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Wrinkling, a common manufacturing-induced defect, can be delayed in dry woven fabrics by applying tension during processing. Such a known solution in industrial practice, is still not sufficiently implemented in the modeling and simulation of woven fabrics. This study proposes a new constitutive model considering fabric's inherent coupling, non-orthogonality, and non-linearity. First, the intrinsic coupling in question is distinguished from the typical coupling presented by the Hook's law. Then, the most influential forms of coupling are chosen and introduced to the model. Moreover, via experimental evidence, we reveal that the concept of inherent coupling raises a new issue: the load history dependency of the fabric. Accordingly, the base model was evolved to a hypoelastic form to capture this dependency. The stiffness functions of the introduced model are determined, considering the mechanical behavior of a typical polypropylene (PP)/glass plain weave and the underlying meso-scale sources of the coupling. An inverse method was employed to identify the unknown model parameters. While the basic loading modes such as the uniaxial tension and picture frame tests are utilized for model calibration, the more complex loading conditions such as the simultaneous biaxial tensile-shear test, which would be more comparable to actual forming processes, are selected as the independent datasets to validate accuracy of the model. Comparisons with the experimental results demonstrated that the coupled model predicts the behavior of the fabric more accurately than the uncoupled model. [ABSTRACT FROM AUTHOR]

Polyethylene terephthalate (PET) is a versatile polymer widely used in textiles because of its chemical stability, mechanical strength, and ease of processing. However, the increasing consumption of PET, particularly in the textile industry, has led to significant environmental concerns owing to its resistance to degradation. To address these issues, chemical recycling methods, particularly glycolysis, have attracted attention for depolymerizing PET into valuable monomers for repolymerization. This review focuses on recent advances in catalysts for PET glycolysis, with special emphasis on their application in textile recycling. We categorized the catalysts into homogeneous and heterogeneous types and discussed their effectiveness in reducing the reaction temperatures and times, thereby decreasing energy consumption and operational costs. Although homogeneous catalysts achieved efficient depolymerization at lower temperatures, their post-reaction separation and purification steps remain challenging and costly. In contrast, heterogeneous catalysts offer simpler separation processes but require significant energy input. Research on the application of glycolytic catalysts in fiber recycling was also highlighted, considering the substantial use of PET in the textile industry. Finally, we suggested future research directions for developing cost-effective and sustainable catalysts that are applicable to PET fibers with the aim of enhancing the efficiency and environmental sustainability of PET recycling processes. [ABSTRACT FROM AUTHOR]

In contrast to the common small target detection problems, it is more difficult to locate and identify the small surface defects of fabric due to its own texture and complex background interference. Therefore, this paper proposes an effective detector for small-scale block defects on fabric surface by taking advantage of the backbone which integrates the Coordinate Attention module to enhance the acquisition of small-scale block defect location information. The FPN + PAN multi-scale detection structure is adopted to effectively integrate the feature information between different levels and deal with the multi-scale problem of defects. In the Neck section, a small target detection layer is set to expand the receptive field to prevent the loss of small-scale defect feature information. Moreover, we propose to use the GhostBottleneck module instead of the ordinary downsampling process to eliminate redundant convolutional calculations to improve the detection speed. The experimental results show that the optimal detection results of 0.56 and 0.842 are achieved in the detection recall and accuracy of the public fabric dataset; compared with other detectors, the result of small-scale defect detection rate is reduced by at least 2.7%, and the detection process meets the real-time requirement of automatic defect detection, which verifies the effectiveness of our method. Code and data are available at: https://github.com/VIMLab-hfut/SCG-NET. [ABSTRACT FROM AUTHOR]

The cellulase enzyme has significant potential for applications in textile chemical processing, offering an environmentally friendly alternative to traditional chemical methods. In conventional enzymatic treatments, the enzymes act as biocatalysts and are typically discarded as effluent after completing their function. However, the single-use nature, high production costs, and limited biological activity of cellulase enzymes hinder their widespread commercial use in the textile industry. This study focuses on the immobilization of a commercial cellulase enzyme onto two distinct reversible soluble–insoluble polymers Chitosan and Eudragit S-100 for the recovery and reusability. Chitosan and Eudragit were chosen as support materials due to their pH-dependent soluble–insoluble properties. These properties allow them to act as homogeneous catalysts in their soluble phase during application (since textile materials are heterogeneous) and enable easy recovery in their insoluble phase for subsequent reuse. The immobilization process was optimized to achieve maximum enzyme activity with ideal enzyme loading percentages. After immobilization on chitosan, the cellulase retained 92% of its initial activity with a loading efficiency of 73.7%, while on Eudragit, it maintained 86.5% activity with a loading efficiency of 75.6%. Fourier-transform infrared spectroscopy (FTIR) was employed to confirm the successful attachment of the cellulase enzyme to the polymers. The immobilized cellulase demonstrated equivalent fading effects compared to the native cellulase in terms of color depth (K/S value) and color metrics (L*, a*, b*), while also reducing physical damage and back-staining—common issues in the traditional denim fading process. Scanning electron microscopy (SEM) and back-staining analyses of the denim samples provided further evidence of these benefits. Moreover, the immobilized cellulase maintained approximately 50% of its activity even after recovery from five denim washing cycles, showcasing the potential for reuse across multiple applications, particularly in textile processing. Thus, cellulase immobilized on chitosan and Eudragit S-100 represents a promising solution for the sustainable use of enzymes in the textile industry. [ABSTRACT FROM AUTHOR]

Composting offers a solid waste management alternative to landfilling resulting in soil amendment products with fertilization and moisture retention benefits and collateral methane reduction by diverting organic wastes from anaerobic landfill environments. An increasing array of materials and products are marketed as compostable, though only a limited range of these are covered by certification schemes. Greater accessibility and deeper understanding of compostability testing is needed to promote meaningful evaluation of the viability and optimal conditions for composting wider ranges of materials. This article describes various critical aspects of laboratory-scale methodology that can be optimized for more consistent, accurate, and efficient testing. While most of the reviewed studies are based on standardized international test methods, modifications to vessel design, medium, control systems, and evaluation show promise. Learnings are also drawn from biodegradation tests using soil and aqueous media. Particular consideration is given to evaluating compostability of textiles, including nonwovens, which today are primarily disposed of in landfills yet have the potential to supplement organic carbon in compost mixtures with nitrogen rich food waste. Furthermore, biodegradation properties of both natural and man-made textile fibers have received growing attention in recent years. Fiber fragments found in oceans and the surge of disposable face coverings used during the COVID-19 pandemic have been widely covered in the mainstream media, highlighting the importance of understanding biodegradation properties for textiles. This review consolidates and organizes diverse and essential procedural details reported in various standards and studies with the goal of encouraging and guiding successful implementation of compostability testing more broadly in laboratories. Certain gaps in test methodologies are identified to help focus future research. Reliable, accessible testing is crucial to expand the beneficial impacts of composting in waste management. [ABSTRACT FROM AUTHOR]

Voting plays a vital role in democratic societies. Adopting electronic voting can effectively increase voter participation and significantly reduce the financial burden on the organizers. In recent years, with the prevalence of blockchain technology, numerous blockchain-based electronic voting schemes have emerged. Compared with traditional electronic voting schemes, they have more favorable security features. However, existing schemes generally suffer from inefficient voting procedures, limited functionality, and dependence on specific blockchain platforms, making them challenging to deploy in diverse voting scenarios. This paper proposes an efficient and versatile electronic voting scheme on blockchain that addresses these problems using our proposed smart contract-based aggregated blind signature, zero-knowledge proofs, and threshold encryption scheme. In the paper, the scheme's various features, including security, are analyzed in detail, and the scheme is deployed and tested on the Hyperledger Fabric and Ethereum blockchain platform. The experiment results demonstrate that the voting scheme satisfies the security requirement, and it has outstanding advantages in performance. [ABSTRACT FROM AUTHOR]

Spindle whorls are a kind of spinning tools of crucially important in prehistoric period. Haidai region is one of the main historical and cultural areas of prehistoric China, and had a relatively developed textile handicraft production, where a large number of spindle whorls were unearthed, including many decorated ones. In order to make clear the uses of the decorated spindle whorls and the meanings of the patterns on them, the data of prehistoric decorated spindle whorls unearthed in archaeological sites of the period from Beixin Culture to Yueshi Culture in Haidai region of China were collected and analyzed, and the morphological data (dimension and shapes) of them were compared with that of undecorated ones. The patterns on the decorated spindle whorls were analyzed and classified, and the characteristics of the patterns in different cultural periods were studied, and based on this, the purpose of making decorated spindle whorls and the function and meanings of the patterns were discussed. The results showed that the range of morphological data of the decorated spindle whorls in Haidai region is consistent with that of undecorated ones belonging to the same period and unearthed in the same site, while compared with undecorated spindle whorls, decorated ones are more affected by regional culture and thus show different features from the ones unearthed in other archaeological sites belonging to the same cultural period. It is concluded that the most basic function of the decorated spindle whorls should be as practical tools for normal spinning production, and the patterns on them should have multiple meanings such as practical, decorative, distinctive and symbolic. The study of decorated spindle whorls can help us understand the prehistoric regional culture in Haidai region. [ABSTRACT FROM AUTHOR]

This study introduces a methodology to easily estimate the composition of textile fibre mixtures, whether they are arranged in fabrics or fibre bundles, in order to assist the examination of fibre release from textiles, simplifying the process of analysis. The proposed method consists of the deconvolution of DTG signals, and the identification of the constituent associated with each peak. To assess the effectiveness of this method, the study analyses raw textile fibres, both individually and blended in different systems to characterize the thermal degradation of each one and their interaction when mixed. Such analysis of raw fibres is applied to commercial fabrics with known compositions and their released fibres in pilling experiments. The studied fibres are polyester (PET), cotton, wool and acrylic, while the blends encompass the systems PET + cotton, PET + wool, PET + cotton + acrylic. Hence, the method is evaluated. It can be applied to fast identification of textile compositions and can further aid in the identification of fibres in pilling tests. [ABSTRACT FROM AUTHOR]

Copyright of Österreichische Wasser- und Abfallwirtschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a textile defect detection method is introduced based on à trous wavelet transform. The processing procedure has two stages: training and testing. In each stage, the images are decomposed using à trous wavelet transform, and the approximate sub-bands are extracted at an appropriate level. This transform is a time-invariant transform. It is useful for several applications, such as textile defect detection. The purpose of the proposed method is to reinforce the energy of the defected area and reduce the energy of the background to allow the detection process, efficiently. The threshold values are energy estimations for rows and columns. The method is applied on different datasets; such as dataset 4 of DAGM dataset, the dataset from industrial automation research laboratory and our own practical dataset. [ABSTRACT FROM AUTHOR]

Fabric defect detection has always been a key issue, and it positively correlated its efficiency with productivity. From manual visual methods to machine vision and deep learning-based techniques, a variety of methods have been studied to improve production efficiency and product quality. Although deep learning-based methods have proven to be powerful tools for segmentation, there are still many pressing issues that need to be addressed in practical applications. First, the scarcity of defective samples compared to normal samples can cause data imbalance and thus affect accuracy. Second, high real-time performance is also required in the actual detection process. To overcome these problems, we propose a high real-time convolutional neural network, named Mobile-Deeplab, to implement end-to-end defect segmentation. In addition, we proposed a loss function to consider the fabric image sample imbalance problem. We evaluated the performance of the model with two public structured datasets and three self-constructed structured datasets. The experimental results show that the segmentation method has better segmentation accuracy than other segmentation models, which verifies the segmentation effect of the method. In addition, 87.11 frames per second on a 256 × 256 size image meet industrial real-time requirements. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to explore structural design of conductive core-spun yarn woven composites to improve their electromagnetic shielding effectiveness. Conductive core-spun yarns were woven into single-layer and double-layer fabrics with different structural parameters, and their electromagnetic shielding effectiveness was compared. Between different structures, in general, woven composites with high compact ratio have high shielding effectiveness. However, for honeycomb structures, due to their honeycomb appearance, they exhibit a higher electromagnetic shielding effectiveness when its compact ratio is slightly lower than other structures. The electromagnetic shielding effectiveness of double-layer woven composites is higher than that of single-layer woven composites. In double-layer woven composites, especially double-layer honeycomb woven composites with opposite sides, their electromagnetic shielding effectiveness is the best. The electromagnetic shielding effectiveness of woven composites can be improved through structural design. For example, the compact rate of the composite is increased, the surface of the composite is formed into a concave and convex shape, the number of conductive core-spun yarns per unit area is increased, and a double-layer or multi-layer structure with intermediate air layer and multiple reflections is formed. [ABSTRACT FROM AUTHOR]

While woven fabric is integral to carbon fiber reinforced polymer (CFRP) plates manufacturing, there has been a notable absence of research focused on the nonlinear detection of CFRP woven fabric. This article employs nonlinear acoustic methods to assess the response of CFRP plates, including unwoven, twill weave, and plain weave structures, to surface microcrack damage. A set of air-coupled nonlinear ultrasonic automatic detection systems was designed to acquire the amplitudes of the fundamental frequency and the second harmonic in the received signal, enabling the calculation of relative nonlinear parameters. Furthermore, the nonlinear cumulative effect was verified by varying the propagation distance. The experimental results highlight the significant influence of woven fabric on the outcomes of nonlinear acoustic testing. Specifically, the nonlinear response of unwoven CFRP to microcracks is the most pronounced, followed by twill weave CFRP plates. Notably, plain weave CFRP can significantly hinder the effectiveness of nonlinear acoustic detection. In cases where CFRP plates are loaded with such woven fabric, there is a potential for lower relative nonlinear parameters when damaged compared to their healthy state. [ABSTRACT FROM AUTHOR]

Methods of qualitative evaluation of performance properties of textile elastomers used in implantology are reviewed. Development of methods of qualitative analysis of functionality of textile elastomers as well as numerical prediction of various deformation processes is urgent because this facilitates improvement of their functionality and enhancement of their competitiveness. [ABSTRACT FROM AUTHOR]

Green and facile route was employed for development of smart flame-retardant, antibacterial and reinforced textile fabric's coatings. The multifunctional coatings were fabricated from sustainable chitosan functionalized via one pot method with α-aminophosphonates with different groups (methyl and phenyl groups). Phenyl- and methyl-based α-aminophosphonates were grafted on chitosan chains individually. Additionally, magnetic chitosan-Fe3O4 nanoparticles functionalized α-aminophosphonates-based phenyl moiety were also prepared. Moreover, tetra-n-butylammonium hexafluorophosphate was also dispersed in coating dispersion. The different prepared functionated chitosan was then exploited as efficient flame-retardant, reinforced and antibacterial-based multifunctional coatings for cotton fabrics. Different mass loadings of methyl- and phenyl-based functionalized chitosan and magnetic chitosan were dispersed in chitosan solution and then coated on cotton surface. The influence of mass loading and different side groups was studied. Flammability, tensile strength and antibacterial properties of developed cotton fabrics were evaluated. The flammability of coated cotton fabrics was strongly improved achieving reduction in rate of burning by 48% compared to uncoated one. This is in addition to LOI value of 23.5% compared to 18% for uncoated fabric. This is due to the influence of organic phosphate in coating layer which stimulates the formation of protective char layer. The tensile strength of coated fabrics was improved recording 29% enhancement compared to uncoated one. Moreover, the developed coating layer strongly inhibits the growth of well-known bacterial strains Escherichia coli and Staphylococcus aureus, achieving clear antibacterial inhibition zones of 16.7 and 23.6 mm, respectively. Additionally, the flame retardancy mechanism was proposed and elucidated. [ABSTRACT FROM AUTHOR]

A Raman spectroscopic study of radiation-synthesized tetrafluoroethylene telomers with different terminal groups was carried out for the first time. The influence of gamma radiation on the molecular structure of telomers and hydrophobic coatings of aluminoborosilicate glass fabric based on them has been studied. Changes induced by gamma radiation in the molecular and morphological structures of the telomers have been observed. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the feasibility of substituting the conventional inorganic alkalis (Na2CO3) with different organic alkoxide5s to explore the potential of replacing aqueous dyeing medium with environmentally friendly solvents like ethanol (EtOH). The study aimed to assess the dyeing characteristics, such as exhaustion, fixation, fastness qualities, physical properties, and environmental implications when EtOH and water were present, while also examining the impact of alkoxides. Time-gated Raman spectra provide insights into the allomorphic forms of cellulose-II after the alkoxide pretreatment, which significantly influences the overall reactivity of cellulose fibers and their dye absorption capabilities. Notably, organic alkoxide pretreated dyeing demonstrated improved color strength, exhaustion, and fixation efficiency compared to Na2CO3 pretreatment, as one of the work's key findings. For instance, the conventional dyed fabric exhibits 3.4 times lower color strength than alkoxide-based (RR239 + C2H5ONa 3 g/L + EtOH) dyeing, showing substantial potential for upscaling at commercial level as it requires no chemical auxiliaries (such as NaCl and Na2CO3). Importantly, the alkoxide pretreatment does not significantly alter the physical, mechanical and fastness properties of the fabrics, which are comparable to both the pristine and aqueous dyed fabrics, as confirmed by scanning electron microscopy experiments. The effluent's color intensity resulting from dyeing with C2H5ONa + EtOH (3 g/L) is approximately 24 times lower (250 Pt/Co) in comparison to the aqueous dyeing using 40 g/L of NaCl and 20 g/L of Na2CO3 in an aqueous medium (6154 Pt/Co). Apart from the color intensity, alkoxide pretreatment and EtOH-based dyeing drastically reduce the COD, BOD, TDS, TSS and total hardness values, which could have cost-saving implications for wastewater treatment plants (WWTPs). [ABSTRACT FROM AUTHOR]

Shoe upper materials are often subjected to repeated creasing in use, which affects their shape retention and aesthetic. The crease recovery property of fabrics is an important property that affects its serviceability. Six variants of warp-knitted jacquard spacer fabrics with different jacquard layer structures were selected and the crease recovery angles on six orientation angles were tested for 30 cycles. The relationship between different orientation angles and crease recovery angles was studied and the comprehensive index for evaluating the crease recovery property of fabric was extracted by the grey relation analysis. The effect of the jacquard layer structure and the number of cycles on crease recovery property was analyzed with MATLAB. The results indicated that the crease recovery angles on different orientation angles were significantly different, and the comprehensive index could evaluate the crease recovery property more reasonably. The influence of the 3-needle underlap and the chain underlap on the crease recovery property was the most significant, and the influence of the 3-needle underlap was greater than that of the chain underlap. Base on solid structure, reducing the proportion of mesh structure or adding semi-transparent structure could improve the crease recovery property. The change rule of crease recovery angles with the number of cycles could be elucidated by the exponential function. [ABSTRACT FROM AUTHOR]

Because of the serious separation of ammonia silicone oil and general hydrophilic resin in the drying process of fabric surfaces, it is difficult to use the composite to improve the hydrophilicity and hand feeling of textile. In this paper, the nonlinear polysiloxane-modified polyurethane was prepared by trihydroxy polyether, hydroxyl-terminated polysiloxane and isoflurane ketone diisocyanate, and blending with amino silicone oil. The results of SEM, EDS, TG and DSC revealed that nonlinear polysiloxane-modified WPU can effectively entangle with amino silicone oil in molecular level by common hydrophobic long chain (polysiloxane), which reduces the migration capacity of the auxiliary macromolecules transfer to the hydrophobic fiber gap effectively. Compared with the traditional amino-modified polysiloxane softener, the hydrophilicity and elasticity of the treated fabric with POET-8L/AJ have been significantly improved. The hydrophilicity of finished fabric reached from 360.8 to 97.9 s, and the crease recovery angle was raised from 9.8° to 62.0°, showing a new high elastic and supple style. [ABSTRACT FROM AUTHOR]

This study investigates the influence of various clamping methods on the ballistic performance of 3D shallow bend-joint woven fabrics (3DSBWF). Utilizing a mesoscopic yarn-level full-size model based on the finite element method (FEM), we analyze energy absorption patterns, backface deformation (BFD), and damage morphology under different clamping conditions. The results demonstrate that clamping methods significantly impact the ballistic performance of 3DSBWF. Specifically, fabrics clamped on the weft side exhibited ballistic limit velocities around 170 m/s and failure morphologies similar to four-side clamping, with minimal energy absorption and failure areas accounting for 25% and 15% of the total energy absorbed, respectively. The warp-side clamped scenario, with a ballistic limit velocity of approximately 220 m/s, ranked second in performance, while the corner-held configuration, with the highest ballistic limit velocity of around 260 m/s, experienced the highest out-of-plane displacement and energy absorption (55%), indicating a distinct failure mechanism. This study highlights the critical role of clamping methods in optimizing the design and performance of ballistic protective fabrics. [ABSTRACT FROM AUTHOR]

In this paper, a textile two-port multi-input multi-output (MIMO) half-circle antenna with corrugated borders is designed and fully analyzed for nearfield ultra-wideband biomedical diagnostic applications. The orthogonal polarization technique is introduced for mutual coupling reduction, signal quality, and imaging accuracy improvement. A low pass filter with significant out-of-band rejection is inserted between the two MIMO structural elements to improve the isolation from element to element. For simple system integration, A 50 ohms feed coplanar waveguide is suggested for each element. The entire antenna is constructed using textile materials, with the radiating element composed of conductor fabric. The substrate, on the other hand, is crafted from cotton material which possesses a relative permittivity of and a tan δ value of 0.025. The size of each antenna element has low-profile dimensions of 40 × 40 × 0.3 mm 3 . The measured results obtained demonstrate an extremely broad bandwidth extends from 2.5 GHz to 12 GHz. Omnidirectional radiation pattern is obtained, with 6 dBi maximum gain and 96% efficiency. In order to ensure flexibility, a range of bending conditions were analyzed, and the durability was assessed through wash ability testing. Safety was confirmed by measuring the specific absorption rate. Furthermore, time domain and MIMO performance assessments were performed to ensure its suitability for imaging systems. [ABSTRACT FROM AUTHOR]

The article focused on the problem of evaluating the effects of organizational and technical conditions on the continuity parameters of the manufacturing cycle. Research results on the effects of the blend composition together with given end-user properties of the textile materials on the parameters of their material capacity and processibility under nonconjugated manufacturing conditions are reported. A mechanism for evaluating the achieved level of continuity using organizational measures to reduce time losses upon changing the yarn fiber composition is proposed. The results enabled the development of standards for evaluating the continuity parameters with technical preparation of manufacturing and monitoring its effectiveness under given organizational and technical conditions. [ABSTRACT FROM AUTHOR]

This study proposes to study the sliding and slippage mesoscopic defects that appear during the preforming phase of dry reinforcements to produce complex composite shapes. For this purpose, experimental preforming tests were conducted on a plain weave fabric with low cohesion using a specific punch developed specifically for this purpose, and which combines the geometric facets of a square and a tetrahedron. The tests were conducted under several configurations varying the blank holder pressure intensity as well as its distribution, through the number and springs position that generate normal forces on the blank holders. The results showed that the corners of the geometry formed by orthogonal faces favor the appearance of mesoscopic defects and specifically slippage because of its severity. Sliding has shown itself to be very sensitive both to the singularities of the geometry where it appears, and to the heterogeneity of the pressure distribution of the blank holders. On the other hand, the sliding, which appears in the vicinity of the slippage on flat faces, is rather sensitive to the distribution of the pressure. The increase in the blank holder pressure, regardless of the conditions of its application, leads to an almost linear increase in the extent and number of these mesoscopic defects. [ABSTRACT FROM AUTHOR]

Untreated natural cotton fabrics are hydrophobic due to the high content of pectin and waxes, and the fabrics need to improve the wettability by scouring for the demands of the subsequent processing such as dyeing and finishing. In this study, a new scouring method for cotton fabrics was developed by pectin hydrolase combined with Fenton system (Fe2+/H2O2). The mechanism of pectin degradation by pectinase combined with Fe2+/H2O2 was investigated. The results showed that α-1,4-glycosidic bonds of water-soluble pectin could be degraded more efficiently by pectinase hydrolysis and Fe2+/H2O2 oxidation. The fabrics scoured with pectinase → Fe2+/H2O2 had less wetting time (4.25 s) and higher vertical wicking height (9.80 cm) compared to the fabrics treated with pectinase alone (> 120 s, 5.15 cm). The results of bleaching and dyeing experiments indicated that the whiteness and dyeing properties of the fabrics were not negatively affected by pectinase → Fe2+/H2O2 treatment, and the strength loss of the fabrics (11.79%) was lower than that of the fabrics with conventional NaOH treatment (20.16%). The results of the experiments in the pilot-scale machine showed that various performance indicators of the fabrics scoured by pectinase → Fe2+/H2O2 reached the level of the conventional NaOH treatment, which demonstrated the possibility for practical application of pectinase → Fe2+/H2O2 scouring. The cotton fabric scouring using pectinase → Fe2+/H2O2 is an effective and feasible new method with advantages such as environmental friendliness, mild treatment conditions, and good treatment effect. [ABSTRACT FROM AUTHOR]

The repaid renewal of fabric products increases the difficulty of retrieving existing products in enterprises. The unimodal retrieval methods can take advantage of historical production experiences, but they cannot meet the user's variable retrieval requests. Cross-modal image-text retrieval can quickly obtain technical descriptions or intentional images, which is an urgent demand in textile industries. In this paper, a novel cross-modal fabric image-text retrieval is proposed based on the fabric characteristics. A convolutional neural network with a compact structure and cross-domain connection is designed to represent the visual content of the fabric images. Then, the fine-tuned TinyBERT is applied to embed the textual description into a vector. The representations of the two modalities are aligned in the same Hamming space. Finally, a cross-modal retrieval strategy is designed based on the features of different modes. A fabric dataset that contains over 40000 pairs of images and texts is built as the benchmark to verify the proposed cross-modal image-text retrieval method. Extensive experiments have been performed on the built dataset. Results indicate that the proposed scheme is feasible and effective, being superior to the existing methods proposed for public datasets. The proposed method can provide referential assistance for the production crew in the fabric factory. [ABSTRACT FROM AUTHOR]

Black textile filaments having good mechanical strength and low water absorbency were spun from carboxymethylated cellulose cross-linked with amine functionalized carboxylated carbon nanotubes (afc-CNT). Incorporating 1 wt.% afc-CNT by crosslinking resulted in filaments with increased mechanical strength (tenacity ~ 1.2 cN/dtex) and a lower water absorbency (water absorbency ~ 0.62 g water/g fiber). The fabricated cellulosic textile filaments also demonstrated antibacterial activity and UV protection capability due to incorporation of afc-CNT. Excellent level of color fastness was achieved for the composite textile filaments because of a chemical bond formation between carboxymethylated cellulose and afc-CNT. [ABSTRACT FROM AUTHOR]

A simplified numerical method for simulating projectile penetration into fabric targets is proposed which takes into account the weavearchitecture, inter-yarn friction, projectile–fabric friction, and the transverse dimensions of the fabric. The necessary design parameters were selected from a comparison of experimental and calculated data. The proposed method was used to estimate the ballistic resistance of a composite armor reducing the after-penetration effect. The influence of the projectile shape on the ballistic resistance of fabric armor was investigated. [ABSTRACT FROM AUTHOR]

Copyright of Österreichische Wasser- und Abfallwirtschaft is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nanocellulose (NC) has been extensively researched for its diverse applications due to its inherent properties, natural availability, biodegradability, and non-toxicity. Biomass-derived NC is an appealing renewable material for replacing non-biodegradable petrochemical products in developing sustainable textiles. However, the textile industry must be able to adequately explore its potential applications in the textile sector. The textile industry produces global and diversified products with specific physicochemical properties that find applications in various facets of our lives. Therefore, it is essential to understand the structure–function relationship between textile substrates and NC and to adapt NC-based renewable approaches for sustainable materials to meet the growing demand and specific end uses of textile products. In this context, the primary aim of this review is to provide a comprehensive summary of recent research findings on NC applications in enhancing the textile wet processing performances, reinforcing fibres, imparting functional properties and coatings, dye adsorption from wastewater, etc. In addition, we highlighted current challenges and promising opportunities for developing functionalized NC to improve the cost-effectiveness and sustainability of textiles. The emergence of NC could expand the scope of green and sustainable nanomaterials in textile applications. [ABSTRACT FROM AUTHOR]

We present a novel learning method using a two-stream network to predict cloth deformation for skeleton-based characters. The characters processed in our approach are not limited to humans, and can be other targets with skeleton-based representations such as fish or pets. We use a novel network architecture which consists of skeleton-based and mesh-based residual networks to learn the coarse features and wrinkle features forming the overall residual from the template cloth mesh. Our network may be used to predict the deformation for loose or tight-fitting clothing. The memory footprint of our network is low, thereby resulting in reduced computational requirements. In practice, a prediction for a single cloth mesh for a skeleton-based character takes about 7 ms on an nVidia GeForce RTX 3090 GPU. Compared to prior methods, our network can generate finer deformation results with details and wrinkles. [ABSTRACT FROM AUTHOR]

Аbstract: With the rapid growth of industrialization/urbanization, the discharge of industrial effluent into the environment is increasing day by day. Conventional technologies for wastewater treatment possess difficulties in meeting all the requirements of wastewater discharge standards, and therefore, the exploration and establishment of new procedures to treat wastewater is a need of hour. Advanced oxidation processes (AOPs) possess an ability of strong oxidation, complete destruction and mineralisation, and no secondary pollutants. It has been proved in the literature that the addition of the catalyst in AOPs can largely enhance the treatment efficiency. The current study mainly emphasizes the practical applications of AOPS towards textile wastewater treatment. The study includes the pros and cons of individual AOPs, fixed photocatalysis, the dual process of photocatalysis and photo-Fenton coupled photocatalysis/photo-Fenton and biological treatment process. This article summarizes the useful theoretical foundation of the scientists and discusses the key areas that need more investigation in the fields of AOPs for industrial wastewater treatment. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerator (TENG) converts mechanical energy into valuable electrical energy, offering a solution for future energy needs. As an indispensable part of TENG, textile TENG (T-TENG) has incredible advantages in harvesting biomechanical energy and physiological signal monitoring. However, the application of T-TENG is restricted, partly because the fabric structure parameter and structure on T-TENG performance have not been fully exploited. This study comprehensively investigates the effect of weaving structure on fabric TENGs (F-TENGs) for direct-weaving yarn TENGs and post-coating fabric TENGs. For direct-weaving F-TENGs, a single-yarn TENG (Y-TENG) with a core-sheath structure is fabricated using conductive yarn as the core layer yarn and polytetrafluoroethylene (PTFE) filaments as the sheath yarn. Twelve fabrics with five different sets of parameters were designed and investigated. For post-coating F-TENGs, fabrics with weaving structures of plain, twill, satin, and reinforced twill were fabricated and coated with conductive silver paint. Overall, the twill F-TENGs have the best electrical outputs, followed by the satin F-TENGs and plain weave F-TENGs. Besides, the increase of the Y-TENG gap spacing was demonstrated to improve the electrical output performance. Moreover, T-TENGs are demonstrated for human-computer interaction and self-powered real-time monitoring. This systematic work provides guidance for the future T-TENG's design. [ABSTRACT FROM AUTHOR]

The problems of digital modeling of performance processes of polymer textile materials, which are of great importance for formulating recommendations for designing materials with improved functional properties, are discussed. [ABSTRACT FROM AUTHOR]

This paper presents research into the parameters affecting the real and virtual drapability of silk fabrics for traditional Hanbok. The KES-FB (Kawabata evaluation system of fabric) and the CLO (3D fashion design software) fabric kit system were used to evaluate the physical properties. The relationships between physical properties and the drape coefficient of silk fabrics were statistically analyzed. It was determined whether the objectified real fabric data could be used and applied as a database when implementing virtual clothing using a 3D virtual clothing program. The KES-FB properties and CLO fabric kit results indicated that the bending property and weight were significantly correlated with the drape coefficient. The bending rigidity and weight per unit area were influential parameters of the drape coefficient. The regression analysis results of both the KES-FB and the CLO fabric kit revealed that bending rigidity and thickness were statistically significantly correlated with the drape coefficient. The bending property was the most determining parameter for the drape coefficient of silk fabrics. A statistically significant difference between the real and virtual drape coefficients of stiffer silk fabrics was found. In the CLO 7.1 program, the accuracy of drape implementation of virtual fabrics was found to be useful when the bending stiffness of the actual fabric was less than 0.089 gf·cm2/cm and the elasticity was more than 1.03%. The results revealed that more research on the program modeling method considering the mechanical and physical properties of the real fabric and its structure is needed. [ABSTRACT FROM AUTHOR]

As the fashion industry increasingly adopts digital platforms, fashion designers face challenges in digitally exploring and selecting fabrics. This transition underscores the complexities in digitally capturing fabrics. Extending our previous research, this study specifically examined the relationship between designers' certainty and their perceptual accuracy in digital fabric evaluations because uncertainty can diminish confidence in digital assessments, leading to an over-reliance on physical samples and slowing digital progress. The research employed a comparative analysis, engaging designers in both digital and physical fabric evaluations. The objectives of this study were: to evaluate the precision of digital fabric representations against physical assessments; to assess designers' psychological certainty and its correlation with assessment accuracy; and to identify fabric attributes that are most prone to misrepresentation digitally. The findings indicated that there is no direct correlation between evaluation accuracy and psychological certainty. Notably, for drapability, digital evaluations exhibited both high levels of certainty and accuracy, implying potential enhancements in digital representations of other textile attributes. The results of this study will contribute to the development of advanced digital fabric evaluation systems that can accurately replicate the physical fabric experience, thus ensuring a high level of certainty. This, in turn, will enhance the reliability and effectiveness of fabric selection on digital platforms. [ABSTRACT FROM AUTHOR]

In the fast-paced fashion world, unique designs are like early birds, grabbing attention as online shopping surges. Fabric texture plays an immense role in selecting the perfect design. Indian Traditional textile motifs are pivotal, showing rich cultural origins and attracting worldwide art fanatics. Yet, technology-driven abstract forms are posing a challenge for them. The decline of handmade artistic ability due to computerization is concerning. Crafting new designs associated with the latest trends is time- consuming and requires diligence. In this work an interactive CBIR (content-based image retrieval) system is presented. It utilizes deep features from InceptionV3 and InceptionResNetV2 models to match query designs with a database of traditional Indian textiles. Its performance is tested with Caltech-101, Corel-1K state-of-the-art datasets, and Indian Textiles datasets and the results are shown to be finer than the existing approaches. The similarity-based fine-grained saliency maps (SBFGSM) approach is employed to visualize the importance of features. Our approach combines deep feature fusion with PCA dimensionality reduction and speeds up search using a clustering approach. Relevance feedback is employed to refine the retrievals. This tool is expected to benefit designers by accelerating the design cycles by bridging the gap between human creativity and A.I. assistance. [ABSTRACT FROM AUTHOR]

The influence of duration of continuous needlepunch loom operation for additional punching on conventional stress and relative tensile elongation of fabrics was studied. Duration of operation considerably affects the conventional breaking stress and has practically no effect on relative breaking elongation. During the first fabric passage through the needlepunch loom, the conventional stress in the longitudinal and vertical directions of the fabric in 2 days of continuous operation of the loom diminishes by 10−15 % and totally in 8 days, by 20−30 %. Conventional breaking stress correlates with /bulk density of the fabric. An equation is proposed for predicting the ratio between the change of the conventional stress and the bulk density of the fabric in the process of additional punching as a function of duration of continuous operation of the loom. [ABSTRACT FROM AUTHOR]

Fabric defect detection is an important part of the textile industry to ensure product quality. To solve the problems such as the difficulty of detecting small defects and the coexistence of multi-scale defects in fabric defect detection, a fabric defect detection method based on the feature purification fusion structure is proposed in this paper. Specifically, we improve the feature extraction network to enhance the network's ability to focus on small defective features and effectively reduce the model parameters. The existing methods use direct fusion between multi-level feature maps, which will lead to feature confusion. Therefore, we propose the feature purification fusion structure (FPF), which includes the semantic information supplementation strategy (SIS) and the detail information supplementation strategy (DIS). SIS extracts valid information from the deep feature map and supplements it to the shallow feature map, weakening feature information irrelevant to the shallow feature map detection task. DIS adaptively supplements the feature information required by the deep feature map detection task from the shallow feature map. FPF improves the ability of the network to detect small defects and effectively mitigates the aliasing effect generated during feature fusion. The experimental results show that compared to the baseline model YOLOv5s algorithm, our model achieved a 6.8% improvement in detection accuracy, with an average inference frame rate of 37.6 FPS, demonstrating better detection performance in fabric defect detection. Furthermore, extending this model to aluminum profile defect datasets also demonstrates strong performance. [ABSTRACT FROM AUTHOR]

Lyocell fibers are currently claimed as green fibers with good application prospects, but the major problem of fibrillation restricts further promotion and application. In this study, based on the theory of hydration lubrication at the solid/liquid interface, hexamethylene-diisocyanate trimer (HDIt), polyethylene glycol (PEG), and butanone oxime (MEKO) were used to synthesize a reactive hydrophilic triblock polyurethane (RHT-PU). RHT-PU could construct a water lubrication layer on the lyocell fiber surface via H-bonding between polyoxyethylene ether and water molecules, reducing the coefficient of friction (CoF) of the fiber interface in the water. It was found that PEG molecular weight and grafting density had significant effects on the CoF of modified fabrics, with CoFs decreased with increased PEG molecular weight and grafting density. In water, the CoFs of fabrics modified by RHT-PU were reduced from 0.45 to 0.32 and maintained lower CoFs at higher temperature and higher normal loads. After mechanical friction and household washing tests, the surface abrasion of modified fabrics were significantly improved and color fading grade of fabrics raised from 3 grade to 4–5 grade. Construction of water-lubricated interfaces effectively reduced fibrillation. [ABSTRACT FROM AUTHOR]

On-demand color switching systems that utilize synchronized semiconductor-catalyzed reduction and photothermal-accelerated oxidation in liquid/solid are highly appealing. Herein, on-demand single/multi-color switching fabrics have been constructed by using defective SnO2:Sb-based color switching systems. SnO2:Sb nanocrystals with the suitable doping concentration accord lattices with abundant free electrons, conferring high photocatalytic and photothermal performances. A well-crafted set of dual light-responsive semiconductor-catalyzed systems with rapid color change can be attained via the homogenous mixture of SnO2:Sb with suitable redox dyes to produce single-color (RGB (red, green, blue)) and multi-color transitioning (purple and green) systems. The illumination of these systems by 450 nm light triggers rapid photocatalytic discoloration, while irradiation by 980 nm light confers the photothermal effect that accelerates recoloration in air. Besides, the inks can be extended to rewritable fabrics by embedding the nanocrystals and redox dyes into hydroxyethyl cellulose (as the polymer matrix) and then coating on hydrophobic cotton fabrics to produce photo-switchable fabrics with excellent single/multi-color response. By exploiting the dual light interactions with the semiconductor-mediated systems, various images/letters can be remotely printed and erased on the rewritable fabrics which show promise for potential applications as information storage media and visual sensors. Importantly, the present rewritable fabric shows good stability and reversibility. The present work provides insights into the development of novel color-switching materials. [ABSTRACT FROM AUTHOR]

To improve the deficiencies of prefabricated autoclaved lightweight aerated concrete (ALC) panel such as susceptibility to cracking and low load-bearing capacity, a textile-reinforced mortar-autoclaved lightweight aerated concrete (TRM-ALC) composite panel was developed in this study. One group of reference ALC panels and five groups of TRM-ALC panels were fabricated and subjected to four-point flexural tests. TRM was applied on the tensile side of the ALC panels to create TRM-ALC. The variable parameters were the plies of textile (one or two), type of textile (basalt or carbon), and whether the matrix (without textile) was applied on the compression side of panel. The results showed that a bonding only 8-mm-thick TRM layer on the surface of the ALC panel could increase the cracking load by 180%–520%. The flexural capacity of the TRM-ALC panel increased as the number of textile layers increased. Additional reinforcement of the matrix on the compressive side could further enhance the stiffness and ultimate load-bearing capacity of the TRM-ALC panel. Such panels with basalt textile failed in flexural mode, with the rupture of fabric mesh. Those with carbon textile failed in shear mode due to the ultra-high tensile strength of carbon. In addition, analytical models related to the different failure modes were presented to estimate the ultimate load-carrying capacity of the TRM-ALC panels. [ABSTRACT FROM AUTHOR]

Commemorative textiles play a significant role in shaping Africa's cultural identity and expressions of power. Embedded within the cultural fabric of Africa, these textiles represent the intellectual and artistic vitality of Africa, serving as powerful symbols of its rich heritage and diversity. Through the lens of memory and commemoration, this study investigated how African cultural memory and identity are negotiated and conveyed through textiles. Drawing upon Bodnar's theoretical framework, the study examines secondary data, using a content analysis approach to discern how commemorative textiles recollect both official and vernacular cultural memories. The result showed that African commemorative textiles are mirrors of local cultures expressing social status, political authority, and economic worth, which play a crucial role in shaping collective memory, preserving cultural knowledge, and fostering a sense of belonging among communities. As preservers of the past and inspirations for the future, the textiles weave the African narrative of identity and power, perpetuating cultural heritage across generations. [ABSTRACT FROM AUTHOR]

One of the current important issues is the management of used textiles. One method is recycling, but the processes are characterized by a high environmental burden and the products obtained are of lower quality. Used textiles can be successfully used to produce SRF (solid recovered fuels). This type of fuel is standardized by ISO 21640:2021. In the paper, an analysis of used textiles made from fibers of different origins was performed. These were acrylic, cotton, linen, polyester, wool, and viscose. A proximate and ultimate analysis of the investigated samples was performed, including mercury and chlorine content. The alternative fuel produced from used textiles will be characterized by acceptable parameters for consumers: a lower heating value at 20 MJ/kg (class 1–3 SRF), mercury content below 0.9 µg Hg/MJ (class 1 SRF), and a chlorine content below 0.2% (class 1 SRF). However, the very high sulfur content in wool (3.0–3.6%) and the high nitrogen content in acrylic may limit its use for power generation. The use of alternative fuel derived from used textiles may allow 3% of the coal consumed to be substituted in 2030. The reduction in carbon dioxide emissions from the substitution of coal with an alternative fuel derived from used textiles will depend on their composition. For natural and man-made cellulosic fibers, the emission factor can be assumed as for plant biomass, making their use for SRF production preferable. For synthetic fibers, the emission factor was estimated at the level of 102 and 82 gCO2/MJ for polyester and acrylic, respectively. [ABSTRACT FROM AUTHOR]

Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by the renowned "gamucha" from India's northeast, from counterfeit powerloom imitations. Our study's objective is to create an AI tool for effortless detection of authentic handloom items amidst a sea of fakes. Six deep learning architectures—VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and DenseNet201—were trained on annotated image repositories of handloom and powerloom towels (17,484 images in total, with 14,020 for training and 3464 for validation). A novel deep learning model was also proposed. Despite respectable training accuracies, the pre-trained models exhibited lower performance on the validation dataset compared to our novel model. The proposed model outperformed pre-trained models, demonstrating superior validation accuracy, lower validation loss, computational efficiency, and adaptability to the specific classification problem. Notably, the existing models showed challenges in generalizing to unseen data and raised concerns about practical deployment due to computational expenses. This study pioneers a computer-assisted approach for automated differentiation between authentic handwoven "gamucha"s and counterfeit powerloom imitations—a groundbreaking recognition method. The methodology presented not only holds scalability potential and opportunities for accuracy improvement but also suggests broader applications across diverse fabric products. [ABSTRACT FROM AUTHOR]

Rock textures observed via thin section are skewed from their true 3D nature. This is due to various cut effects—artifacts introduced due to the lower dimensional nature of the thin section relative to the rock. These cut effects can be corrected, and several methods have been developed to invert crystal shape and crystal size, but with each process performed separately and sequentially. With the ongoing adoption of electron backscatter diffraction (EBSD) by petrologists, an additional data stream has now become available: the 3D orientation of 2D grain sections. For EBSD analysis, no stereological corrections are typically applied for interpreting the data. This study tests whether this orientational information is skewed due to a fabric cut effect. We test this by numerically generating synthetic crystal datasets representative of several crystal shapes and population sizes. We find that EBSD orientational data has a fabric cut effect since crystals oriented with long axes perpendicular to the thin section are more likely to be sampled compared to those with long axes oriented parallel to it. This effect must be accounted for to interpret the true 3D fabric accurately. Towards this end, we develop two new tools for working with EBSD-derived fabric: (1) a simple first-order test for determining if a measured fabric exceeds that of the fabric cut effect, and (2) a method of inverting cut fabrics that provides robust error estimations. We demonstrate the applicability and accuracy of these methods using a range of synthetic examples and a natural sample. With these newly developed tools, there is clear potential for a new textural toolkit framework, to further our ability to correct for the various cut effects while also providing accurate uncertainty estimates. [ABSTRACT FROM AUTHOR]

Background: Characterizing material properties of thin sheets for design or manufacturing purposes is an essential concern in many engineering applications. This task is particularly challenging for materials with a pronounced anisotropic and nonlinear mechanical behavior. Objective: A hybrid, experimental-numerical approach for the characterization of the mechanical, nonlinear response of thin, anisotropic, deformable materials is proposed. In contrast to classical approaches where various biaxial tension tests are analyzed, the main goal here is the complete characterization based on one single experiment. Methods: The proposed approach is based on a novel non-standard experimental setup which is on the one hand easy to install and use, and which on the other hand intentionally induces a strongly inhomogeneous strain field in the specimen capturing as many deformation modes and intensities as possible. The resulting displacement field can be measured using e.g., digital image correlation, and is then accessible to the parameter identification as full-field data. To allow for an efficient identification, an extended equilibrium gap method is presented, where unknown boundary force distributions applied in the experiment are computed iteratively. The approach's feasibility is assessed through virtual full-field data obtained by numerical simulation of the proposed experimental setup using predefined parameter values and applying realistic noise. That way, a quantitative assessment of the method's performance regarding two specifically chosen material models is enabled. Results: Provided that the stiffness-related material parameters are indeed linear in the stress equations, a quadratic optimization problem can be constructed to allow for a unique identification of the parameter values. Analysis show that reference parameter values for calendered rubber as well as coated textile fabric can be identified, even when realistic noise is applied to the virtual test data. Conclusion: Based on the presented investigations, the proposed method has been found to be feasible for the accurate identification of stiffness-related parameters of anisotropic, nonlinear thin sheets using a single experiment. [ABSTRACT FROM AUTHOR]

Fabric defect detection is an important part of the textile industry, aiming at the problems of many types of fabric defects, small size defects and unbalanced samples, an improved YOLOv5 fabric defect detection algorithm, FD-YOLOv5, was proposed. First, the coordinate attention module is embedded in the YOLOv5 backbone network structure to replace the bottleneck structure in the original network model. While reducing the amount of parameters and calculation, it enhances the ability of the network to extract features and improves the model's ability to detect small target defects. Secondly, a smoother Mish activation function is used in the original model convolution structure for model training, which improves the nonlinear expression ability of the model; the SIoU loss function considering the direction of the anchor box is used to improve the convergence speed and detection accuracy of the model. Finally, combining the focal loss and GHM loss functions as the target confidence loss function to solve the problem of sample imbalance in the fabric defect dataset. The experimental results based on the public fabric defect dataset of Aliyun TianChi shows that the mAP@.5 and mAP@.5:.95 of the improved algorithm are 65.1% and 30.4%, respectively, which are 8.3% and 3.2% higher than the original model, respectively, and the parameter amount, calculation amount and weight of the model are reduced by 8.4%, 11.2% and 14.3%, respectively, compared with the original model. Even compared with the state-of-the-art YOLOv7 model, the mAP@.5 value of the proposed model is improved by 6.5%. Although the FPS value is lower than YOLOv7 model, it also achieves a detection speed of 79 frames per second, which can meet the real-time demand. The experimental results demonstrate the effectiveness of the method in this paper, which can provide a reference for the automatic detection method of fabric defects. [ABSTRACT FROM AUTHOR]

The optimization of pretreatment parameters is one of the most important factors for the successful dyeing of textile materials. The aim of this study was to investigate the most effective way of pre-treating cellulose based lyocell knitted fabric in order to achieve an optimal result when dyeing it with reactive dyestuffs. This research investigates the effects of pretreatment by exhaust method on lyocell knitted fabric, which is analyzed using five different recipes. Pretreatment techniques that are being used are mild pretreatment, hot wash without detergent, hot wash with detergent, causticization, and hot wash with causticization. Pretreated and untreated lyocell fabric is dyed with bi-functional reactive dyestuffs in a shade of 1%, to produce color. Three key factors are considered in this research work: optimum pretreatment technique, causticization time selection, and causticization concentration selection. In order to optimize the pretreatment technique, 16 trials are performed by varying the time and concentration. Compared to other pretreatment techniques, causticization is the best for dyeing lyocell fabric with reactive dyes. Additionally, causticization treatment is observed to have the best performance on lyocell fabric when applied for 30 min. The optimum concentration of NaOH for causticizing lyocell fabric is 3 mol/l, as determined by the results. This assessment of results is done based on four factors such as K/S value (color strength), strength loss %, weight loss % as well as pilling test results. [ABSTRACT FROM AUTHOR]

Natural fibers possess good specific mechanical strength, flexibility, superior biodegradability, and composite stability, making them ideal materials for environmentally friendly, sustainable, and cost-effective manufacturing of composite components. This study aims to utilize cotton fibers and direct ink writing (DIW) technology to fabricate cotton-containing 3D printed fabrics (3Dp-Fs) to address issues related to poor mechanical properties. Herein, cotton fibers were dissolved in a LiCl/DMAc solvent and hydroxyethyl cellulose was added to prepare a solution matrix. Subsequently, the screened chopped cotton fibers were uniformly dispersed in the matrix to create homogeneous cotton-containing inks. The rheological properties of inks were thoroughly investigated, revealing that all the inks exhibited shear-thinning behavior. Finally, cotton-containing 3Dp-Fs were produced using the DIW technique. The analysis of 3Dp-Fs encompasses their appearance, morphology, internal structure, mechanical strength, and wearability. Results indicate that increasing the cotton fiber content from 0 to 0.8 wt% enhances the tensile strength of 3Dp-F by 60.40 N and the bursting strength by 55.46 N. Surprisingly, the addition of cotton fibers significantly improves the air permeability of 3Dp-F to 233.36 mm s−1, and after 120 s, the water content difference is 640.31%—both of which surpass the performance of 3Dp-F without cotton fibers. As a conceptual validation, a well-shaped 3D garment was created using these 3Dp-Fs. This study presents a novel approach that holds great promise for 3D printing fabrics and clothing with excellent mechanical properties and comfort. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]