Hasil untuk "Textile bleaching, dyeing, printing, etc."

Juro Živičnjak, Antoneta Tomljenović, Igor Zjakić

During use, the surface of textile fabrics is prone to wear, which can cause changes such as pilling. Pilling (entanglement of fibers) is primarily assessed using the standard visual method EN ISO 12945-4:2020, but it can also be quantitatively measured by instrumental methods with image analysis software. Due to non-uniform digital imaging conditions, such as variations in magnification and analyzed surface area, the assessed area is often inconsistent. As a result, the total percentage of the fabric specimen surface area covered with pills is often omitted. To ensure uniform digital imaging, an innovative apparatus was designed and constructed in this research and applied to woven fabrics made from 100% cotton, wool, viscose, polyamide 6.6, polyester, and acrylic fiber. Pilling in the fabric specimens was induced by rubbing with the Martindale pilling tester (EN ISO 12945-2:2020) using two different abradant materials, through predefined pilling rubs ranging from 125 to 30,000. Pilling assessment was conducted using both the visual method and the improved instrumental method, following established grading classes based on the total percentage of the fabric specimen surface area covered with pills. The research results highlight the importance of uniform digital imaging and digital grading, as these demonstrate the high comparability of pilling grades assigned by the standard visual method while providing better distinction between consecutive grades.

T. Nageshkumar, Prateek Shrivastava, L. Ammayapan et al.

Machine learning model coupled with graphical user interface was developed to predict mechanical properties of flax fiber. The experiment was conducted using test setup which applies constant rate of loading (CRL). Flax fiber was tested under five independent parameters i.e, type of fiber (Tf), moisture content (Mc), weight of sample (Ws), gauge length (Gl) and loading rate (Lr) with response variables, i.e., breaking load and elongation. In this study, a total of 432 patterns of input and output parameters obtained from laboratory experiments were used to develop machine learning algorithms (Random forest, support vector, and XGBoost). Among the machine learning models, random forest regressor yielded high R2 value, low mean squared error (MSE), and mean absolute error (MAE). The SHapley Additive exPlanations (SHAP) analysis was performed and found sample weight and gauge length were the most influential features for breaking load and elongation, respectively. The developed GUI, integrated with a random forest regressor, predicted breaking load and elongation with an error range of −2.5% to 2.3% for raw fiber and 1.5% to 6.5% for cleaned fiber. The developed GUI coupled random forest regressor can be used to predict the mechanical properties of fibers with ease.

José Augusto de la Fuente León, Ma. Alejandrina Martínez Gámez, José Luis Lucio Martinez et al.

In this study, we demonstrate a proof of principle of an all-fiber random laser due to the plasmonic effect. This was achieved with a fiber co-doped with bismuth/aluminum/yttria/silver in which a microsphere (microcavity) at the fiber’s tip was made using a splicing machine. The presence of bismuth and silver nanoparticles in the fiber along with bismuth–aluminum phototropic centers stands behind the observed phenomenon. The effect can be attributed to the in-pair functioning of this unit as an active medium and volumetric plasmonic feedback, resulting in lasing at 807 nm under 532 nm pumping with a notably low (~2 mW) threshold.

علیرضا طاهری مقدم, سمانه کاظم نژاد

گنبد سبز مشهد یکی از بناهای تاریخی دوره صفویه و تزیینات اصلی این آرامگاه شامل کاشی‌کاری‌های بیرونی متعلق به دوره پهلوی است. هدف از این پژوهش، شناسایی پالت رنگی کاشی‌کاری‌های گنبد سبز و تطبیق کمّی رنگ‌ها براساس سیستم رنگی NCS است. نظر به اهمیت و نقش محوری عنصر رنگ در هنر کاشی‌کاری ایرانی، این پژوهش در پی پاسخ به دو پرسش اساسی است: نخست آنکه معادل‌سازی کمّی پالت رنگی کاشی‌های بنای گنبد سبز براساس سیستم استاندارد رنگی NCS چیست؟ و دوم اینکه میزان فراوانی و قدرت رنگی در پالت کاشی‌کاری بنای گنبد سبز چگونه است و رنگ‌های غالب آن کدام هستند؟ روش گردآوری داده‌ها بر اساس مطالعات میدانی میدانی و از طریق انطباق سیستم رنگ NCS با کاشی‌های اصیل بنا است. در نتیجه تطبیق رنگ‌ها، ۱۲۲ کد رنگی از ۷ خانواده رنگی به دست آمد و با روش توصیفی-تحلیلی مورد بررسی و ارزیابی قرار گرفت. علاوه بر این، رنگ های غالب، قدرت رنگی و درصد تنوع رنگی نیز مشخص شد. نتایج نشان می‌دهد که هر چه رنگ‌ها روشن‌تر باشند، دامنه رنگی آن‌ها گسترده‌تر می‌شود و هر چه به سمت رنگ‌های تیره نزدیک می‌شویم، تنوع رنگی آن‌ها کاهش یافته و رنگ‌ها یکنواخت‌تر می‌شوند.

Suman Kumar Das

India's growing population and economy have significantly increased the demand and consumption of natural resources. As a result, the potential benefits of transitioning to a circular economic model have been extensively discussed and debated among various Indian stakeholders, including policymakers, industry leaders, and environmental advocates. Despite the numerous initiatives, policies, and transnational strategic partnerships of the Indian government, most small and medium enterprises in India face significant challenges in implementing circular economy practices. This is due to the lack of a clear pathway to measure the current state of the circular economy in Indian industries and the absence of a framework to address these challenges. This paper examines the circularity of the 93-textile industry in India using the C-Readiness Tool. The analysis comprehensively identified 9 categories with 34 barriers to adopting circular economy principles in the textile sector through a narrative literature review. The identified barriers were further compared against the findings from a C-readiness tool assessment, which revealed prominent challenges related to supply chain coordination, consumer engagement, and regulatory compliance within the industry's circularity efforts. In response to these challenges, the article proposes a strategic roadmap that leverages digital technologies to drive the textile industry towards a more sustainable and resilient industrial model.

Chandler Moore, Michael Febbraro, Juan Manfredi et al.

Additive manufacturing offers efficient production of plastic scintillators with nontrivial geometries using vat polymerization, allowing fabrication of geometries which would be difficult or even impossible to produce using conventional subtractive manufacturing. This work presents a novel photocurable scintillator formula that includes coumarin 450 as a tertiary dye to enable high-resolution 3D printing via the manipulation of the 405 nm cure light. Bulk photocured and 3D printed (with and without tertiary dye) samples were compared through observational assessment and spectral response. All samples showed pulse shape discrimination between neutron and gamma events. Inclusion of the tertiary dye has minimal impact on emission spectrum and light output, but significant impact on print resolution as shown by comparison of printed high-complexity geometries and feature resolution test objects. With the use of a cure-limiting dye, unsupported features, such as freestanding pillars, were resolvable down to 0.7 mm. Even finer resolution at or below 0.1 mm was achieved in fully supported, integrated structures printed with off-the-shelf 405 nm desktop 3D printer. Scintillators demonstrated a light output up to 50% of EJ-200 with a PSD figure of merit up to 1.35 at 0.9-1.1 MeVee.

Soham Sarkar, Arnab Hazra

Coral bleaching is a major concern for marine ecosystems; more than half of the world's coral reefs have either bleached or died over the past three decades. Increasing sea surface temperatures, along with various spatiotemporal environmental factors, are considered the primary reasons behind coral bleaching. The statistical and machine learning communities have focused on multiple aspects of the environment in detail. However, the literature on various stochastic modeling approaches for assessing coral bleaching is extremely scarce. Data-driven strategies are crucial for effective reef management, and this review article provides an overview of existing statistical and machine learning methods for assessing coral bleaching. Statistical frameworks, including simple regression models, generalized linear models, generalized additive models, Bayesian regression models, spatiotemporal models, and resilience indicators, such as Fisher's Information and Variance Index, are commonly used to explore how different environmental stressors influence coral bleaching. On the other hand, machine learning methods, including random forests, decision trees, support vector machines, and spatial operators, are more popular for detecting nonlinear relationships, analyzing high-dimensional data, and allowing integration of heterogeneous data from diverse sources. In addition to summarizing these models, we also discuss potential data-driven future research directions, with a focus on constructing statistical and machine learning models in specific contexts related to coral bleaching.

Katarzyna Grabowska, Łukasz Januszkiewicz, Ewelina Pabjańczyk-Wlazło

This study explores the electromagnetic properties of flat textile products enhanced with carbon nanotube (CNT) threads used as the weft. CNT threads, fabricated via dry-spinning, were integrated into fabrics by wrapping them around steel threads to form a solenoid-like structure. To further improve electromagnetic attenuation, the CNT yarn was coated with graphene oxide and silver nanoparticles. The research assessed the impact of these modifications on the fabric’s ability to attenuate alternating electromagnetic fields across a range of frequencies. Results showed enhanced attenuation at 30 MHz and 500 MHz. CNT yarn wrapped around steel threads achieved attenuation efficiencies of 18 dB at 30 MHz and 22 dB at 500 MHz, with a notable 10 dB improvement at 30 MHz over the reference. Fabrics with CNT yarn coated with graphene oxide demonstrated similar performance to the reference fabric at 500 MHz and an 8 dB increase at 30 MHz. Similarly, CNT yarn with silver nanoparticles showed comparable performance at higher frequencies but matched the reference at 30 MHz. These results indicate significant enhancement at lower frequencies, with benefits diminishing at higher. This study underscores the potential of integrating CNTs and metal nanoparticles into textiles to improve electromagnetic shielding, especially across specific frequencies.

Zheng Yong, Qi Yexiong, Qi Xiaoling et al.

The helmet shell material featuring a gradient in bending is urgently required for the next-generation integrated helmet system. However, achieving a bending gradient design for orthogonal woven composites on a 3D shell surface is a significant challenge. Here, nonorthogonal woven composites at 30°, 45°, and 60° were fabricated, and their bending properties are discussed. Furthermore, their bending properties are compared to those of plain off-axis woven composites, which indicates that the bending linearity trend of nonorthogonal woven composites is evident. Notably, the bending strength of the 30° and 60° nonorthogonal woven composites is 66.9 and 67.4% higher, respectively, than that of the plain off-axis woven composites, and the bending modulus is 169.8 and 196.9% higher, respectively. Finally, a finite element analysis of the bending properties of nonorthogonal woven composites was conducted, and a stress analysis of the inner layers was also conducted. This work paves the way for designing gradient materials for helmet shells.

Silas M. Mbeche, Paul M. Wambua, David N. Githinji

Human hair (HH) is considered a waste material generated in salons and barbershops in most societies, especially highly populated cities, where it is produced in large quantities, thus rekindling the interests of academics. Several studies are ongoing on the possibility of utilizing it as a reinforcement in polymer composites, either in its raw form or as extracted keratin nanoparticles, due to its unique features and the current global emphasis on circular economy. The present review seeks to provide a synopsis of recent developments in the utilization of HH and keratin in polymer composites. Composites from different HH loading, length, and chemical treatments were made using hand lay-up and hot compression molding methods. HH has been investigated in diverse composite systems, encompassing HH/natural fiber composites, HH/synthetic fiber composites, and keratin-reinforced composites. Our study revealed that these innovative materials exhibit enhanced energy absorption capacity, mechanical strength, hardness, and thermal properties, positioning them as promising choices for a wide range of engineering applications. The review further revealed that keratin nano-particles can be extracted from waste HH using various methods such as reduction alkaline hydrolysis and can be used as reinforcement in polymer composites.

سحر آیار, حسن تاجیک, نیازمحمد محمودی et al.

در این تحقیق، چارچوب ایمیدازولات زئولیتی 67 (ZIF-67) و کامپوزیت زیستی کربوکسی متیل سلولز CMC/ZIF-67 (CMC/ZIF-67) سنتز شد. مواد سنتز‌شده با آنالیز‌های مختلف شناسایی شدند. سپس از کربوکسی متیل سلولز و کامپوزیت زیستی CMC/ZIF-67 برای حذف رنگزای مالاکیت گرین استفاده شد. نتایج نشان داد که قابلیت حذف رنگزا با کامپوزیت زیستی (35/92 درصد) بیشتر از پلیمر کربوکسی متیل سلولز (41/9 درصد) است. با افزایش مقدار جاذب، درصد حذف مالاکیت گرین نیز افزایش ‌‌می‌یابد. با افزایش مقدار جاذب، مکان‌های فعال سطح جاذب در دسترس‌‌‌تر‌ است و درصد حذف رنگزا بیشتر ‌‌می‌شود. درصد حذف رنگزا در مقادیر 1، 2، 3 و 4 میلی‌گرم جاذب کامپوزیت به ترتیب 25، 54، 79 و 35/92 درصد بود. با افزایش غلظت رنگزا، میزان حذف رنگزا کاهش یافت. میزان حذف رنگزا در غلظت‌های 20، 30، 40 و 50 میلی گرم در لیتر با کامپوزیت به ترتیب 35/92، 85، 79 و 71 درصد بود. وجود حلقه‌های ایمیدازول در ساختار ZIF-67 به عنوان لیگاند ‌‌می‌تواند یکی از دلایل اصلی ظرفیت جذب بالای کامپوزیت زیستی باشد. با توجه به پیوند‌های دوگانه در حلقه‌های ایمیدازول، برهم‌کنش‌های انباشتگی Π-Π با حلقه‌های آروماتیک مالاکیت گرین رخ ‌‌می‌دهد. این برهم‌کنش ویژه کامپوزیت زیستی را قادر می‌سازد تا ظرفیت بالای مالاکیت گرین را جذب کند. جذب رنگزا توسط کامپوزیت زیستی CMC/ZIF-67 از ایزوترم لانگمویر و سینتیک شبه مرتبه دوم پیروی ‌‌می‌کند.

Arun Aneja, Karel Kupka, Jiří Militký et al.

Textile products comprise approximately 10% of the total global carbon footprint. Standard practice is to discard apparel textile waste after use, which pollutes the environment. There are professional collectors, charity organizations, and municipalities that collect used apparel and either resell or donate them. Non-reusable apparel is partially recycled, mainly through incineration or processed as solid waste during landfilling. More than 60 million tons of textiles are burnt or disposed of in landfills annually. The main aim of this paper is to model the heterogeneous kinetics of hydrolysis of multicomponent textile waste containing polyester (polyethylene terephthalate (PET) fibers), by using water without special catalytic agents or hazardous and costly chemicals. This study aims to contribute to the use of closed-loop technology in this field, which will reduce the associated negative environmental impact. The polyester part of waste is depolymerized into primary materials, namely monomers and intermediates. Reaction kinetic models are developed for two mechanisms: (i) the surface reaction rate controlling the hydrolysis and (ii) the penetrant in terms of the solid phase rate controlling the hydrolysis. A suitable kinetic model for mono- and multicomponent fibrous blends hydrolyzed in neutral and acidic conditions is chosen by using a regression approach. This approach can also be useful for the separation of cotton/polyester or wool/polyester blends in textile waste using the acid hydrolysis reaction, as well as the application of high pressure and the neutral hydrolysis of polyester to recover primary monomeric constituents.

Carlos Rodriguez-Pardo, Dan Casas, Elena Garces et al.

We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i.e., the tileability). Existing methods for tileable texture synthesis focus on general texture quality, but lack explicit analysis of the intrinsic repeatability properties of a texture. In contrast, our TexTile metric effectively evaluates the tileable properties of a texture, opening the door to more informed synthesis and analysis of tileable textures. Under the hood, TexTile is formulated as a binary classifier carefully built from a large dataset of textures of different styles, semantics, regularities, and human annotations.Key to our method is a set of architectural modifications to baseline pre-train image classifiers to overcome their shortcomings at measuring tileability, along with a custom data augmentation and training regime aimed at increasing robustness and accuracy. We demonstrate that TexTile can be plugged into different state-of-the-art texture synthesis methods, including diffusion-based strategies, and generate tileable textures while keeping or even improving the overall texture quality. Furthermore, we show that TexTile can objectively evaluate any tileable texture synthesis method, whereas the current mix of existing metrics produces uncorrelated scores which heavily hinders progress in the field.

Shu Zhong, Miriam Ribul, Youngjun Cho et al.

The rise of Machine Learning (ML) is gradually digitalizing and reshaping the fashion industry. Recent years have witnessed a number of fashion AI applications, for example, virtual try-ons. Textile material identification and categorization play a crucial role in the fashion textile sector, including fashion design, retails, and recycling. At the same time, Net Zero is a global goal and the fashion industry is undergoing a significant change so that textile materials can be reused, repaired and recycled in a sustainable manner. There is still a challenge in identifying textile materials automatically for garments, as we lack a low-cost and effective technique for identifying them. In light of this, we build the first fashion textile dataset, TextileNet, based on textile material taxonomies - a fibre taxonomy and a fabric taxonomy generated in collaboration with material scientists. TextileNet can be used to train and evaluate the state-of-the-art Deep Learning models for textile materials. We hope to standardize textile related datasets through the use of taxonomies. TextileNet contains 33 fibres labels and 27 fabrics labels, and has in total 760,949 images. We use standard Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to establish baselines for this dataset. Future applications for this dataset range from textile classification to optimization of the textile supply chain and interactive design for consumers. We envision that this can contribute to the development of a new AI-based fashion platform.

Halil Faruk Karagoz, Gulcin Baykal, Irem Arikan Eksi et al.

The problem of text-guided image generation is a complex task in Computer Vision, with various applications, including creating visually appealing artwork and realistic product images. One popular solution widely used for this task is the diffusion model, a generative model that generates images through an iterative process. Although diffusion models have demonstrated promising results for various image generation tasks, they may only sometimes produce satisfactory results when applied to more specific domains, such as the generation of textile patterns based on text guidance. This study presents a fine-tuned diffusion model specifically trained for textile pattern generation by text guidance to address this issue. The study involves the collection of various textile pattern images and their captioning with the help of another AI model. The fine-tuned diffusion model is trained with this newly created dataset, and its results are compared with the baseline models visually and numerically. The results demonstrate that the proposed fine-tuned diffusion model outperforms the baseline models in terms of pattern quality and efficiency in textile pattern generation by text guidance. This study presents a promising solution to the problem of text-guided textile pattern generation and has the potential to simplify the design process within the textile industry.

P. S. N

Ngan Yi Kitty Lam, Jeanne Tan, Anne Toomey et al.

Abstract For the integration of conductive yarns in e-textiles, knitting offers structural versatility and malleability for wider product applications in the contexts of wearables and interiors. To enable mass adoption of conductive materials, it is imperative for users to be able to launder these materials as part of product maintenance. Interactive textiles knitted from polymeric optical fibres (POFs) and silver-coated conductive yarns are able to illuminate and change colours via integrated touch sensor systems. Current research only focuses on the washability and abrasion resistance of conductive yarns solely and not both POF and conductive yarn within the same fabric structure. This study is novel as it investigates the washability and abrasion resistance of POF and silver-coated conductive yarn integrated knitted textiles with different loop structures and the impact to their illuminative function. POFs were knitted within the same fabric structure by the inlay method using a 7-gauge industrial hand-operated flatbed knitting machine. This study examined how washing and abrasion affect POFs and silver-coated conductive yarn in five different knit structures, and the illuminative function of the knitted textiles. Washing and abrasion affected the resistance of conductive yarns. Scratches and bent POFs were observed after 20 gentle washing cycles. However, washing had minimal impact on the illuminative function of the knitted e-textiles examined in this study. The experiments provide evidence that e-textiles knitted with POFs and conductive yarns in the same fabric structure withstand washing and abrasion and thus have the potential for mass market adoption in fashion and interior applications.

Budhodeb Biswas, Soumya Ray, C. Majumder

Alberta Longhini, Michael C. Welle, Ioanna Mitsioni et al.

Identification of textile properties is an important milestone toward advanced robotic manipulation tasks that consider interaction with clothing items such as assisted dressing, laundry folding, automated sewing, textile recycling and reusing. Despite the abundance of work considering this class of deformable objects, many open problems remain. These relate to the choice and modelling of the sensory feedback as well as the control and planning of the interaction and manipulation strategies. Most importantly, there is no structured approach for studying and assessing different approaches that may bridge the gap between the robotics community and textile production industry. To this end, we outline a textile taxonomy considering fiber types and production methods, commonly used in textile industry. We devise datasets according to the taxonomy, and study how robotic actions, such as pulling and twisting of the textile samples, can be used for the classification. We also provide important insights from the perspective of visualization and interpretability of the gathered data.

S. Basak, T. Senthilkumar, G. Krishnaprasad et al.