Textile Production Process Research Articles

Problems of the process of moistening raw cotton and cotton fiber. Cotton fiber wetting process problems

The moistening of raw cotton and cotton fiber is a critical stage in the textile production process, significantly influencing the quality of the final product. However, achieving optimal moisture content presents numerous challenges. One of the primary problems is the uneven distribution of moisture during processing, which can lead to variations in fiber strength and quality. The inappropriate application of water or humidification methods often causes fiber shrinkage, reduced durability, and increased contamination. Furthermore, the control of temperature and humidity plays a vital role in preventing over-drying or excessive moisture retention, both of which can negatively affect the spinning and handling properties of cotton fibers. Technological limitations, coupled with the complexity of monitoring moisture levels accurately, result in inefficiencies in the cotton processing industry. Research has also shown that improper moisture regulation can lead to decreased efficiency in machinery, increased energy consumption, and a higher likelihood of fiber damage during transportation and storage. Therefore, addressing these issues requires advancements in moisture control technologies and the development of standardized procedures to ensure consistent and high-quality cotton fiber production.

Synthesis and characterization of potato starch as a feasible alternative to the commercial yarn sizing materials

In Bangladesh, surplus potatoes pose challenges, leading to substantial wastage and losses. To mitigate this issue, exploring diverse applications for excess potatoes is crucial. This study focuses on using locally produced potato starch as an alternative sizing material. The potato starch was extracted and employed to size 100% cotton yarns of 12, 20, and 30 Ne counts. Comparative analyses were conducted with commonly used commercial sizing agents, including tapioca starch, one-shot, and Amatrolid 8923. Technical parameters such as size solution viscosity, yarn pick-up percentage, coating quality, hairiness reduction, tensile strength, elongation percentage, abrasion resistance, and coefficient of variation (CV%) were investigated for the different sizing agents. Potato starch exhibited performance on par with commercial agents, yielding favorable results. Particularly for coarser yarns (12 and 20 Ne counts), potato starch outperformed the alternatives, showcasing its efficacy. While one-shot proved most effective for the 30 Ne yarn, potato starch displayed slightly diminished performance due to its higher viscosity. Overall, potato starch emerged as a promising alternative to conventional sizing agents. It surpassed tapioca starch and Amatrolid 8923 across various parameters, reaffirming its potential as a feasible substitute in yarn sizing applications. This study underscores the viability of potato starch in enhancing the sustainability and efficiency of textile production processes.

Treatment of textile dyes with steel flakes via Fenton and Electro-Fenton processes: An experimental analysis

The textile production process is well recognised as one of the most water-intensive industries, with an estimated daily water consumption of several thousand cubic metres. The main objective of the study is to remove the colour and COD of three reactive dyes namely reactive blue, reactive yellow and reactive black by Fenton and Electro Fenton Process. In this research paper, new novel catalyst steel flakes have been employed as part of the Fenton and Electro-Fenton process, replacing the conventional Fenton and Electro-Fenton catalyst. Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Analysis (EDAX), Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Thermo Gravimetric Analysis (TGA) was done to characterize the steel flakes. The pH, dye concentration, FeSO4 dosage and H2O2 dosage were studied. The maximum colour and Chemical Oxygen Demand (COD) removal in Fenton process was 98.47 % and 74 % respectively. In Electro Fenton Oxidation the maximum colour and COD removal was 99.65 % and 78 % respectively. The best colour and COD removal was observed by Fenton and Electro Fenton oxidation with optimum Fenton molar ratio of 50:1 and pH 3 in all three dyes. In the laboratory-scale feasibility tests on the Electro-Fenton process, the highest colour removal effectiveness was observed at catalyst dosages of 1 g/L, 132 mM H2O2, and pH 3. The novel modified heterogeneous Electro-Fenton approach presents a distinct cost-effectiveness benefit in comparison to both the conventional Electro-Fenton reaction and the Electro-Fenton reaction employing alternative iron sources.

YOLO-BGS Optimizes Textile Production Processes: Enhancing YOLOv8n with Bi-Directional Feature Pyramid Network and Global and Shuffle Attention Mechanisms for Efficient Fabric Defect Detection

Timely detection of fabric defects is crucial for improving fabric quality and reducing production losses for companies. Traditional methods for detecting fabric defects face several challenges, including low detection efficiency, poor accuracy, and limited types of detectable defects. To address these issues, this paper chose the YOLOv8n model for continuous iteration enhancement in order to improve its detection performance. First, multiscale feature fusion was realized by the Bi-directional Feature Pyramid Network (BiFPN). Second, the Shuffle Attention Mechanism (SA) is introduced to optimize feature classification. Finally, the Global Attention Mechanism (GAM) was used to improve global detection accuracy. Empirical findings demonstrated the improved model’s efficacy, attaining a test set mean average precision (mAP) value of 96.6%, which is an improvement of 3.6% compared to the original YOLOv8n. This validates that YOLO-BGS excels in detecting textile defects. It effectively locates these defects, minimizes resource waste, and fosters sustainable production practices.

The impact of temperature and relative humidity on cotton fiber moisture regain and specific resistance

This paper investigates the effects of temperature and relative humidity on the moisture regain and specific resistance of cotton fibers when a new water-cooled textile air conditioner is used as humidification equipment in the textile production process. Cotton fiber is used as the test object, and the temperature and relative humidity are adjusted to simulate the humidification of the textile production process environment, during which the moisture regain and specific resistance values are measured. The experimental results show that: the time required for cotton fibers to reach the nominal moisture regain shortens with the increase of temperature and relative humidity, and the shortening time is more obvious when the temperature remains unchanged at 75%-80% relative humidity, and the change is obvious at 25 °C-30 °C when the relative humidity is certain. The specific resistance of cotton fiber decreases with the increase of temperature and relative humidity; the specific resistance of the fiber decreases rapidly at the beginning of humidification, and then becomes smaller slowly; with the increase of moisture regain, the specific resistance of fiber decreases in an exponential trend.

Towards autonomous learning and optimisation in textile production: data-driven simulation approach for optimiser validation

AbstractThe textile industry is a traditional industry branch that remains highly relevant in Europe. The industry is under pressure to remain profitable in this high-wage region. As one promising approach, data-driven methods can be used for process optimisation in order to reduce waste, increase profitability and relieve mental burden on staff members. However, approaches from research rarely get adopted into practice. We identify the high dimensionality of textile production processes leading to high model uncertainty as well as an incomplete problem formulation as the two main problems. We argue that some form of an autonomous learning agent can address this challenge, when it safely explores advantageous, unknown new settings by interacting with the process. Our main goal is to facilitate the adoption of promising research into practical applications. The main contributions of this paper include the derivation and formulation of a probabilistic optimisation problem for high-dimensional, stationary production processes. We also create a highly adaptable simulation of the textile carded nonwovens production process in Python that implements the optimisation problem. Economic and technical behavior of the process is approximated using both Gaussian Process Regression (GPR) models trained with industrial data as well as physics-motivated explicit models. This ’simulation first’-approach makes the development of autonomous learning agents for practical applications feasible because it allows for cheap testing and validation before physical trials. Future work will include the comparison of the performance of different agent approaches.

The Effects of Weaving Process on the Damage Formation of the E- Glass Yarn

The woven fabric production can be executed in the weaving machine by the using of different mechanisms, which are working simultaneously. Because of the weaving mechanisms, the yarn has under tension and changed between the determined range within the repetitive cycles. The mechanisms and unsteady tension cause to damage on the yarns in the woven fabric production process. The breakable fibers like carbon or glass are more sensitive to degrade in the weaving machine because of their low friction resistance in comparison with the traditional fibers. The several of studies had been carried out to determine the tension of yarns and their damage mechanisms in the weaving process. However, they focused on the damage evolutions of warp yarns in the weaving process. In addition to the warp yarns, the damage evolution of weft yarns was investigated for industrial production in this study. The prevalent weft tensioning systems were evaluated, and the spring yarn tensioning system shown the best performance. Moreover, the yarn- to- machinery part type of friction was determined as the main reason to degrade warp yarns in the industrial production of glass woven fabric.

A novel non-woven fabric from bamboo fiber in medical lifestyle products

The present study aims to investigate the appropriate size of bamboo fibers derived from waste bamboo and determine the optimal duration for soaking in bio-fermented water to facilitate fabric molding. Additionally, we seek to explore the properties of non-woven fabric products manufactured from bamboo fibers. The study factors encompass three grades of bamboo fibers, designated A, B, and C, as well as five levels of fermentation time: 2, 4, 6, 8, and 10 days. A Completely Randomized Design (CRD) experiment is planned to assess both physical and microbial properties. Furthermore, we will analyze the inhibition of Staphylococcus aureus (S. aureus) and Escherichia coli (E.coli) bacteria in accordance with AATCC test standards and determine the optimal ratio for non-woven fabric molding. Regarding the ratio of bamboo fiber to polyester fiber, we will investigate three levels: 50:50, 70:30, and 100:0, with the objective of designing medical lifestyle products.Preliminary findings indicate that grade B bamboo fibers exhibit a light brown color and a relatively thin structure, with lengths ranging from 1 to 3 cm, rendering them suitable for most fabric production processes. Notably, unfermented bamboo fibers demonstrate higher inhibition of S. aureus and E.coli bacterial growth compared to their fermented counterparts. Additionally, our results suggest that the ingredient ratio significantly impacts the molding process, with a 99.94% effect. Remarkably, a blend of bamboo fiber and polyester fiber in a 70:30 ratio can be mechanically processed to create needle-pressed fabric, utilizing a method that involves pressing and tight weaving. This innovative approach facilitates the production of antibacterial non-woven fabric products suitable for elderly individuals, ready for practical use in community settings as a best practice solution.

Reducing the carbon footprint of the textile sector: an overview of impacts and solutions

The clothing, footwear, and household textiles sectors play a significant role, providing employment opportunities on a global scale. However, they are also associated with a considerable consumption of natural resources, leading to high levels of water pollution and emissions of greenhouse gases, contributing to climate change. The carbon footprint (CF) of textiles depends generally on fabric types and production processes. Natural fibers such as cotton, wool, and silk, generally have a lower CF when compared to synthetic fabrics such as polyester and nylon, requiring more energy and chemicals during production. In addition, fabrics manufactured through eco-friendly methods, such as closed-loop systems and sustainable production processes, have a significantly lower CF than those produced using conventional means. Accordingly, there is a perceived need for comprehensive studies that investigate the environmental and climate implications of the textile sector. Considering these concerns, a study was undertaken on the topic, encompassing a literature review and data collection so as to present an overview of the environmental impacts and CF associated with the textile sector. The results from the research show that the resource consumption associated with the textile sector, particularly its CF, demands urgent action in order to reduce its impacts. Also, actionable measures are needed so as to reduce the CF of the textile industry. These are described in the paper, along with the obstacles that need to be overcome so as not only to reduce the CF of this sector, but also to pursue the objectives outlined in the sustainable development goals.

Detection and Classification of Fabric Defects Using Deep Learning Algorithms

The textile industry primarily relies on fabric as a crucial raw material, the production of which involves multiple complex stages. Due to the multitude and complexity of these stages, fabric defects can frequently occur. With the modern fabric production process being nearly fully automated, and given the variety of potential defects, detecting errors on fabrics has become increasingly challenging. The rapid pace of production and the substantial market share of the sector mean that relying on human inspection for error detection can lead to significant time losses and can reduce the accuracy of defect detection to around 60%. Consequently, recent years have seen a shift towards the development of intelligent systems for fabric defect detection in parallel with technological advancements. With the rapid progression of artificial intelligence, the application of image processing techniques has commenced in this field. This study has developed a real-time defect detection system for fabrics using deep learning techniques. Initially, a network model was created using an open-source neural network library, CNN, achieving 89% accuracy. Subsequent implementations using the VGG16 and InceptionV3 architectures reached accuracies of 89% and 86%, respectively. To further improve the study, fabrics were classified into two categories: defective and non-defective, and the pre-trained Convolutional Neural Networks model ResNet50-v2 was employed as a feature extractor. This approach yielded an approximate accuracy of 95%.

Effects of structural parameters on air consumption and mechanical energy characteristics of airflow in the vortex spinning nozzle

Reducing energy consumption during textile production processes has currently become one of the key concerns. In order to design the nozzle of the vortex spinning machine with reduced air consumption, numerical simulation of the airflow in the nozzle is performed to investigate the effect of the length and the inlet diameter of the conical chamber in the intermediate section of the vortex tube on the air consumption and the mechanical energy characteristics of the airflow. A spinning experiment conducted to measure the flow rate and yarn tenacity is adopted to verify the numerical simulation results. The simulation results show that the air consumption of the nozzle is insignificantly affected as the length increases from 7.3 mm to 7.7 mm, while a decreasing trend has been found as the length increases from 7.7 mm to 8.1 mm. As the inlet diameter increases from 4.6 mm to 5.0 mm, the air consumption of the nozzle increases monotonically. The mechanical energy of airflow in the nozzle exhibits a minor difference between cases of lengths of 7.3 mm, 7.5 mm, and 7.7 mm, while it decreases significantly in cases of lengths of 7.9 mm and 8.1 mm. The mechanical energy of airflow in the vortex chamber increases as the inlet diameter increases. The experimental results are consistent with the numerical predictions. This work is expected to provide a reference for the design of the vortex spinning nozzle and an approach to reducing the energy consumption in the yarn production process.

Empowering Eco-Friendly Choices: An Environmental Impact Assessment Decision Support System for Textiles and Clothing

The textile and clothing industry is one of the industries with the highest environmental impact, with a huge amount of waste during the product life cycle. However, there are few tools available for companies to assess the environmental impact of their production process. In this way, a decision support system was developed so that producers can calculate the environmental impact associated with the textile and clothing production process, with the main objective of helping producers and, consequently, consumers to make environmentally conscious decisions given the increasingly demanding market. The methodology of this study integrated the accounting of a set of indicators, allowing producers to calculate the environmental impact associated with the textile and clothing production process. The decision support system returns, depending on the results of the calculations of the indicators, different ecolabels that allow consumers to compare different products based on their environmental performance. In short, the study in question allows us to contribute to environmentally conscious decision making, both for the producer and the consumer, to promote sustainable practices in the textile and clothing industry. The decision support system is flexible and adaptable for different companies and industries in order to meet their needs and improve their environmental performance.

Innovations and Opportunities in Sustainable Textile Recycling

Waste or wastage is the term for materials that remain unutilized after a product's initial use or process completion. It is a type of substance that is worthless, ineffective, or flawed. Thus, material that loses its value or becomes unusable after the creation of any textile product has concluded is referred to as textile waste. Produce waste occurs during all phases of the textile production process, including spinning, weaving, knitting, dying, finishing, and garment making. This study examines the effects textile waste on the environment and the difficulties in recycling procedures within the industry, which is crucial for the world economy and ecosystem. It is crucial to move towards a circular economy model since the textile industry produces a lot of trash because of its fast fashion cycles and high levels of production to meet demand. This study emphasizes the textile industry's significant environmental impact. The conclusions show that although textile recycling can help lessen the industry's environmental impact, there are still a number of obstacles to overcome, including the need for low-grade materials to have commercially viable recycling processes, the high cost of recovery procedures, and the logistical and technical challenges associated with separating fiber blends. In order to deal with these issues, the study emphasizes how crucial it is to develop environmentally friendly procedures and cutting-edge recycling technologies.

Supervised and unsupervised techniques in textile quality inspections

Quality inspection is a critical step in ensuring the quality and efficiency of textile production processes. With the increasing complexity and scale of modern textile manufacturing systems, the need for accurate and efficient quality inspection and defect detection techniques has become paramount. This paper compares supervised and unsupervised Machine Learning techniques for defect detection in the context of industrial textile production, in terms of their respective advantages and disadvantages, and their implementation and computational costs. We explore the use of an autoencoder for the detection of defects in textiles. The goal of this preliminary work is to find out if unsupervised methods can successfully train models with good performance without the need for defect labelled data.

RETRACTED: Displacement of the selvage of the fabric on shuttless looms as a factor in optimizing the silk fabric production process

See the retraction notice BIO Web of Conferences 116, 00001 (2024), https://doi.org/10.1051/bioconf/202411600001

Process Prediction and Feature Visualization of Meltblown Nonwoven Fabrics Using Scanning Electron Microscopic (SEM) Image-Based Deep Neural Network Algorithms

Meltblown nonwoven fabrics are used in various products, such as masks, protective clothing, industrial filters, and sanitary products. As the range of products incorporating meltblown nonwoven fabrics has recently expanded, numerous studies have been conducted to explore the correlation between production process conditions and the performance of meltblown nonwoven fabrics. Deep neural network algorithms, including convolutional neural networks (CNNs), have been widely applied in numerous industries for tasks such as object detection, recognition, classification, and fault detection. In this study, the correlation between the meltblown nonwoven fabric production process and performance was analyzed using deep neural network algorithms for classifying SEM images. The SEM images of meltblown nonwovens produced under various process conditions were trained using well-known convolutional neural network models (VGG16, VGG19, ResNet50, and DenseNet121), and each model showed high accuracy ranging from 95% to 99%. In addition, LRP (Layer-wise Relevance Propagation) and Gradient-weighted Class Activation Mapping (Grad-CAM) models were applied to visualize and analyze the characteristics and correlation of the SEM images to predict the meltblown nonwoven fabric production process.

Application of biopolymers in clothing and fashion

The use of biopolymer fibres in textile technology predates written human history. Researchers and academics are looking at plant fabrics for potential use in cutting-edge technologies because of their low environmental impact and long lifespan. The use of nonbiodegradable petrochemical materials in fast fashion's trendy, low-priced clothes has increased in frequency. The development, manufacturing, and disposal of these materials have all contributed to environmental degradation. Materials that incorporate biopolymers are often promoted as biobased, biodegradable, or possessing all of these qualities. Biopolymers textiles with improved degradability performance are reviewed, as is the utilisation of this intelligent bio textile as a low or no-cost replacement for commercial chemical-based textiles. This book will also explain how these cutting-edge modelling techniques might help polymer designers settle on the most optimal solution. In addition to reducing expenditures on resources, production, and processing, the entirely biodegradable polymers also provide support for the textiles industry, Biodegradable polymers' recent advancement and prospective future uses and trends in the contemporary biodegradable textiles industry will be discussed. The transition from synthetic to bio-based materials is inevitable, although it is happening slowly due to technological hurdles. The use of biopolymers offers several benefits, including the manufacture of an eco-friendly final product and lightweight textiles with cheap production costs. A lot of work has gone into making bio-textiles that can meet the needs of the future, like ensuring their superiority in all respects and acquiring textile substrates with prospective capabilities like antimicrobial, fireproof, resistant to ultraviolet light, conductive to electricity, and very hydrophobic. We'll be discussing we'll examine how eco-friendly biopolymers can be incorporated at various points in the textile production process. Biopolymers are more environmentally conscientious than other cutting-edge technologies because they could help future generations save money, energy, and raw resources.

Bioinspired multi-scale core-spun yarn-based solar evaporator for ultra-efficient and durable high-salinity brine desalination

Solar-driven interfacial desalination has been considered a promising and green technology for relieving worldwide water shortage because of its zero carbon emission. However, salt accumulation during evaporation results in a significant reduction in solar evaporation performance and sustained service life. High-performance and long-term salt-rejecting solar evaporators are urgently desirable. Inspired by the rapid water transfer driven by leaf transpiration and the capillary pressure in woody plants, we developed electrospun polyacrylonitrile (PAN) @carbon nanotubes (CNTs) nanofiber/cotton core-spun yarn (PCCS yarn) based solar evaporator enabled by the multi-branch microchannels and sub-microchannels for ultra-efficient and durable high-salinity brine desalination. The optimal PCCS yarn-based solar evaporator exhibits a record-high evaporation rate of 3.46 kg m−2h−1 under one sun illumination among 2D evaporators. Meanwhile, an excellent and stable brine desalination rate of ∼2.75 kg m−2h−1 for 100 h continuous solar irradiation is achieved even in 20wt% NaCl solution. The above results are attributed to the massive micro evaporation surfaces formed between nanofibers, rapid water replenishment in the radius direction, and orientational fast water transport by Laplace pressure along and across the PCCS yarn. In addition, the continuous preparation of the core-spun yarn by the conjugated electrospinning technology and the complete fabric production process in the textile industry make it possible for the practical application of the PCCS yarn-based solar evaporator. This work promotes the development of high-performance, long-term and scalable solar desalination devices.

Silk wastes and autoclaved degumming as an alternative for a sustainable silk process

Silk degumming is considered the first point in the preparation of silk-based materials since this process could modify the silk fiber and the properties of its related products. This study evaluated the differences in morphology, secondary structure, amino acid content, thermal stability, and mechanical properties of two types of raw materials, defective cocoons (DC) and silk fibrous waste (SW), degummed by chemical (C) and autoclaving (A) methods. Subsequently, silk fibroin films were prepared by dissolving each type of degummed fibers, and thermal and structural films properties were determined. The findings demonstrated that autoclaving is an efficient alternative to remove silk sericin, as the resulting fibers presented improved structural, thermal, and mechanical properties compared to those obtained by the chemical method. For films preparation, autoclave resulted in a good option, but dissolution parameters need to be adjusted for defective cocoons. Furthermore, similarities between the physicochemical properties of fibers and films from both fibrous wastes suggest that SW is a promising raw material for producing fibrous resources and regenerated silk fibroin materials. Overall, these findings suggest new recycling methods for fibrous waste and by-products generated in the silk textile production process.

WASTE IDENTIFICATION AT FABRIC PRODUCTION LINE IN TEXTILE COMPANY IN INDONESIA

In the last four years, the price of fabric raw materials has increased globally supported by fluctuations in the price of the Dollar exchange rate causing the costs required by textile companies to increase by 81.97%. Based on production data obtained from the company, product quality decreased by 10.28% from 2019 to 2022. Poor product quality accompanied by increasing costs of production raw materials adversely affect company finances, so this study aims to reduce the costs required in the production process. The cost of this production process can be reduced by reducing the waste that occurs during the production process. To deal with this problem, this research was conducted on one of the textile companies in Indonesia that produces fabric. The research began by identifying the existing waste problem by conducting a special questionnaire survey called waste assessment questionaire (WAQ). The respondents in this survey were three people from the textile company who were responsible for the course of the fabric production process in the factory. The results of this questionnaire were used to model the relationship between the types of waste and the types of waste that occurred the most. The results of data analysis showed that there were four types of waste that significantly occurred, namely idle time by 23.9%, unnecessary processes by 20.7%, waste due to product defects by 20.1%, and waste due to excess production by 18.9%. These four types of waste will be further analyzed to eliminate the waste effectively and efficiently.