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This study aims to investigate if engineered compression variations using moisture-responsive knitted fabric design can improve breast support in seamless knitted sports bras.

An experimental approach was used to integrate a novel moisture-responsive fabric panel into a seamless knitted bra, and the resulting compression variability in dry versus wet conditions were compared with those of a control bra. Air permeability and elongation testing of between breasts fabric panels was conducted in dry and wet conditions, followed by three-dimensional body scanning of eight human participants wearing the two bras in similar conditions. Questionnaires were used to evaluate perceived comfort and breast support of both bras in both conditions.

Air permeability test results showed that the novel panel had the highest variance between dry and wet conditions, confirming its moisture-responsive design, and increased its elongation coefficient in both wale and course directions in wet condition. There were significant main effects of bra type and body location on breast compression measurements. Breast circumferences in the novel bra were significantly larger than in the control bra condition. The significant two-way interaction between bra type and moisture condition showed that the control bra lost compressive power in wet condition, whereas the novel bra became more compressive when wet. Changes in compression were confirmed by participants’ perception of tighter straps and drier breast comfort.

These findings add to the limited scientific knowledge of moisture adaptive bra design using engineered knitted fabrics via advanced manufacturing technologies, with possible applications beyond sports bras, such as bras for breast surgery recovering patients.

Under the widely used testing methods for fabric bending behavior, only one result for one direction can be obtained by using one fabric which is low efficient. To obtain fabric bending anisotropy, it is necessary to conduct a great many testing experiments. The purpose of this paper is to investigate a novel, efficient and visual method that can measure fabric bending anisotropy.

Fabrics are first cut into special shapes with eight strips including four directions, 0°(warp direction), 90°(weft direction), 45 and 135°(true bias), then are put onto the self-designed instrument. After that a camera is used to take picture from the right above the prism. New parameters, projection area, projection length, projection length, falling height and falling index (S, L, H and I in short) are obtained. Furthermore, standard deviation of them (SDS, SDL, SDH and SDI in short) are extracted for the characterization of bending anisotropy.

Results show that the new method has good feasibility and S, L and I can be used to express fabric bending property well. Of all the four new parameters, SDL has the highest correlation with SD of bending length, SDS the second and SDH the third. That is, SDL can characterize bending anisotropy best. Taken convenience of data acquisition and correlation into consideration, bending length L is the best parameter. Average L and SDL in four directions can be combined to express the comprehensive bending behavior of fabrics.

The new method can measure and characterize both the fabric bending property and bending anisotropy. Besides its high efficiency, it can display fabric bending or bending anisotropy visually and directly.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

The textile sector is moving towards new technologies, where the application of nanotechnology is offering fabrics with multifunctional properties making fabric odourless, hydrophobic, durable and self-cleaning. This aim of this research is to investigate self-cleaning ability of denim fabric with the application of zinc oxide nanoparticles (ZnO NPs) synthesized naturally. The primary focus of this investigation is achieving sustainability mark through green synthesis of ZnO NPs.

In this analysis, ZnO NPs being one of the metal oxides exhibiting self-cleaning, UV-protective and anti-microbial properties were synthesized naturally using Azadirachta Indica leaves. The prepared NPs were characterized by using X-ray diffraction and scanning electron microscopy analyses confirming their size and crystalline structure. Different formulations were investigated with varying concentration of zinc oxide and auxiliaries onto the denim fabric using pad-dry-cure application technique.

XRD analysis confirmed the successful green synthesis of ZnO NPs. SEM analysis revealed the homogeneous and hexagonal wurtzite NPs deposition on the denim fabric. It was ascertained that with 5% ZnO NPs and 7% Binder concentrations, the formulation resulted in a smooth and even layer on the denim fabric maintaining the appearance and feel at the same time offers appreciable grading (Grade 4) against the stringent stains of Ketchup, Coffee, Grape and Orange Juice with insignificant change in tensile strength.

In this study, self-cleaning attributes of denim fabric with zinc oxide nano formulations of different composition was studied to achieve promising functional properties in a single step not studied earlier.

The essential properties of active sports fabrics are moisture management, quick-drying, body heat management and thermal regulations. Fibre type, blending nature, yarn and fabric structure and the finishing treatment are the key parameters that influenced the performance of the clothing meant for sportswear. This study aims to investigate the effect of fibre blending and structural tightness factors on bi-layer sport fabric's dimensional, moisture management and thermal properties.

In this study, 12 different bi-layer inter-lock fabrics were produced. Polyester filament (120 Denier) yarn was fed to form the backside of the fabric, and the face side was varied with cotton, modal, wool and soya spun yarns of 30sNe. Three different types of structural tightness factors were considered, such as low, medium and high were taken for sample development. The assessment towards dimensional, moisture management and thermal properties was carried out on all the samples.

The polyester-modal blend with a high tightness factor has shown maximum overall moisture management capability (OMMC) values of 0.73 and air permeability of 205.3 cm³/cm²/s. The same sample has shown comparatively higher thermal conductivity of 61.72 × 10^–3 W m^-1 °C^-1(Under compression state) and 58.45 × 10^–3 W m^-1 °C^-1 (under recovery state). In the case of surface roughness is concerned, polyester-modal blends have shown the lowest surface roughness, surface roughness amplitude and surface friction co-efficient. Among the selected fibre combinations, the overall comfort level of polyester-modal bi-layer knitted structure with a higher tightness factor is appreciable. Polyester-modal is more suitable for active sportswear among the four fiber blend combinations.

The outcome of this study will help to gain a better understanding of fibre blends, structural tightness factor and other process specifications for the development of bi-layer fabric for active sportswear applications. The dynamic functional testing methods (Moisture management and Thermal properties) were carried out to simulate the actual wearing environment of the sports clothing. This study will create a new scope of research opportunities in the field of bi-layer sports textiles.

This study was conducted to explore the influence of fibre blend and structural tightness factor on the comfort level of sportswear and to find the suitable fibre blend for active sportswear clothing.

The purpose of this study is to investigate the mechanical properties of denim fabrics constructed from ring-spun and open-end rotor spun yarns.

Yarns of 10s Ne count using cotton fibers were spun using the ring and open-end rotor spinning technologies. The yarns were used to produce a denim fabric on an air-jet loom with a 3/1 twill weave structure. Mechanical tests – tensile strength, tear strength, abrasion resistance and pilling resistance – of denim fabrics were evaluated. The test results were analyzed using analysis of variance with the help of Software Package for Social Sciences.

Denim fabrics made by using ring-spun yarns exhibited better tensile and tear strength properties than denim fabrics made by using open-end rotor spun yarns. On the contrary, denim produced using open-end rotor yarns have better abrasion resistance, pilling resistance and air permeability than those produced using ring-spun yarns.

Both spinning techniques have a significant influence on the properties of denim fabrics. Whenever better tensile and tear strength is required, it is better to use ring-spun yarns, while if the requirement is better abrasion resistance and pilling resistance with high air permeability, then open-end rotor spun yarns shall be used.

This paper aims to investigate the influence of picking sequence, weave design and weft yarn material on the thermal conductivity of the woven fabrics.

This work includes the development of 36 woven samples with two weave designs (1/1 plain and 3/1 twill), three picking sequences (single, double and three pick insertion) and six different weft yarn materials (cotton, polyester having 48 filaments, polyester with 144 filaments, spun coolmax having Lycra in core and coolmax in sheath, filament coolmax and polypropylene). The thermal conductivity was measured using ALAMBETA tester.

The results showed that weft yarn material, weave design and picking sequence have a meaningful impact on the thermal conductivity of woven fabric. The value of thermal conductivity was lowest for the fabrics with three pick insertion and 3/1 twill weave in all weft yarn materials.

Plain woven fabric with single pick insertion is feasible for summer wear to enhance the comfort of wearer. By changing the warp yarn grouping and material, improved thermal conductivity/resistance can also be achieved.

The authors have studied the combined effect of different weft yarn materials with different picking sequences and different weave designs on thermal conductivity of the woven fabrics.

The purpose of this study is to apply silver nanoparticles on the cellulosic fabric via a green cross-linking approach to obtain antibacterial textiles. The cellulosic fabrics may provide an ideal enclave for microbial growth due to their biodegradable nature and retention of certain nutrients and moisture usually required for microbial colonization. The application of antibacterial finish on the textile surfaces is usually done via synthetic cross-linkers, which, however, may cause toxic effects and halt the biodegradation process.

Herein, we incorporated citrate moieties on the cellulosic fabric as eco-friendly crosslinkers for the durable and effective application of nanosilver finish. The nanosilver finish was then applied on the citrate-treated cellulosic fabric under the pad-dry-cure method and characterized the specimens for physicochemical, textile and antibacterial properties.

The results expressed that the as-prepared silver particles possessed spherical morphology with their average size in the nano range and zeta potential being −40 ± 5 mV. The results of advanced analytical characterization demonstrated the successful application of nanosilver on the cellulosic surface with appropriate dispersibility.

The nanosilver-treated fabric exhibited appropriate textile and comfort and durable broad-spectrum antibacterial activity.

The treated cellulosic fabric expressed that the cross-linking, crystalline behavior, surface chemistry, roughness and amphiphilicity could affect some of its comfort and textile properties yet be in the acceptable range for potential applications in medical textiles and environmental sectors.

Fabrics, which are one of the raw materials of the clothing industry, constitute approximately 40–45% of the total cost of an apparel product. Due to the labor-intensive nature of this industry and failure to apply scientific methods along with the manufacturing processes, the wastes in the raw materials, including fabrics, become higher. Besides, quality deficiencies are encountered due to the same reasons. This study aims to determine the optimum total fabric layer height based on the fabric type during the cutting process with a straight knife cutting machine, which provided a decrease in the cutting errors.

Frequently used fabric types in an enterprise operating in organic cotton knitwear were listed. During the cutting tests, the straight knife cutting machine was used as the cutting device. The weight and thickness values of the fabrics were obtained to provide a comparison basis. Two different algorithms were created to evaluate the defective pieces according to fabric type, cutting height and error placement. Cutting resistances of these fabrics were also determined to evaluate the defect reasons. In the end, optimum total fabric layer count and total cutting height suggestions were proposed for each fabric type for a minimum cutting error.

At the end of this study, the error-free layers were identified per fabric type. At the same time, the optimum cutting height was suggested for each fabric basis. For 40/1 single jersey fabrics, the cutting height should be between 2.10 cm and 10.40 cm; for 30/1 single jersey fabrics, between 1.65 cm and 5.70 cm; for 20/1 single jersey fabrics, between 1.83 cm and 6.70 cm; for two-thread fleece fabrics, between 2.13 cm and 4.70 cm; and for three-thread fleece fabrics, between 0 cm and 4.90 cm.

Within the scope of the study, since the products made of knitted fabric were produced more frequently and in large quantities, the study was carried out with 15 different types of knitted fabrics at 10 different layers. The same methods should be applied for woven, denim and nonwoven fabric types, which would shed light on the following studies.

Due to scarce research carried out on the cutting procedure of the clothing industry in regards to sustainability, this study aims to contribute to this area. The main difference between this study and the studies that mostly make mathematical predictions about the cutting procedure is that it is practice-oriented.

Dehydrated bacterial cellulose’s (BC) intrinsic rigidity constrains applicability across textiles, leather, health care and other sectors. This study aims to yield a novel BC modification method using glycerol and succinic acid with catalyst and heat, applied via an industrially scalable padding method to tackle BC’s stiffness drawbacks and enhance BC properties.

Fabric-like BC is generated via mechanical dehydration and then finished by using padding method with glycerol, succinic acid, catalyst and heat. Comprehensive material characterizations, including international testing standards for stiffness, bending properties (cantilever method), tensile properties, moisture vapor transmission rate, moisture content and regain, washing, thermal gravimetric analysis, derivative thermogravimetry, Fourier-transform infrared spectroscopy and colorimetric measurement, are used.

The combination of BC/glycerol/succinic acid dramatically enhanced porous structure, elongation (27.40 ± 6.39%), flexibility (flexural rigidity of 21.46 ± 4.01 µN m; bending modulus of 97.45 ± 18.20 MPa) and moisture management (moisture vapor transmission rate of 961.07 ± 86.16 g/m²/24 h; moisture content of 27.43 ± 2.50%; and moisture regain of 37.94 ± 4.73%). This softening process modified the thermal stability of BC. Besides, this study alleviated the drawbacks for washing (five cycles) of BC and glycerol caused by the ineffective affinity between glycerol and cellulose by adding succinic acid with catalyst and heat.

The study yields an effective padding process for BC softening and a unique modified BC to contribute added value to textile and leather industries as a sustainable alternative to existing materials and a premise for future research on BC functionalization by using doable technologies in mass production as padding.