Table of Content

10 January 2024, Volume 32 Issue 1

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Objective evaluation of pilling of woven fabrics based on deep learning

WU Jun, XU Tian, YU Kun

2024, 32(1):  1-8. 

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At present, China's textile production is huge, and textile import and export trade affects the domestic economic development.Now, the most prominent problem of textiles is quality, of which fabric pilling condition is an extremely important part. The domestic assessment of pilling grade is mainly carried out by professional scientific personnel in some specific scenarios with the standard pilling samples for comparison, for which there are many limitations. First of all, the rating results may be affected by the subjective influence of the testers. Secondly, this method is time-consuming and costly, so there is a need for an objective rating system to grade the fabric pilling.
In recent years, foreign scholars have began to use the computer to process some traditional images by extracting such parameters as the size, number, density, volume and other parameters of the pilling through traditional image processing methods, and such parameters can be used for fabric pilling rating. With the development of deep learning, high accuracy, convenience and other advantages are becoming increasingly prominent and they are widely used by domestic researchers. In the image field, feature extraction by convolutional neural network is effective and avoids the problem of subjective feature extraction. Therefore, we proposed a new Wide-SqueezeNet network for objective rating of fabric pilling based on deep learning.
In this paper, two kinds of woven fabrics with different compositions and contents were used as samples, a ball-box pilling instrument was used to obtain different grades of pilling samples, and the fabrics were put under the light source for image acquisition by using a grayscale camera. A total of 4,376 samples of both kinds were collected. As for the network model, SqueezeNet with fewer parameters and fast training was used as the main body of the network which was innovatively designed. The network model has ten layers, but the number of parameters is small, so the expression ability of complex problems is weak. The new Wide-Fire module was formed by adding a short connection to the original Fire module and using two 3×3 small convolutions to obtain the information of the pilling feature map at different scales and fusing the features with the output of the original Fire module, while using a depth-separable convolution to replace the ordinary convolution in the network to reduce the computation to increase the training speed. A Wide-SqueezeNet network model with deep separable convolution was finally designed.
After the training, the accuracy of Wide-SqueezeNet with improved Fire module is 2% higher than that of the base network, and the accuracy of Wide-SqueezeNet with deep separable convolution is increased by 0.5% and the speed is improved. The final network model is significantly more accurate than some classical classification network models. Two 3×3 convolutional kernels and one 5×5 convolutional kernel are used for training, and the results show that the accuracy of the network with two 3×3 convolutional kernels is higher, so the two 3×3 convolutions are used for feature extraction.
The experimental results show that the improvements in this paper improve the accuracy of the network classification, and the model size and computational effort are basically the same compared to the original network. Finally, the reliability of the network is further verified by the feature map and heat map of the network output, which proves that Wide-SqueezeNet is reliable in the objective rating method of woven fabric pilling. The comprehensive evaluation shows that the network model proposed in this paper can meet the requirements of pilling rating in the fabric industry.


A measurement method for dynamic moisture transfer performance of fabrics based on infrared images

HU Song, TONG Mengxia, ZHANG Jun, FAN Zhenyuan, ZHANG Yi

2024, 32(1):  9-17. 

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The performance of dynamic moisture transfer for fabric, which refers to the diffusion ability of liquid water on clothing, is an important factor affecting thermal and moisture comfort of clothing. When the human body sweats, the sweat is mainly transmitted through the clothes in the form of liquid water. Therefore, it is important to explore the property of dynamic moisture transfer of fabrics to develop clothing products and to improve the clothing comfort. The traditional methods for testing the property of dynamic moisture transfer for fabrics include vertical wicking and Moisture Management Tester (MMT). With the development of computer technology, image processing technology has been widely used in the field of textile property testing such as defect detection, permeability measurement and wetting area test of fabrics for its advantages of convenience, high efficiency and accuracy. 
At present, there are still some deficiencies in the testing of dynamic moisture transfer of fabrics based on image technology: (1) the fabric wetting image taken by an CCD camera is difficult to show the capillary wetting of the edge of the wetting area, which leads to the inability to accurately obtain and identify the real edge of the wetting area of the fabric. (2) The effect of image acquisition is greatly affected by the image acquisition device, fabric color and texture, and the environment conditions. It is easy to cause image noise, which makes it difficult to extract the wetting area. In order to improve the accuracy and convenience of the measurement of dynamic moisture transfer properties of fabrics, and to extract the wetted areas on fabrics during the moisture transfer, in this paper, we proposed a method to measure the property of moisture dynamic transfer on fabrics based on the infrared image. In this method, a thermal infrared camera was used to observe the dynamic moisture transfer process of fabrics, and the temperature difference between dry and wet areas of fabrics was used to identify the wetting area. The application of infrared imaging technology overcame the interference of environmental light, fabric color and texture, and was convenient for image processing. In this paper, we also developed an automatic system to measure the dynamic moisture transfer property of fabrics based on infrared images. The system has the capacity to automatically calculate the wetting area of fabrics at any time in the dynamic moisture transfer process, and can provide accurate and reliable data for property analysis of fabrics. The program contains five modules, including the image extraction module, the image segmentation module, the image denoising module, the image binarization module and the area calculation module. The bilateral filtering algorithm was used for image denoising, and the Otsu algorithm was applied to image binarization. After importing the infrared imaging video file, the interval of the image frame was set according to sampling requirements to extract image and to segment image by the coordinates of the target wetted area. After storing the segmented image, the proposed system automatically performs image denoising and binarization, and finally converts it into the actual wetted area value of the fabric according to the ratio between pixel value and actual area and then outputs the wetted area value. 
In order to verify the accuracy of this method, six kinds of fabric samples were selected for testing, and the difference in moisture transfer property between fabrics was analyzed. The results show that the method can identify and extract the wetting area of fabrics correctly with easy operation and, high accuracy, and continuous observation of the moisture transfer process. Additionally, the results indicate that the fabric surface density and total tightness have a great impact on the initial speed of dynamic moisture transfer of the fabrics, which shows a significant negative correlation. The results are also consistent with the theoretical analysis, further proving the high reliability of the proposed method.


Hydrostatic pressure inspection of woven fabrics based on machine vision

NI Jialu, WANG Ruowen, SHI Wenhui, YUAN Zhilei, XU Pinghua

2024, 32(1):  18-26. 

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Hydrostatic pressure resistance of textiles is an important indicator affecting the wet comfort of textiles. In fabric research and testing stage, the hydrostatic pressure method is commonly used to assess the water resistance of textiles. Current standards such as ISO 811:2018, GB/T 4744—2013, and AATCC 127—2017 are applicable to evaluating the water resistance of various fabrics and non-woven materials (such as canvas, geotextiles, and tent fabrics) that have undergone waterproofing treatments. However, these standards still require inspectors to stop the equipment when the third water droplet is observed. Manual judgment has many disadvantages, such as the delay in human-machine operation, the inability to accurately describe the water discharge position, the need for inspectors' presence, and poor reproducibility. Therefore, exploring the automatic ispection of the hydrostatic pressure of woven fabrics is of great significance. Machine vision-based hydrostatic pressure testing can be understood as dynamically tracking transparent, nearly circular water droplet targets on the substrate of fabric. Currently, existing methods for detecting moving targets include optical flow, frame difference, and background subtraction.
There are still some shortcomings in the current image-based detection of dynamic water droplets on fabrics. First, optical flow method has a high computational complexity, which can easily lead to delay and misjudgment in video droplet tracking. Second, the frame difference method is sensitive to light and holes are easy to appear in the segmented motion foreground when water droplets move slowly. There are many limitations in its application. Third, Gaussian mixture model has weak convergence and poor contour detection integrity, and is not robust to external factors such as environmental noise and lighting. Fourth, infrared images have poor detection results in static water pressure testing due to the small temperature difference between water droplets on the fabric surface and the fabric surface caused by prolonged contact. To improve the efficiency of static water pressure testing of woven fabrics and verify the effectiveness of the image analysis method in detecting the static water pressure of fabrics, we adopt a machine vision-based automatic detection method for fabric hydrostatic pressure. By utilizing 3D printing technology, the encapsulation of the acquisition equipment and light source is achieved. Real-time masking, denoising, and segmentation processing of video frames are performed to obtain a stable and effective observation area. By using the  background subtraction method improved by the background updating strategy, and combining with a mixture of Gaussian models, we achieve the real-time recording of the water outlet point position of the fabric and frame number, which can be used to calculate the fabric's resistance to static water pressure. We also develop a dynamic detection system that can monitor fabric hydrostatic water pressure, automatically stop testing, extract keyframe images, record time, and calculate the fabric hydrostatic pressure. The system include four modules of video image acquisition, pre-processing, motion droplet detection and data recording conversion.
To verify the adaptability of the proposed testing method, experiments were conducted and compared with the existing equipment's built-in detection module, conventional background subtraction method, and Gaussian mixture model subtraction method. Compared with existing methods, results show that this improved algorithm performs well in detecting fabrics with solid color or wide stripe, and the errors range from 0.37% to 2.77%. However, for fine stripes and irregular printed fabrics, the error rate is higher, being above 9.27%. This method can effectively detect solid color and some regular patterned fabrics, but its applicability to complex textured fabrics needs to be improved.


Research on the fabric defect detection algorithm based on semantic segmentation

ZHAO Haoming, ZHANG Tuanshan, MA Haoran, REN Jingqi

2024, 32(1):  27-35. 

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Defect detection is an important link for textile enterprises to improve product quality. The fabric with defects cannot be used in production, which greatly reduces the production efficiency of the factory. At present, the detection of fabric defects in most enterprises in China is still mainly based on manual visual inspection. With the extension of working hours, human function is limited, human eyes are tired, defects will be missed and misjudged, and the objectivity is poor and the detection efficiency is low. Affected by physiology, psychology and external environment, it will have an important impact on the health of testers. As there are many kinds of fabrics and the types, sizes and shapes of defects are different, it is impossible to meet the requirements of factory production efficiency and detection accuracy only by manual visual inspection. Therefore, intelligent inspection is introduced into the factory and gradually replaces manual visual inspection. Fabric defect detection has become a research hotspot. Convolutional neural network and algorithm research have a significant effect on defect detection, while the reasonable collection of data sets is a big problem. There are many kinds of fabric textures and fibers, and fabric defects account for a small proportion relative to the pixels of the whole image, usually between 0.5% and 15%, so it is impossible to achieve a balanced proportion of pixels. Due to the uneven data classification in the data set, the detection accuracy cannot be further improved. 
Many scholars have designed different neural networks to detect defects, such as U-net and ResNet50. The accuracy rate can reach 95% for fabric defects with large pixels, such as broken warp and weft, but only 80% for defects with small pixels, such as holes and stains, and the effect is not good. The imbalance of data types in data sets is very common, including defects with large pixel ratio and defects with small pixel ratio. The network needs to be adjusted to improve the detection accuracy of small-category defects. To solve the problem of imbalanced data classification and improve the accuracy of small-category defect detection, we put forward a CS model network designed on the basis of Resnet and U-net network structure, and adds MSCA attention mechanism suitable for small-category defect and strip defect feature detection, which makes the network pay more attention to this kind of defects. The multi-class Focal Loss function is introduced into it, which makes the segmentation result more accurate by increasing the weight of small-class defects. The small-scale defects are given a large initial weight and dynamically adjusted to keep it balanced. By adjusting the parameters of Focal loss function, mIoU, Acc and Loss values are used as experimental evaluation indicators to compare the experimental results, and CS model is compared with the semantic segmentation models of U-net, ResNet50, DeepLabV3 and VGG16 networks, respectively.
The experimental results show that the detection accuracy of the CS model network model proposed in this study is improved by 2.97% compared with U-net, 7.89% compared with mIoU, 3.86% compared with ResNet50, and 6.98% compared with mIoU, all of which have been significantly improved, and the problems of uneven classification of data categories and detection accuracy of small-category defects in data sets have been solved. The research results can provide reference suggestions for fabric defect detection research.


Automatic detection of spiral mesh defects based on feature extraction and image classification

WANG Borun, ZHANG Ning, LU Yuzheng

2024, 32(1):  36-44. 

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The spiral mesh is a kind of polymer filter, which is formed by polyester, polyamide, polyethylene and other polymer monofilaments through the winding device to form a left and right opposite spiral ring line similar to the spring structure. Through the left and right spiral ring lines, the mesh is formed by repeatedly meshing and inserting the connecting core wire in the overlapping part, and then the spiral mesh is finally formed by the subsequent processes such as heat setting, core insertion, cutting, edge sealing and gluing. It has special void structure, high interception precision, high mesh flatness, free splicing, high structural strength, long service life and strong corrosion resistance, and can realize solid-liquid separation in various scenarios. It is widely used in environmental protection, papermaking, coal mine, food, medicine and other fields. Spiral mesh defects are mainly divided into three categories: holes, missing core wires and staggered rings. Although most of its production processes have been automated, the final quality inspection process still depends on manual inspection. However, manual detection has shortcomings such as strong subjectivity, high cost, low efficiency, high labor intensity, and damage to vision, which cannot guarantee the detection precision and accuracy. The speed and accuracy of manual detection can no longer meet the requirements of spiral mesh production. It is particularly important to use machine vision to realize the automation of spiral mesh defect detection.
In order to solve the problems of low efficiency and high false detection rate of spiral network manual defect detection, we proposed a method of spiral network defect detection based on classification. By extracting multi-mode and multi-scale LBP features, the information of spiral network image was fully characterized. Based on the classification idea, in the case of small sample size, the SVM classifier was used to effectively learn and classify the small sample, high-dimensional and non-linear data, and to distinguish the local damaged and abnormal defective images and defect-free images to realize the automatic detection of spiral mesh defects. By building an image acquisition device, we collected the defective and non-defective images of the spiral mesh, and verified the proposed method. By extracting the texture features of the spiral mesh, we discussed the classification effects of LBP features of different modes and scales and other features on SVM and K-NN classifiers. It is found that the most suitable feature for spiral mesh defect detection is the uniform mode LBP operator with eight sampling points and two sampling radius, and the optimal classifier is SVM. The effectiveness of the proposed method was verified by collecting images to establish a data set. The experimental results show that the classification accuracy of the spiral network with and without defects reaches 100 %, which verifies that the LBP operator has good anti-noise and robustness. The average classification speed is 0.48 s/sheet.
This paper proposes a comprehensive and efficient spiral network defect detection algorithm, which has certain practical significance for the spiral network industry. The sample size of the spiral network defect images collected in this paper is small. On this basis, the number of samples will be further increased to establish a more stable spiral network defect detection system.


A Method for Identifying Women's Sleeves Based on Improved YOLOv5 and ResNet50

CAO Hanying, TUO Jiying

2024, 32(1):  45-53. 

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In response to the problems of numerous classifications of women's clothing sleeve shapes, difficulty in feature recognition, and unsatisfactory detection results, on the basis of fully utilizing the correlation information between different women's clothing sleeve shapes, an improved YOLOv5 object detection and ResNet50 object classification deep learning method was used to achieve automatic recognition of women's clothing sleeve shapes.
Firstly, two methods of sleeve type classification were combined based on the length and shape of women's clothing sleeves. The sleeves were divided into four primary classifications based on the length of women's clothing: sleeveless, ultra short, short, and long sleeves. On the basis of the primary classifications, they were further divided into 15 secondary classifications based on their morphological characteristics, including bra, neck hanging, raglan sleeves, bubble sleeves, and leg sleeves. Secondly, clothing sample images were collected from e-commerce platforms, taking into account factors such as different angles, lighting, and backgrounds. On the basis of balancing different sleeve types, 3600 sleeve type images of women's clothing were collected, screened, and labeled. A clothing sleeve type dataset containing approximately 6500 sleeve type samples was obtained, and sleeve type labeling was performed using Labelimg software. Once again, based on the analysis of the YOLOv5 object detection network, CBAM attention mechanism, ResNet50 residual network principle, and network features, an improved YOLOv5 and ResNet50 combined deep learning method based on CBAM attention mechanism is proposed for women's sleeve automatic recognition. Among them, YOLOv5 model gradually adjusts the parameters of the network model through the back propagation and gradient descent characteristics of the Convolutional neural network on the self labeled garment sleeve shape data set to obtain the network parameters suitable for the detection of women's sleeve shape, thus realizing the target detection of women's sleeve shape in the primary level classification. The convolutional attention module CBAM, which combines channel attention mechanism and spatial attention mechanism, is beneficial for solving the problem of no attention preference in the original network, thereby enhancing the effectiveness of sleeve detection. Four independent ResNet50 residual networks were used to carry out sleeve type secondary classification recognition based on the improved YOLOv5 network detection of four sleeve types: sleeveless, ultra short sleeved, short sleeved, and long sleeved, respectively, in order to obtain the final results of women's sleeve type recognition. Finally, based on the Python language and Pytorch framework, the proposed deep learning algorithm for women's clothing sleeve recognition was designed and implemented, and the model was trained on the sleeve dataset to verify the effectiveness of sleeve recognition through experiments.
The results indicate that ① compared to the YOLOv5 method and the CBAM improved YOLOv5 method, the CBAM improved YOLOv5 and ResNet50 combined method, which introduces the correlation information between women's sleeve shapes, has more advantages in the accuracy of women's sleeve shape recognition. The overall recognition accuracy is about 93.3, and its overall accuracy is 12.2 and 8 percentage points higher than the YOLOv5 model improved by YOLOv5 and CBAM, respectively; ② in the task of identifying women's sleeve type by YOLOv5, improved YOLOv5, YOLOv5 and ResNet50 combined methods, compared with sleeveless and long sleeves, the identification of ultra-short sleeves and short sleeves is more difficult, and the overall accuracy is more difficult to improve.


2D Image reconstruction of nonwoven fabrics based on generative adversarial networks

WANG Zhilu , HOU Jue, YANG Yang, LIU Zheng

2024, 32(1):  54-63. 

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 Nonwovens are used in a wide range of applications, including medical and health care, tourism, construction and waterproofing, and agriculture, for a variety of filtration materials, gas or particle adsorption materials, sound insulation, and more. The functions of nonwovens are closely related to the structure of their fiber collections. Based on the premise that nonwovens are randomly arranged in the layers of the fiber network, researchers usually use parametric simulation, fractal theory or image generation algorithms to simulate the fibers of nonwovens. However, the fiber structure generated in this way has the following problems: (1) The fiber morphology based on parametric simulation is linear and does not agree with the actual fiber curl state. (2) The fractal theory-based simulations cannot reconstruct the real nonwoven fabric structure concretely. (3) The single fiber morphology reconstructed based on the generation algorithm is different from the real state of the fiber morphology.
In order to reconstruct a realistic image of the nonwoven fiber structure, this paper constructs a generative adversarial network (FGAN) with a multiscale training strategy on the GAN base framework. In order to improve the stability of model training, a multi-scale training strategy is used to train the model, and PixelwiseNormalization layer and standardization layer are also introduced in the construction of the model. The reconstructed fiber structure is randomly arranged, which is consistent with the real nonwoven fiber arrangement. To increase the diversity of fiber structures in the generated images, a weight diversity loss WMI Loss is proposed. the model is trained for a long time, and the final generator is stable to generate nonwoven images that are consistent with the real images. Compared with other generative models, FGAN has better stability and the reconstructed nonwoven images have higher quality and more diverse fiber structures. Also, to verify the effectiveness of multiple-degree training strategy and weight diversity loss, ablation experiments are performed on the model. The experimental results show that the evaluation index FID of the model-generated images is reduced by 24.52% under the effect of the multiscale training strategy and by 20.31% under the effect of the weight diversity loss. Finally, in order to verify the structural consistency between the generated images and the real images, the average porosity of the real images is calculated to be 30.87% and the average porosity of the generated images is 30.02%, which are very close to each other. And the pore number distribution curves of the generated image and the real image have a high overlap. From the above analysis, it can be verified that the porosity and pore distribution of the nonwoven fabric images generated by the model are consistent with the real images.
FGAN can reconstruct high quality 2D images of nonwovens, from which detailed information on fiber distribution and morphology can be obtained. This information can be used for nonwoven performance analysis, optimization of production processes, etc. The method is able to reconstruct a diverse range of thin nonwoven structures, while the analysis of thicker nonwoven structures is yet to be investigated.


Effects of carbon nanotube mass fraction on electromagnetic shielding properties of nano-aramid composite aerogel fibers

WANG Jianing, SI Suqiu, ZHENG Xingyi, LIU Wei

2024, 32(1):  64-72. 

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The aerogel, a porous material with low density and large specific surface area, has promising applications in environmental treatment, catalytic carriers, energy storage devices, etc. It has been proposed that aerogels are shaped into one-dimensional fibres, which may lead to some good properties such as good flexibility and small density. This facilitates the design and manufacture of textiles. In the past decade, efforts have been devoted to the development of aerogel fibers such as graphene aerogel fibres, titanium oxide aerogel fibres, silica aerogel fibers and cellulose aerogel fibers with multiple different functions. Graphene aerogel composite fibers with tunable thermal conversion and storage under multiple stimuli offer a wide range of applications in next-generation wearable systems. Cellulose aerogel fibers have high mechanical strength and low thermal conductivity at room temperature. When combined with phase change materials, nickel and fluorocarbon (FC) resins, aerogel fibers can also be used as phase change fibers, conductive fibers and hydrophobic textiles. Aramid nano fibers (ANFs for short) are polymeric nanofibers with high porosity, large specific surface area, high aspect ratio and unique nano-scale effects. These excellent properties make them promising for many emerging applications. ANFs are prepared as aerogel fibers and made to be used as a base material to compound with other materials to prepare high performance aerogel fibers. This can solve some of the problems of excellent performance materials that cannot be moulded due to poor mechanical properties.
In recent years, more and more scholars have been preparing various high performance materials into aerogel fibers. 
In this paper, carbon nanotubes (CNTs), a conductive material, were dispersed in the same system with ANF through DMSO and the effects of different CNT contents on the properties of aerogel fibers were discussed. Based on the electrical conductivity of the aerogel fibres, their electromagnetic shielding properties were investigated. The mechanical properties and electrical conductivity of the fibers were tested to determine the optimum parameters of CNT within the experimental range. The results show that when the content of CNT in the dispersion reaches 25%, the fiber has a breaking strength of 7.29 MPa and the electromagnetic shielding effectiveness reaches 18 dB. The CNT/ANF composite aerogel fiber can shield 80 % of electromagnetic waves and meet the industrial requirements.


Melt polycondensation and characteristics of waste PET fibers

WANG Kexin, GAO Feng, WANG Yongjun, CHEN Wenxing, LÜ Wangyang

2024, 32(1):  73-79. 

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Since its invention in 1948, polyester has been widely used in different areas, including fibers, films, polyesters bottle and so on, because of its excellent fibre forming property, thermal stability and mechanical properties. The total production of polyester fibers for 2020 in China exceeded 60 million tons. However, it's hard to ignore the recycling of waste polyester with the development of the industry. For example, only on the production line will produce about 2% waste fibers, which has caused a great burden on enterprises and the environment. The molecular weight of PET can be increased, and its utilization value can be improved by polycondensation. In this study, melt polycondensation was used to increase the viscosity of waste PET fibers, and the characteristics were investigated in order to provide support for the recovery of waste PET fibers.
The polycondenstion reactions were conducted at different temperature and time conditions, and the influence on molecular weight changes were investigated by testing the characteristic viscosity. The molecular weight and distribution were tested by using APC-MALLS-RID, so as to show the change of molecular weight in the process of viscosity increasing. APC-PDA-RID was used to calculate the absolute content of oligomers and determine the composition of different cyclic oligomers. The thermal properties were tested by DSC to analyze the influence of viscosity increasing behavior on thermal properties. It was found that the waste PET powder, coarse cut fibers and virgin PET powder were melted and thickened at different temperatures and times. The results showed that at 270~280 ℃, the intrinsic viscosity increases with reaction time, and can increase to more than 1.0 dL/g within 50 min. The rate of melt polycondensation is obviously accelerated after the temperature increases, but when the temperature is higher than 270 ℃, the thermal degradation reaction will be promoted, causing the viscosity of the polyester to decline instead of rising. Therefore, 270 ℃ is an appropriate reaction temperature. The viscosity increasing time at 270 ℃ is positively correlated with the molecular weight, and the molecular weight distribution becomes wider after viscosity increasing. Cyclic trimers are the most abundant component of oligomers. The content of oligomers decreases with the increase of the reaction time. The content of oligomers decreases after reaction at 270 ℃ for 40 min. The content of oligomers in the waste PET powder is always higher than that in the virgin PET under the same conditions due to the intrinsic difference. The molecular weight and chain of the waste PET powder increase. The entanglement of the polymer chains hinders their mobility, leading to a reduction in chain regularity and a decrease in crystallization temperatures. The presence of small molecules, such as diethylene glycol generated through side reactions, exerts an influence on the melting point. In this study, characteristics of melt polycondensation and viscosification of waste polyester fibers were manifested from various aspects and compared with the virgin PET. It is found that the waste fibers have better viscosification effect, which id of great significance for recycling. 


Synthesis of quaternary ammonium salt cationic modifiers and their application in dyeing cotton fabrics with gardenia yellow

ZHU Qiuyu, ZHANG Bin, SHEN Xiaojie, CHEN Qiulin, WANG Lei, YU Zhicheng

2024, 32(1):  80-89. 

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 To improve the dyeing performance of gardenia yellow on cellulose fibers, we designed and synthesized a series of cellulose fiber cationic modifiers with 3-chloro-2-hydroxypropyl as the active group and quaternary ammonium cation as the cationic group. Then, we introduced quaternary ammonium groups into cellulose fibers by nucleophilic addition reaction, and then dyed the fibers with gardenia dyes to improve the dyeing performance of gardenia yellow dyes.
The molecular structures of quaternary ammonium modifiers with different alkyl chain lengths were characterized by ESI-MS and proton nuclear magnetic resonance. FT-IR and X-ray photoelectron spectroscopy confirmed that quaternary ammonium compounds were successfully grafted onto the cotton fabrics through ether bonds. HPLC-MS was used to detect the binding mode between gardenia yellow dye and modified cotton fabrics.
The absorption peak of methyl bending vibration of quaternary ammonium compounds connected to nitrogen atoms on quaternary ammonium groups at 1,470 cm-1 was analyzed by Fourier infrared spectroscopy and the characteristic peak of binding energy of nitrogen elements at 399 eV was analyzed by X-ray photoelectron spectroscopy. It was confirmed that quaternary ammonium compounds were successfully grafted onto cotton fabrics in the form of ether bonds.
The cotton fabric was cationic treated with modifiers with different alkyl chain lengths, and the modified cotton fabric was dyed with gardenia yellow dye to compare the dyeing K/S values of the modified cotton fabric. The experimental results show that the optimal alkyl chain length of the modifier is 12, with a dye uptake rate of 85.46% and a K/S value of 4.33. This is because the growth of the alkyl chain of the modifier increases the positive charge on the surface of the fabric, enhancing the adsorption of dyes. But at the same time, the growth of the alkyl chain increases the molecular volume of the modifier, resulting in most of the modifier being grafted on the surface of the fiber, hindering the diffusion of dyes into the interior of the fiber. Therefore, when the alkyl chain is 18, the K/S value actually decreases.
The cotton fabric was modified with cationic modifier with the same concentration and different alkyl chain lengths, and the cotton fabric modified with quaternary ammonium cation with different alkyl chain lengths was dyed with gardenia yellow dye. And the effects of dyeing process conditions on K/S value, dyeing rate and color fastness of dyed fabrics were studied. The experimental results show that the dyeing performance of the fabric modified with CH-12 is the best, with a dyeing rate of 88.07% and a K/S value of 4.71.  At the same time, the cotton fabric modified with CH-12 has a soaping and rubbing fastness of over 3 levels, and a light fastness of 2‒3 levels.


Effect of ultraviolet aging on the structure and isotopes of rhamnus utilis-dyed silk fabrics

CAI Yilan, YANG Dan, JIA Liling, ZHOU Yang, PENG Zhiqin

2024, 32(1):  90-99. 

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The Silk Road is a bridge for political, economic and cultural exchanges between the East and West. Silk is composed of silk protein and silk glue protein. Silk protein, accounting for about 80% of the silk component, is mainly composed of 18 amino acids such as glycine, alanine, serine and tyrosine. Some amino acids in silk are easily photochemically interacted with ultraviolet light, resulting in embrittlement of the silk fabric. Silk artifacts are often not intact after excavation, which puts forward new requirements for origin tracing of silk artifacts. Stable isotope technology is a potential method for origin tracing of unknown objects and is gradually applied to trace textile artifacts. To trace the origin of silk fabrics by using isotope technology, it is necessary to figure out the influence of dyeing and aging on the isotopes of silk fabrics.
Light has great influence on the preservation of silk fabrics. In this study, silk fabrics were mordant-dyed with natural rhamnus utilis dyestuff and under UV accelerated photoaging for 5, 10, 15 d and 30 d to simulate the possible state of silk artifacts under UV lighting. Then they were dried at 50℃ for one hour and stored in a sealed bag for further testing. The color differences of the samples were tested by a spectrophotometer. The microscopic morphology and mechanical properties of the samples were examined by scanning electron microscopy (SEM) and a universal material testing machine. The chemical structure of colored silk fabrics with different photo-aging times were tested by using fourier transform infrared spectroscopy (FTIR). And the amide III region was fitted to obtain the relative content changes of secondary structures. Stable isotope ratios of silk fabrics with different degrees of photoaging were tested. On this basis, the morphological and structural changes of silk fibers under the influence of light aging and the changes of stable isotopes of silk fibers under the condition of ultraviolet light aging were analyzed to provide basic data for the traceability of silk cultural relics. The results show that UV aging had great influence on the morphology and structure of silk fabrics. UV aging induced a rapid decrease in the color difference of silk fabrics. The color difference of mordant-dyed silk fabrics changed most obviously. Cracks were shown in the surfaces of the aged silk fibers and the mechanical properties of the aged silk fabrics were greatly reduced, with the greatest reduction happening in rhamnus utilis-dyed samples. The β-sheet structure of the silk fibers was destroyed after aging. More heavy carbon isotopes were left in the aged silk fabrics compared with the control samples, especially for the mordant-dyed silk fabrics.


Impact of fabric performance on placket's appearance of men's tailored jackets

KONG Yuan , LAO Jiawei

2024, 32(1):  100-107. 

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The tailored jacket plays a unique and important role in the fashion industry. The placket, located in the center of the vision, is a crucial part of both the structure and the shape of the tailored jacket. In addition to the pattern design, the fabric performance has a great impact to the shape of the placket. It is vital to choose the suitable fabric according to the placket shape in the design and production of tailored jackets.
After testing the functions and performances of 28 kinds of fabrics, we chose 10 kinds of fabrics with quite different performance through cluster analysis and made 10 tailored jacket samples with the same patterns, accessories, and process. Based on the SPSS data analysis results, the impact of fabric performance on the shape of the placket was discussed. The relation between the fabric performance and evaluation indexes of the placket shape was elaborated. The regression equations of them were established.
Inclination and overlap were selected as the indexes of objective evaluation to placket shape. The research show that the fabric performance indexes which are correlated with the inclination include gram weight(W), thickness(T), warp density(Mj), drape coefficient(Xc), warp bending strength(Kj), zonal bending strength(Kw), zonal breaking strength(Dw), warp elongation(Sj), zonal elongation(Sw). The fabric performance indexes that are correlated with the overlap index include gram weight(W), drape coefficient(Xc), warp breaking strength(Dj), zonal breaking strength(Dw), warp elongation(Sj), and zonal elongation(Sw). Through regression analysis, the regression equations were established. The placket inclination equation: Y=-3.819W-0.071Sj-0.072kj+31.076; the placket overlap equation: Y=-0.032Xc+0.174Dw-0.174Sj+0.125Sw+2.854. 
Aesthetics and flatness were selected as the indexes of subjective evaluation to placket shape. The study shows that the fabric performance indexes which are correlated with the aesthetics include gram weight (W), drape coefficient(Xc), zonal breaking strength(Dw), warp elongation(Sj), and zonal elongation(Sw). The fabric performance indexes that are correlated with the flatness include gram weight(W), thickness(T), drape coefficient(Xc), zonal bending strength (Kw), zonal breaking strength(Dw) and zonal elongation(Sw). The research established the regression equations based on the regression analysis of subjective evaluation result and fabric performance indexes. Equation of aesthetics: Y=0.248W-0.013Xc+0.045Dw+3.311; Flatness equation: Y=0.454W-0.018Xc+3.245.
In this study, the performance indexes of fabric and the objective evaluation indexes of placket shape were analyzed. The results show that gram weight, warp elongation and zonal elongation are negatively correlated with the inclination of placket; gram weight, zonal breaking strength and zonal elongation are positively correlated with the overlap of placket. The performance indexes of fabric and subjective evaluation indexes of placket were studied, and the results show that gram weight, zonal breaking strength and the aesthetics of the placket are significantly positively correlated. Gram weight and drape coefficient are negatively correlated with the flatness of the placket, while thickness, zonal bending strength, zonal breaking strength and zonal elongation are positively correlated with the flatness of the placket. At the same time, the study shows that there is a great correlation between subjective and objective evaluation indexes. The inclination of placket is small, the overlap is large, and the aesthetics and flatness of the placket are satisfactory.


Thermal-wet comfort evaluation of fire physical  training clothing based on EEG technology#br#

REN Jiayuan, JIN Jian, ZHENG Jingjing

2024, 32(1):  108-118. 

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Fire physical training clothing is the base layer clothing that is most closely in contact with the skin during the daily training of firefighters. In the hot summer, firefighters have to spend a long time in a hot and humid environment for physical training. A large amount of physiological heat accumulates in the training suit, so that the ability of systemic circulation is reduced, resulting in heat regulation dysfunction. In severe cases, firefighters may experience dizziness, convulsions and other physiological reactions. In the process of visiting and researching Shengzhou Fire and Rescue Brigade in Shaoxing city, Zhejiang province and Baiyang Fire and Rescue Brigade in Hangzhou, Zhejiang province, we learned that firefighters hope to optimize the thermal-wet comfort performance of training clothing to improve their wearing comfort, improve heat stress problems, and improve training efficiency. However, in the past research on the comfort of fire physical training clothing, the traditional evaluation system is mainly used, and the results obtained have certain limitations.
To solve the problem of heat stress during firefighters' daily training and discuss the influence of phase change fabric on human thermal-wet comfort, this study is based on the research basis of electrophysiology and basic characteristics of EEG, and starts from the evaluation dimension of both objective EEG experiment and subjective perception. Firstly, the study conducts EEG monitoring experiments on the subjects in five different dressing states. The power spectral density of the subject is extracted by fast Fourier transform in frequency domain analysis. This study selects the average power spectral density of α wave and β wave, which can reflect the mental state of human brain as the index of EEG data to characterize the intensity of brain wave rhythm. Power spectrum analysis and statistical analysis are used to investigate the difference in intensity of α and β waves associated with mood. On this basis, the subjective score after normalization is used as the subjective comfort evaluation index, and the relationship between the subjective evaluation index and the objective EEG index is discussed by correlation analysis. On the whole, the state of dressing under hot and wet conditions affects the mood and comfort of the participants and the power spectral density of α and β waves can be used as the evaluation index of EEG. In the condition of wearing PCM fire physical training clothing, the intensity of α wave is the highest, the brain positive emotion is more than other states, the intensity of β wave is the least, and the tension and irritability are the least. The relationship between the subjective evaluation index and the EEG data index shows that the α wave intensity would be inhibited and the β wave intensity would be higher when the subjects thought that the humidity and heat sensation and discomfort caused by wearing the fire physical training suit are stronger.
Questionnaire survey is a traditional and important research method, and EEG technology can provide some technical help for objectively quantifying subjects' emotions. EEG data can provide more quantitative and objective data support. Through the analysis of the relationship between EEG data and subjective comfort evaluation index, the thermal and humid comfort feeling under different dressing conditions can be explored from both physiological and psychological dimensions. Such research results can provide reference for the study of clothing comfort evaluation by using EEG technology.


Research progress and development prospects of the fabric recycling of waste military uniforms in China

LIU Yang , YAO Xiang , YANG Gesheng , YU Xiufeng, ZHANG Yaopeng

2024, 32(1):  119-129. 

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With the continuous development of society and the rapid growth of global textile production, the annual textile consumption per capita and the output of waste textiles have also increased significantly. While the recycling of waste fabrics in China is still at a low-level stage, with a comprehensive utilization rate of less than 15%, resulting in a serious waste of resources and environmental pollution. Due to the variety types of waste civilian clothing and the complexity of corresponding fabrics, it is relatively difficult and costly to recycle them. Waste military uniforms (WMUs) have uniform styles, clear fabric components, and a large amount of usage, and thus become an ideal recycled raw material. If appropriate separation strategies can be adopted to separate various components and further fabricate recycled products based on the fabric components, the high-value recycling and reuse of WMUs will be achieved effectively, which can provide valuable references for the high-value recycling and reuse of wasted civilian clothing. 
In order to promote the high-value fabric recycling of WMUs in China, we summarized the types and fabrics features of type 07 military uniforms, systematically introduced the present recycle strategies of WMUs, such as splitting spinning, recycled polyester pellets, as well as the separation of polyester/cotton, and projected the progress of the recycling of WMUs. Current fabric recycling of WMUs mainly focuses on the simple reusage, as well as the recycling of polyester components to prepare recycled polyester pellets. While there are few studies on the high-value recycling of other components, especially on the cotton fibers with high proportion in military uniforms. If effective polyester/cotton separation technologies can be adopted to effectively separate the polyester and cotton fibers, then corresponding recycled polyester pellets and dissolved cotton pulp can be prepared by using the separated components. Regenerated cellulose products can be further fabricated based on the recycled cotton pulp by using this promising strategy. Therefore, the development of high-efficient separation of polyester and cotton, and corresponding high-value utilization of the recycled polyester and cotton components are important development trends of the comprehensive high-value recycling of WMUs.  


Research and application progress of nano-filler reinforced composites with polylactic acid

SHAO Yehua, GAO Zhaoyang, WANG Longfei, TIAN Wei, QI Dongming, YAN Xiaofei

2024, 32(1):  130-139. 

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Traditional petroleum-based plastics, such as polyethylene (PE) and polypropylene (PP), can be seen almost everywhere in our life. However, the overwhelming majority of the billions of tons of plastic produced worldwide ultimately ends up in landfills or natural environments, with only a tiny fraction being incinerated or recycled. As plastic consumption continues to increase, so does its production, leading to a more severe issue of "white pollution" and raising concerns about the extensive use of oil resources. With the growing awareness of environmental protection, polylactic acid, a biodegradable plastic made from renewable resources, is widely used to replace traditional plastics due to its excellent degradation performance.
The polylactic acid industry is also developing with the increasing proportion of biodegradable plastics in the market. Polylactic acid is made from renewable resources like corn and potatoes, making it fully biodegradable and biocompatible. Therefore, in the field of life medicine, polylactic acid is often used as a medical dressing or drug carrier. With good mechanical and physical properties, polylactic acid is suitable for thermoplastic, blow molding, and other processing methods. It can not only become one of the most suitable bio-based plastics to replace traditional plastics, but also be used in packaging, agriculture, and other fields. Polylactic acid nanocomposites, with nanofillers like stone carbon nanotubes as the reinforcement, effectively improve the mechanical properties of polylactic acid, even reduce the resistivity, and expand the application of polylactic acid in engineering fields, such as sensors, electromagnetic waves, and so on. Moreover, by strengthening and toughening polylactic acid, polylactic acid nanocomposites can be used as scaffolds or composite membranes and other applications in life medicine.
At present, polylactic acid nanocomposites have great advantages. The excellent performance of nanofillers can effectively improve the mechanical properties, crystallinity, and toughness of polylactic acid, and expand its application in packaging, engineering, agriculture, and other fields. Although a large number of valuable studies have been carried out on polylactic acid nanocomposites, the current research on them is still faced with problems of poor compatibility between the nano-filler and polylactic acid, uneven dispersion or functional endogeneity of the nanocomposites. Consequently, it is necessary to speed up the research on the interface and dispersion of polylactic acid nanocomposites and to promote the research and development of functional polylactic acid nanocomposites.