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The purpose of this study is to analyse the characteristics of cellulose fibres derived from the pseudo-stems of Curcuma longa and to evaluate the properties of non-woven fabric produced using these fibres.

The fibres were extracted via a decortication method. The acquired intrinsic qualities of the fibres were used to assess the feasibility of using them in textile applications. The thermal bonding approach was used for the development of the non-woven fabric, using a hot press machine with low-melt polyester fibre as a binder.

The mean length of Curcuma longa fibres was determined to be 52.73 cm, with a fineness value of 4.00 tex. The fibres exhibited an uneven cross-sectional morphology, characterized by a diverse range of oval-shaped lumens. The fibre exhibited a tenacity of 1.45 g/denier and an elongation value of 4.30%. The fibres possessed a moisture regain value of 11.30%. The experimental non-woven fabrics had consistent weight and thickness, while exhibiting different properties in terms of tensile strength and air permeability, with Fabric C having the highest tensile strength and the lowest air permeability value.

The features of Curcuma longa fibre, obtained with the decortication process, exhibited suitability for textile applications. Three experimental non-woven fabrics comprising different compositions of Curcuma longa fibre and low-melt polyester fibre were produced. The tensile strength and air permeability properties of these fabrics were influenced by the composition of the fibres.

The purpose of this study is to investigate the value of Ghanaian weaving art tradition to humanity from socio-cultural and economic points of view. This study sought to answer questions such as “What is the interest of researchers regarding Ghanaian textile weaving art tradition?”, “To what extent has weaving art tradition projected the Ghanaian culture?” and “What is the socio-economic value of Ghanaian weaving art tradition?”

The systematic literature review approach was used to analyse data obtained from the Scopus online database. The PRISMA framework was adopted to select 22 relevant studies for analysis and conclusions. Also, the VOSviewer software was used to analyse and understand the co-occurrence of keywords.

It was revealed that Ghanaian weaving art tradition stands as a major craft that projects Ghana globally. Researchers around the world keep adding knowledge on Ghanaian traditional weaving and its value to humanity, resulting in a significant rise recently in the publication trend. Also, the rich cloth from the art is celebrated annually to invite people around the globe to learn the Ghanaian culture for social development. This study again found that traditional weaving serves as a major source of income for weavers and marketers of indigenous Ghanaian woven fabrics.

The findings of this study serve as a wake-up call to the Government of Ghana, institutional actors and national leaders to practically engage in the projection of the local art by playing individual roles such as financially supporting the craftsmen, initiating and implementing appropriate policies and displaying the local cloth on international occasions. This will make the local art more attractive for effective marketing and cultural preservation.

With reliable information extracted from the Scopus online database, this study presents original results and makes appropriate suggestions worth adopting for the improvement of the Ghanaian weaving art tradition.

This paper aims to investigate sustainability-led innovation, focusing on the interplay between product and process innovation for sustainability goals and the underlying supplier–customer relationships. Thus, the paper delves into sustainability-led innovation and how it affects supplier–customer relationships, and vice versa, thus providing a twofold perspective.

The textile industry is the empirical context of this study, which is exploratory research based on in-depth, semi-structured interviews with entrepreneurs, managers and experts in the textile industry.

In the textile industry, sustainability-led product innovation concerns mainly product durability and performance, product recyclability and the use of waste for new product development. Process innovation deals with circular economy, traceability and water and chemical use minimization. The paper also shows how sustainability-led innovation is implemented in more technical terms and regarding supplier–customer relationships.

The paper adopts an original perspective on how processes take place in the relationships between suppliers and customers, where there is no dominance of one actor, but innovation emerges from interdependence and interaction. Such perspective allows to provide an in-depth analysis of the supplier–customer relationships and underlying dynamics that affect sustainability-led innovation; moreover, the authors study how such innovation impacts supplier–customer relationships and the underlying relational dynamics. The value of the paper also stands in delivering a real representation of the innovation processes grounded in the textile industry.

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.

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.

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.

This paper aims to find a suitable solution to degrade the C.I. Reactive Red 24 (RR24) dyeing wastewater by using sodium persulphate to recycle water and inorganic salts.

The effects of temperature, the concentration of inorganic salts and Na₂CO₃ and the initial pH value on the degradation of RR24 were studied. Furthermore, the relationship between free radicals and RR24 degradation effect was investigated. Microscopic routes and mechanisms of dye degradation were further confirmed by testing the degradation karyoplasmic ratio of the product. The feasibility of the one-bath cyclic dyeing in the recycled dyeing wastewater was confirmed through the properties of dye utilization and color parameters.

The appropriate conditions were 0.3 g/L of sodium persulphate and treatment at 95°C for 30 min, which resulted in a decolorization rate of 98.4% for the dyeing wastewater. Acidic conditions are conducive to rapid degradation of dyes, while ·OH or SO₄⁻· have a destructive effect on dyes under alkaline conditions. In the early stage of degradation, ·OH played a major role in the degradation of dyes. For sustainable cyclic dyeing of RR24, inorganic salts were reused in this dyeing process and dye uptake increased with the times of cycles. After the fixation, some Na₂CO₃ may be converted to other salts, thereby increasing the dye uptake in subsequent cyclic staining. However, it has little impact on the dye exhaustion rate and color parameters of dyed fabrics.

The recommended technology not only reduces the quantity of dyeing wastewater but also enables the recycling of inorganic salts and water, which meets the requirements of sustainable development and clean production.

In this paper the aim is that Aramid/alginate blended nonwoven fabrics were prepared, and the flame retardancy of the blended nonwoven fabrics was studied by thermogravimetric analysis, vertical flame test, limiting oxygen index (LOI) and cone calorimeter test.

The advantages of different fibers can be combined by blending, and the defects may be remedied. The study investigates whether incorporating alginate fibers into aramid fibers can enhance the flame retardancy and reduce the smoke production of prepared aramid/alginate blended nonwoven fabrics.

Thermogravimetric analysis indicated that alginate fibers could effectively inhibit the combustion performance of aramid fibers at a higher temperature zone, leaving more residual chars for heat isolation. And vertical flame test, LOI and cone calorimeter test testified that the incorporation of alginate fibers improved the flame retardancy and fire behaviors. When the ratio of alginate fibers for aramid/alginate blended nonwoven fabrics reached 80%, the incorporation of alginate fibers could notably decreased peak-heat release rate (54%), total heat release (THR) (29%), peak-smoke production rate (93%) and total smoke production (86%). What is more, the lower smoke production rate and lower THR of the blends vastly reduced the risk of secondary injury in fires.

This study proposes to inhibit the flue gas release of aramid fiber and enhance the flame retardant by mixing with alginate fiber, and proposes that alginate fiber can be used as a biological smoke inhibitor, as well as a flame retardant for aramid fiber.

As an Internet fashion brand, HSTYLE has developed into an Internet enterprise with annual sales of 1.5 billion RMB within 10 years, establishing its position as the top industry performer in China. This case studies HSTYLES' innovation in business model and organizational management. HSTYLE's workgroups have achieved the balance of responsibilities and rights in a small team of three members at minimum, while mobilizing the enthusiasm and initiative of the line managers with the support of public service sector. At the same time, HSTYLE enriches its brand style, establishes a fashion cloud platform, and integrates individual and organizational consumers into its existing fashion design, manufacturing and sales system.

Titanium(IV) oxide nanoparticles (TiO₂ NP) were deposited to cotton denim fabrics using a self-crosslinking acrylate – a polymer dispersion to extend the lifetime of the products. This study aims to determine the optimum conditions to increase abrasion resistance, to provide self-cleaning properties of denim fabrics and to examine the effects of these applications on other physical properties.

The denim samples were first treated with nonionic surfactant to increase their wettability. Three different amounts of the polymer dispersion and two different pH levels were selected for the experimental design. The finishing process was applied to the fabrics with pad-dry-cure method.

The presence of the coatings and the adhesion of TiO₂ NPs to the surfaces were confirmed by scanning electron microscope and Fourier transform infrared spectroscopy analysis. It was ascertained that the most appropriate self-crosslinking acrylate amount and ambient pH level is 10 mL and “2”, respectively, for providing increased abrasion resistance (2,78%) and enhanced self-cleaning properties (363,4%) in the denim samples. The coating reduced the air permeability and softness of the denim samples. Differential scanning calorimetry and thermogravimetry analysis results showed that the treatments increased the crystallization temperatures and melting enthalpy values of the denim samples. Based on the thermal test results, it is clear that mass loss of the denim samples at 370°C decreased as the amount of self-crosslinking acrylate increased (at pH 3).

This study helped us to find out optimum amount of self-crosslinking acrylate and proper pH level for enhanced self-cleaning and abrasion strength on denim fabrics. With this finishing process, an environmentally friendly and long-life denim fabric was designed.