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The heat transfer properties play significant roles in the thermal comfort of the clothing products. The purpose of this paper is to find the relationship between heat transfer properties and fabrics' structure, yarn properties and predict the effective thermal conductivity of single layer woven fabrics by a parametric mathematical model.

First, the weave unit was divided into four types of element regions, including yarn overlap regions, yarn crossing regions, yarn floating regions and pore regions. Second, the number and area proportion of each region were calculated respectively. Some formulas were created to calculate the effective thermal conductivity of each element region based on serial model, parallel model or series–parallel mixing model. Finally, according to the number and area proportion of each region in weave unit, the formulas were established to calculate the fabric overall effective thermal conductivity in thickness direction based on the parallel models.

The influences of yarn spacing, yarn width, fabric thickness, the compressing coefficients of air layers and weave type on the effective thermal conductivity were further discussed respectively. In this model, the relationships between the effective thermal conductivity and each parameter are some polynomial fitting curves with different orders. Weave type affects the change of effective thermal conductivity mainly through the numbers of different elements and their area ratios.

In this model, the formulas were created respectively to calculate the effective thermal conductivity of each element region and whole weave unit. The serial–parallel mixing characteristics of yarn and surrounding air are considered, as well as the compression coefficients of air layers. The results of this study can be further applied to the optimal design of mixture fabrics with different warp and filling yarn densities or different yarn thermal properties.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

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.

Lightweight, open construction cotton knitted fabrics generally do not impart good protection from solar ultraviolet radiation (UVR). As lightweight 100% cotton single jersey is highly cherished for summerwear, it is sine qua non to understand the structural parameters that effectively strike a good balance between UV protection and thermophysiological comfort of the wearer. Relatively heavy fabrics protect from UVR, but comfort is compromised because of waning porosity, increase in thickness and thermal insulation. The purpose of this paper is to engineer knits that will bestow maximum UV protection while preserving the thermophysiological comfort of the wearer.

In total, 27 cotton single jersey fabrics with different areal densities and yarn counts were selected. Ultraviolet protection factor (UPF) was calculated based on the work of Imrith (2022). To précis, the authors constructed a UV box to measure the UPF of fabrics, denoted as UPF_B. UPF_B data were correlated with AATCC 183-2004 and yielded high correlation, R² 0.977. It was concluded that UPF 50 corresponds to UPF_B 94.3. Thermal comfort properties were measured on the Alambeta and water-vapour resistance on the Permetest. Linear programming (LP) was used to optimize UPF_B and comfort. Linear optimization focused on maximizing UPF_B while keeping the thermophysiological comfort and areal density as constraints.

The resulting linear geometrical and sensitivity analyses generated multiple technically feasible solutions of fabrics thickness and porosity that gave valid UPF_B, thermal absorptivity and water-vapour and thermal resistance. Subsequently, an interactive optimization software was developed to predict the stitch length, tightness factor and yarn count for optimum UPF_B from a given areal density. The predicted values were then used to knit seven 100% cotton single jersey fabrics and were tested for UV protection. All seven fabrics gave UPF_B above the threshold, that is, higher than 94.3. The mathematical model demonstrated good correlations with the optimized parameters and experimental values.

The optimization software predicted the optimum UPF_B reasonably well, starting from the fabric structural and constructional parameters. In addition, the models were developed as interactive user interfaces, which can be used by knitted fabric developers to engineer cotton knits for maximizing UV protection without compromising thermophysiological comfort. It has been demonstrated that LP is an efficient tool for the optimization and prediction of targeted knitted fabrics parameters.

Through this study, four different types of woven fabric structures were created by using cotton/banana blends with a 70:30 ratio by varying the weaving specifications. This study aims to investigate the comfort and mechanical properties of these woven materials.

Taguchi L16 experimental design (5 factors and 4 levels) with response surface methodology tool was used to optimize mechanical and comfort characteristics. The yarn samples used in this study are cotton/banana with a blend ratio of 70:30. Fabric type (A), grams per square metre (GSM; B), yarn count (C), fabric thickness (D) and cloth cover factor (E) are the chosen process characteristics.

The highest tensile strength and tearing strength of the cotton/banana blended fabric samples were obtained as 326.3 N and 90.3 k.gf/cm, respectively. Similarly, the highest thermal conductivity and overall moisture management capacity values were found to be 0.6628 and 3.06 W/mK X10⁻⁴, respectively. The optimized process parameters for obtaining maximum mechanical properties were using canvas fabric structure, 182 GSM, 36^s Ne yarn count, 0.48 mm fabric thickness and 23.5 cloth cover factor. Similarly, the optimized process parameters for obtaining maximum comfort properties were achieved using a twill fabric structure, 182 GSM, 32^s Ne yarn count, 0.4 mm fabric thickness and 23 cloth cover factor.

In contrast to synthetic fabrics, banana fibre and its blended materials are significant ecological solutions for apparel and functional clothing. Products made from banana fibre are a sustainable and green alternative to conventional fabrics. Banana fibre obtained from the pseudostem of the plant has an appearance similar to ramie and bamboo fibres. Numerous studies showed that banana fibre could absorb significant moisture and be spun into yarn through ring and rotor spinning technology. On the other hand, this fibre can be easily combined with cotton, jute, wool and synthetic fibre. The present utilization of pseudostem of banana plant fibre is very minimal. This type of research improves the usability of bananas their blended fabrics as apparel and functional wear.

The purpose of this work is to investigate the effect of filament composition with different specifications on the thermal comfort properties of bi-layer knitted fabrics.

In this paper eight bi-layer knitted fabrics with the same knitting structure but different filament compositions were prepared, and the thermal-wet comfort properties of these fabrics were examined. According to experimental data, the effect of filament composition on the thermal comfort properties of fabric was analyzed.

The increasing difference of hydrophilicity between inner and outer layers resulted in the enhancement of moisture management properties. Better thermal-physiology performance was exhibited by fabrics made up of finer and circular section fibers. Excellent thermal transfer, drying performance and one-way water transport capacity benefited the improvement of dynamic cooling effect of fabrics.

This work provides a useful and effective method for the development of bi-layer knitted fabric applied for sports and summer clothing.

The thermophysiological comfort of fabrics is prerequisite as customers covet adequate moisture, heat management-supported and UV protective clothing that measure up to their levels of activities and environmental conditions. Hitherto, scant tasks have been reported with the purpose of engineering both comfort and UV protection simultaneously. From that vantage point, the objective of this work is to develop a model for optimum UPF, air permeability, water-vapour resistance, thermal resistance, thermal absorptivity and areal density of knitted fabrics.

Weft knitted fabrics of various compositions were investigated. UPF was tested using the Labsphere UV transmittance analyser. The FX 3300 (Textest instruments) air permeability tester was used to test air permeability. Thermal comfort and water-vapour resistance were evaluated using the Alambeta and Permetest instruments, respectively. Based on image processing, the porosity was measured. Fabrics thickness and areal density were measured according to standard methods. Furthermore, parametric and non-parametric statistical test methods were applied to the data for analysis.

Linear regression was substantiated by Kolmogorov-Smirnov test. Then multiple linear regression of porosity and thickness together on UPF and comfort parameters were visually depicted by virtue of 3D linear plots. Residual analysis with quantile-quantile and probability plots, advocated the tests using the Shapiro-Wilk test. The result was validated by comparison with experimental data tested. The samples gave satisfactory relative errors and were supported by the z-test method. All tests indicated failure to reject the null hypothesis.

The predictive models were embedded into an interactive computer program. Fabric thickness and porosity are the inputs needed to run the program. It will predict the optimum UPF, areal density and thermophysiological comfort parameters. In a nutshell, knitters may use the program to determine optimum structural parameters for diverse permutations of UPF and thermophysiological comfort parameters; scilicet high UV protection together with low thermal insulation combined with low water-vapour resistance and high air permeability.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

The purpose of this study is to investigate the physical, ultraviolet (UV), colour appearance and colour fastness properties of selected fabrics dyed with natural dyes from Daboya and Ntonso communities of Ghana. The study further highlights the rich cultural heritage of traditional dyeing from these two communities. Craftsmen in West Africa especially Ghana, have sustained the traditional dyeing methods to produce textile products for consumers.

In this study, two sample fabrics were purchased from craftsmen at Ntonso and Daboya communities in Ghana. These fabrics were analysed at the laboratory under standard test methods for their physical, UV, colour appearance and colour fastness properties.

Results showed that all the sample fabrics have good UV shielding performance (ratings above 50+). Daboya sample fabrics (dyed with indigo dyes) produced more colour stains than the sample fabrics from Ntonso (dyed with black “kuntunkuni” dyes). The K/S_sum value or colour yield reduced after washing but that alternatively increased the calculated ultraviolet protection factor.

Findings from this study exposed the unique UV performance of dyed traditional fabrics (using natural dyes) from Ntonso and Daboya communities in Ghana. This inspires and enforces the need for craftsmen to improve their production cycle to produce these fabrics in different sizes which provides the necessary UV shielding abilities for consumers in the wake of climate changes.

This study demonstrated that the natural dyeing process at the two communities produced relatively good UV and colour fastness properties of the sample fabrics. These eco-friendly dyeing practices have survived over time to maintain and promote the concept of sustainability within the textile and fashion industry in Ghana.

This paper aims to develop bilayer woven fabrics with different picking sequences with enhanced comfort without any change in the constituent materials.

Six bilayer woven fabrics were produced on Dobby loom with 3/1 twill weave using micro-polyester yarn. Three different picking sequences, i.e. single pick insertion (SPI), double pick insertion (DPI) and three pick insertion (3PI), were used in both face and back layers. The effect of picking sequence on air permeability (AP), volume porosity, thermal resistance and overall moisture management capability (OMMC) of the samples were analyzed.

The results showed that 3PI–3PI picking sequence gives the highest OMMC, AP and thermal resistance in bilayer woven fabrics and the least results exhibited by SPI–SPI picking sequence.

This research uses a bilayer woven system that develops channels and trapes the air causing higher thermal resistance; therefore, applicable for winter sports clothing rather than for summer wear. Developed bilayer woven fabrics can be used in winter sportswear to improve the comfort of the wearer and reduce fatigue during activity.

Authors have developed bilayer fabrics by changing the picking sequences, i.e. SPI, DPI and 3PI of weft yarns in both layers and compared their thermo-physiological comfort properties.