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The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Occurrence of fabric rupture is a problem that can influence fabric performance during wear. In this regard, fabric tearing resistance is considered by manufacturers and consumers and enhancing tear resistance through optimization of related parameters is beneficial.

In this study, the tearing resistance of a series of shirting fabrics with various weave patterns and weft densities were investigated by both static and dynamic tear test methods. Moreover, the constituent yarn's frictional and tensile behaviour was evaluated and their relation with tear resistance was analysis.

According to the outcomes, the fabric firmness and density and friction of yarns affect the tear resistance, reversely. However an improvement in yarn's tenacity can raise the tear resistance.

In this study it was aimed to not only consider influence of both static and dynamic tear test approach on the tearing performance of fabrics regarding their structural parameters, the impact of the constituent's yarn properties include tensile behaviour and friction coefficient on the tearing performance of fabric considered, as well.

The purpose of this paper is to predict a global quality index of a ring spun yarn whose count Ne is ranging between 7.8 (76.92 tex) and 22.2 (27 tex). To fulfill this goal, a hybrid model based on artificial neural network (ANN) and fuzzy logic has been established. Fiber properties, yarn count and twist level are used as inputs to train the hybrid model and the output would be a quality index which includes the major physical properties of ring spun yarn.

The hybrid model has been developed by means of the application of two soft computing approaches. These techniques are ANN which allows the authors to predict four important yarn properties, namely: tenacity, breaking elongation, unevenness and hairiness and fuzzy expert system which investigates spinner experience to give each combination of the four yarn properties an index ranging from 0 to 1. The prediction of the model accuracy was estimated using statistical performance criteria. These criteria are correlation coefficient, root mean square error, mean absolute error and mean relative percent error.

The obtained results show that the constructed hybrid model is able to predict yarn quality from the chosen input variables with a reasonable degree of accuracy.

Until now, there is no sufficiently information to evaluate and predict the global yarn quality from raw materials characteristics and process parameters. Therefore, this present paper’s aim is to investigate spinner experience and their understanding about both the impact of various parameters on yarn properties and the relationship between these properties and the global yarn quality to predict a quality index.

Dref-3 friction spun core yarns produced using staple fibre yarn as the core, e.g. Jute core yarn wrapped with cotton fibre, have poorer mechanical properties compared to the core yarn itself. The purpose of this study was to understand the structure of such yarns, that will lead to the optimization of fibre, machine and process variables for production of better quality yarn from the Dref-3/3000 machines.

The Dref spinning trials were conducted following a full factorial design with six variables, all with two operative levels. The Dref-3 friction spun yarn, in which the core is a plied, twisted ring yarn composed of cotton singles and the sheath, formed from the same cotton fibres making the singles, has been examined. The structures have also been studied by using the tracer fibre technique.

It was observed that rather than depending on the plied core yarn, the tensile properties of the Dref-3 yarn are significantly determined by the parameters those affect the constituent single yarn tensile properties, i.e. the amount of twist and its twist direction, yarn linear density and the sheath fibre proportion used during the Dref spinning in making the final yarn. Further, when the twist direction of single yarn, double yarn and the Dref spinning false twisting are in the same direction, the produced core-sheath yarn exhibits better tensile properties.

The understanding of the yarn structure will lead to optimized production of all staple fibre core Dref spun yarns.

The research work may lead to utilization of coarse and harsh untapped natural fibres to the production of value-added textile products.

Though an earlier research has reported the effects of sheath fibre fineness and length on the tensile and bending properties of Dref-3 friction yarn, the present study is the first documented attempt using the tracer fibre technique to understand Dref-3 yarn structure with plied staple fibrous core.

The emergence of tissue engineering has led to the development of three‐dimensional cellular scaffolds that reconstruct the tissue structure. Research into the use of biodegradable materials in scaffolds has grown; the aim is that when tissue growth is complete, the scaffold degrades completely. This research aims to design novel scaffolds and investigates biodegradable polylactide (PLA) yarns; in particular, poly(l‐lactide) (PLLA) yarns extruded in‐house. To study degradation and determine the effect on the biodegradable yarns/textiles, they were immersed in phosphate buffer solution (PBS, pH=7.4) for various durations at 37°C. Mechanical properties were evaluated on tensile testing rigs and they were observed, before and after the immersion period. Cells were then cultured (37°C, 5 per cent carbon dioxide in air) on the textiles for 1 week. As expected, after immersion, the yarns exhibit a decrease in elongation and tenacity. Initial results indicate that the yarn properties influence cell attachment and spreading.

Fancy multicount yarn (9.5s Ne) is prepared on a ring frame with an Amsler fancy yarn attachment per the Box-Bhenken design for three variables and at three levels. The ring frame process parameters selected are spindle speed, traveller mass and twist multiplier. Different yarn properties, such as yarn tenacity, breaking elongation, yarn irregularity, yarn hair index, imperfections and thin places -40% are tested. An analysis of the result is done by using statistical software. It is observed that a 9000 rpm spindle speed with a 5 twist multiplier gives maximum yarn tenacity, a 9000 rpm spindle speed with traveller mass of 120 mg gives minimum yarn irregularity and traveller mass of 120 mg with a 4.8 twist multiplier gives minimum yarn imperfections within the experimental zone explored.

Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both the mechanics involved in yarn spinning and the quality of the yarn produced. However, information is lacking about the degree of influence that those properties impart on the spinnability of cotton fiber and the strength of the final yarn. This paper aims to discuss this issue.

This paper proposes the application of discriminant analysis as a multivariate regression tool to develop the causal relationships between six cotton fiber properties, i.e. fiber strength (FS), fiber fineness (FF), upper half mean length (UHML), uniformity index (UI), reflectance degree and yellowness and spinning consistency index (SCI) and yarn strength (YS) along with the determination of the respective contributive roles of those fiber properties on the considered dependent variables.

Based on the developed discriminant function, it can be revealed that FS, UI, FF and reflectance degree are responsible for higher YS. On the other hand, with increasing values of UHML and fiber yellowness, YS would tend to decrease. Similarly, SCI would increase with higher values of FS, UHML, UI and reflectance degree, and its value would decrease with increasing FF and yellowness.

The discriminant functions can effectively envisage the contributive role of each of the considered cotton fiber properties on SCI and YS. The discriminant analysis can also be adopted as an efficient tool for investigating the effects of various physical properties of other natural fibers on the corresponding yarn characteristics.

A soft computing model for predicting yarn tenacity from fiber properties and yarn parameters is developed. Because the number of samples is limited, the artificial neural network to be established must be a small-scale one. This soft computing model includes two stages. Firstly, the fiber properties and yarn parameters were selected by utilizing a ranking method for identifying the most relevant fiber properties and yarn parameters as the input variables to fit the small-scale artificial neural network model. The first part of this method takes human knowledge of yarn tenacity into account. The second part utilizes a data sensitivity criterion based on a distance method. Secondly, the artificial neural network model of the relationship between fiber properties, yarn parameters and yarn tenacity is established. The results show that the artificial neural network model yields an accurate prediction, and a reasonably effective artificial neural network model can be achieved with relatively few data points integrated with the input variable selecting method developed in this research. The results also show that there is great potential for this research in the field of computer-assisted design in spinning technology.

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.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.