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The use and importance of mélange yarn in apparel sector is increasing day by day. With the gradual increase in market share, achieving the desired quality level of mélange yarn remains a challenge for yarn manufacturing industry. The purpose of this paper is to investigate the effect of raw material (dyed fiber percentage in the mixing), important spinning process variable (yarn twist multiplier) and productivity (spindle rpm of ring frame) on properties of cotton mélange spun yarn.

Box and Behnken Design of experiment has been used to investigate the important yarn quality parameters like evenness, imperfection, hairiness, breaking strength and breaking elongation of blow room blended cotton mélange yarn. The quadratic regression model is used to derive the statistical inferences about sensitivity of the yarn quality parameters to the different process variables. The response surfaces are constructed for depicting the geometric representation of yarn quality parameters plotted as a function of process variables.

The study shows that shade depth and spindle speed have significant effects on the mélange yarn unevenness and imperfections. Mélange yarn strength and hairiness are significantly affected by shade depth and yarn twist multiplier (TM). Yarn elongation at break is only influenced by the spindle speed. A darker shade is responsible for higher yarn unevenness, imperfection, hairiness and lower yarn strength. A higher spindle speed is also liable for deterioration of yarn quality.

Many spinning industries are planning to convert their existing spindles from normal gray yarn production to mélange yarn manufacturing. The outcome of this study will lead to achieve better mélange yarn quality and productivity by the industry.

Research on mélange yarn is itself scant. This study is exclusively conducted to analyze the individual and interactive effect of various process parameters on the mélange yarn quality.

The main objectives of this research work were to investigate the effect of production speed on intermingled yarn properties and melange fabric properties with special reference to melange appearance.

Polyester/nylon intermingled yarns were produced using an SSM DP3-C air-intermingling machine using commercial process parameters and Heberlein P212 nozzle. Melange fabric samples were knitted from polyester/nylon intermingled yarns while maintaining the same parameters to avoid knitting variations. The fabric samples were dyed using a sample dyeing machine while maintaining dye recipe and dyeing parameters constant to avoid dyeing variations.

The production speed has significant effect on intermingled yarn and melange fabric properties. When the production speed is increased, mingle points, mingle stability, linear density, strength and the elongation of the intermingled yarns decreases. When the production speed is increased, fabric weight decreases and the melange effect varies from dot-like appearance to line-like appearance.

Only the effect of production speed on intermingled yarn and melange fabric properties is discussed in this paper. Appearance evaluating systems developed in this research are limited to melange fabrics produced using air-intermingled yarns with two colour components.

Results indicate that the intermingled yarns for the application of melange fabrics should be developed with optimum intermingling speeds, and it should not be changed during the production since production speed has significant effect on yarn and fabric parameters. Therefore, melange appearance and fabric weight may vary between fabric lots with different production speeds even though all the other parameters are kept constant. Further, melange appearance evaluation method developed in this research could be used as a guide in developing melange fabrics.

This research introduced a qualitative and a quantitative method to analyse melange fabric appearance. This melange appearance evaluation method can be used as a guide to achieve specific melange effect in the sample development stage. Further, when a melange sample appearance catalogue is developed for all the variables for a particular fabric type using this evaluation method, customer requested appearance can be achieved in minimum sample trials which save time, capacity, money and customer credibility.

The purpose of this article is to design and make fabrics with colorful melange yarns spun by a three-channel rotor spinning machine.

The three-channel digital rotor-spun machine controls the blending proportion by adjusting the feeding of three-primary color fiber slivers online, so that a piece of colorful yarn presents a variety of colors along the longitudinal direction where constant yarn linear density can be produced flexibly. Various fabric patterns can be produced by three-channel rotor-spun colorful melange yarn with different periods of color.

The fabric, made by the rotor-spun colorful melange yarn, is rich in color, clear in layers, soft in the hand and has a hazy three-dimensional effect. The product is environmentally friendly and saves energy.

An innovative idea to develop various colorful fabrics is proposed by combining their pattern with colorful melange yarn produced by a three-channel rotor spinning machine.

Grey cotton fibers with a mean fiber length and fineness of 29 mm and 4.2 micronair was pretreated, scoured and dyed. Three ring yarns were spun separately from 100% grey cotton (R.R.Y.), 50% dyed and 50% grey cotton blend (M.R.Y.) and 100% dyed cotton (D.R.Y.). The extent of fiber damage was assessed by measuring the length and the mechanical characteristics of cotton fibers after passing the fibers through the lap machine and the draw frame II. Properties of R.R.Y., M.R.Y. and D.R.Y. samples were examined. In terms of tenacity and elongation at break, grey and dyed cotton fibers, which were selected after being processed by the lap machine and the draw frame II, were very similar. The fiber length by number and weight of grey cotton was longer than that of dyed cotton, while the amount of fiber nep and short fiber content of dyed cotton were more than those of grey cotton.

The three yarn samples were the same in terms of elongation at break. The tenacity of R.R.Y. was the highest but the yarn sample was the lowest in terms of coefficients of mass variation (Cv%), imperfection and hairiness in comparison with the M.R.Y. and D.R.Y. samples.

A project aimed at the 3D simulation of textile yarns from technical specifications was begun in 1993 and attracted support from both industry and research councils to enable the formation of a multi-national team of researchers to come together in the UK. Considerable progress has been made particularly in the simulation of knitting yarn and plain knit fabric. In recent times the team has tackled the problem of simulating mixture coloured yarns sometimes called melange. This has necessitated a more complicated model of yarn structure based on queuing theory which is outlined at the end of the paper. References are provided for readers who wish to learn more of the various theories and models that have been used throughout the course of this project.

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.

The purpose of this paper is to propose a new recognition algorithm on quadratic local extremum for fabric density recognition.

The density wave is established to correctly detect density by searching local extremes. The gray wave of each line in fabric image is extracted first. The derivation of gray wave is calculated, extreme waves including all true extreme values and false extreme values are obtained. And then the second derivative of extreme waves are calculated and the result makes local correction. The density wave, which can represent position and quantity of yarn and interstice, is established. According to the resolution and size parameters of image, the function of density with density wave statistics is given.

The experiment and analysis proved that the method proposed can detect fabric density simply and successfully with less calculation and no image preprocessing.

The algorithm provides practical guidelines for fabric density detection and provides a new thought for fabric characteristic identification. Future work could be focused on the development of methods for the automatic algorithm with color fabrics.

The algorithm based on quadratic local extremum presented in this paper is a new method to successfully detect fabric density and can be applied to the recognition for other categories of clothing fabrics and images.

This paper aims to determine the most practically applicable color-difference formula for yarn-dyed fabrics woven from warp and weft yarns in different color depths and to establish color-difference tolerance for perceptibility by evaluating yarn-dyed fabrics visually and instrumentally.

A total of 108 sample pairs were evaluated by a panel of 13 observers with perceptibility method under three typical light sources (A, D65 and cool white fluorescent). The data sets were statistically analyzed by the homogeneity of variance test (F-test), analysis of variance, standardized residual sum of squares and performance factor/3.

Light sources had a slight influence on the visual assessments of yarn-dyed fabrics. Among the eight color-difference formulae for measurements of yarn-dyed fabrics, CIEDE2000(2:1:1) outperformed all other tested formulae, and the color tolerance for the perceptibility of CIEDE2000(2:1:1) was 0.62. When the homochromy index (K) of warp and weft yarns of yarn-dyed fabric was lower than 1.25, the color difference based on ΔE*00(2:1:1) between the two samples was acceptable in terms of the color tolerance for perceptibility (i.e. 0.62).

The warp and weft yarns in different color depths could be woven in fabric with a relatively uniform color appearance.

This study could contribute to cost savings by reusing disqualified dyed yarns during the weaving manufacturing process.

No study was published about the tensile properties of different sections of multicount yarn. Hence there was a need to conduct a detailed section-wise tensile study of fancy multicounty yarn. The purpose of this study is to identify the different sections of the same multicount yarn and test them separately.

Multicount yarn with slubs were prepared on a ring frame with the Amsler Textile Effect System. Different yarn sections from each multicount yarn are identified as fine normal, fine slub, medium normal, medium slub, coarse normal, coarse slub and the changeover sections. These sections are tested for the breaking load and breaking elongation. Broken ends of the yarn sections are also studied, and these broken ends are classified as sharp broken end, tapered broken end and slipped broken end. A study is also conducted on the location of break or the place of break at the changeover sections.

It was found that the twist of yarn at slub sections was lower than the twist at normal sections. In spite of the low yarn twist at slub sections, the breaking load of the yarn was higher at slub sections than at normal sections by 12 to 30 per cent owing to the presence of more number of fibres that share the breaking load. The breaking load at the changeover section (normal to slub and slub to normal) was found to be higher than that at normal section and lower than at slub sections. No significant difference was found between the breaking load of the two changeover sections (normal to slub and slub to normal). The majority of breaks in normal yarn sections were of the tapered broken end type and the majority of breaks in slub yarn sections were slipped broken ends.

Efforts are taken to identify different sections of the same multicount yarn with the help of specially designed square black board and tensile study of these sections are carried out.