When deep learning is applied to intelligent textile defect detection, the insufficient training data may result in low accuracy and poor adaptability of varying defect types of the trained defect model. To address the above problem, an enhanced generative adversarial network for data augmentation and improved fabric defect detection was proposed. Firstly, the dataset is preprocessed to generate defect localization maps, which are combined with non-defective fabric images and input into the network for training, which helps to better extract defect features. In addition, by utilizing a Double U-Net network, the fusion of defects and textures is enhanced. Next, random noise and the multi-head attention mechanism are introduced to improve the model’s generalization ability and enhance the realism and diversity of the generated images. Finally, we merge the newly generated defect image data with the original defect data to realize the data enhancement. Comparison experiments were performed using the YOLOv3 object detection model on the training data before and after data enhancement. The experimental results show a significant accuracy improvement for five defect types – float, line, knot, hole, and stain – increasing from 41%, 44%, 38%, 42%, and 41% to 78%, 76%, 72%, 67%, and 64%, respectively.
{"title":"FabricGAN: an enhanced generative adversarial network for data augmentation and improved fabric defect detection","authors":"Yiqin Xu, Chao Zhi, Shuai Wang, Jianglong Chen, Runjun Sun, Zijing Dong, Lingjie Yu","doi":"10.1177/00405175241237479","DOIUrl":"https://doi.org/10.1177/00405175241237479","url":null,"abstract":"When deep learning is applied to intelligent textile defect detection, the insufficient training data may result in low accuracy and poor adaptability of varying defect types of the trained defect model. To address the above problem, an enhanced generative adversarial network for data augmentation and improved fabric defect detection was proposed. Firstly, the dataset is preprocessed to generate defect localization maps, which are combined with non-defective fabric images and input into the network for training, which helps to better extract defect features. In addition, by utilizing a Double U-Net network, the fusion of defects and textures is enhanced. Next, random noise and the multi-head attention mechanism are introduced to improve the model’s generalization ability and enhance the realism and diversity of the generated images. Finally, we merge the newly generated defect image data with the original defect data to realize the data enhancement. Comparison experiments were performed using the YOLOv3 object detection model on the training data before and after data enhancement. The experimental results show a significant accuracy improvement for five defect types – float, line, knot, hole, and stain – increasing from 41%, 44%, 38%, 42%, and 41% to 78%, 76%, 72%, 67%, and 64%, respectively.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"108 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148878","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 : 2024-03-15DOI: 10.1177/00405175241237827
Yixin Li, Jiatai Gu, Kai Cao, Xiaohong Qin
Waterproof and breathable fibrous membrane is a new type of polymer functional material widely used in outdoor protection, sports equipment, industrial production, and other fields. To impart waterproof ability and breathability, a common practice is to modify the electrospun membrane with low surface-energy materials, such as fluorosilane. However, when directly added to the electrospinning solution, the effects of fluorosilane on the property of the prepared membrane have been scarcely reported. Herein, we prepared the electrospun polyurethane membrane by adding two types of fluorosilane into the polyurethane solution. The effects of fluorosilane type and concentration on the conductivity and viscosity of the electrospinning solution were investigated. Moreover, the prepared polyurethane/fluorosilane fibrous membranes are analyzed in terms of their surface morphology, structure, water contact angle, breathable performance, and mechanical properties. Results show that the dual effects of fluorosilane content exert an opposing influence on the jet drawing process, which yields a minimum fiber diameter when the fluorosilane content is 20%. Under this condition, the membrane shows the highest water vapor transmission rate (10.4 kg/(m2·d). In addition the membranes have the maximum water contact angle (138°) and breaking strength (12 MPa) when the fluorosilane content is 40%. This study provides insights into fabricating electrospun waterproof and breathable membranes in equipment for outdoor sports and protection.
{"title":"Effects of fluorosilane addition on polyurethane electrospun membranes with waterproof and breathable performance","authors":"Yixin Li, Jiatai Gu, Kai Cao, Xiaohong Qin","doi":"10.1177/00405175241237827","DOIUrl":"https://doi.org/10.1177/00405175241237827","url":null,"abstract":"Waterproof and breathable fibrous membrane is a new type of polymer functional material widely used in outdoor protection, sports equipment, industrial production, and other fields. To impart waterproof ability and breathability, a common practice is to modify the electrospun membrane with low surface-energy materials, such as fluorosilane. However, when directly added to the electrospinning solution, the effects of fluorosilane on the property of the prepared membrane have been scarcely reported. Herein, we prepared the electrospun polyurethane membrane by adding two types of fluorosilane into the polyurethane solution. The effects of fluorosilane type and concentration on the conductivity and viscosity of the electrospinning solution were investigated. Moreover, the prepared polyurethane/fluorosilane fibrous membranes are analyzed in terms of their surface morphology, structure, water contact angle, breathable performance, and mechanical properties. Results show that the dual effects of fluorosilane content exert an opposing influence on the jet drawing process, which yields a minimum fiber diameter when the fluorosilane content is 20%. Under this condition, the membrane shows the highest water vapor transmission rate (10.4 kg/(m<jats:sup>2</jats:sup>·d). In addition the membranes have the maximum water contact angle (138°) and breaking strength (12 MPa) when the fluorosilane content is 40%. This study provides insights into fabricating electrospun waterproof and breathable membranes in equipment for outdoor sports and protection.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148637","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 : 2024-03-15DOI: 10.1177/00405175241237137
Jie Yuan, Changliang Xu, Jiahui Fang, Lin Lou
The mechanism of the neuro-electrophysiological response of the human brain to skin tactile stimulation has always been an important research topic in the field of comfort perception of textile materials. In order to explore the characteristic neural potential components induced by various fabric tactile stimulation and their influence rules, event-related potential technology with ultra-high time resolution was introduced to monitor the somatosensory brain region under tactile stimulation of fabrics with different tactile properties, and the signal changes of related potentials were extracted and analyzed. The results showed that the amplitudes of N100 increased with the stiffness of the fabric, the amplitudes of P200 decreased with the smoothness of the fabric, and the amplitudes of P300 decreased with the smoothness and softness of the fabric. These results indicated that N100, P200, and P300 potentials could be used as neurophysiological response indexes of brain neurons to distinguish the subtle differences in fabric tactile properties. This finding not only laid a scientific theoretical basis for the brain perception mechanism of fabric tactile properties, but also provided a possibility for further quantification characterization of textile comfort perception.
{"title":"Investigation of fabric tactile perception using the event-related potential method","authors":"Jie Yuan, Changliang Xu, Jiahui Fang, Lin Lou","doi":"10.1177/00405175241237137","DOIUrl":"https://doi.org/10.1177/00405175241237137","url":null,"abstract":"The mechanism of the neuro-electrophysiological response of the human brain to skin tactile stimulation has always been an important research topic in the field of comfort perception of textile materials. In order to explore the characteristic neural potential components induced by various fabric tactile stimulation and their influence rules, event-related potential technology with ultra-high time resolution was introduced to monitor the somatosensory brain region under tactile stimulation of fabrics with different tactile properties, and the signal changes of related potentials were extracted and analyzed. The results showed that the amplitudes of N100 increased with the stiffness of the fabric, the amplitudes of P200 decreased with the smoothness of the fabric, and the amplitudes of P300 decreased with the smoothness and softness of the fabric. These results indicated that N100, P200, and P300 potentials could be used as neurophysiological response indexes of brain neurons to distinguish the subtle differences in fabric tactile properties. This finding not only laid a scientific theoretical basis for the brain perception mechanism of fabric tactile properties, but also provided a possibility for further quantification characterization of textile comfort perception.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"132 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148646","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 : 2024-03-14DOI: 10.1177/00405175241235652
Tamara Ruiz-Calleja, Alberto Jiménez-Suárez, Rocío Calderón-Villajos, Silvia G Prolongo
Achieving proper dispersion of pigments, dyes, or other additives, such as microcapsules or nanoparticles, within printing pastes or textile coatings is crucial for obtaining a homogeneous result. In certain specialized applications, such as coloration technology, it is possible to use colorimetry tools, visual examination, and even artificial vision to identify defects. However, none of these techniques comprehensively map the specific additive distribution. This paper proposes a novel approach: monitoring the distribution of conductive particles (graphene nanoplatelets, referred to as GNPs) within an acrylic coating paste using the Joule’s effect. Four different dispersion systems (ultrasound mixer, blender, toroidal agitation, and three-roll mill) are employed. Thermographic images provide an accurate view of how conductive particles are distributed. This complements data from numerical values, such as the maximum and average temperatures recorded for each sample. In certain cases, relying solely on numerical values can be inadequate or insufficient, hence the novelty of this article emphasizing the significance of using the Joule’s effect to assess the distribution of conductive particles. Concerning the mixing systems, optimal dispersion of GNPs in distilled water is most effectively achieved using an ultrasound mixer, with enhanced uniformity as dispersion time increases. For mixing the components of the coating paste, the toroidal agitation method yields the best result. Employing the three-roll mill is discouraged for this application due to its propensity to induce phase separation.
{"title":"Characterization of conductive particle dispersion in textile coatings through Joule’s effect monitoring analysis","authors":"Tamara Ruiz-Calleja, Alberto Jiménez-Suárez, Rocío Calderón-Villajos, Silvia G Prolongo","doi":"10.1177/00405175241235652","DOIUrl":"https://doi.org/10.1177/00405175241235652","url":null,"abstract":"Achieving proper dispersion of pigments, dyes, or other additives, such as microcapsules or nanoparticles, within printing pastes or textile coatings is crucial for obtaining a homogeneous result. In certain specialized applications, such as coloration technology, it is possible to use colorimetry tools, visual examination, and even artificial vision to identify defects. However, none of these techniques comprehensively map the specific additive distribution. This paper proposes a novel approach: monitoring the distribution of conductive particles (graphene nanoplatelets, referred to as GNPs) within an acrylic coating paste using the Joule’s effect. Four different dispersion systems (ultrasound mixer, blender, toroidal agitation, and three-roll mill) are employed. Thermographic images provide an accurate view of how conductive particles are distributed. This complements data from numerical values, such as the maximum and average temperatures recorded for each sample. In certain cases, relying solely on numerical values can be inadequate or insufficient, hence the novelty of this article emphasizing the significance of using the Joule’s effect to assess the distribution of conductive particles. Concerning the mixing systems, optimal dispersion of GNPs in distilled water is most effectively achieved using an ultrasound mixer, with enhanced uniformity as dispersion time increases. For mixing the components of the coating paste, the toroidal agitation method yields the best result. Employing the three-roll mill is discouraged for this application due to its propensity to induce phase separation.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"98 5 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148648","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}
The complex evaluation of thermo-physiological comfort for a particular garment is still challenging, as it depends on the different structural parameters and individual properties of textiles. Measurement of relevant fabric characteristics requires very specific laboratory equipment, such as an M 290 moisture management tester (SDL ATLAS) or similar. For this reason, it is obvious that there is a great demand to predict the overall moisture management capability ( OMMC) based on the individual properties that are responsible for clothing comfort and testing according to different standards rather than OMMC-specific calculation using the M 290 tester. Therefore, in this research, linear regression analysis was performed using MATLAB software to predict the OMMC for cotton–polyester fabrics knitted in two patterns, namely 1 × 1 rib and half-Milano rib, using four percentages of fibers. Water vapor permeability, water vapor resistance, water absorption capacity, water absorption time, and air permeability were used as input variables for linear regression analysis to predict the OMMC of fabrics. The performed analysis has shown that the OMMC is directly dependent on the relative water vapor permeability and air permeability, and the linear regression equation suggested in this research can predict the suitability of a textile for a particular garment concerning its moisture management behavior.
对特定服装的热生理舒适性进行复杂的评估仍具有挑战性，因为这取决于纺织品的不同结构参数和个别特性。相关织物特性的测量需要非常特殊的实验室设备，如 M 290 湿度管理测试仪（SDL ATLAS）或类似设备。因此，根据不同的标准和测试对服装舒适性负责的单项特性来预测整体湿度管理能力（OMMC），而不是使用 M 290 测试仪进行特定的 OMMC 计算，显然有很大的需求。因此，在本研究中，使用 MATLAB 软件进行线性回归分析，以预测棉-涤纶织物的 OMMC，该织物采用两种模式，即 1 × 1 罗纹和半米拉诺罗纹，使用四种百分比的纤维。水蒸气渗透率、水蒸气阻力、吸水能力、吸水时间和透气性被用作线性回归分析的输入变量，以预测织物的 OMMC。分析结果表明，OMMC 直接取决于相对透湿性和透气性，本研究提出的线性回归方程可以预测纺织品的湿度管理行为是否适合特定服装。
{"title":"Linear regression analysis of properties related to moisture management using cotton–polyester knitted fabrics","authors":"Norina Asfand, Stasė Petraitienė, Virginija Daukantienė","doi":"10.1177/00405175241236495","DOIUrl":"https://doi.org/10.1177/00405175241236495","url":null,"abstract":"The complex evaluation of thermo-physiological comfort for a particular garment is still challenging, as it depends on the different structural parameters and individual properties of textiles. Measurement of relevant fabric characteristics requires very specific laboratory equipment, such as an M 290 moisture management tester (SDL ATLAS) or similar. For this reason, it is obvious that there is a great demand to predict the overall moisture management capability ( OMMC) based on the individual properties that are responsible for clothing comfort and testing according to different standards rather than OMMC-specific calculation using the M 290 tester. Therefore, in this research, linear regression analysis was performed using MATLAB software to predict the OMMC for cotton–polyester fabrics knitted in two patterns, namely 1 × 1 rib and half-Milano rib, using four percentages of fibers. Water vapor permeability, water vapor resistance, water absorption capacity, water absorption time, and air permeability were used as input variables for linear regression analysis to predict the OMMC of fabrics. The performed analysis has shown that the OMMC is directly dependent on the relative water vapor permeability and air permeability, and the linear regression equation suggested in this research can predict the suitability of a textile for a particular garment concerning its moisture management behavior.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"98 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148653","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 : 2024-03-13DOI: 10.1177/00405175241236916
Huimin Su, Peishan Zhang, Ziyi Guo, Tao Li, Fengyuan Zou
Breast feature parameters could represent breast morphology. It is significant for improving bra fit, and is an important aspect of garment ergonomics. To obtain the important feature parameters that can effectively represent breast morphology, this study proposed a feature parameter extraction method based on the machine learning model. First, the human body point-cloud data of 201 female college students were obtained by a three-dimensional body scanner, and 24 feature parameters related to breast morphology were acquired. Then, the cluster analysis was used to classify breast morphology into four categories: uniform hemisphere, outward expanding circular, converging water drop, and outward expanding hemisphere. Finally, principal component analysis was used to reduce the dimensionality of feature parameters, and the three machine learning models, naive Bayes, support vector machine, and random forest, were utilized to extract the parameters after dimensionality reduction. The results showed that principal component analysis could reduce the dimensions of breast feature parameters to seven main parameters. Based on the above three models, the seven main parameters were further reduced to three important feature parameters. They were sorted sequentially: breast volume, breast surface area, and longitudinal breast cup straight line length, and the Fisher discriminate function was used to distinguish breast morphology. The recognition accuracy based on the three important feature parameters reached 99%, higher than 97.5% for full feature parameters recognition, and 98% for seven feature parameters recognition. It is proved that the three important feature parameters obtained by the machine model are effective in characterizing breast morphology.
{"title":"Extraction and recognition of breast morphological feature parameters based on machine learning models","authors":"Huimin Su, Peishan Zhang, Ziyi Guo, Tao Li, Fengyuan Zou","doi":"10.1177/00405175241236916","DOIUrl":"https://doi.org/10.1177/00405175241236916","url":null,"abstract":"Breast feature parameters could represent breast morphology. It is significant for improving bra fit, and is an important aspect of garment ergonomics. To obtain the important feature parameters that can effectively represent breast morphology, this study proposed a feature parameter extraction method based on the machine learning model. First, the human body point-cloud data of 201 female college students were obtained by a three-dimensional body scanner, and 24 feature parameters related to breast morphology were acquired. Then, the cluster analysis was used to classify breast morphology into four categories: uniform hemisphere, outward expanding circular, converging water drop, and outward expanding hemisphere. Finally, principal component analysis was used to reduce the dimensionality of feature parameters, and the three machine learning models, naive Bayes, support vector machine, and random forest, were utilized to extract the parameters after dimensionality reduction. The results showed that principal component analysis could reduce the dimensions of breast feature parameters to seven main parameters. Based on the above three models, the seven main parameters were further reduced to three important feature parameters. They were sorted sequentially: breast volume, breast surface area, and longitudinal breast cup straight line length, and the Fisher discriminate function was used to distinguish breast morphology. The recognition accuracy based on the three important feature parameters reached 99%, higher than 97.5% for full feature parameters recognition, and 98% for seven feature parameters recognition. It is proved that the three important feature parameters obtained by the machine model are effective in characterizing breast morphology.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"14 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140127919","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}
A hexahedron reverberation box was utilized to measure the reverberation time of samples in a diffuse acoustic field. The aim of this study is to design a mathematical model that accurately represents the correlation between frequency and absorption coefficient for analysis. The same was obtained by aligning the noise absorption coefficients (NACs) determined through an impedance tube and calculated based on the reverberation time. The analysis indicated that jute fiber exhibits superior performance compared to other natural fibers, such as rock and glass wool, in the attenuation of sound frequencies above 2200 Hz. The results motivated further investigation over jute nonwoven fabric to conduct layer analysis (1–7) and to examine the impact of thickness. Thereby, a dataset was compiled consisting of 11 fibrous samples (nine natural and two commercial fibers) and jute nonwoven fabric. The empirical model was developed regardless of the type of fiber or thickness of the nonwoven fabric, and it was successfully validated for three additional fibers (banana, pineapple, and ramie). The predictive model exhibited a high level of accuracy in estimating the NAC, displaying a strong similarity to that of impedance tube measurements. The achieved mean absolute error ranges for the predictions are between 0.02 and 0.03 only. The main discovery of this study revolves around the recognition of frequency of sound as a crucial variable and its application in predicting the NAC for the samples.
{"title":"Non-destructive reverberant testing of natural fibrous samples in a diffused acoustic field environment","authors":"Mallika Datta, Gautam Basu, Devarun Nath, Sayandeep Debnath, Surajit Sengupta, Kartick K Samanta","doi":"10.1177/00405175241235396","DOIUrl":"https://doi.org/10.1177/00405175241235396","url":null,"abstract":"A hexahedron reverberation box was utilized to measure the reverberation time of samples in a diffuse acoustic field. The aim of this study is to design a mathematical model that accurately represents the correlation between frequency and absorption coefficient for analysis. The same was obtained by aligning the noise absorption coefficients (NACs) determined through an impedance tube and calculated based on the reverberation time. The analysis indicated that jute fiber exhibits superior performance compared to other natural fibers, such as rock and glass wool, in the attenuation of sound frequencies above 2200 Hz. The results motivated further investigation over jute nonwoven fabric to conduct layer analysis (1–7) and to examine the impact of thickness. Thereby, a dataset was compiled consisting of 11 fibrous samples (nine natural and two commercial fibers) and jute nonwoven fabric. The empirical model was developed regardless of the type of fiber or thickness of the nonwoven fabric, and it was successfully validated for three additional fibers (banana, pineapple, and ramie). The predictive model exhibited a high level of accuracy in estimating the NAC, displaying a strong similarity to that of impedance tube measurements. The achieved mean absolute error ranges for the predictions are between 0.02 and 0.03 only. The main discovery of this study revolves around the recognition of frequency of sound as a crucial variable and its application in predicting the NAC for the samples.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"32 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140127926","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 : 2024-03-13DOI: 10.1177/00405175241236494
Kai Yang, Xiuling Zhang, Mohanapriya Venkataraman, Kun Chen, Yuanfeng Wang, Jakub Wiener, Guocheng Zhu, Juming Yao, Jiri Militky
Textiles incorporating phase change material have attracted increasing attention due to their temperature regulating function. Although a great progress has been made in the development of phase change material textiles, it has been found that the loading amount of phase change materials is limited by other final properties. Recently, we have proposed a sandwich fibrous phase change material encapsulation with a relatively high phase change material loading amount, which is a multi-layer fabric structure containing phase change material. However, the breathability of sandwich fibrous phase change material encapsulation should be improved because there is no path for air to penetrate through. In this work, the sandwich fibrous phase change material encapsulation structure with polyethylene glycol as phase change material is modified by introducing different air pockets in the thermal function layer ranging from 19% to 64%. The leakage phenomenon, phase transition behavior, thermal energy storage, breathability, T-history and practicality of the breathable sandwich fibrous phase change material encapsulations are investigated. As a result, the maximum polyethylene glycol loading amount of the phase change materials pocket is 83 wt%, and there is no leakage of polyethylene glycol during working time. The overall enthalpy value of the breathable sandwich fibrous phase change material encapsulation ranges from 27 J/g to 48 J/g. The optimal air permeability and water vapor resistance of the breathable sandwich fibrous phase change material encapsulation is 9 mm/s under 100 Pa and 34.5 m2 Pa W−1. Furthermore, the heterogeneous heat transfer through the breathable sandwich fibrous phase change material encapsulation is found due to the complicated thermal resistances of the hybrid thermal functional layer. In addition, for breathable sandwich fibrous phase change material encapsulation, the flexibility, hydrophobicity, self-cleaning property, abrasion resistance, and stability after water immersion are found. We believe the research has a great potential in various applications related to phase change material.
{"title":"Thermal behavior of flexible and breathable sandwich fibrous polyethylene glycol (PEG) encapsulations","authors":"Kai Yang, Xiuling Zhang, Mohanapriya Venkataraman, Kun Chen, Yuanfeng Wang, Jakub Wiener, Guocheng Zhu, Juming Yao, Jiri Militky","doi":"10.1177/00405175241236494","DOIUrl":"https://doi.org/10.1177/00405175241236494","url":null,"abstract":"Textiles incorporating phase change material have attracted increasing attention due to their temperature regulating function. Although a great progress has been made in the development of phase change material textiles, it has been found that the loading amount of phase change materials is limited by other final properties. Recently, we have proposed a sandwich fibrous phase change material encapsulation with a relatively high phase change material loading amount, which is a multi-layer fabric structure containing phase change material. However, the breathability of sandwich fibrous phase change material encapsulation should be improved because there is no path for air to penetrate through. In this work, the sandwich fibrous phase change material encapsulation structure with polyethylene glycol as phase change material is modified by introducing different air pockets in the thermal function layer ranging from 19% to 64%. The leakage phenomenon, phase transition behavior, thermal energy storage, breathability, T-history and practicality of the breathable sandwich fibrous phase change material encapsulations are investigated. As a result, the maximum polyethylene glycol loading amount of the phase change materials pocket is 83 wt%, and there is no leakage of polyethylene glycol during working time. The overall enthalpy value of the breathable sandwich fibrous phase change material encapsulation ranges from 27 J/g to 48 J/g. The optimal air permeability and water vapor resistance of the breathable sandwich fibrous phase change material encapsulation is 9 mm/s under 100 Pa and 34.5 m<jats:sup>2</jats:sup> Pa W<jats:sup>−1</jats:sup>. Furthermore, the heterogeneous heat transfer through the breathable sandwich fibrous phase change material encapsulation is found due to the complicated thermal resistances of the hybrid thermal functional layer. In addition, for breathable sandwich fibrous phase change material encapsulation, the flexibility, hydrophobicity, self-cleaning property, abrasion resistance, and stability after water immersion are found. We believe the research has a great potential in various applications related to phase change material.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"36 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140127887","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 : 2024-03-10DOI: 10.1177/00405175241236886
Zhijia Dong, Xueming Fang, Yuqin Ding, Honglian Cong, Pibo Ma
In this work, two functional superfine polyester yarns were selected as face yarns, three conventional yarns were selected as ground yarns, and 18 fabrics were designed by using a combination of three different plating structures. Then, through contact angle measurements, moisture management performance, thermal management performance, and breathability tests, we evaluated the effects of raw material, structure, and pore characteristics on the fabric’s hygrothermal comfort, and furthermore assessed the prioritization of the three factors using a range analysis. The results show that fabrics with face yarn of 75 D/288 F have better hydrophilicity, and fabrics with face yarn of 50 D/216 F have a better temperature control effect. The fabric with spandex/polyester covered yarn or spandex/nylon covered yarn has the best moisture transmission, and the unidirectional transfer index can reach 300%. The microstructure has a great influence on air permeability, and the surface porosity is positively correlated with air permeability. Combined with the heat and humidity division of the sports body, the development of efficient zonal positioning of heat and moisture management sports undershirts can provide certain theoretical support for the design of weft-knitted seamless sports products.
在这项工作中，我们选择了两种功能性超细聚酯纱线作为面纱，三种常规纱线作为地纱，并通过三种不同电镀结构的组合设计了 18 种织物。然后，通过接触角测量、湿度管理性能、热管理性能和透气性测试，评估了原材料、结构和孔隙特性对织物湿热舒适性的影响，并进一步使用范围分析评估了三个因素的优先级。结果表明，面纱为 75 D/288 F 的织物具有更好的亲水性，面纱为 50 D/216 F 的织物具有更好的温度控制效果。氨纶/涤纶包覆纱或氨纶/锦纶包覆纱的织物透湿性最好，单向透湿指数可达 300%。微观结构对透气性有很大影响，表面孔隙率与透气性呈正相关。结合运动人体的热湿分区，开发高效分区定位的热湿管理运动内衣，可为纬编无缝运动产品的设计提供一定的理论支持。
{"title":"Fixed position varying pore structure of weft-knitted seamless fabric and heat/moisture transfer mechanism","authors":"Zhijia Dong, Xueming Fang, Yuqin Ding, Honglian Cong, Pibo Ma","doi":"10.1177/00405175241236886","DOIUrl":"https://doi.org/10.1177/00405175241236886","url":null,"abstract":"In this work, two functional superfine polyester yarns were selected as face yarns, three conventional yarns were selected as ground yarns, and 18 fabrics were designed by using a combination of three different plating structures. Then, through contact angle measurements, moisture management performance, thermal management performance, and breathability tests, we evaluated the effects of raw material, structure, and pore characteristics on the fabric’s hygrothermal comfort, and furthermore assessed the prioritization of the three factors using a range analysis. The results show that fabrics with face yarn of 75 D/288 F have better hydrophilicity, and fabrics with face yarn of 50 D/216 F have a better temperature control effect. The fabric with spandex/polyester covered yarn or spandex/nylon covered yarn has the best moisture transmission, and the unidirectional transfer index can reach 300%. The microstructure has a great influence on air permeability, and the surface porosity is positively correlated with air permeability. Combined with the heat and humidity division of the sports body, the development of efficient zonal positioning of heat and moisture management sports undershirts can provide certain theoretical support for the design of weft-knitted seamless sports products.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"28 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140116076","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}
The wicking effect constitutes a pivotal determinant in facilitating the ingress and transference of liquid water within yarns and fabrics. Its significance looms prominently in the context of subsequent product processing, particularly concerning the immersion and interface bonding of textile matrix composites. The twist exerts profound influence over the fiber disposition and density within yarns, as well as the yarn and the wicking pathways for liquid water. We use a mathematical model grounded in the three-dimensional helical capillary permeation mechanism, inherently linked to the twist factor. This model operates under the assumption that the yarn's fibers exhibit uniform diameters and arrangements. Leveraging the macroscopic force equilibrium method, a function of liquid capillary rise with wicking time was deduced. and the dynamic progression of liquid water ascent within the yarn was simulated using the COMSOL platform. Subsequently, a series of wicking experiments were executed on polyester filament yarns, each characterized by varying twist levels. The results revealed that the experimental data coincided well with the theoretical prediction, thus affirming the model's accuracy.
{"title":"Three-dimensional spiral model of the wicking effect for continuous polyester filaments","authors":"JiaHao He, Jiugang Li, Qiang Yang, Zhiyun Xie, Xinpeng Jin, Xiaoxi Sun, Wenlu Zhang, Zhijiang Liu, Xiaopeng Xu, Wenbin Li, Jing Guo","doi":"10.1177/00405175241227933","DOIUrl":"https://doi.org/10.1177/00405175241227933","url":null,"abstract":"The wicking effect constitutes a pivotal determinant in facilitating the ingress and transference of liquid water within yarns and fabrics. Its significance looms prominently in the context of subsequent product processing, particularly concerning the immersion and interface bonding of textile matrix composites. The twist exerts profound influence over the fiber disposition and density within yarns, as well as the yarn and the wicking pathways for liquid water. We use a mathematical model grounded in the three-dimensional helical capillary permeation mechanism, inherently linked to the twist factor. This model operates under the assumption that the yarn's fibers exhibit uniform diameters and arrangements. Leveraging the macroscopic force equilibrium method, a function of liquid capillary rise with wicking time was deduced. and the dynamic progression of liquid water ascent within the yarn was simulated using the COMSOL platform. Subsequently, a series of wicking experiments were executed on polyester filament yarns, each characterized by varying twist levels. The results revealed that the experimental data coincided well with the theoretical prediction, thus affirming the model's accuracy.","PeriodicalId":22323,"journal":{"name":"Textile Research Journal","volume":"64 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140075153","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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