Articles de revues sur le sujet « Textile industry – Classification »
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1
Hermawan, Iwan. « ANALYSIS OF THE IMPACT OF MACROECONOMIC POLICIES ON TEXTILE INDUSTRY AND ITS PRODUCTS IN INDONESIA ». Buletin Ekonomi Moneter dan Perbankan 13, no 4 (28 juin 2011) : 357–90. http://dx.doi.org/10.21098/bemp.v13i4.398.
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Textile and textile’s product play an important role in the Indonesian economy. During the last five years, however, share of these industries and commodities to gross domestic product tend to decrease. The objectives of this study are to analyze factors affecting Indonesian textile and textile’s product, and the prospect of Indonesian textile and textile’s product in the future. Results of the study show that domestic textile production was affected by world cotton price and wage rate, while the domestic garment production was affected by wage rate in the garment sector. Indonesia’s textile export to world market was influenced by domestic textile price, and Indonesia’s export garment was influenced by exchange rate (Rp/US$). Indonesian textile demand was affected by wage rate and domestic garment demand was affected by income per capita of Indonesia. In general, the prospect of Indonesian textile and textile’s product seems not too good. In fact, Indonesian textile and textile’s product had depended on high import cotton, investment, and exchange rate. So why, economy policies are still needed to accelerate Indonesian textile and textile’s product developmentJEL Classification: C53, E60, F43, and F4.Keywords: export, open economy, forecasting, simulation, textile and textile’s product.
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Harmsen, Paulien, Michiel Scheffer et Harriette Bos. « Textiles for Circular Fashion : The Logic behind Recycling Options ». Sustainability 13, no 17 (30 août 2021) : 9714. http://dx.doi.org/10.3390/su13179714.
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For the textile industry to become sustainable, knowledge of the origin and production of resources is an important theme. It is expected that recycled feedstock will form a significant part of future resources to be used. Textile recycling (especially post-consumer waste) is still in its infancy and will be a major challenge in the coming years. Three fundamental problems hamper a better understanding of the developments on textile recycling: the current classification of textile fibres (natural or manufactured) does not support textile recycling, there is no standard definition of textile recycling technologies, and there is a lack of clear communication about the technological progress (by industry and brands) and benefits of textile recycling from a consumer perspective. This may hamper the much-needed further development of textile recycling. This paper presents a new fibre classification based on chemical groups and bonds that form the backbone of the polymers of which the fibres are made and that impart characteristic properties to the fibres. In addition, a new classification of textile recycling was designed based on the polymer structure of the fibres. These methods make it possible to unravel the logic and preferred recycling routes for different fibres, thereby facilitating communication on recycling. We concluded that there are good recycling options for mono-material streams within the cellulose, polyamide and polyester groups. For blended textiles, the perspective is promising for fibre blends within a single polymer group, while combinations of different polymers may pose problems in recycling.
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Angelova, Yordanka, Silvija Mežinska et Lyubomir Lazov. « INNOVATIVE LASER TECHNOLOGY IN TEXTILE INDUSTRY : MARKING AND ENGRAVING ». Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (15 juin 2017) : 15. http://dx.doi.org/10.17770/etr2017vol3.2610.
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The advent of laser technology in textiles industry has established a new innovative solution, which successfully prevents some of the weaknesses in the conventional technologies. Lasers are being used in Laser Marking (Only the surface of fabric is processed, fading), Laser Engraving (Controlled cutting to depth). It has been used extensively as the replacement of some conventional dry processes like sand blasting, hand sanding, destroying, and grinding etc., which are potentially harmful and disadvantageous for the environment.
The article considers some innovative laser technologies, such as marking and engraving on various textile materials. The laser applications for leather and textile processing were analysed. The report overviews systems and ways of laser marking and engraving implementations. Classification of markings was proposed. The advantages of laser marking and engraving technologies in textile fields were pointed.
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Zhang, Jianlei, Lin He et Longdi Cheng. « Is China’s Textile Industry Still a Labour-Intensive Industry ? » Fibres and Textiles in Eastern Europe 29, no 1(145) (28 février 2021) : 13–16. http://dx.doi.org/10.5604/01.3001.0014.5038.
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Is China’s textile industry (CTI) still a laboor-intensive one? To answer this question, this study measures the capital-labour intensity and technology intensity of CTI and its sub-sectors during 2006-2018, then applies factor intensity classification and cluster analysis to identify their industrial attributes. The results show that CTI and its sub-sectors are still the labour- and non-technology-intensive. All the indexes of capital-labour intensity and technology intensity of CTI and its sub-sectors are below 100, lower than the average of industry sectors, indicating that they are not separate from the category of labour-intensive industry and still heavily dependent on labour. And cluster analysis verifies the industrial classification results. So CTI still needs to keep on increasing its capital intensity and technology intensity to achieve the goal of industrial transformation and upgrading in the future.
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Riba, Jordi-Roger, Rosa Cantero, Pol Riba-Mosoll et Rita Puig. « Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach ». Polymers 14, no 12 (17 juin 2022) : 2475. http://dx.doi.org/10.3390/polym14122475.
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The textile industry is generating great environmental concerns due to the exponential growth of textile products’ consumption (fast fashion) and production. The textile value chain today operates as a linear system (textile products are produced, used, and discarded), thus putting pressure on resources and creating negative environmental impacts. A new textile economy based on the principles of circular economy is needed for a more sustainable textile industry. To help meet this challenge, an efficient collection, classification, and recycling system needs to be implemented at the end-of-life stage of textile products, so as to obtain high-quality recycled materials able to be reused in high-value products. This paper contributes to the classification of post-consumer textile waste by proposing an automatic classification method able to be trained to separate higher-quality textile fiber flows. Our proposal is the use of near-infrared (NIR) spectroscopy combined with a mathematical treatment of the spectra by convolutional neural networks (CNNs) to classify and separate 100% pure samples and binary mixtures of the most common textile fibers. CNN is applied for the first time to the classification of textile samples. A total of 370 textile samples were studied—50% used for calibration and 50% for prediction purposes. The results obtained are very promising (100% correct classification for pure fibers and 90–100% for binary mixtures), showing that the proposed methodology is very powerful, able to be trained for the specific separation of flows, and compatible with the automation of the system at an industrial scale.
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Yao, Gui Fen. « Quality Evaluation for Automobile Seat Woven Fabric ». Advanced Materials Research 1004-1005 (août 2014) : 1427–31. http://dx.doi.org/10.4028/www.scientific.net/amr.1004-1005.1427.
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Industrial textiles is designed with engineering structure textiles. Transportation textiles is one of the main types of industrial textiles. Automobile seat fabric is one of the decorative materials in automobile textile fabrics. The fabric should have soft handle, good air permeability, coordinate color, luxury and generous pattern, wear-resisting, anti-fouling, flame retardant, certain friction factor and antistatic property. In recent years, requirements for automobile textiles of safety, health, environmental protection is more and more high. In order to evaluate the automobile seat woven fabric quality, need to have a simple and effective standard. Quality indexes should be comprehensive. The test method of quality index should be operable. In the existing relevant standards, the test content is not consistent. The existing relevant standards are national standards, textile industry standards and automotive industry standards. Within textile industry standard FZ/T 24005-2010 wool textiles for chair, the technical requirements include safety specification, classification rules, physical quality rating, internal quality rating, appearance quality rating. For flame retardant performance, must meet the following requirements: damaged length ≤200mm, afterflame time ≤15s. Within national standard GB 8410-2006, flammability of automotive interior materials, for flame retardant performance, must meet the following requirements: burning rate ≤100mm/min. Within automotive industry standard QC/T 633-2009 the seats of passenger vehicles, for safety specification, seat fabric must meet the B grade in GB 18401. Based on some effective standard, established suitable standard for automobile seat woven fabric. The standard covers quality evaluation content, performance levels and the methods of test to be used to determine these performance levels.
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Cao, Fei, Jian Ping Shi, Xian Yan Liu et Chang Sheng Zhang. « Modern Home Textiles Database Query System ». Advanced Materials Research 175-176 (janvier 2011) : 398–401. http://dx.doi.org/10.4028/www.scientific.net/amr.175-176.398.
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This study made a discussion of the exploitation and application of home textile bedding data-base design. The target is to closely follow the International Textile Fashion trend and to design a lot of world-class home textile bedding products. This article bases on the practicality of the textile bedding design. Database is divided into three modules: style classification database, design theme classification database and the processing technology database. In a comprehensive basis of market research, database is using VB programming language and Access database development tools. Because it has the function of flexible, quickly and accurately find the technical parameters, design styles and product styles and other related information of the processing. There are many descriptions, pictures, process technology and so on of home textiles bedding in the data-base. From the data-base, designers can easily inquiry the color, variety, fashion, style, function, process technology etc. Then, designers can exchange and innovation by using what they inquiry from the database. Through the innovation, home textile enterprises enhance the market competitiveness. It’s conducive to improve the overall development of textile industry by the data-base.
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Liu, Zhengdong, Wenxia Li et Zihan Wei. « Qualitative classification of waste textiles based on near infrared spectroscopy and the convolutional network ». Textile Research Journal 90, no 9-10 (5 novembre 2019) : 1057–66. http://dx.doi.org/10.1177/0040517519886032.
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The recycling of waste textiles has become a growth point for the sustainable development of the textile and clothing industry. In addition, sorting is a key link in the follow-up recycling process. Since different fabrics are required to be processed by different technologies, manual sorting not only takes time and effort but also cannot achieve accurate and reliable classification. Based on the analysis of near infrared spectroscopy, the theory and methods of deep learning are used for the qualitative classification of waste textiles in order to complete the automatic fabric composition recognition in the sorting process. Firstly, a standard sample set is established by waveform clipping and normalization, and a Textile Recycling Net deep web suitable for near infrared spectroscopy is established. Then, a pixilated layer is used to facilitate the deep learning of features, and the multidimensional features of the spectrum are extracted by using the multi-layer convolutional and pooling layers. Finally, the softmax classifier is adopted to complete the qualitative classification. Experimental results show that the convolutional network classification method using normalized and pixelated near infrared spectroscopy can realize the automatic classification of several common textiles, such as cotton and polyester, and effectively improve the detection level and speed of fabric components.
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Lysova, Marina A., Lyudmila V. Dryagina, Natalia A. Gruzintseva et Boris N. Gusev. « UNIFICATION OF THE CODING SYSTEM TEXTILE PRODUCTS ». Technologies & ; Quality 53, no 3 (28 octobre 2021) : 24–29. http://dx.doi.org/10.34216/2587-6147-2021-3-53-24-29.
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One of the ways to achieve the required level of quality of industrial products and, consequently, to increase the competitiveness of Russian manufacturers is to implement the task of optimising and unifying the nomenclature of products produced by enterprises. However, at present, due to the differences in the classification and coding systems of consumer products at the corresponding stages of its life cycle, there are problems with the unification of the nomenclature of industrial products, including textiles, since textile and light industry enterprises, trade organisations and customs authorities each use their own classification of these types of products. The paper analyses the functional capabilities of the currently existing coding systems for textile products, and also proposes and implements a method for matrix coding of textile products on the range of geosynthetic materials produced. In addition, the possibility of combining matrix coding of products with information about its manufacturer and quality in the framework of a two-dimensional barcode is shown.
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Lee, So Young, Hye Seon Jeong, Yoon Sung Choi et Choong Kwon Lee. « Textile material classification in clothing images using deep learning ». Korean Institute of Smart Media 12, no 7 (31 août 2023) : 43–51. http://dx.doi.org/10.30693/smj.2023.12.7.43.
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As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.
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Abdulla et Shiv Kumar. « Technical efficiency and its determinants in the Indian textile garments industry ». Research Journal of Textile and Apparel 25, no 4 (5 mai 2021) : 346–60. http://dx.doi.org/10.1108/rjta-09-2020-0110.
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Purpose This paper aims to examine technical efficiency and its determinants in Indian textile garments industry in post-agreement on textiles and clothing regime and evaluate the technical efficiency among micro, small and medium enterprises (MSMEs) firms. Design/methodology/approach This study uses unbalanced panel data for the period 2005–2010 to 2015–2016. The stochastic frontier function is used to estimate technical efficiency and its determinants. Findings The results show that the overall ecosystem of textile garments’ value chains could be improved to enhance the technical efficiency thereof. The result also reveals that small-scale firms have the highest technical efficiency scores, and medium-scale firms have the least technical efficiency score among all the categories of MSMEs. Research limitations/implications The textile garments industry needs to define its innovation strategies, as these strategies lead to different results that can be achieved only through the management of resources dedicated to the generation and implementation of innovations. Practical implications This study has shown that to offset India’s cost disadvantage in the international markets, there is a need to develop an ecosystem of textile manufacturing and value chains, eliminate the inverted duty structure (where inputs are taxed at a higher rate than the final product) and switch over from shuttle looms toward shuttle-less looms. This would unleash the potential of textile and garments industry and make it globally competitive and technically efficient. Further, there will be an alignment with the ease of doing business with an appropriate mix of policy, technology, institution, infrastructure, information and services. Originality/value Using frontier production function takes stochastic context into account for the dynamic character of technical efficiency and its components. Most of the past studies have assessed technical efficiency at the aggregate level using three-digit National Industrial Classification (NIC) or four-digit NIC code. An analysis at higher levels of aggregation masks the variation in technical efficiency. This study used five-digit NIC data to measure the firm-specific technical efficiency of the textile industry. According to the authors’ knowledge, this study is the first of its kind in the Indian textile industry using stochastic frontier approach and panel data. Further, it also looks at the contribution of different determinants in technical efficiency to the firms.
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Zhu, Xiangmei, Bin Zhang et Hui Yuan. « Digital economy, industrial structure upgrading and green total factor productivity——Evidence in textile and apparel industry from China ». PLOS ONE 17, no 11 (4 novembre 2022) : e0277259. http://dx.doi.org/10.1371/journal.pone.0277259.
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According to the standard of GB/T4754-2017 Classification of National Economic Industry and the characteristics of the textile and apparel industry, the textile and apparel industry is divided into three categories: textile industry, clothing industry and chemical fiber manufacturing industry. Based on the panel data of the textile and apparel industry from 2010 to 2019, this paper measures green total factor productivity (GTFP) by using the unexpected output super efficiency SBM model and the ML index. On this basis, this paper empirically tests the impact of digital economy on the GTFP of textile and apparel industry, and the dual intermediary effects of rationalization of industrial structure and advanced industrial structure are discussed. The results show that: (1) The GTFP of the textile and apparel industry shows a fluctuating upward trend, but it is in a state of low growth. (2) Digital economy has a significant effect on promoting the GTFP. Among them, it has a positive effect on the improvement of GTFP in textile industry, but has no obvious effect on the clothing industry, and has a restraining effect on the chemical fiber manufacturing industry. (3) In the process of the impact of digital economy on GTFP, the rationalization of industrial structure has a partial intermediary effect, and the level of effect reaches 35.81%, while the advancement of industrial structure does not necessarily have a "structural dividend", and its influence on GTFP is non-linear. This paper enriches the research on the influencing factors of GTFP, and is also an effective supplement to the research on digital economy. The conclusions provide a reliable empirical basis for digital economy to help the textile and apparel industry pollution control, and also provide policy references for giving full play to the green value of digital economy.
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Sandhya, NC, Nihal Mathew Sashikumar, M. Priyanka, Sebastian Maria Wenisch et Kunaraj Kumarasamy. « Automated Fabric Defect Detection and Classification : A Deep Learning Approach ». Textile & ; Leather Review 4 (14 décembre 2021) : 315–35. http://dx.doi.org/10.31881/tlr.2021.24.
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A computer-based intelligent visual inspection system plays a major role in evaluating the quality of textile fabrics and its demand is continuously increasing in the textile industry, especially when the quality of textile is to be considered. In this paper, we propose an AI-based automated fabric defect detection algorithm which utilizes pre-trained deep neural network models for classifying possible fabric defects. The fabric images are enhanced by pre-processing at various levels using conventional image processing techniques and they are used to train the networks. The Deep Convolutional Neural Network (DCNN) and a pre-trained network, AlexNet, are used to train and classify various fabric defects. With the exiting textile dataset, a maximum classification accuracy of 92.60% is achieved in the conducted simulations. With this accuracy, the detection and classification system based on this classifier model can aid the human to find faults in the fabric manufacturing unit.
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Hodge, George, et Christine Cagle. « BUSINESS-TO-BUSINESS E-BUSINESS MODELS : CLASSIFICATION AND TEXTILE INDUSTRY IMPLICATIONS ». AUTEX Research Journal 4, no 4 (1 décembre 2004) : 211–27. http://dx.doi.org/10.1515/aut-2004-040407.
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Abstract Since the introduction of the Internet and e-commerce in the mid-1990s, there has been a lot of hype surrounding e-business, the impact that it will have on the way that companies do business, and how it will change the global economy as a whole. Since the crash of the dotcom companies in 2001, there has been much less hype surrounding the use of the Internet for business. There seems to have been a realization that e-business may not be the answer to all of a company’s problems, but can be a great asset in the struggle to increase efficiencies in daily business dealings, and that the Web is primarily a new way of relating to customers and suppliers. This paper categorizes and discusses the different types of business-to-business electronic business models currently being used by businesses and discussed in the academic literature, and shows how these business models are being implemented within the textile industry. This paper is divided into three parts. Part I gives an overview and some important definitions associated with businessto- business e-business, and discusses some characteristics that are unique to doing business on the Internet. Risks and benefits associated with doing business online are also discussed. Part II analyzes the different types of e-business models seen in the academic literature. Based on the analysis of the literature, a taxonomy of e-business models was developed. This new classification system organized e-business models into the following categories: sourcing models, ownership models, service-based models, customer relationship management models, supply chain models, interaction Models and revenue models. Part III reviews how these e-business models are currently being used within the textile industry. A preliminary analysis of 79 textile manufacturing companies was conducted to identify the applications of e-business.
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Beljadid, Aafaf, Adil Tannouche et Abdessamad Balouki. « Fabric defect classification using transfer learning and deep learning ». IAES International Journal of Artificial Intelligence (IJ-AI) 12, no 3 (1 septembre 2023) : 1378. http://dx.doi.org/10.11591/ijai.v12.i3.pp1378-1385.
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The internal inspection of fabrics is one of the most important phases of production in order to achieve high quality standard in the textile industry. Therefore, developing efficient automatic internal control mechanism has been an extremely major area of research. In this paper, the famous architecture Googlenet was fine-tuned into two configurations for texture defect classification that was trained on a textile texture database (TILDA). The experimental result, for both configurations, achieved a significant overall accuracy score of 97% for motif and a non-motif-based images and 89% for mixed images. In the results obtained, it was observed that the second model, which updates the last six layers, was more successful than the first one; which updates the last two layers.
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Prasetyani, Dwi, Ali Zainal Abidin, Nanda Adhi Purusa et Fahrein All Sandra. « The Prospects and The Competitiveness of Textile Commodities and Indonesian Textile Product in the Global Market ». ETIKONOMI 19, no 1 (22 mars 2020) : 1–18. http://dx.doi.org/10.15408/etk.v19i1.12886.
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This study has two objectives: first, to test the competitiveness of Textile Commodities and Indonesian Textile Product (TPT) in the global market and identify the prospects of the new export markets. Second, identify the competitiveness of the textile industry using case studies in the Solo Raya region. The Revealed Comparative Advantage (RCA) and Export Product Dynamics (EPD) methods are using in this study. The results show that Indonesian TPT commodities have a lost opportunity category in the central export destinations countries, such as a decline in market share. Indonesian TPT commodities have prospects in Austria, Canada, Finland, Norway, Portugal, Qatar, and Sweden due to competitiveness and domination in the market. Besides, the condition of the Indonesian textile industry competitiveness shows low competitiveness in terms of factor conditions, demand conditions, supporting and related industries, strategy, structure, and competition that are components of Porter's diamond model.JEL Classification: L6, L67How to Cite:Prasetyani, D., Abidin, A. Z., Purusa, N. A., & Sandra, F. A. (2020). The Prospects and The Competitiveness of Textile Commodities and Indonesian Textile Product in the Global Market. Etikonomi: Jurnal Ekonomi, 19(1), 1 – 18. https://doi.org/10.15408/etk.v19i1.12886.
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Clark, Don P., et Kathleen Rees. « Intra-Industry Specialization in Textiles and Apparel ». Global Economy Journal 6, no 4 (23 novembre 2006) : 1850098. http://dx.doi.org/10.2202/1524-5861.1202.
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This paper examines changes in intra-industry specialization indicators over the 1992-2004 period to assess potential for structural adjustment problems that may arise in U.S. textile and apparel products with growth in trade. Separate analyses are conducted for U.S. bilateral trade with China, Mexico, and DR-CAFTA members. Seven of the sixteen three-digit Standard International Trade Classification (SITC) product groups are expected to experience significant structural adjustment problems. With the exception of one group, all fall within the apparel and clothing (SITC 84) category. Results suggest substantial increases in U.S. imports from China are influencing these findings.
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Khalil, Samina. « Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs : A Case of Textile Processing Units in Pakistan. » Pakistan Development Review 50, no 4II (1 décembre 2011) : 685–98. http://dx.doi.org/10.30541/v50i4iipp.685-698.
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This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output
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Ashraf, Rehan, Yasir Ijaz, Muhammad Asif, Khurram Zeeshan Haider, Toqeer Mahmood et Muhammad Owais. « Classification of Woven Fabric Faulty Images Using Convolution Neural Network ». Mathematical Problems in Engineering 2022 (27 août 2022) : 1–16. http://dx.doi.org/10.1155/2022/2573805.
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Convolution neural network (CNN) is one of the most popular machine learning techniques that is being used in many applications like image classification, image analysis, textile archives, object recognition, and many more. In the textile industry, the classification of defective and nondefective fabric is an essential and necessary step to control the quality of fabric. Traditionally, a user physically inspects and classifies the fabric, which is an ineffective and tedious activity. Therefore, it is desirable to have an automated system for detecting defects in the fabric. To address these issues, this research proposes a solution for classifying defective and nondefective fabric using deep learning-based framework. Therefore, in this research, an image processing technique with CNN-based GoogleNet is presented to classify defective and nondefective fabric. To achieve the purpose, the system is trained using different kinds of fabric defects. The performance of the proposed approach was evaluated on the textile texture TILDA dataset, and achieved a classification accuracy of 94.46%. The classification results show that the proposed approach for classifying defective and nondefective fabric is better as compared to other state-of-the-art approaches such as Bayesian, BPNN, and SVM.
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Sousa, Ana Rita, Renata Matos, José R. M. Barbosa, O. Salomé G. P. Soares, João Ferreira, Gilda Santos, Augusta Silva et al. « Scalable Flexible Electromagnetic Interference Shielding Textiles Based on MWCNTs and PEDOT:PSS ». Solid State Phenomena 333 (10 juin 2022) : 161–69. http://dx.doi.org/10.4028/p-t2u2zu.
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With the rise of electromagnetic radiation-based technologies, considerable attention has been drawn to developing and implementing innovative electromagnetic shielding materials. Carbon nanomaterials and conductive polymers have been appealing to both academia and industry as promising alternatives for the traditionally used metallic materials, owing to their lightness, flexibility, easy processability and resistance to corrosion, which are of special importance for textile applications. In this work, multiwalled carbon nanotubes (MWCNTs) and poly (3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) have been applied to cotton textile substrates by straightforward scalable dyeing and coating processes, respectively. These processes led to uniform and homogeneous coatings with distinct properties: the fabric coated with MWCNT presented higher thickness and lower loading of incorporated material than the textile coated with PEDOT:PSS (thickness: 995 μm vs. 208 μm; material loading: 9.4 wt.% vs. 70.7 wt.%). The electromagnetic shielding properties were outlined for each shielding textile in the frequency range of 5.85–18 GHz: an average shielding effectiveness of ~35.6 dB was obtained for MWCNT@tex, while PEDOT:PSS@tex reached ~38.3 dB. Thus, PEDOT:PSS provided enhanced radiation shielding with lower coating thickness, while the MWCNTs led to improved attenuation with less material usage. Shielding effectiveness values above 30 dB were obtained for both electromagnetic interference shielding textiles, which corresponds to an excellent classification for general use applications, such as casual clothing and maternity wear.
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TAO, PENG, CAO WENLI, CHEN JIA, LV XINGHANG, ZHANG ZILI, LIU JUNPING et HU XINRONG. « Research on fabric classification based on graph neural network ». Industria Textila 74, no 01 (28 février 2023) : 3–11. http://dx.doi.org/10.35530/it.074.01.202224.
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Fabric classification plays a crucial role in the modern textile industry and fashion market. In the early stage, traditional neural network methods were adopted to identify fabrics with the drawback of restricted fabric type and poor accuracy. Combining multi-frame temporality and analysing fabric graph data made from fabric motion features, this paper proposes a novel hybrid model that introduces the concept of graph networks to classify 30 textile materials in a public database. We utilize the graph inductive representation learning method (GraphSAGE, Graph Sample and Aggregate) to extract node embedding features of the fabric. Moreover, bidirectional gated recurrent unit and layer attention mechanism (BiGRU-attention) are employed in the last layer of aggregation to calculate the score of previous cells. Intending to further enhance performance, we link the jump connection with adaptive selection aggregation frameworks to determine the influential region of each node. Our method breaks through the limitation that the original methods can only classify a few fabrics with great classification results.
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Zhu, Yaolin, Jiameng Duan et Tong Wu. « Animal fiber imagery classification using a combination of random forest and deep learning methods ». Journal of Engineered Fibers and Fabrics 16 (janvier 2021) : 155892502110093. http://dx.doi.org/10.1177/15589250211009333.
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Feature extraction is a key step in animal fiber microscopic images recognition that plays an important role in the wool industry and textile industry. To improve the accuracy of wool and cashmere microscopic images classification, a hybrid model based on Convolutional Neural Network (CNN) and Random Forest (RF) is proposed for automatic feature extraction and classification of animal fiber microscopic images. First, use CNN to learn the representative high-level features from animal fiber images, then add dropout layers to avoid over-fitting. And the backward propagation algorithm are used to optimize the CNN structure. Random forest, which is robust and has strong generalization ability, is introduced for the classification of animal fiber microscopic images to obtain the final results. The study shows that, the proposed method has better generalization performance and higher classification accuracy than other classification methods.
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Das, Subrata, Sundaramurthy S, Aiswarya M et Suresh Jayaram. « Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric ». Indian Journal of Artificial Intelligence and Neural Networking 1, no 2 (10 avril 2021) : 9–13. http://dx.doi.org/10.35940/ijainn.b1011.041221.
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Inspection is the most important role in textile industry which declares the quality of the apparel product. Many Industries were improving their production or quality using Artificial Intelligence. Inspection of fabric in textile industry takes more time and labours. In order to reduce the number of labours and time taken to complete inspection, computerized image processing is done to identify the defects. It gives the accurate result in less time, thereby saves time and increases the production. The convolutional neural network in deep learning is mainly used for image processing for defect detection and classification. The high quality images are given as input, and then the images were used to train the deep learning neural network. Thewovenfabricdefects such as Holes, Selvedge tails, Stains, Wrong drawing and Snarlswere identified by using Convolutional Neural Network. The sample images were collected from the SkyCotex India Pvt.Ltd. The sample images were processed in CNN based machine learning ingoogle platform; the network has a input layer, n number of hidden layer and output layer. The neural network is trained and tested with the samples and the result obtained is used to calculate the efficiency of defect identification.
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Das, Subrata, Sundaramurthy S, Aiswarya M et Suresh Jayaram. « Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric ». Indian Journal of Artificial Intelligence and Neural Networking 1, no 2 (10 avril 2021) : 9–13. http://dx.doi.org/10.54105/ijainn.b1011.041221.
Texte intégralRésumé :
Inspection is the most important role in textile industry which declares the quality of the apparel product. Many Industries were improving their production or quality using Artificial Intelligence. Inspection of fabric in textile industry takes more time and labours. In order to reduce the number of labours and time taken to complete inspection, computerized image processing is done to identify the defects. It gives the accurate result in less time, thereby saves time and increases the production. The convolutional neural network in deep learning is mainly used for image processing for defect detection and classification. The high quality images are given as input, and then the images were used to train the deep learning neural network. The woven fabric defects such as Holes, Selvedge tails, Stains, Wrong drawing and Snarls were identified by using Convolutional Neural Network. The sample images were collected from the Sky Cotex India Pvt. Ltd. The sample images were processed in CNN based machine learning in google platform; the network has a input layer, n number of hidden layer and output layer. The neural network is trained and tested with the samples and the result obtained is used to calculate the efficiency of defect identification.
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Santikarama, Irma, Faiza Renaldi, Fatan Kasyidi et Agya Java Maulidin. « Use Case Framework of Computerized Production Monitoring Processes in Textile Industry ». Journal of Applied Informatics and Computing 6, no 1 (26 avril 2022) : 31–39. http://dx.doi.org/10.30871/jaic.v6i1.3977.
Texte intégralRésumé :
Use cases are a description of system functions resulting from needs analysis and obtained from interviews and observations. In standard practices, this stage is also known as the most time-consuming stage. Although every use case produced in software development is unique, there is always a similarity in its function to systems made previously in other organizations. These similarities are studied to reduce time in the process during the requirements analysis stage. Many studies have built and used a Use Case Framework (UCF) to be used together by software developers. So far, UCF has been owned by the banking industry in mapping use case standards in ATMs, health in standardizing use cases in electronic medical records, libraries in standardizing information retrieval, and mapping processes in crowdfunding. This research adds to the list of the latest UCFs produced, namely in the related textile industry, in standardizing the functions that exist in computer-based production monitoring systems. It is based on the fact that there are many textile companies globally, with more than 1.000 of them are established in Indonesia. This study investigated eight Indonesian textile companies to obtain information data to determine what functions are required, t. The data collection techniques used were interviews and observation. More stages were carried out in this study afterward, namely defining Actor Analysis and Functional Methods, Combining Analysis, Classification of Use Cases, Describing Use Case Scenarios, and Visualizing Frameworks. The data analysis results obtained from each company, we managed to define 10 main use cases, 4 supporting use cases, and four specific use cases. This study’s products can help provide a reference in using case design to create a computer-based textile company monitoring system.
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Lu, Sheng. « What Will Happen to the US Textile and Apparel Industry if the NAFTA Goes ? » Margin : The Journal of Applied Economic Research 12, no 2 (12 avril 2018) : 113–37. http://dx.doi.org/10.1177/0973801018754624.
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This study provides a quantitative evaluation of how the termination of the North American Free Trade Agreement (NAFTA) proposed by the Trump administration will affect the US textile and apparel (T&A) industry. By adopting the Global Trade Analysis Project (GTAP) computable general equilibrium model based on the GTAP9 database, the study finds that: first, the termination of NAFTA will significantly reduce US apparel imports from NAFTA members but lead to an increase of US apparel imports from Asian countries; second, ending NAFTA will substantially reduce US textile exports to the NAFTA region, which currently is the single largest export market for the US textile industry; and third, rather than encouraging more ‘Made in the USA’, the termination of NAFTA will reduce further the output of T&A manufacturing in the United States. The findings of this study augment our understanding of the potential economic impact of ending a major free trade agreement, which has been studied little, and shed new lights on the debate regarding the T&A-specific sectoral impact of NAFTA. The findings of the study also provide valuable inputs for policymakers regarding what should or should not be done with NAFTA from the perspective of the US T&A industry. JEL Classification: F14, F15, F17
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Hasan, Syed Mehmood, Muhammad Asad Ali et Satya Shah. « Inventory Management System for a General Items Warehouse of the Textile Industry ». International Journal of Applied Sciences & ; Development 2 (29 août 2023) : 101–10. http://dx.doi.org/10.37394/232029.2023.2.11.
Texte intégralRésumé :
This research is based on Inventory Management System for a General Items Warehouse of the Textile Industry. The overall inventory is managed by applying classification tools such as ABC, FSN & HML that categorize inventory based on consumption value, issuance rate and unit price respectively. Also, it helps to appropriately position the items on the desired rack and position. The optimized layout is designed that reduces the retrieval time, uplift the storage capacity, and have cross aisles that reduce the retrieval time of any item from the warehouse. The system for proper traceability & tracking of the items is also studied that is based on the 1D Barcode. This whole study improves the overall operation of the Supply Chain.
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Alviari, Luana Putri, Merlina Fitri Anggamawarti, Yudistira Sanjiwani et Victor Yuardi Risonarta. « Classification of Impact Damage on A Rubber-Textile Conveyor Belt : A Review ». International Journal of Mechanical Engineering Technologies and Applications 1, no 1 (27 janvier 2020) : 21. http://dx.doi.org/10.21776/mechta.2020.001.01.4.
Texte intégralRésumé :
A conveyor belt is one type of goods transportation in technological processes, particularly in the mining industry. The belt is the important material and principal part of the conveyor belt. The overall quality of the conveyor belt as its service life and impact loads are very important factors. Therefore, the purpose of this paper is to classify the types of impact damage that may occur in rubber-textile conveyor belts. In many works, many types of conveyor belts are tested at various levels and the type of impacting material. The level of damage occurred is investigated by using probability theory. Particularly, the evaluation of experimental test data and predictive modeling is carried out using the Naïve Bayes classification.
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Zeshan, Muhammad. « A Practical Narrative of Tariff Protection in Pakistan ». Global Trade and Customs Journal 18, Issue 1 (1 janvier 2023) : 37–45. http://dx.doi.org/10.54648/gtcj2023004.
Texte intégralRésumé :
This research work quantifies the changes in effective rates of protection in Pakistan during the last decade of 2011–20. Based on its results, it supports a more flexible trade policy in Pakistan. Further, it identifies the sectors with strong and weak long-run productive capacities and highlights the role of trade barriers in these industries. A key concern is the decreasing productive capacity of textile and leather sectors where the textile industry has the largest share in the total exports from Pakistan. Hence, there is a dire need to invest more in research and development activities in such industries. Finally, the country needs to increase its range of export items and export destinations with more favourable terms of trade, which can be achieved by reducing its historically high import tariff rates. Effective Rate of Protection, Input-Output Table, Industry, Trade, Pakistan JEL Classification: C67, D57, F6, L5, R15
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Seçkin, Mine, Ahmet Çağdaş Seçkin, Pinar Demircioglu et Ismail Bogrekci. « FabricNET : A Microscopic Image Dataset of Woven Fabrics for Predicting Texture and Weaving Parameters through Machine Learning ». Sustainability 15, no 21 (24 octobre 2023) : 15197. http://dx.doi.org/10.3390/su152115197.
Texte intégralRésumé :
This research presents an approach aimed at enhancing texture recognition and weaving parameter estimation in the textile industry to align with sustainability goals and improve product quality. By utilizing low-cost handheld microscopy and machine learning, this method offers the potential for more precise production outcomes. In this study, textile images were manually labeled for texture, specific mass, weft, and warp parameters, followed by the extraction of various texture features, resulting in a comprehensive dataset comprising four hundred and fifty-eight inputs and four outputs. Prominent machine learning algorithms, including XGBoost, RF, and MLP, were applied, resulting in noteworthy achievements. Specifically, XGBoost demonstrated an impressive texture classification accuracy of 0.987, while RF yielded the lowest MAE (5.121 g/cm) in specific mass prediction. Additionally, weft and warp estimations displayed superior accuracy compared to manual measurements. This research emphasizes the crucial role of AI in improving efficiency and sustainability within the textile industry, potentially reducing resource wastage, enhancing worker safety, and increasing productivity. These advancements hold the promise of significant positive environmental and social impacts, marking a substantial step forward in the industry’s pursuit of its objectives.
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Lee, Khai-Loon. « Supply Chain Technology Adoption : Its Clarification, Evolution, Classification, and Practicality in Textile and Apparel Industry ». International Journal of Business and Economics Research 3, no 6 (2014) : 15. http://dx.doi.org/10.11648/j.ijber.s.2014030601.13.
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Gnanaprakash V, Et al. « Novel MobileNet based Multipath Convolutional Neural Network for defect detection in fabrics ». International Journal on Recent and Innovation Trends in Computing and Communication 11, no 9 (5 novembre 2023) : 2417–23. http://dx.doi.org/10.17762/ijritcc.v11i9.9308.
Texte intégralRésumé :
Automatic fabric defect detection and classification is the most important process in the textile industry to ensure the fabric quality. In the existing systems, a learning based method is used for detecting defects in plain weave fabrics. In this paper, a novel MobileNet based Multipath Convolutional Neural Network (MMPCNN) architecture is proposed for detection and classification of simple and complex patterned fabric defects. In the proposed MMPCNN architecture, MobileNet model is used in the first path. In this, Gabor filter bank is used instead of conventional filters in the first convolution layer. A simple convolutional neural network architecture with Gray Level Co-occurrence Matrix (GLCM) features as an input is used in the second path of the MMPCNN architecture. Gabor filters are more useful for analyzing the texture with different orientations and scales. Each Gabor filter parameter has its own impact on analyzing the texture and extracting the information from the texture. Therefore, in this paper, the use of Gabor filter parameters in MMPCNN architecture is analyzed. The proposed model is experimented on the TILDA textile image database and it is able to achieve 100% accuracy with reduced trainable parameters for fabric defect detection and classification.
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Ziyeh, Paula, et Marco Cinelli. « A Framework to Navigate Eco-Labels in the Textile and Clothing Industry ». Sustainability 15, no 19 (25 septembre 2023) : 14170. http://dx.doi.org/10.3390/su151914170.
Texte intégralRésumé :
Considering the increasing demand for more sustainable products across many industries, eco-labels are a useful tool for communicating the sustainability-related performance of a product to the eco-conscious consumer. However, the abundance of different eco-labels and a lack of harmonization concerning their assessment methods can hamper their effectiveness. To address these shortcomings, this paper considers the methods employed by eco-labels in the textile and clothing industry to assess the sustainability-based performance of products. Using a sample of 10 eco-labels from the Ecolabel Index, a new framework for classifying eco-labels based on their assessment methods is developed. The framework includes two categories of label assignments ((i) binary and (ii) different levels of performance) and six types of assessment methods. These types are characterized according to the decision support features employed by the labels, such as lists of mandatory criteria, minimum (average) scores, percentage scores, and the weighting of sub-categories. The proposed framework shows the benefits of cascading decision science notions in the eco-labeling domain. It provides a harmonized vocabulary of components (i.e., a roadmap) to perform a consistent and traceable advancement of eco-labels. Consequently, it can be expanded at present to allow for the classification of other eco-labels in the textile and clothing industry and beyond.
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Selvarasi, A. « Enhancing fabric quality control : Implementing real-time defect detection with image processing techniques and arduino ». i-manager’s Journal on Image Processing 10, no 4 (2023) : 37. http://dx.doi.org/10.26634/jip.10.4.20194.
Texte intégralRésumé :
The textile industry is a rapidly growing sector globally and plays a momentous role in many sectors like manufacturing, employment, and business operations in many developed countries. Cloth flaws account for over 85% of the failures experienced in the garment industry. Efforts are currently underway to enhance fabric consistency, making the identification of defects a critical step in the textile manufacturing process. However, the traditional manual inspection technique for detecting cloth flaws is time-consuming and labor-consuming. Consequently, automation has been introduced through image processing technology. This approach utilizes image processing techniques in MATLAB to locate faults, with fault detection carried out using an Arduino. To improve the accuracy of fabric defect identification, an electronic fabric inspection method has been proposed. This framework incorporates image processing techniques, employing MATLAB, and real-time applications implemented on the Arduino kit. Neural Networks serve as the optimal classifiers for fault classification. Upon detecting a flaw in the fabric, the system breaks shortly to remove the defective component. The identified fault is then displayed on the LCD, and the buzzer is activated.
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Hussain, Ijaz. « Growth and Financing Behaviour of Firms of Textile Industry in Pakistan : A Panel Data Analysis. » Pakistan Development Review 50, no 4II (1 décembre 2011) : 699–714. http://dx.doi.org/10.30541/v50i4iipp.699-714.
Texte intégralRésumé :
High economic growth, extremely low nominal interest rate and negative real interest rate gave a boost to financial leverage (gearing ratio) of the textile sector to its peak in 2005. Firms are now are facing the consequence of high gearing. An explosion in their financing costs along with removal of textile quota from 2005 onwards and later on an acute energy crisis hampered their profitability and ability to repay their debt. This in turn contributed to non-performing loans which is now is likely to pose a big challenge for financial sector and push economy into another crisis. Most of the previous studies including a very few on capital structure of Pakistani firms focus on understanding only the firm specific determinants of financial leverage and completely ignore macroeconomic or institutional factors. Findings of this paper prove that all firm specific determinants including profitability and efficiency, firms‘ growth, risk and collateral excluding size significantly influence corporate financial leverage of textile industry in Pakistan. All macroeconomic variables including overall economic growth, equity market conditions and nominal cost of debt also have significant impact on corporate gearing. Negative sign with the composite measure of profitability and efficiency implies that banks are compelled to fund inefficient and unprofitable firms because demand for loans comes more from inefficient and unprofitable firms. Positive sign with growth and negative sign with risk is indicative of the fact that banks prefer to lend to growing rather than riskier firms. JEL classification: C13, C23, C51, L65, G10, G30 Keywords: Capital Structure Determinants, Corporate Financial Leverage, Corporate Gearing Ratio
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Furferi, Rocco, et Michaela Servi. « A Machine Vision-Based Algorithm for Color Classification of Recycled Wool Fabrics ». Applied Sciences 13, no 4 (14 février 2023) : 2464. http://dx.doi.org/10.3390/app13042464.
Texte intégralRésumé :
The development of eco-sustainable systems for the textile industry is a trump card for attracting expanding markets aware of the ecological challenges that society expects in the future. For companies willing to use regenerated wool as a raw material for creating plain, colored yarns and/or fabrics, building up a number of procedures and tools for classifying the conferred recycled materials based on their color is crucial. Despite the incredible boost in automated or semi-automated methods for color classification, this task is still carried out manually by expert operators, mainly due to the lack of systems taking into account human-related classification. Accordingly, the main aim of the present work was to devise a simple, yet effective, machine vision-based system combined with a probabilistic neural network for carrying out reliable color classification of plain, colored, regenerated wool fabrics. The devised classification system relies on the definition of a set of color classes against which to classify the recycled wool fabrics and an appositely devised acquisition system. Image-processing algorithms were used to extract helpful information about the image color after a set of images has been acquired. These data were then used to train the neural network-based algorithms, which categorized the fabric samples based on their color. When tested against a dataset of fabrics, the created system enabled automatic classification with a reliability index of approximately 83%, thus demonstrating its effectiveness in comparison to other color classification approaches devised for textile and industrial fields.
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Nugroho, Emmanuel Agung, Diki Mulyadi et Nanang Roni wibowo. « Sistem Klasifikasi Citra untuk Proses Inspeksi Kain Menggunakan Teachable Machine dan Raspberry Pi ». Jurnal Teknologika 14, no 1 (31 mai 2024) : 49–60. http://dx.doi.org/10.51132/teknologika.v14i1.368.
Texte intégralRésumé :
The textile industry has been growing rapidly, even the growth of textile products exceeds the growth of the national economy. The demand for textile products is not only for domestic consumption but also for export. In an effort to meet quality standards and maintain customer satisfaction, quality control of fabric production is very important, especially in controlling fabric production defects. The types of defects that exist in fabrics are holes, stains, rare defects due to broken/lost threads, floating, color fading, broken patterns, double threads, thick threads (slubs), mixed ends, pin marks, and others. In this research, a system is designed that can detect production defects in fabrics using machine learning-based image processing methods using Raspberry Pi. The types of defects modeled are sparse defects and stain defects, or in factory terms often called slap defects. The test results show that this system has an average frame per second (FPS) of 4.85, an average inference time of 181.1 ms, with an image classification result accuracy of 98.4%
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Correia, Jeferson, Ana Júlia Dal Forno, Cintia Marangoni et José Alexandre Borges Valle. « Waste management system in the clothing industry in Santa Catarina State Brazil ». Management of Environmental Quality : An International Journal 29, no 4 (11 juin 2018) : 594–607. http://dx.doi.org/10.1108/meq-10-2017-0109.
Texte intégralRésumé :
Purpose The purpose of this paper is to identify and diagnosis waste management practices used by clothing manufacturing companies in Santa Catarina state Brazil. Design/methodology/approach The data for this multiple case study were obtained from interviews and by using a questionnaire to collect company data. After the analysis of the responses to questionnaires issued to 22 companies, a scoring system was developed to systematically classify these companies at either a basic, intermediate or advanced levels. Findings According to the classification used, eight companies were characterized at the basic level, eight at the intermediate level and six as advanced. Most of the companies have already implemented measures for reuse or recycling of textile scraps, probably because of the economic value added. Research limitations/implications The classification system proposed proved to be an effective tool for identifying: if each company had a plan of action involving requirements of Brazil’s National Solid Waste Policy; if the company had a management system in accordance with Law 12,305; the quality of solid waste treatment at the entire company and in its clothing sector; if the company adopted shared responsibility actions; and if it had knowledge of the negative environmental impacts. Originality/value This paper presents a classification system for companies based on a questionnaire. The system allows determining the degree of compliance with Brazilian waste management legislation.
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Mahmood, Tariq. « Effects of Input Composition on Technical Efficiencies of Textile Industries in Pakistan ». Pakistan Development Review 51, no 2 (1 juin 2012) : 117–30. http://dx.doi.org/10.30541/v51i2pp.117-130.
Texte intégralRésumé :
This paper studies the technical efficiencies of the textile manufacturing industries in Pakistan using 5-digit level industry data. Technical efficiencies are computed by the Data Envelopment Analysis technique assuming constant as well as variable returns to scale. The efficiency scores thus obtained are analysed by the TOBIT regression technique to determine how input composition influences these efficiency scores. It is found that imported raw material and machinery exercises a positive effect, whereas non-industrial costs affect technical efficiencies in a negative way. Electricity does not play its due role in affecting technical efficiencies. JEL Classification: C24, D24, L6, O14 Keywords: Technical Efficiency, Data Envelopment Analysis, TOBIT Analysis, Manufacturing Industries
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Andalia, Debbi, et Kurniawati Kurniawati. « Sustainable Textile Practices by Integrated Viscose Rayon and Yarn Producers : An Empirical Study ». GATR Journal of Finance and Banking Review Vol. 8 (1) APRIL - JUNE 2023 8, no 1 (29 juin 2023) : 28–42. http://dx.doi.org/10.35609/jfbr.2023.8.1(1).
Texte intégralRésumé :
Objective – Modest fashion growth rapidly while it was reported that world textile fibre production is dominated by non-biodegradable fossil fibres (e.g.: polyester). In this situation, viscose rayon presents as an alternative to substitute non-biodegradable textile fibres, because it primarily produced from wood, having natural characteristic, and biodegradable. In contrast, the nature of viscose rayon manufacturing steps involves significant amount of chemicals and utilities, which is also high risk to environment. This qualitative research aims to examine the sustainability practices in first largest viscose rayon and yarn producers in Indonesia as well as their customers response and find out the organization’s contribution to green textile manufacturing. Methodology – content analysis Findings –This study found that the producers implement the sustainability practices in the field of textile, apparel and fashion industry. Positive response from their customers also found toward the implementation of sustainability practices, including green purchase intention. Novelty – Some implication found for first largest integrated viscose rayon and yarn producers in Indonesia in order to enhance their strategy and contributed to sustainable textile practices globally. Furthermore, some recommendations are given to relevant party to support viscose rayon and yarn sustainable practices while at the same time contribute to ISO 26000 and Sustainable Development Goals (SDGs). Type of Paper: Empirical JEL Classification: L23, L73, Q01, Q53. Keywords: Content Analysis; Manufacture; Sustainable Practices; Textile, Apparel And Fashion; Viscose Rayon And Yarn. Reference to this paper should be made as follows: Andalia, D; Kurniawati. (2023). Sustainable Textile Practices by Integrated Viscose Rayon and Yarn Producers: An Empirical Study, J. Fin. Bank. Review, 8(1), 28 – 42. https://doi.org/10.35609/jfbr.2023.8.1(1)
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Oh, Jungmi. « Analysis of Clothes and Textile Waste Classification System to Facilitate a Circular Economy in the Fashion Industry ». Journal of the Korean Society of Costume 73, no 4 (31 août 2023) : 1–15. http://dx.doi.org/10.7233/jksc.2023.73.4.001.
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Boykov, Alexey V., Valentin M. Nikonorov, Dmitry A. Luchin et Igor V. Ilin. « Neural network approach for detection of defects “weft crack” and “water damage” in textile fabrics ». Journal Of Applied Informatics 19, no 2 (29 avril 2024) : 55–66. http://dx.doi.org/10.37791/2687-0649-2024-19-2-55-66.
Texte intégralRésumé :
At present, the automation of production processes, including the use of computer vision, machine learning and artificial intelligence methods, is of relevance at light industry enterprises due to the fourth industrial revolution. The key role in the production processes is played by the quality of manufactured products – textile fabrics, which is directly affected by the process of defectoscopy. Due to the development of digital technologies and the growth of computing power, it is possible to automate the process of defectoscopy of textile fabrics using computer vision to reduce labor costs and increase the accuracy of defect detection. The purpose of this paper is to conduct experimental studies of the marking and detection of specific classes of textile defects using a hardware-software complex of computer vision and using a neural network approach. To achieve this goal, the paper describes the existing classification of textile web defects, describes the used hardware-software system, and presents the application of the neural network model of the Mask R-CNN architecture to solve the problem of exemplar defect segmentation. As part of the study, a manual partitioning of more than 400 tissue photographs into two classes of defects was performed as an extension of the training sample: “weft crack” and “water damage”, the obtained results of the neural network model were evaluated by IoU metrics: the best result for the class “weft crack” DIoU = 0.2, for the class “water damage” DIoU = 0.87. Based on the results of the experimental studies, conclusions are made about the existing potential of using neural network approach for defectoscopy of similar classes of defects. The presented results can be used for training and retraining of various models of object detection, the gained experience can be applied in other spheres of industry.
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Aktar, Mst Afroja. « Green Insights of Textile Industry in Bangladesh : A Case Study on Mozart Knitting Ltd. » Global Disclosure of Economics and Business 3, no 1 (30 juin 2014) : 93–108. http://dx.doi.org/10.18034/gdeb.v3i1.176.
Texte intégralRésumé :
The effectiveness of individual units of an Effluent Treatment Plant (ETP) agrees on the entire performance of the plant and the ultimately echoing effluent standard. In this study, an ETP of a composite textile industry in Bangladesh was investigated using this method. After completion of the stipulated study, findings came into force illustrated that water samples had been collected from the dyeing unit and accordingly had been used for the ETP and also been determined for the key parameters. The results were used to identify problems with the treatment units and broadly suggested for modifications. Simple but fully-bodied methodology was developed for assessing the performance of various treatment units and the ETP as a whole that could be implemented by ETP managers on a regular basis for improving the performance so that the effluent meets national standard as well as conforms to the requirements of international standard which is a compliance of the international buyers. To comply with the BB Guidelines commercial banks have taken initiatives on financing to the textile dyeing units in question with the implementation of ETP. The ETP’s performance on Mozart Knitting Ltd. shown that it has been performing with the underprivileged management, especially chemical dosing were affecting the ETP’s performance and that simple measures could address the problems. A number of equipment i.e middle pathways rolling, water transferring pipes should repair without more ado.
JEL Classification Code: L67, K32
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Abid, Sabeur. « Texture defect detection by using polynomial interpolation and multilayer perceptron ». Journal of Engineered Fibers and Fabrics 14 (janvier 2019) : 155892501882527. http://dx.doi.org/10.1177/1558925018825272.
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This article deals with fabric defect detection. The quality control in textile manufacturing industry becomes an important task, and the investment in this field is more than economical when reduction in labor cost and associated benefits are considered. This work is developed in collaboration with “PARTNER TEXTILE” company which expressed its need to install automated defect fabric detection system around its circular knitting machines. In this article, we present a new fabric defect detection method based on a polynomial interpolation of the fabric texture. The different image areas with and without defects are approximated by appropriate interpolating polynomials. Then, the coefficients of these polynomials are used to train a neural network to detect and locate regions of defects. The efficiency of the method is shown through simulations on different kinds of fabric defects provided by the company and the evaluation of the classification accuracy. Comparison results show that the proposed method outperforms several existing ones in terms of rapidity, localization, and precision.
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., Zia Ur Rehman. « Impact of Macroeconomic Variables on Capital Structure Choice : A Case of Textile Industry of Pakistan ». Pakistan Development Review 55, no 3 (1 septembre 2016) : 227–39. http://dx.doi.org/10.30541/v55i3pp.227-239.
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The financing decision of a firm is influenced by both internal (firm specific) and external (macroeconomic) factors. However, most of the empirical investigations have focus on internal factors whereas the impact of macroeconomic variables on capital structure decisions is somewhat under researched particularly in the context of developing countries. The aim of the study is to analyse the impact of macroeconomic variables on the capital structure decisions of all listed textile firms in Pakistan for the period 2004-2013. Panel data regression (fixed effects model) was used to estimate the effect of macroeconomic variables on capital structure. The findings of the study reveal that public debt, exchange rates and interest rates are negatively related whereas corporate taxes, stock market development, inflation rate and GDP growth rate are positively related with economic leverage. Moreover, the relationship of corporate taxes, stock market development and exchange rates is significant with the economic leverage. JEL Classification: E44, E52, E62, F31, G32 Keywords: Capital Structure, Interest Rates, Inflation, Public Debt, Exchange Rates, GDP Growth Rate, Stock Market Development, Pakistan
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Chen, Ran. « An empirical study of COVID-19's stock returns to the whole industry in the US stock market ». Advances in Economics and Management Research 1, no 1 (18 mai 2022) : 166. http://dx.doi.org/10.56028/aemr.1.1.166.
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Based on the daily stock data of 49 industry classification data in Kenneth R. French database, this paper adopts the Fama-French five-factor model and adopts multiple linear regression method to empirically study the changes of stock return impact factors of 49 us industries before and after COVID-19. The results show that the marginal effects of market risk factors and investment style factors on stock returns weaken, while the marginal effects of market value factors and value factors increase. The influence of profit factor on stock return is not significant. Post-pandemic, the market favors small-cap stocks, value stocks, and conservative portfolios. Specific to the industry level, the small market value and value stock portfolio of the hotel and catering industry bring greater excess returns; The rare metal industry of high market value, growth stock portfolio excess return is greater; Textile industry value stocks, investment style aggressive portfolio to get better returns. Based on this, when the "black Swan" event comes, we should pay attention to grasp the switch of investment style in order to achieve better returns. At the same time, specific to each industry investment strategy should be different.
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Chen, Chun, Xiaoyan Deng, Zhuliang Yu et Zhengtao Wu. « Fabric Defect Detection Using a One-class Classification Based on Depthwise Separable Convolution Autoencoder ». Journal of Physics : Conference Series 2562, no 1 (1 août 2023) : 012053. http://dx.doi.org/10.1088/1742-6596/2562/1/012053.
Texte intégralRésumé :
Abstract Fabric defect detection is anomaly detection, which is widely studied in the textile industry. Like most anomaly detection tasks, there are some problems hindering detection results, such as class imbalance, defective sample scarcity, and feature selection. This paper proposes a method applying depthwise separable convolution autoencoder on dimensionality reduction and one-class classifier support vector data description (SVDD) to detect fabric defects. A depthwise separable convolution autoencoder can effectively extract sample features with less computation and fewer parameters than the regular convolution, which will be easily used in industrial production. SVDD can only use non-defective samples to train the classifier and solve the difficulty and heavy cost of collecting negative samples (defective samples). In this paper, we will demonstrate the effectiveness of the method on polyester fibers by using accuracy and AUC as evaluation criteria.
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Paksoy, H. Mustafa, Yakup Durmaz, B. Dilek Özbezek et Filiz Çopuroğlu. « The mediating role of organizational commitment in the relationship between internal marketing and job performance : Application in Turkiye ». Journal of Economics and Management 46 (2024) : 111–42. http://dx.doi.org/10.22367/jem.2024.46.05.
Texte intégralRésumé :
Aim/purpose – The study aims to investigate the role of organizational commitment as a mediator between internal marketing and job performance. Design/methodology/approach – For this purpose, data were collected from 239 per- sonnel working in the textile sector in Turkiye’s Organized Industrial Zone. The data obtained from the participants through the survey method were examined by explanatory factor analysis, descriptive statistics, correlation, and bootstrap regression analyses. Findings – The findings of the research clearly show that internal marketing practices have a significant impact on job performance. Internal marketing variable explained 52.24% of the change in organizational commitment. However, it has been revealed that organizational commitment has a partial mediating role in the relationship between internal marketing practices and job performance. Research implications/limitations – This study has limitations in taking samples from Turkiye, focusing on the textile industry, and using the convenience sampling method. Originality/value/contribution – These results strongly support the argument that internal marketing practices further increase job performance through organizational commitment. Keywords: internal marketing, organizational commitment, job performance. JEL Classification: M10, M12, M31, M19
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Kim, Hye Jin, Seonyoung Youn, Jeein Choi, Hyeonji Kim, Myounghee Shim et Changsang Yun. « Indexing surface smoothness and fiber softness by sound frequency analysis for textile clustering and classification ». Textile Research Journal 91, no 1-2 (29 juin 2020) : 200–218. http://dx.doi.org/10.1177/0040517520935211.
Texte intégralRésumé :
Cutting-edge technology is being used in the fashion industry for three-dimensional (3D) virtual fitting programs to meet the demand for clothing manufacturing as well as textile simulating. For expanding the textile choices of the program users, this research looks at the indexation of tactile sensations, the texture of fabrics, which has been subjectively evaluated by the human hand. Firstly, this study objectively measured and indexed the surface smoothness and fiber softness of 749 fabrics through a tissue softness analyzer that mimics human hands. Secondly, after statistical analyses of the drape coefficient, each bending distance and Young's modulus for the initial tensile strength in the warp–weft directions, the thickness, and the weight of the fabrics, it was found that drape (Pearson coefficient = 0.532) and bending properties are the key factors in the fabric surface smoothness (TS750), while the fiber softness (TS7) showed a weak correlation with thickness (Pearson coefficient = 0.364), followed by the log value of the Young's modulus in the weft direction. Thirdly, we classified nine clusters for TS750 based on the 11 regression variables with significant Pearson coefficients, and characterized each cluster in order of surface smoothness (TS750) after Duncan post-hoc tests and analyses of variance (all statistically significant, p < 0.01) with microscopic surface images of one sample for each cluster. For precise TS750 classification, we finally trained the 267 samples with the same 11 variables, resulting in 93.3% prediction through an artificial neural network with multiple hidden layers. This prediction with Fisher discriminants for the clusters will enable the 3D virtual program users to predict further clustering of newly added fabrics.
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Wan, Caiyu, et Zhenlong Hu. « Development and Prospect of Automatic Inspection System for Fabric Defects ». Academic Journal of Science and Technology 3, no 3 (22 novembre 2022) : 215–17. http://dx.doi.org/10.54097/ajst.v3i3.2985.
Texte intégralRésumé :
Summarize the development of automatic inspection system for fabric defects. The main problems existing in traditional manual cloth inspection are pointed out. The development of automatic cloth inspection technology at home and abroad is reviewed. The classification and detection requirements of fabric defects are introduced. This paper probes into the functions and related technologies of automatic cloth inspection system, and looks forward to the prospect of automatic cloth inspection technology. It is considered that the use of automatic cloth inspection machine can improve labor productivity; Automatic cloth inspection machine is an important measure to upgrade the textile industry and get rid of labor-intensive industries, and it is an inevitable trend of future development.