabrics
This study investigates the influence of knitting structure on the electrical performance and durability of conductive fabrics for smart clothing applications. Fabrics with plain (PN), cable (CB), and miss (MS) stitches were knitted using cotton and silver-coated yarns, and subjected to ten cycles of artificial perspiration immersion and washing (PW). Despite using the least amount of conductive yarn, the MS structure exhibited the lowest surface resistance (3.18 Ω/m
2
) and the highest heating temperature (156 °C), due to its floating segments. In contrast, the CB structure showed the highest resistance (3.85 Ω /m
2
) and the lowest heating performance (129 °C), while the PN structure demonstrated intermediate performance (3.38 Ω /m
2
, 148 °C) with the most stable pre-treatment resistance. These results suggest that knitting structure affects performance more than conductive yarn quantity. After 10 PW cycles, all fabrics exhibited silver oxidation, delamination, and cotton fibrillation. Resistance increased (PN: 6.72 Ω/m
2
; CB: 10.79 Ω/m
2
; MS: 4.72 Ω/m
2
), yet the MS structure retained the highest heating temperature (97 °C), indicating excellent electrothermal durability. However, MS showed significant dimensional shrinkage (−64%), highlighting a trade-off between performance and structural stability. Optimizing knitting structure is essential for enhancing the functionality, durability, and practicality of conductive fabrics in smart clothing applications.
Journal Article
Hyperlocal Knitting: Building Sustainable Networks with 3D Seamless Technology
Traditional manufacturing models are increasingly unsustainable and vulnerable to environmental and geopolitical challenges. Industry 4.0 principles integrated with cutting-edge 3D knit technology form the basis for the proposed Future Factory Network (FFN). The FFN enables localised production on a global scale, where the physical location of designers and consumers becomes irrelevant; aligning with circular economy principles and offering extensive design options. Produced with near-zero waste, minimal post-processing, and highly customisable, 3D knit products offer an ideal solution for sustainable manufacturing.This research will develop and test a novel manufacturing model that leverages existing resources to adopt slow fashion principles within a scalable, efficient, and agile framework. The FFN model aims to enhance brand sustainability and circular economies through smart manufacturing systems and advanced 3D knit technology. Whilst this model addresses manufacturing processes, the behavioural shifts needed among consumers and stakeholders are intrinsic issues beyond the scope of this research.
Journal Article
Thermal Comfort Properties of Biodegradable Hemp and Polylactide Fiber Knitted Fabrics
According to the global strategy of Green course, the production of sustainable textiles using different biodegradable fibres has immense potential for the development of sustainable products. Using one of the most sustainable biobased pure hemp and polylactide fibers yarns, four new biodegradable three-layer weft knitted fabrics with good thermal comfort properties were developed. The inner layer (worn next to the skin) and the middle layer of the knits were formed of hydrophobic polylactide fibers, the outer layer of different amounts (36–55%) of hydrophilic natural hemp fibers. Biodegradable polylactide fiber yarns were used as a replacement for conventional petroleum-based synthetic fibers. Natural hemp fibers are one of the most sustainable fibers derived directly from Cannabis sativa L. plants. The properties of the knitted fabrics were analysed and compared under thermoregulatory-moisture management, thermal resistance, air and water vapour permeability-properties. The results showed that all newly developed knits are ascribed to ‘moisture management’ fabrics according to the summary grading of all indices of moisture management parameters. In addition, it was found that the highest overall moisture management capability is related to the quantity of natural hemp fiber composition in different knitting structures. Based on the overall moisture management capacity (OMMC) index and thermal resistance values of developed knitted fabrics, the performance levels for these materials contacting the skin and intended for the intermediate layer were determined.
Journal Article