Pub Date : 2024-09-18DOI: 10.1177/00405175241268619
Nilesh Ingle, Warren J Jasper
This review focuses on the transformative applications of deep learning and artificial intelligence in textile dyeing, printing, and finishing. In textile dyeing, the topics span color prediction, color-based classification, dyeing recipe prediction, dyeing pattern recognition, and the nuanced domain of color fabric defect detection. In textile printing, applications of artificial intelligence and machine learning center around pattern detection in printed fabrics, the generation of novel patterns, and the critical task of detecting defects in printed textiles. In textile finishing the prediction of fabric thermosetting parameters is discussed. Artificial neural networks, diverse convolutional neural network variations like AlexNet, traditional machine learning approaches including support vector regression, principal component analysis, XGBoost, and generative artificial intelligence such as generative adversarial networks, as well as genetic algorithms all find application in this multifaceted exploration. At its core, the interest to use these methodologies is because of the need to minimize repetitive and time-consuming manual tasks, curtail prototyping costs, and promote process automation. The review unravels a plethora of innovative architectures and frameworks, each tailored to address specific challenges. However, a persistent hurdle looms – the scarcity of data, which remains a significant impediment. While unveiling a collection of research findings, the review also spotlights the inherent challenges in implementing artificial intelligence solutions in the textile dyeing and printing domain.
{"title":"A review of deep learning and artificial intelligence in dyeing, printing and finishing","authors":"Nilesh Ingle, Warren J Jasper","doi":"10.1177/00405175241268619","DOIUrl":"https://doi.org/10.1177/00405175241268619","url":null,"abstract":"This review focuses on the transformative applications of deep learning and artificial intelligence in textile dyeing, printing, and finishing. In textile dyeing, the topics span color prediction, color-based classification, dyeing recipe prediction, dyeing pattern recognition, and the nuanced domain of color fabric defect detection. In textile printing, applications of artificial intelligence and machine learning center around pattern detection in printed fabrics, the generation of novel patterns, and the critical task of detecting defects in printed textiles. In textile finishing the prediction of fabric thermosetting parameters is discussed. Artificial neural networks, diverse convolutional neural network variations like AlexNet, traditional machine learning approaches including support vector regression, principal component analysis, XGBoost, and generative artificial intelligence such as generative adversarial networks, as well as genetic algorithms all find application in this multifaceted exploration. At its core, the interest to use these methodologies is because of the need to minimize repetitive and time-consuming manual tasks, curtail prototyping costs, and promote process automation. The review unravels a plethora of innovative architectures and frameworks, each tailored to address specific challenges. However, a persistent hurdle looms – the scarcity of data, which remains a significant impediment. While unveiling a collection of research findings, the review also spotlights the inherent challenges in implementing artificial intelligence solutions in the textile dyeing and printing domain.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"11 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1177/00405175241265510
Nilesh Ingle, Warren J Jasper
In the textile production chain, fibers serve as the foundational units for yarn, and yarn, in turn, acts as a fundamental component for woven or knitted fabrics. The quality control of fabrics is intricately tied to the management of fibers and yarns. Traditional laboratory methods have been utilized to assess their quality, but the advent of machine learning and deep learning introduces a transformative approach. This review explores the application of machine learning methods such as principal component analysis, support vector machine, and deep learning methods such as artificial neural networks, convolutional neural networks, you look only once, and genetic algorithms to predict various properties of fibers and yarns. In the context of fibers, the review delves into topics such as cotton fiber grading based on color, characterization of jute fiber, and the identification of medullation in alpaca fibers. For yarns, the focus shifts to predicting parameters such as yarn tenacity, evenness, abrasion index of spun yarns, inspection of false twist textured yarn packages, breaking elongation of ring-spun cotton yarns, tensile properties of cotton/spandex yarns, yarn thickness, and yarn hairiness. The review also provides insights into the advantages and limitations of the discussed studies. Despite the comprehensiveness of this review, it is acknowledged that there might be additional relevant work not covered. The review encourages the sharing of data to expedite the integration of these technologies in future applications within the field.
{"title":"A review of deep learning within the framework of artificial intelligence for enhanced fiber and yarn quality","authors":"Nilesh Ingle, Warren J Jasper","doi":"10.1177/00405175241265510","DOIUrl":"https://doi.org/10.1177/00405175241265510","url":null,"abstract":"In the textile production chain, fibers serve as the foundational units for yarn, and yarn, in turn, acts as a fundamental component for woven or knitted fabrics. The quality control of fabrics is intricately tied to the management of fibers and yarns. Traditional laboratory methods have been utilized to assess their quality, but the advent of machine learning and deep learning introduces a transformative approach. This review explores the application of machine learning methods such as principal component analysis, support vector machine, and deep learning methods such as artificial neural networks, convolutional neural networks, you look only once, and genetic algorithms to predict various properties of fibers and yarns. In the context of fibers, the review delves into topics such as cotton fiber grading based on color, characterization of jute fiber, and the identification of medullation in alpaca fibers. For yarns, the focus shifts to predicting parameters such as yarn tenacity, evenness, abrasion index of spun yarns, inspection of false twist textured yarn packages, breaking elongation of ring-spun cotton yarns, tensile properties of cotton/spandex yarns, yarn thickness, and yarn hairiness. The review also provides insights into the advantages and limitations of the discussed studies. Despite the comprehensiveness of this review, it is acknowledged that there might be additional relevant work not covered. The review encourages the sharing of data to expedite the integration of these technologies in future applications within the field.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"6 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/00405175241267769
Qiaoli Cao, Guangming Zheng, Chongwen Yu
The performance of blended yarns is affected by the distribution of component fibers within blended yarn and the blending uniformity. However, there is a lack of comprehensive and quantitative investigations on the relationship between them. In this paper, various specifications for two-component blended yarns were prepared, and the main factors affecting blending uniformity were analyzed. Then the yarn blending irregularity and yarn performance, including tenacity, tenacity coefficient of variation, extension and yarn unevenness were tested. Finally, Pearson correlation analysis and linear regression were performed between blending irregularity and each yarn performance to characterize the influence of blending uniformity on yarn performance quantitatively. The results show that the blending irregularity is effectively improved by uniform feeding of slivers, and increasing the passage of sliver blending. The blending irregularity has no significant influence on the relationship between twist factor and yarn performance. The blending irregularity has the most positive and highest effect on tenacity coefficient of variation, followed by tenacity and unevenness in third place, and the Pearson correlation coefficient ( P) were all above 0.5, and the linear regression coefficient was above 10−3, but the breaking extension was weakest and negatively correlated with blending irregularity. Except for breaking extension, the effect of blending irregularities on yarn performance becomes more obvious when there are large differences in fiber linear density and fiber length. This paper reveals the relationship between blending uniformity and yarn performance, to provide a basis for theoretical research on the properties of blended yarns.
{"title":"Study on the relationship between blending uniformity and yarn performance of blended yarn","authors":"Qiaoli Cao, Guangming Zheng, Chongwen Yu","doi":"10.1177/00405175241267769","DOIUrl":"https://doi.org/10.1177/00405175241267769","url":null,"abstract":"The performance of blended yarns is affected by the distribution of component fibers within blended yarn and the blending uniformity. However, there is a lack of comprehensive and quantitative investigations on the relationship between them. In this paper, various specifications for two-component blended yarns were prepared, and the main factors affecting blending uniformity were analyzed. Then the yarn blending irregularity and yarn performance, including tenacity, tenacity coefficient of variation, extension and yarn unevenness were tested. Finally, Pearson correlation analysis and linear regression were performed between blending irregularity and each yarn performance to characterize the influence of blending uniformity on yarn performance quantitatively. The results show that the blending irregularity is effectively improved by uniform feeding of slivers, and increasing the passage of sliver blending. The blending irregularity has no significant influence on the relationship between twist factor and yarn performance. The blending irregularity has the most positive and highest effect on tenacity coefficient of variation, followed by tenacity and unevenness in third place, and the Pearson correlation coefficient ( P) were all above 0.5, and the linear regression coefficient was above 10<jats:sup>−3</jats:sup>, but the breaking extension was weakest and negatively correlated with blending irregularity. Except for breaking extension, the effect of blending irregularities on yarn performance becomes more obvious when there are large differences in fiber linear density and fiber length. This paper reveals the relationship between blending uniformity and yarn performance, to provide a basis for theoretical research on the properties of blended yarns.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"39 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/00405175241275620
Ching Lee, Jeanne Tan, Jun Jong Tan, Hiu Ting Tang, Wing Shan Yu, Ngan Yi Kitty Lam
Human thermal comfort, crucial for well-being and productivity, is often improved by personal comfort systems that offer tailored control over environmental conditions while promoting energy efficiency. Previous studies have explored various textile technologies in thermoregulation systems according to user preferences. However, limited research has focused on temperature prediction by artificial intelligence to maximize thermal comfort for varied users. This study proposes a design approach to optimize thermal comfort in electric heating textiles using artificial intelligence, considering user preferences related to age and gender differences. A fuzzy logic model is established as a proof of concept for temperature regulation by varying ambient temperature, followed by developing an artificial neural network model to predict the optimal temperature for maximum comfort. Subsequently, a smart electric heating jacket is fabricated to assess preferred heating temperatures among 50 subjects with varying ages and genders. Results from the artificial neural network model show promising temperature prediction, while subject tests reveal significant differences in skin temperatures based on gender. This emphasizes the need for artificial intelligence-based heating e-textiles to accommodate diverse user needs. The study’s findings are expected to contribute to intelligent temperature regulation in thermal textiles and wearables, benefitting both the industry and consumers through customized heating products.
{"title":"Integrating artificial intelligence for optimal thermal comfort: A design approach for electric heating textiles aligned with user preferences","authors":"Ching Lee, Jeanne Tan, Jun Jong Tan, Hiu Ting Tang, Wing Shan Yu, Ngan Yi Kitty Lam","doi":"10.1177/00405175241275620","DOIUrl":"https://doi.org/10.1177/00405175241275620","url":null,"abstract":"Human thermal comfort, crucial for well-being and productivity, is often improved by personal comfort systems that offer tailored control over environmental conditions while promoting energy efficiency. Previous studies have explored various textile technologies in thermoregulation systems according to user preferences. However, limited research has focused on temperature prediction by artificial intelligence to maximize thermal comfort for varied users. This study proposes a design approach to optimize thermal comfort in electric heating textiles using artificial intelligence, considering user preferences related to age and gender differences. A fuzzy logic model is established as a proof of concept for temperature regulation by varying ambient temperature, followed by developing an artificial neural network model to predict the optimal temperature for maximum comfort. Subsequently, a smart electric heating jacket is fabricated to assess preferred heating temperatures among 50 subjects with varying ages and genders. Results from the artificial neural network model show promising temperature prediction, while subject tests reveal significant differences in skin temperatures based on gender. This emphasizes the need for artificial intelligence-based heating e-textiles to accommodate diverse user needs. The study’s findings are expected to contribute to intelligent temperature regulation in thermal textiles and wearables, benefitting both the industry and consumers through customized heating products.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"49 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/00405175241268799
Qi Jia, Xinyi Diao, Kun Li, Ling Han
To address the issue of viral and bacterial contamination in air filtration materials, specifically focusing on the accumulation of viruses on aerogels and long-term bacterial growth, a hydrophobic and antimicrobial polyvinylidene fluoride (PVDF)/tea polyphenols (TPs) nanofibers membrane was prepared by electrospinning technique with natural antimicrobial TPs and ferroelectric PVDF as raw materials. By scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR) and testing on contact angle and antimicrobial properties, the performances of the nanofiber membranes were characterized. It was verified by XRD and FTIR analyses that the TPs facilitated the transition of PVDF from α-crystalline phase to the β-crystalline phase, thereby enhancing the polarization effect of PVDF nanofiber membranes and fortifying the electrostatic adsorption filtration capacity of the material’s trapped charges. Therefore, the incorporation of TPs not only bolstered the material’s antimicrobial efficacy but also reinforced the in-situ polarized electret effect of PVDF, consequently augmenting the high filtration efficiency and low filtration resistance capabilities of the PVDF/TPs membrane. The research found that filter membranes containing TPs exhibit exceptional filtration performance, effectively maintaining filtration resistance in 20–25 Pa while achieving a filtration efficiency of over 90% for aerosols with diameters of 2.5 μm. Notably, the PVDF/TPs membrane containing 20% TPs demonstrated outstanding filtration efficiency against 1.5 μm aerosol particles, reaching 99.98% with a filtration resistance of only 23.26 Pa, and a high inhibition rate against Staphylococcus aureus of 96.5%. The PVDF/TPs nanofiber air filtration material developed in this study presents a novel approach for high-efficiency, low-resistance, antibacterial filtration for diverse applications in antibacterial air filtration fields.
{"title":"Tea polyphenols-enhanced in-situ polarization of polyvinylidene fluoride nanofiber material with antibacterial and high-filtration, low-resistance filtering performances","authors":"Qi Jia, Xinyi Diao, Kun Li, Ling Han","doi":"10.1177/00405175241268799","DOIUrl":"https://doi.org/10.1177/00405175241268799","url":null,"abstract":"To address the issue of viral and bacterial contamination in air filtration materials, specifically focusing on the accumulation of viruses on aerogels and long-term bacterial growth, a hydrophobic and antimicrobial polyvinylidene fluoride (PVDF)/tea polyphenols (TPs) nanofibers membrane was prepared by electrospinning technique with natural antimicrobial TPs and ferroelectric PVDF as raw materials. By scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR) and testing on contact angle and antimicrobial properties, the performances of the nanofiber membranes were characterized. It was verified by XRD and FTIR analyses that the TPs facilitated the transition of PVDF from α-crystalline phase to the β-crystalline phase, thereby enhancing the polarization effect of PVDF nanofiber membranes and fortifying the electrostatic adsorption filtration capacity of the material’s trapped charges. Therefore, the incorporation of TPs not only bolstered the material’s antimicrobial efficacy but also reinforced the in-situ polarized electret effect of PVDF, consequently augmenting the high filtration efficiency and low filtration resistance capabilities of the PVDF/TPs membrane. The research found that filter membranes containing TPs exhibit exceptional filtration performance, effectively maintaining filtration resistance in 20–25 Pa while achieving a filtration efficiency of over 90% for aerosols with diameters of 2.5 μm. Notably, the PVDF/TPs membrane containing 20% TPs demonstrated outstanding filtration efficiency against 1.5 μm aerosol particles, reaching 99.98% with a filtration resistance of only 23.26 Pa, and a high inhibition rate against Staphylococcus aureus of 96.5%. The PVDF/TPs nanofiber air filtration material developed in this study presents a novel approach for high-efficiency, low-resistance, antibacterial filtration for diverse applications in antibacterial air filtration fields.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"18 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/00405175241268790
Jianxin Zhang, Jin Ma, Miao Qian, Ming Wang
Hyperspectral images possess abundant information and play a pivotal role in enhancing the accuracy of color difference detection in textiles. However, traditional hyperspectral imaging methods necessitate costly equipment and intricate operational procedures. A novel deep learning model based on a multihead attention mechanism was proposed in this article to facilitate the extensive application of hyperspectral imaging technology in textile quality inspection. This model enabled the reconstruction of the hyperspectral information of plain weave textiles from a single RGB image. In this model, encoder-decoder architecture and pyramid pooling convolutional operations were employed to integrate multiscale features of plain weave cotton-linen textiles. This could capture details and contextual information in textile images more precisely, enhancing the accuracy of hyperspectral image reconstruction. Simultaneously, an attention mechanism was introduced to increase the model’s receptive field and improve its focus on key regions in the input image and feature maps. This resulted in a reduced weighting of redundant information during network learning, leading to an improved feature extraction capability of the network. Through these methods, successful reconstructions of plain weave textiles hyperspectral information from a single RGB image was achieved. Quantitative and qualitative tests were conducted on two datasets, namely, the NTIRE 2020 dataset and a self-made textile dataset, to evaluate the performance of the proposed method. The approach proposed in this article exhibited promising results on both datasets. Specifically, the reconstructed textile hyperspectral images achieved a root mean square error of 0.0344, a peak signal-to-noise ratio of 29.945, a spectral angle mapper of 3.753, and a structural similarity index measure of 0.955 on the textile dataset. In the reconstructed hyperspectral colorimetric experiment, the maximum value of average color difference was 2.641. These results demonstrate that the method can meet the requirements for textile color measurement applications.
{"title":"Reconstructing hyperspectral images of textiles from a single RGB image utilizing the multihead self-attention mechanism","authors":"Jianxin Zhang, Jin Ma, Miao Qian, Ming Wang","doi":"10.1177/00405175241268790","DOIUrl":"https://doi.org/10.1177/00405175241268790","url":null,"abstract":"Hyperspectral images possess abundant information and play a pivotal role in enhancing the accuracy of color difference detection in textiles. However, traditional hyperspectral imaging methods necessitate costly equipment and intricate operational procedures. A novel deep learning model based on a multihead attention mechanism was proposed in this article to facilitate the extensive application of hyperspectral imaging technology in textile quality inspection. This model enabled the reconstruction of the hyperspectral information of plain weave textiles from a single RGB image. In this model, encoder-decoder architecture and pyramid pooling convolutional operations were employed to integrate multiscale features of plain weave cotton-linen textiles. This could capture details and contextual information in textile images more precisely, enhancing the accuracy of hyperspectral image reconstruction. Simultaneously, an attention mechanism was introduced to increase the model’s receptive field and improve its focus on key regions in the input image and feature maps. This resulted in a reduced weighting of redundant information during network learning, leading to an improved feature extraction capability of the network. Through these methods, successful reconstructions of plain weave textiles hyperspectral information from a single RGB image was achieved. Quantitative and qualitative tests were conducted on two datasets, namely, the NTIRE 2020 dataset and a self-made textile dataset, to evaluate the performance of the proposed method. The approach proposed in this article exhibited promising results on both datasets. Specifically, the reconstructed textile hyperspectral images achieved a root mean square error of 0.0344, a peak signal-to-noise ratio of 29.945, a spectral angle mapper of 3.753, and a structural similarity index measure of 0.955 on the textile dataset. In the reconstructed hyperspectral colorimetric experiment, the maximum value of average color difference was 2.641. These results demonstrate that the method can meet the requirements for textile color measurement applications.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"14 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/00405175241268796
Michael Kisiel, Nagarajan M Thoppey, Michael M Morlock, Sebastian Bannwarth
Compression pressure changes in dynamic conditions under textile compression devices have a critical impact on the success of compression therapy. Models exist to predict the level of compression pressure, but not the actual change in pressure. This paper aims to derive a formula to accurately determine the pressure change under textile compression devices, to investigate the factors influencing the pressure change and to verify their effects through theoretical analysis. Firstly, a formula based on Laplace’s law is presented which mathematically describes the dependencies of pressure changes. Secondly, a simulation is carried out to demonstrate the effect of these dependencies on pressure changes using theoretical textile curve functions. Finally, the effect of these dependencies is demonstrated by testing short- and long-stretch bandages in a tensile testing machine and using the recorded material curves to simulate a theoretical application of these bandages to the lower limbs. The results show that the change in pressure is not solely determined by the intrinsic properties of the material, but is influenced by several variables, including the mechanical performance of the textile materials during stretching, the target pressures for application of the textile material, and the body geometries to which the material is applied. Pressure change cannot be a constant for textile compression devices such as bandages. The research increases the understanding of the factors that influence pressure changes in compression device materials. The findings may have implications for the design and selection of compression textiles in clinical applications.
{"title":"Stiffness in compression therapy: Analytical estimation of pressure changes beneath textile compression devices","authors":"Michael Kisiel, Nagarajan M Thoppey, Michael M Morlock, Sebastian Bannwarth","doi":"10.1177/00405175241268796","DOIUrl":"https://doi.org/10.1177/00405175241268796","url":null,"abstract":"Compression pressure changes in dynamic conditions under textile compression devices have a critical impact on the success of compression therapy. Models exist to predict the level of compression pressure, but not the actual change in pressure. This paper aims to derive a formula to accurately determine the pressure change under textile compression devices, to investigate the factors influencing the pressure change and to verify their effects through theoretical analysis. Firstly, a formula based on Laplace’s law is presented which mathematically describes the dependencies of pressure changes. Secondly, a simulation is carried out to demonstrate the effect of these dependencies on pressure changes using theoretical textile curve functions. Finally, the effect of these dependencies is demonstrated by testing short- and long-stretch bandages in a tensile testing machine and using the recorded material curves to simulate a theoretical application of these bandages to the lower limbs. The results show that the change in pressure is not solely determined by the intrinsic properties of the material, but is influenced by several variables, including the mechanical performance of the textile materials during stretching, the target pressures for application of the textile material, and the body geometries to which the material is applied. Pressure change cannot be a constant for textile compression devices such as bandages. The research increases the understanding of the factors that influence pressure changes in compression device materials. The findings may have implications for the design and selection of compression textiles in clinical applications.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"149 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, a cotton/polyester combination yarn with a hydrophobic–hydrophilic gradient across the yarn cross-section was developed using twinning and twisting technologies, and the hermos-physiological comfort properties of the cotton/polyester combination yarn-based double-layer knitted fabrics, prepared from the cotton/polyester combination yarn together with cotton yarn and polyester filaments, were systematically investigated and compared with the cotton (outer)–polyester filaments (inner) fabric. The results show that the cotton/polyester fabric has a better one-way transfer capacity and drying property due to the hydrophobic–hydrophilic gradient from inside to outside, as well as a lower thermal resistance. The cotton/polyester (outer)–polyester filaments (inner) fabric exhibits a weaker hydrophobic–hydrophilic gradient than the cotton/polyester fabric, offering superior water vapor permeability and dynamic cooling property. Although the cotton (outer)–cotton/polyester (inner) fabric with a hydrophilic gradient shows a higher thermal resistance and a weaker dynamic cooling property, it also has a higher air permeability, thermal conductivity and qmax, and its drying rate is second only to the cotton/polyester fabric. The use of the cotton/polyester combination yarn in the inner layer significantly improves the fabrics’ wettability, wickability, and tactile comfort. Furthermore, the combination yarn-based fabrics also have very good water transfer ability. As a result, the combination yarn can take advantage of both fibers in the preparation of fabrics that meet different comfort requirements.
{"title":"Study on the thermo-physiological comfort properties of cotton/polyester combination yarn-based double-layer knitted fabrics","authors":"Wanwan Ma, Longdi Cheng, Yunying Liu, Agnes Psikuta, Yimin Zhang","doi":"10.1177/00405175241268802","DOIUrl":"https://doi.org/10.1177/00405175241268802","url":null,"abstract":"In this study, a cotton/polyester combination yarn with a hydrophobic–hydrophilic gradient across the yarn cross-section was developed using twinning and twisting technologies, and the hermos-physiological comfort properties of the cotton/polyester combination yarn-based double-layer knitted fabrics, prepared from the cotton/polyester combination yarn together with cotton yarn and polyester filaments, were systematically investigated and compared with the cotton (outer)–polyester filaments (inner) fabric. The results show that the cotton/polyester fabric has a better one-way transfer capacity and drying property due to the hydrophobic–hydrophilic gradient from inside to outside, as well as a lower thermal resistance. The cotton/polyester (outer)–polyester filaments (inner) fabric exhibits a weaker hydrophobic–hydrophilic gradient than the cotton/polyester fabric, offering superior water vapor permeability and dynamic cooling property. Although the cotton (outer)–cotton/polyester (inner) fabric with a hydrophilic gradient shows a higher thermal resistance and a weaker dynamic cooling property, it also has a higher air permeability, thermal conductivity and q<jats:sub>max</jats:sub>, and its drying rate is second only to the cotton/polyester fabric. The use of the cotton/polyester combination yarn in the inner layer significantly improves the fabrics’ wettability, wickability, and tactile comfort. Furthermore, the combination yarn-based fabrics also have very good water transfer ability. As a result, the combination yarn can take advantage of both fibers in the preparation of fabrics that meet different comfort requirements.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"195 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1177/00405175241268793
Fei Zheng, Yanping Liu
Three-dimensional mesh fabrics of one-piece spacer structure are an essential component of automotive seat ventilation systems due to their excellent cushion and ventilation performance. The mesh structure is manufactured by stretching across the width of as-knitted structure with closed surfaces in a coupled thermo-mechanical stentering and heat-setting process. This paper presents a numerical study to examine the effect of stentering on the mesh structure and yarn architecture of a typical three-dimensional mesh fabric by establishing a finite element model based on micro X-ray computed tomography reconstruction at the yarn level. The finite element model is verified with the global and local deformation of the mesh during the stentering process. The evolution of the yarn architecture in the stentering process is demonstrated and quantitatively analyzed in terms of curvature and torsion. Three-dimensional mesh fabrics of different mesh sizes and thicknesses after stentering at different ratios are also simulated to study their compression properties. The numerical and experimental results showed that stentering opens the meshes and simultaneously shortens, widens and thins the fabric. The meshes are unevenly distributed across the width, and the intermediate meshes are more open and uniform than the two selvage meshes. To obtain a three-dimensional mesh fabric with uniform and symmetrical meshes, the as-knitted fabric should be stretched coursewise to be wider than 2 times and narrower than 2.6 times its initial width. Stentering disperses, lengthens, tilts, bends and twists the spacer monofilaments, thereby broadening the compression plateau stage and decreasing the compression resistance.
一体式间隔结构的三维网眼织物具有出色的缓冲和通风性能，是汽车座椅通风系统的重要组成部分。网状结构是在热机械拉幅和热定型耦合工艺中，通过拉伸具有封闭表面的原针织结构的宽度来制造的。本文介绍了一项数值研究，通过在纱线层面建立基于微 X 射线计算机断层扫描重建的有限元模型，研究拉幅对典型三维网眼织物的网眼结构和纱线结构的影响。该有限元模型通过拉幅过程中网格的整体和局部变形进行了验证。演示了拉幅过程中纱线结构的演变，并从曲率和扭转方面进行了定量分析。此外，还模拟了不同网孔尺寸和厚度的三维网状织物在不同比率拉幅后的压缩特性。数值和实验结果表明，拉幅使网孔张开，同时使织物变短、变宽和变薄。网孔在幅宽上分布不均，中间网孔比两边网孔更开阔、更均匀。要获得网眼均匀对称的三维网眼织物，应将针织后的织物进行纵向拉伸，使其宽度大于初始宽度的 2 倍，宽度小于初始宽度的 2.6 倍。拉伸可使间隔单丝分散、延长、倾斜、弯曲和扭曲，从而扩大压缩平台阶段并降低压缩阻力。
{"title":"Yarn-level numerical simulation based on micro-CT reconstruction for the stentering process of warp-knitted three-dimensional mesh fabric","authors":"Fei Zheng, Yanping Liu","doi":"10.1177/00405175241268793","DOIUrl":"https://doi.org/10.1177/00405175241268793","url":null,"abstract":"Three-dimensional mesh fabrics of one-piece spacer structure are an essential component of automotive seat ventilation systems due to their excellent cushion and ventilation performance. The mesh structure is manufactured by stretching across the width of as-knitted structure with closed surfaces in a coupled thermo-mechanical stentering and heat-setting process. This paper presents a numerical study to examine the effect of stentering on the mesh structure and yarn architecture of a typical three-dimensional mesh fabric by establishing a finite element model based on micro X-ray computed tomography reconstruction at the yarn level. The finite element model is verified with the global and local deformation of the mesh during the stentering process. The evolution of the yarn architecture in the stentering process is demonstrated and quantitatively analyzed in terms of curvature and torsion. Three-dimensional mesh fabrics of different mesh sizes and thicknesses after stentering at different ratios are also simulated to study their compression properties. The numerical and experimental results showed that stentering opens the meshes and simultaneously shortens, widens and thins the fabric. The meshes are unevenly distributed across the width, and the intermediate meshes are more open and uniform than the two selvage meshes. To obtain a three-dimensional mesh fabric with uniform and symmetrical meshes, the as-knitted fabric should be stretched coursewise to be wider than 2 times and narrower than 2.6 times its initial width. Stentering disperses, lengthens, tilts, bends and twists the spacer monofilaments, thereby broadening the compression plateau stage and decreasing the compression resistance.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the authors’ previous work, slub yarn was simulated by randomly determining the fiber position with the Monte Carlo method according to the draft principle of ring-spun slub yarn. In this paper, the total torque of each micro-element yarn was calculated by considering the contribution of fiber bending, twisting and stretching to the torque of yarn body. The micro-element yarn is a regular ring-spun yarn. The twist of each micro-element yarn can be calculated according to the equal torque between micro-element yarns and the conservation law of twists. The twist curve along the yarn axis also can be obtained. The simulation values of the slub twist and base twist can be obtained by calculating the average twist of the slub apparent segment and that of the base apparent segment, respectively. The twist angle and diameters of the slub and base apparent segments of the spun slub yarn were measured using scanning electron microscopy images, enabling determination of the measured values for both slub twist and base twist. The average error rate of the simulated value compared with the measured value for slub twist was 7.654%, while for base twist, it was 7.745%. The relationships between slub length, base length, slub multiple, design twist, and both slub twist and base twist were investigated. The correlation coefficient ( R) of the simulated and measured values was generally above 0.9, and the trend of the two was consistent. The work presented in this paper provides a basis for the development of virtual spinning technology.
{"title":"A prediction method and its application for twist of slub yarn based on micro-element yarn","authors":"Shifeng Wu, Chongwen Yu, Xiaoye Zhang, Hengshu Zhou, Fengxiang Luo, Qiaoli Xu","doi":"10.1177/00405175241271046","DOIUrl":"https://doi.org/10.1177/00405175241271046","url":null,"abstract":"In the authors’ previous work, slub yarn was simulated by randomly determining the fiber position with the Monte Carlo method according to the draft principle of ring-spun slub yarn. In this paper, the total torque of each micro-element yarn was calculated by considering the contribution of fiber bending, twisting and stretching to the torque of yarn body. The micro-element yarn is a regular ring-spun yarn. The twist of each micro-element yarn can be calculated according to the equal torque between micro-element yarns and the conservation law of twists. The twist curve along the yarn axis also can be obtained. The simulation values of the slub twist and base twist can be obtained by calculating the average twist of the slub apparent segment and that of the base apparent segment, respectively. The twist angle and diameters of the slub and base apparent segments of the spun slub yarn were measured using scanning electron microscopy images, enabling determination of the measured values for both slub twist and base twist. The average error rate of the simulated value compared with the measured value for slub twist was 7.654%, while for base twist, it was 7.745%. The relationships between slub length, base length, slub multiple, design twist, and both slub twist and base twist were investigated. The correlation coefficient ( R) of the simulated and measured values was generally above 0.9, and the trend of the two was consistent. The work presented in this paper provides a basis for the development of virtual spinning technology.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"56 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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