Textile Manufacturing Research Articles

Treatment of the Textile Wastewater using Malaysian Ganoderma lucidum Mycelial Pellets

Purification of textile wastewater using biomass and in particular different fungi is gaining exponential interest to minimize the impacts of current physical-chemical and biological wastewater treatment by-products. This study investigates the potential of Malaysian Ganoderma lucidum mycelium pellets (GLMP) for the decontamination of wastewater samples received from a commercial textile manufacturer. All studies were performed under ambient temperature (26-35 ℃) and unsterilized conditions using a simple bioreactor design (stirred batch bioreactor) for a more practical assimilation of the current available wastewater treatment process system. The optimal conditions of adsorption by GLMP were determined by variation effects of adsorbent concentration (0, 8.75, 12.5 and 25 g/L), pH (unadjusted 7.10 – 8.22, 4 and 6), and wastewater dilution factor (1:0, 1:4, and 2:3, v/v). This method was proved to be effective in both decolorization and chemical oxygen demand (COD) reduction, simultaneously. The most significant percentage of decolorization observed was 77.24% in a 72 h treatment, whereas COD reductions were 78.32% in a 36 h treatment. The present study fits both Langmuir and Freundlich adsorption isotherms as the values of R2 both model were close to 1, indicating the favorable adsorption of dyes towards Malaysian GLMP.

Fabricating Health: The Challenges Faced by Women Power loom Weavers

The power loom sector is a significant contributor to fabric manufacturing in the country, encompassing a diverse range of products. These products span from low-value coarse and gauze cloth to high-value shirting, suiting, and made-ups. Additionally, the sector also produces a limited number of grey fabrics or yarn-dyed products. Female weavers operating within the power loom sector faces a multitude of obstacles that are significantly influencing their economic survival and overall welfare. The textile industry holds significant importance in the economic development of India, whereas the power loom industry being a crucial element within this sector. The purpose of this study is to evaluate the female labour force participation and total working hours, as well as the effect on their health, in the power loom sector of Gaya and Bhagalpur. In addition, the paper will discuss other significant obstacles faced by women weavers, such as wage disparities, lengthy working hours, and safety and security concerns. The focus of the research will be the socioeconomic standing of female power loom weavers. The study is supported by both primary and secondary data. Primary data is derived from data collection on the ground. Weavers are the backbone of the power loom sector, but they are perpetually plagued by issues related to finances, electricity, and health care, among other issues, and they receive no support from the government. As a result, the industry as a whole experiences low productivity and to enhance the conditions of the power loom weavers, there is a dire need for government support.

Design, characterizations, and antimicrobial activity of sustainable home furnishing-based waste fabric treated using biobased nanocomposite

Clothing and textile industries are major contributors to environmental pollution including textile manufacturing through garment production, spinning, weaving, and dyeing. In this context, the sustainability textile industry is a big challenge and contributes to serving a large segment of society. Also, textile wastes could be used as a raw material for added-value products. Herein, in this study, recycling of residues fabric was treated with antimicrobial nanocomposite to reach the best use of exhausts and obtain multifunction products of aesthetic via the technical design of the waste raw materials. Besides, solving the unemployment problem by opening fields for small industry projects capable of producing high-value textile artifacts, especially when treated against microbes, can be applied to home furnishings. The waste fabric was treated via green synthesis nanocomposite based on chitosan and in situ prepared ZnONPs and cross-linked with tannic acid. The prepared nanocomposite was characterized using physicochemical analysis including attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and X-ray diffraction (XRD). Additionally, the nanocomposite and treated fabric topographical behavior were studied using scanning electron microscopy (SEM) attachment with energy dispersive X-ray analysis (EDX), and images were processed to evaluate the roughness structure. Additionally, high-resolution transmission electron microscopy (HR-TEM) and dynamic light scattering (DLS) were performed to ensure the size and stability of the nanocomposite. The obtained results affirmed the green synthesis of nanocomposite with a size around 130 nm, as well as the doped ZnONPs average size of 26 nm and treated waste fabric, performed a promising attraction between nanocomposite and fabric fibers. Moreover, the antimicrobial study observed excellent activity of nanocomposite against bacteria and unicellular fungi as well.

Automated machine learning for fabric quality prediction: a comparative analysis.

The enhancement of fabric quality prediction in the textile manufacturing sector is achieved by utilizing information derived from sensors within the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems linked to sensors embedded in textile machinery. The integration of Industry 4.0 concepts is instrumental in harnessing IoT sensor data, which, in turn, leads to improvements in productivity and reduced lead times in textile manufacturing processes. This study addresses the issue of imbalanced data pertaining to fabric quality within the textile manufacturing industry. It encompasses an evaluation of seven open-source automated machine learning (AutoML) technologies, namely FLAML (Fast Lightweight AutoML), AutoViML (Automatically Build Variant Interpretable ML models), EvalML (Evaluation Machine Learning), AutoGluon, H2OAutoML, PyCaret, and TPOT (Tree-based Pipeline Optimization Tool). The most suitable solutions are chosen for certain circumstances by employing an innovative approach that finds a compromise among computational efficiency and forecast accuracy. The results reveal that EvalML emerges as the top-performing AutoML model for a predetermined objective function, particularly excelling in terms of mean absolute error (MAE). On the other hand, even with longer inference periods, AutoGluon performs better than other methods in measures like mean absolute percentage error (MAPE), root mean squared error (RMSE), and r-squared. Additionally, the study explores the feature importance rankings provided by each AutoML model, shedding light on the attributes that significantly influence predictive outcomes. Notably, sin/cos encoding is found to be particularly effective in characterizing categorical variables with a large number of unique values. This study includes useful information about the application of AutoML in the textile industry and provides a roadmap for employing Industry 4.0 technologies to enhance fabric quality prediction. The research highlights the importance of striking a balance between predictive accuracy and computational efficiency, emphasizes the significance of feature importance for model interpretability, and lays the groundwork for future investigations in this field.

Fabric defects identification for textile industry with a deep learning approach

The textile and clothing industries are crucial sectors that make significant contributions to the economic growth and advancement of countries. In addition to low labor costs, the sector’s development performance is influenced by flexible production structure and defect-free products manufacturing. However, in the textile companies, the manufacture, processing, and weaving of fabric can lead to undesirable defects, which ultimately leads to a decline in both quality and cost. This study investigates the effectiveness of different deep learning frameworks to accurately classify fabric defects commonly encountered in the textile industry in Turkey. For this purpose, an innovative data set is generated as comprising fabric defects such as lines, wrinkle marks, machine oil leaks, holes, and bleaching. Furthermore, the effectiveness of Adam and Ranger optimization functions in defect identification has been evaluated using various models in connection with explainable artificial intelligence. The findings indicate that the ResNet18 + Adam model, which is very simple and shallow, obtained a notable level of success with an accuracy of 99.30%. On the other hand, the more complex EfficientNetv2m + Adam model emerged by achieving an accuracy of 99.42%. The research results suggest that deep learning algorithms can effectively replace human manual control in the task of detecting fabric defects.

A FRAMEWORK FOR LEAN MANUFACTURING IMPLEMENTATION IN THE TEXTILE INDUSTRY: A RESEARCH STUDY

This study investigates the implementation of lean manufacturing in the textile industry, focusing on its potential to enhance efficiency, reduce waste, and improve product quality. Lean manufacturing principles, including Just-In-Time (JIT) production, Kanban systems, value stream mapping, 5S workplace organization, and continuous improvement (Kaizen), have been widely recognized for their effectiveness in various manufacturing sectors. However, their application in the textile industry remains underexplored. Through a systematic literature review of 60 review papers and empirical analysis involving case studies, interviews, and surveys with industry experts, managers, and workers, this research identifies the significant benefits of lean practices in textile manufacturing. The findings reveal substantial efficiency gains, waste reduction, and quality improvements among companies adopting lean principles. Nevertheless, the study also highlights several industry-specific challenges, such as high variability in raw materials, complex production processes, and market pressures from fast fashion. These challenges necessitate tailored approaches and effective change management strategies for successful lean integration. Additionally, the research emphasizes the need for investment in advanced technologies and flexible manufacturing systems to enhance responsiveness to market changes. The study concludes that despite the challenges, lean manufacturing offers valuable strategies for textile companies aiming to improve their competitiveness and sustainability in the global market. This research contributes to the growing body of knowledge on lean manufacturing and provides practical insights for textile manufacturers seeking to adopt and optimize lean practices.

RETRACTION: Knowledge map visualization of technology hotspots and development trends in China's textile manufacturing industry

RETRACTION: R. Huang, P. Yan, X. Yang, “Knowledge Map Visualization of Technology Hotspots and Development Trends in China's Textile Manufacturing Industry,” IET Collaborative Intelligent Manufacturing 3, no. 3 (2021): 243–251, https://doi.org/10.1049/cim2.12024.The above article, published online on 27 March 2021 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editors‐in‐Chief, Liang Gao and Weiming Shen; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.The retraction has been agreed on after concerns about this manuscript were raised by a third party. An investigation revealed substantial flaws in the literature analysis presented. The methodical details are described insufficiently. Accordingly, the literature analysis results cannot be reproduced, and the conclusions are considered invalid.The authors have been informed of the decision to retract.

Fibre fragment pollution: source directed intervention through design

This study analyses the difference in fibre fragment pollution generated during the laundering of woven and knit fabrics for clothing and the influence of fabric parameters on the amount of pollution released. 100% polyester single jersey knit and 2/2 twill woven fabrics were created and washed according to AATCC TM212-2021. Results indicated more tightly woven structures released over three times less pollution than looser knit structures, releasing 6.41 mg of fibre fragments per kg of woven fabric (mean ± 1.50 SD, n = 8) compared to 21.21 mg of fibre fragments per kg of knit fabric (mean ± 1.80 SD, n = 8). The first wash for both knit and woven fabrics shed significantly more fibre fragments than subsequent washes. By the fifth wash both fabrics released under a tenth of the amount of pollution released in the first wash. To mitigate fibre fragment pollution that is released by fabrics designed for clothing this work recommends designing out pollution using more tightly constructed fabrics. Furthermore, the implementation of technology in fabric and garment manufacturing processes (e.g. fibre catching devices) should be utilised specifically during first washes allowing pollution to be filtered from the wastewater to stem the flow of contaminants into the environment.

Circularity in Knitting: The Potential of Re-Using Natural Yarns After Unraveling

ABSTRACT Currently, there is an increasing focus on sustainable textile manufacturing in terms of reducing the waste generated during the production and pre-consumer stages. Knitting companies are exploring the promising direction of unraveling and upcycling yarn to achieve this goal. To assess the knittability of unraveled yarn vis-a-vis conventional yarn, a comprehensive indicator of yarn-breaking force changes was proposed. The purpose of this study is to present a method to evaluate the strength property of unraveled yarn and to investigate the potential of 74-tex cotton and 72-tex wool yarn for future upcycling in knitting. The results of the investigation revealed that cotton yarn has a higher potential for upcycling both at pre- and post-consumer stages, compared to wool yarn. The knittability of unraveled cotton yarn is almost 99% after knitting and dry relaxation and over 95% after repeated washing and drying cycles. However, it is pertinent to note that repeated ironing impacts the cotton yarn’s knittability, and hence, it is recommended to label future upcycling products with a special marking prohibiting ironing. The possibility of unraveling and reusing wool yarn is limited to the post-production and pre-consumer stages when its knittability is near 95%.

Gio-Nigeria Economic Relations on the Development of the Nigerian Textile Sector, 2006–2017

The purpose of the study is to investigate how the economic ties between China and Nigeria have affected the growth of Nigeria's textile sector between 2021 and 2023. The study collected data from stakeholders, including policymakers, business owners, and textile industry specialists, using qualitative research methods such as questionnaires, interviews, and documentary reviews. The study's conclusions will provide important light on how the relationship between China and Nigeria has affected the growth of Nigeria's textile sector and will also include useful suggestions for future legislation and company owners. The study found that, especially during the study period, Nigeria's textile sector has developed favorably as a result of the economic ties between China and Nigeria. The partnership has given manufacturers significant opportunities in Nigeria to have access to Chinese resources, money, and technology. New investments, more manufacturing capacity, and access to new markets have resulted from this. Furthermore, new, more effective production methods have been used due to the cooperation between the two nations, improving the productivity and caliber of the textiles produced. Despite the advantages, especially in terms of profit-sharing, intellectual property, and environmental sustainability. The report recommends that both nations should enact more robust policy incentives to promote investment in regional businesses, support the protection of intellectual property, and work to increase the textile industry's environmental sustainability to address these problems. Moreover, the report suggested, among other things, that the two nations work together more closely to exchange resources and knowledge. In its conclusion, the study showed that it has provided valuable opportunities for textile manufacturers to access capital, technology, and resources from China, while also introducing more efficient production methods. However, the economic relationship needs to be further strengthened in terms of policy incentives, intellectual property protection, and environmental sustainability to maximize the benefits of the relationship for both countries. Received: 28 March 2024 / Accepted: 29 June 2024 / Published: 8 July 2024

The Vagaries of the Sea: Evidence on the Real Effects of Money from Maritime Disasters in the Spanish Empire

Abstract We estimate the effect of money supply changes on the real economy by exploiting a recurring natural experiment: maritime disasters in the Spanish Empire (1531–1810) that resulted in the loss of substantial amounts of silver money. We find that negative money supply shocks caused Spanish real output to decline. A transmission channel analysis highlights slow price adjustments and credit frictions as mechanisms through which money supply changes affected the real economy. Especially large output declines occurred in textile manufacturing against the backdrop of a credit crunch that impaired merchants’ ability to supply their manufacturers with inputs.

Mechanically-enhanced structural composites yarns with multifilament-spreading technique using a modified ring spinning system and predicting modelling

To advance spinning technology for multifilament covered cotton fibers, a spreading apparatus was introduced to ring spinning. This innovation allowed the manufacturing of a composite yarn with a wrapped structure. However, an effective strategy for predicting the mechanical properties of multifilament spread composite structural yarns (FS-CSY) was urgently needed. In this study, researchers compared FS-CSY with bi-component composite yarn (b-CY) to determine if the addition of a spreading apparatus and twisting in the manufacturing process resulted in any improvements or enhancements. The FS-CSY structure was found to be novel and exhibited significant mechanical properties, including a 4.7% increase in tensile strength compared to b-CY. A strategy was developed to predict FS-CSY by considering the transfer impact of fiber enhancement within the yarn and by incorporating two-component interaction forces in the yarn-wrapped structure. Meanwhile, a modified three-element tensile constitutive model was established to effectively simulate the stress characteristics under different twist levels, which proved the composite yarn prediction theoretical model. Finally, the fracture morphology analysis shown the b-CY fracture surface was irregular, while FS-CSY exhibited a uniform fracture surface, and the 750TPM showed a more uniform and stable structure. By developing this predictive strategy, enhance existing structural composite yarn by maximizing the efficiency and quality of multi-filament covered cotton fibers. Ultimately, this research contributes to advancements in textile manufacturing processes and lays a foundation for future innovations.

German minority in urban space of a textile manufacturing centre: the case of 19th century city of Łódź

German settlement in Poland has been the subject of numerous studies. The causes and determinants of migratory movements have been discussed extensively, while little attention has been paid to the spatial aspects of the phenomenon. The aim of this study, which is part of geographical and historical research, is to determine the size of the influx of migrants of German nationality into the rapidly developing textile production centre of Łódź in the 19th century, as well as the geographical origins and distribution of this nationality group in the urban space. These considerations are complemented by the identification of key issues related to the social integration and assimilation processes of the German minority. In the first half of the 19th century, the influx of skilled labour, almost entirely recruited from the German population, conditioned the development of the textile industry in Łódź. It also had a key influence on the demographic development of Łódź, which soon became the largest centre of textile production in Poland. In subsequent decades, there was a tendency among German immigrants towards separation in the sphere of professional and social activities, but already by the end of the 19th century the German community settled in Łódź was diverse in terms of the sense of nationality. In addition to a certain proportion of the immigrants who were already Polonised, there were persons declaring German nationality, as well as those undecided about the issue.

Significance of Casein and it’s Applications in the manufacture and finishing of various textiles

Significance of Casein and it’s Applications in the manufacture and finishing of various textiles

Use of Big Data for Textile Fashion and Garment Industry

Big data is such type of data which requires a different processing approach. The feature of data science is to drive the fashion and textile industry forward. Among the revolutionary technologies big data is the most useful tool for industries. One of the most basic questions in research is how block chain empowered by big data can be handled and utilized to manage traceable and information sharing more perfect through global supply chains. At present the number of applications of big data technology in textile manufacturing sections in organized manner is limited. In fashion apparel businesses there are numerous benefits of retail analytics. Data related to style designs is the information about the components and standards of plan, which joined together, give the plan of a design item. Big data can allow fashion industry to examine popular patterns at a granular level to allow them understand about who is buying and why.

Impact of a Projectile on Fabrics – An Experimental Test Method

Fabric manufacturers are striving to produce value-added, high-end technical fabrics, such as smart textiles, protective clothing and accessories for sports, where the performance of the fabric has become a decisive factor. Fabric impact resistance is one such property of importance. Therefore the development of a suitable method to predict the impact on the fabric at the point of fabric development is of great importance. In this article, the authors have attempted to design and develop a test method to predict fabric impact more accurately. A mathematical model has been developed to show the relationship between impact force, impact velocity and the depth of penetration on impact. Finally, test equipment has been developed to measure the impact based on the developed model. The indentation depth on impact was measured against both impact velocity and kinetic energy, and the experimental impact force was calculated against the impact velocity. It was found that the impact velocity of the projectile affects both the impact on fabric and indentation into the backing material as well as the energy absorbed by the fabric. The energy absorption also increased with impact velocity and higher velocities caused greater damage. No great variation in temperature was detected.

Proposal for Prevention of Occupational Disease Risk Using the Hiradc Method at PT X in the Dyeing Department

People in this era have various needs in carrying out life. The needs that need to be used by the community have multiple kinds of things, such as clothing. Clothes used by the community go through various processes before becoming finished goods. Before becoming ready-to-wear, clothes came from raw materials such as fabric rolls. There are multiple production processes, such as yarn, fabric, chemical dyeing, etc. One of the places where fabrics are produced is textile factories. A textile factory is a company that produces products in the form of fabrics. The manufacture of fabrics that occur in textile factories must involve mixing chemicals so that the fabric has good quality. The process of mixing these chemical substances involves workers, and the chemical substances used are classified as Hazardous and Toxic Materials (B3), so these chemicals can have a bad impact on their workers if there is physical contact with the worker's body. Exposure to chemicals in the worker's body will cause a risk of developing Occupational Diseases (PAK). Therefore, in this study, data will be processed to prevent workers' risk of Occupational Diseases (PAK) using the HIRADC (Hazard Identification, Risk Assessment, and Determining Control) method. The proposal given in this study is a proposal for the use of Personal Protective Equipment (PPE) and Standard Operating Procedure (SOP) for preventive measures against Occupational Diseases (PAK) for workers

Gendered Migration in India: Exploring the Challenges and Human Rights Issues Faced by Female Migrant Workers in Perumbavoor, Ernakulam District, Kerala

Most studies on inter-state migrant workers in Kerala focus on male migrants arriving in the state. Kerala began to receive migrant workers in 1950. There has been a significant shift in it since 1990. From the 1960s to the 1990s, migrant workers came to Kerala from Tamil Nadu, Andhra Pradesh, and Karnataka. During those times, women came to Kerala along with men. After the 1990s, migration from Assam, West Bengal, Bihar, Orissa, Jharkhand, and Haryana gained momentum. Many women have moved to Kerala since 2000, as opposed to male migrants from 1990 to 2000. Many human rights issues face migrant women in Kerala, including their workplaces, lifestyles, job searches, and travel. The consequences of not getting significant health interventions and being harassed at work are not well known in society. A study of migrant women begins in the public space, where they are not considered citizens but as objects of utility. My research revealed 516,000 migrant women in Kerala, based on a government document obtained through RTI. There are 25000 female migrant workers among them. It is estimated that around 34 lakh migrant workers work in Kerala, according to the Kerala Planning Board's 2021 report. Out of that, 20% of women work in many workplaces and enterprises in Kerala. According to surveys and a government assessment, Kerala's Ernakulam district has the highest concentration of migrant women workers. The female migrant workers in the Perumbavoor one of the cities of the Ernakulam district are the subject of this study. Female workers who work in plywood, bricks, baji, tailoring, pan shops, plastic manufacturing, textile manufacturing, and other industrial units are the subject of this study. Female migrant workers are personally met and interviewed during fieldwork to obtain data. For this study, qualitative ethnographic research methodology is employed. The findings of this study suggest the government's policy-making for social welfare, social security, job security, the prevention of sexual violence, and making sure that female migrants in Kerala receive equal pay for equal work. This study is being conducted with the human rights of women migrant workers in Kerala foremost in mind. Information is being directly collected from migrant women workers, labour department health department government representatives, trade union members, NGO representatives, etc.

Measurement of effectiveness spinning fiber machines using total productive maintenance method at textile manufacturing

PT XYZ is a textile industry manufacturing company whose main products are Polymer Chips, Pre-Oriented Yarn (POY), Draw Twister Yarn (DTY), Bi Shrankage Yarn (BSY), and Polyester Staple Fiber (PSF) which are marketed domestically and internationally. There are still high breakdown problems, which can hamper the production process and result in a decrease in production capacity. The purpose of this study was to measure the Performance Effectiveness of Fiber Spinning Machines using the Total Productive Maintenance (TPM) Method and to find the causes of the Six Big Losses that affect the Performance Effectiveness of Fiber Spinning Machines. The application of TPM in the manufacturing industry aims to extend machine life and maximize the effectiveness of machine performance. The results of measuring the Performance Effectiveness of Fiber Spinning Machines using the TPM Method obtained an Overall Equipment Effectiveness (OEE) value of 87.85% and the cause of Six Big Losses that affect the effectiveness of the performance of Fiber Spinning Machines is Equipment Failure Losses, the most dominant factor is the Human Factor, namely lack of control, lack of understanding of Standard Operational Procedure (SOP), lack of thoroughness when cleaning spinnerets.

Benefits of Using Data-Driven Lean Manufacturing in Textile and Apparel Manufacturing Units in Pakistan

The objective of the present study is to investigate the advantages of lean manufacturing in textile and apparel manufacturing facilities in Pakistan. This study examines the influence of lean manufacturing on the time-saving, process efficiency, and industry improvement. Modern organizations are employing lean manufacturing as a critical concept in their operations. There are numerous advantages to employing lean manufacturing; consequently, the textile industry is also transitioning to this methodology. The quantitative methodology employed in this investigation is employed to investigate the current subject matter. The study's data is gathered from the customers and employees of textile and apparel manufacturing units in Pakistan. In order to accumulate data from the respondents of the investigation, questionnaires were implemented. The study's findings suggest that lean manufacturing is significantly positively correlated with industry improvement, process efficiency, and time savings. These three factors are also essential for the success of an organization. This investigation also encompasses practical and managerial implications, future direction, and limitations.