Yarn Defects Research Articles

Тарау жылдамдығының иірім жіптің сапалық көрсеткіштеріне әсері

In spinning production, the carding process has a great influence on the sliver’s quality, as well as the resulting yarn’s quality properties. This research work was carried out to substantiate the spinning yarn’s quality by changing the carding release speed. Changes in the yarn’s quality properties obtained at different carding speeds were analyzed using laboratory equipment Uster® system. The study’s main aim is to determine the optimal carding speed required to obtain low linear density yarn. Yarn number Ne 40/1 (14.5 tex) was obtained on a Zinser® ring spinning machine As a result of the study, it was found that when measuring the effect of spreading release speed on yarn inhomogeneity with increasing release speed from 180 m/min to 210 m/min with lower value of yarn inhomogeneity, with increasing spreading release speed from 180 to 210 m/min there is a continuous increase in yarn defects in spinning yarn. In the study, with the increase in release drum speed, the spinning yarn strength decreased from 16.8 cN/tex to 16.0 cN/tex. There was an increase in discharge speed from 180 to 210 m/min from 4.2% to 4.8% with low yarn pile. It was found that yarn elongation decreased from 5.42% to 5.25% due to increase in distributed release speed.

Enhancing Yarn Quality Wavelength Spectrogram Analysis: A Semi-Supervised Anomaly Detection Approach with Convolutional Autoencoder

Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails the examination of component defects through Wavelet Spectrogram Analysis (WSA). Conventional detection techniques heavily rely on manual experience and lack generality. To address this limitation, this current study leverages machine learning technology to formulate a semi-supervised anomaly detection approach employing a convolutional autoencoder. This method trains deep neural networks with normal data and employs the reconstruction mode of a convolutional autoencoder in conjunction with Kernel Density Estimation (KDE) to determine the optimal threshold for anomaly detection. This facilitates the differentiation between normal and abnormal operational modes without the necessity for extensive labeled fault data. Experimental results from two sets of industrial data validate the robustness of the proposed methodology. In comparison to conventional Autoencoder and prevalent machine learning techniques, the proposed approach demonstrates superior performance across evaluation metrics such as Accuracy, Recall, Area Under the Curve (AUC), and F1-score, thereby affirming the feasibility of the suggested model.

IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE

The image processing philosophy is mainly determined by the complexity of the image and provides the necessary information to be derived from the image. In the textile industry, the image processing technique focuses on the determination of the geometric properties of the fibers such as cross-sectional shape, diameter, length, fineness, and curl while the studies on the yarn characteristics mostly focus on the determination of yarn hairiness, yarn unevenness and yarn defects (thick place, thin place and neps). In this study, previous studies about image processing approaches that are applied for fiber characteristics were investigated. A case study was conducted to automatically determine the visible foreign matter in the waste cotton bundle that can be used for recycled cotton yarn production. It was revealed that the image processing methods can be successfully applied for foreign fiber and matter detection in cotton bundle. As a result, it is emphasized that the waste cotton properties can be specified with a sensitive and accurate approach via image processing technique, objective and numerical determination can be obtained instead of visual evaluation based on experience.

A defect detection system of glass tube yarn based on machine vision

Tube yarn is also called glass fiber spun yarn. Due to the excellent properties of glass fiber, various industrial products based on glass fiber are used in a variety of industries. As the most obvious factor affecting the quality of products, quality detection of glass fiber yarns is very important for companies. Due to traditional defect detection relies on the experience and subjective factors of workers, which makes many different views on the same defect. Some traditional methods limit the solution to specific types of defects and do not accurately detect various defects. In this paper, we propose a comprehensive defect detection system of tube yarn via combining machine vision and deep learning methods. In whole system, we inspect the weight through a weight sensor firstly. Then, we propose a multi-scale cross-fusion attention module to improve the MobileNetV2, and combine with machine vision image feature extraction method for hairiness detection. Finally, the modified MobileNetV2 network is used as the backbone of YOLOX network, making the YOLOX is lighter and achieve more efficiently stain detection of tube yarn. Then, the detection results are used to determine whether the glass tube yarn has passed. In addition, we establish an effective and sufficient amount of tube yarn defects dataset. The experimental results show that the proposed hairiness detection algorithm achieve 96% accuracy with 160+ FPS, and the surface stain detection algorithm achieve 0.89 mAP with 71+ FPS on the tube yarn dataset. The system is efficient, precise, and can be applied to actual production.

Effects of meso‐ and micro‐scale defects on hygrothermal aging behavior of glass fiber reinforced composites

AbstractDesign and process‐induced defects in fiber‐reinforced polymers (FRPs) lead to fracture nucleation due to the stress concentrations. In addition to the degradation in mechanical properties, defects can accelerate aging of FRPs and limit their service life. Efforts to understand the impact of defects have largely focused on the mechanical performance of FRPs. However, their impact on aging performance has not yet been extensively investigated. Here, we report the effect of the meso‐scale (missing yarn) and micro‐scale (micro‐crack) defects on the hygrothermal aging behavior of FRPs. Missing yarn defects were generated by pulling‐out yarns in warp and weft directions of glass fabric. Then, micro‐cracks were induced in composite laminates by acoustic emission controlled tensile loading/unloading. After exposing samples to the hygrothermal aging, we found that meso‐scale defects deteriorate mechanical/thermomechanical performance, reaching 30% decrease in the flexural strength. Notably, even though increasing micro‐crack density reduces the moisture saturation time, the aging time is reported as a more predominant design parameter, deteriorating the mechanical performance for micro‐crack‐induced FRPs.

Identification Method for Cone Yarn Based on the Improved Faster R-CNN Model

To solve the problems of high labor intensity, low efficiency, and frequent errors in the manual identification of cone yarn types, in this study five kinds of cone yarn were taken as the research objects, and an identification method for cone yarn based on the improved Faster R-CNN model was proposed. In total, 2750 images were collected of cone yarn samples in real of textile industry environments, then data enhancement was performed after marking the targets. The ResNet50 model with strong representation ability was used as the feature network to replace the VGG16 backbone network in the original Faster R-CNN model to extract the features of the cone yarn dataset. Training was performed with a stochastic gradient descent approach to obtain an optimally weighted file to predict the categories of cone yarn. Using the same training samples and environmental settings, we compared the method proposed in this paper with two mainstream target detection algorithms, YOLOv3 + DarkNet-53 and Faster R-CNN + VGG16. The results showed that the Faster R-CNN + ResNet50 algorithm had the highest mean average precision rate for the five types of cone yarn at 99.95%, as compared with the YOLOv3 + DarkNet-53 algorithm with a mean average precision rate that was 2.24% higher and the Faster R-CNN + VGG16 algorithm with a mean average precision that was 1.19% higher. Regarding cone yarn defects, shielding, and wear, the Faster R-CNN + ResNet50 algorithm can correctly identify these issues without misdetection occurring, with an average precision rate greater than 99.91%.

Robust Fabric Defects Inspection System Using Deep Learning Architecture

Abstract Clothing is one of the fundamental requirements for living. The fabric business is a steadily developing industry because the interest in dress will never diminish. To support the development of the clothing industry, the clothing industry needs to take rigid measures to keep up the quality of the pieces of fabric they produce. The industry needs a worker to screen the quality of the fabric using a manual fabric review framework. The goal of this article is to plan a profound deep learning algorithm to recognize the fabric types using computer vision. This article focuses on identification of fabric defects using convolutional neural network with the use of appropriate pooling layer, softmax layer, and rectified linear activation layer to acquire an undeniable degree of precision. The photographs of garments with various fabric defects like fabric broken pick defect, fabric with pattern, soiled fabric, fabric weft yarn defect, and plain fabric are considered for evaluation of the architecture. The performance of the architecture is measured with various performance measures like sensitivity, specificity, and accuracy. The algorithm produces the highest accuracy of 97.5 and 100 % for the training and testing samples, respectively, for soiled fabric type.

A computer vision based online quality control system for textile yarns

Yarn quality control is a crucial step in producing high quality textile end products. Online yarn testing can reduce latency in necessary process control by providing rapid insights into yarn quality, leading to production of superior quality yarns. However, both widely used capacitance based evenness testers and emerging imaging based evenness testing systems are largely offline in operation (i.e. a posteriori). A suitable online system that could be employed to test quality of a variety of yarns in normal industrial processing conditions does not yet exist.In this study, we propose an online evenness testing system for measurement of a certain type of yarn defect called nep by using imaging and computer vision techniques. The developed system directly captures yarn images on a spinning frame and uses Viola-Jones object detection algorithm for real-time detection of nep defects. The validation of nep detection algorithms and comparison of the new method with an existing evenness tester in terms of nep count demonstrated its reasonable defect detection accuracy and promising potential for application in wider yarn spinning industry.

Investigation on the performance properties of polytrimethylene terephthalate (PTT) based staple fibers and cotton blended OE-rotor yarns

In this study, firstly, it is aimed to spin PTT (Polytrimethylene Terephthalate) based staple Sustans® fibers together with cotton in Open-End rotor yarn production system. In this way, it is aimed to transfer the positive properties of Sustans® fibers such as good elasticity, good dimensional stability, low static electrification and soft hand to the staple yarn structure with a high efficiency spinning technique. In this context, using 5 different ratios of Sustans®/Cotton blends and 100% Cotton, 6 different rotor yarns with Ne 8.25 (71.6 tex) were produced and the quality characteristics of these yarns were tested. In the study, Sustans®/Cotton blends were successfully spun with the Open-End rotor system. As a result, compared to 100% cotton, it was observed that the elongation at break, unevenness, yarn defects and hairiness values were better for yarns containing Sustans® fibers, while the strength values were slightly lower. The optimum blend ratio was concluded to be 50/50% Sustans®/Cotton for the open-end spinning technique.

融合空间模糊C-均值聚类的纱线疵点检测算法

融合空间模糊C-均值聚类的纱线疵点检测算法

Analisis tentang Setting Roller Gauge dan Break Draft Tidak Merubah Total Draft pada Mesin Roving Rieter type F10 / 55011- 00425 Tahun Pembuatan 1998

At PT Indiratex Spindo, there are 2 units that produce 100% cotton yarn. For unit 1 produces open end yarn, and for unit 2 produces ring spinning yarn. The production process in unit 2 starts from the raw material, namely cotton in the blowroom process, opening and mixing of the cotton so that the shape of the feathers will be processed in the carding machine to further open cotton fibers, clean dirt and short fibers with long fibers. The result of the card is that the card sliver will then be duplicated and drafted on the draw frame breaker, after which it will be processed and duplicated again in the finisher draw frame. Then the sliver drawing finisher will be drafted, given a twist and rolled in a bobbin on the roving machine. The output of the roving machine will then be processed in the ring spinning machine, the roving will be drafted, twisted so that the yarn will then be rolled into the cop, then put in the winding machine to remove thread imperfections or yarn defects and change the cop roll to cones rolls. After the process is complete, the thread will be packed in the packing section. To produce good quality yarn, it is necessary to plan production, repair and maintenance as well as repair machines properly. Drafting is one one process that is very important and to be hard to the quality of the yarn that will be produced. If the drafting process is not perfect it will affect the unevenness of the material or yarn. One of the problem topics related to drafting on the Rieter type F10 / 55011-00425 roving machine in 1998 is that if the break draft is changed, the total draft will change. This assumption is really true because if the draft is damaged, what will change is the main draft, and the total draft will remain the same. Meanwhile, for the correct setting of the roller gauge so that the roving does not experience a high level of unevenness, if the setting is wide, the draft break must be small. And if the arrangement is eternal then the break draft used must be large. The second arrangement is so that the fiber can be properly drafted and does not experience floating fibers or fiber cracks.

Analisis tentang Setting Roller Gauge dan Break Draft Tidak Merubah Total Draft pada Mesin Roving Rieter type F10 / 55011- 00425 Tahun Pembuatan 1998

At PT Indiratex Spindo, there are 2 units that produce 100% cotton yarn. For unit 1 produces open end yarn, and for unit 2 produces ring spinning yarn. The production process in unit 2 starts from the raw material, namely cotton in the blowroom process, opening and mixing of the cotton so that the shape of the feathers will be processed in the carding machine to further open cotton fibers, clean dirt and short fibers with long fibers. The result of the card is that the card sliver will then be duplicated and drafted on the draw frame breaker, after which it will be processed and duplicated again in the finisher draw frame. Then the sliver drawing finisher will be drafted, given a twist and rolled in a bobbin on the roving machine. The output of the roving machine will then be processed in the ring spinning machine, the roving will be drafted, twisted so that the yarn will then be rolled into the cop, then put in the winding machine to remove thread imperfections or yarn defects and change the cop roll to cones rolls. After the process is complete, the thread will be packed in the packing section. To produce good quality yarn, it is necessary to plan production, repair and maintenance as well as repair machines properly. Drafting is one one process that is very important and to be hard to the quality of the yarn that will be produced. If the drafting process is not perfect it will affect the unevenness of the material or yarn. One of the problem topics related to drafting on the Rieter type F10 / 55011-00425 roving machine in 1998 is that if the break draft is changed, the total draft will change. This assumption is really true because if the draft is damaged, what will change is the main draft, and the total draft will remain the same. Meanwhile, for the correct setting of the roller gauge so that the roving does not experience a high level of unevenness, if the setting is wide, the draft break must be small. And if the arrangement is eternal then the break draft used must be large. The second arrangement is so that the fiber can be properly drafted and does not experience floating fibers or fiber cracks.

Effect of Structural Changes on the Cotton Composite Yarn Properties

ABSTRACT Worldwide yarn diversity in terms of structure and properties has increased in recent years. Especially composite yarns produced by multiple components have become very preferable in both weaving and knitting sectors. Among these yarns, core and dual core-spun yarn are more prominent in some areas due to their particular performance. In this study, the structure and properties of plain, elastane cored, and elastane + filament cored composite yarns, which were spun by using cotton as sheath fibers, are comparatively investigated. With a constant Ne 16/1 linear density, a carded yarn production line with modified ring-spinning system was used to produce the composite samples using 5.56% elastane ratio for both and 13.79% polybutylene terephthalate (PBT) ratio for dual core. It was observed that the strength of the composite yarns decreased, while the elongation values at the time of breaking tended to increase. There was some deterioration in transition from plain yarn to dual core composite yarn in terms of unevenness and yarn defects, while some improvement was observed in terms of yarn hairiness. In order to dwell on the causes of this situation, the surface and cross-sectional images of the yarns were also studied.

Characterization of twist of fancy yarns using wavelet analysis of sensor signal

Yarn twist variations may cause stripes in the direction of weft yarns or local defects on a fabric surface. Since fast Fourier transform and time analysis cannot directly detect local frequency variations of yarn signal and defect sensors are designed to detect the diameter decreases without considering frequency analysis, no data associated with twist-related frequency changes can be obtained when inspecting Chenille yarn (Cy) defects. This study proposes the prediction of twist level ( T) and twist variations ( ΔT) of Cy whose twist changes in accordance with the spatial period of pile density by using wavelet analysis, allowing localized frequency variations to be obtained. Complex-valued Paul wavelet was used to determine the ΔT of signals with small frequency fluctuations, while Morlet wavelet was addressed for signals with high frequency change. The relation of the signal frequency to the pile yarn density and, correspondingly, twist was modeled by equations. To prevent discontinuities in wavelet cross-spectrum (WCS), the twist simulation signal was generated by equalizing twist oscillation amplitudes without changing their phase. To compare ideal twist to the local twist, another simulation signal demonstrating the ideal twist at sample-specific frequency was generated. The WCS of the simulation signals allowing the segmentation of variation intervals was used for determining ΔT and yarn portions, where the twist is compatible with ideal twist, by establishing correlation between scales and twists. For yarn samples with various T and ΔT types, the T and ΔT results obtained by the proposed wavelet-based algorithm showed the mean absolute relative percentage errors of 1.617% and 37.062%, respectively.

Bending rigidity of yarns using beam method on a two-support configuration

This paper reports a simple, quick and reasonably accurate approach for measuring the bending rigidity of yarns. The beam method has been adapted and applied using a bending frame that has a fixed support and a simple support. The yarns are left to bend under the effect of their own weight. The accuracy and the precision of that bending frame are assessed over the time using an isotropic material and then compared against the ring-loop method and the KES-FB-2 pure bending tester. The findings show that the precision of this bending frame is acceptable. However, this bending frame gives at least 1.6 times greater values of bending rigidity than the KES-FB-2 pure bending tester, though the relationship between these two methods is linear and significant. Moreover, the spun yarns appear to have high levels of variability of the bending rigidity. This study is important as it overcomes the challenges faced while using other methods to measure the bending rigidity of yarn. It also provides a comprehensive account of the variation in this property. Further, it gives an indication of the highly non-uniform structure of spun yarns and the impact of yarn defects on the bending properties of yarns.

A New Method to Evaluate Yarn Appearance Qualities Based on Machine Vision and Image Processing

For improving the efficiency, flexibility and robustness of yarn defect system, a new method is proposed to evaluate the quality of yarn by testing the yarn diameter, defects and hairiness based on machine vision and image processing technology. Firstly, the diameter image processing unit (DIPU) is defined and a series of sampling points are selected from moving yarn, and the DIPU corresponding to each sampling point is segmented from the captured yarn images. The average diameter of DIPU is used to represent the yarn diameter of the test points. In the extraction of yarn images, the DIPU is divided into definite foreground region, definite background region and unknown region according to the characteristics of gray-level projection distribution, and the unknown region is further processed with Poisson matting method, in which the yarn image and background image are completely separated by a defined connectivity classifier. After the yarn core is extracted by the classifier, the hairiness is divided by using image subtraction. Finally, in order to further evaluate the quality of yarn, the yarn defects were analyzed by the method of statistical methods.

KONVANSİYONEL RİNG VE YENİ BİR MODİFİYE RİNG İPLİK EĞİRME SİSTEMİ KULLANILARAK ÜRETİLEN İPLİK VE KUMAŞLARIN BAZI ÖZELLİKLERİNİN KARŞILAŞTIRILMASI

In this study, it is aimed to evaluate the properties of the yarns produced by conventional ring and a new modified ring (ProSPIN®) spinning system developed to solve the problems arising from the spinning triangle and the knitted fabrics obtained from these yarns by comparison. For this purpose, the equivalent ring and ProSPIN yarns with yarn counts Ne 20 and Ne 28 and twist coefficients αe 3,5 and αe 4,1 were produced using 100% combed cotton raw material and tensile strength, elongation, unevenness, yarn defects and hairiness of these yarns were examined. Then, single jersey fabrics were produced from these yarns and their burst strength, abrasion resistance and pilling resistance properties were examined. All test results were also evaluated statistically. As a result, 100% combed cotton ProSPIN yarns were found to have better values in general in all yarn properties than conventional ring yarns, especially in hairiness and breaking tenacity properties. When the test results of single jersey fabrics were evaluated, it was found that knitted fabrics produced using ProSPIN yarns had higher burst strength, abrasion resistance and pilling resistance values.

Analysis of the Strength Parameters of Worsted and Component Spun Yarns after the Rewinding Process

The notion of yarn is understood as a continuous textile product of theoretically endless length and circle-like cross-section, made of staple or continuous fibres. A yarn leaves the spinning mill in a raw state and is used to produce some fabrics, but mostly it is subjected to the process of finishing. The yarn undergoes preparatory processes, such as winding, doubling, twisting, paraffin treatment, singeing and dyeing, depending of the final fabric type. Yarns are rewound on winding frames, and computers control operation of the rewinding frames and systematically monitor the yarn parameters. An electronic cleaner removes yarn defects, whose size has been saved in the computer memory. The purification of yarn improves its quality, resulting in a decrease in the number of breaks in further technological processes. A lower number of yarn breaks contribute to an increase in machine efficiency (weaving and knitting machines). The experimental section of the study is divided into two parts. The first includes tests of yarn strength parameters before and after the rewinding process. It was established in the process of cleaning that the yarn during rewinding affects the strength of wool and blended yarns. In the second part, the strength parameters of yarn doubling points after the rewinding process for various variants of the splicer setting were tested. The blowing time in the doubling chamber was changed and the importance of using the thermosplicer for the yarn joint strength was established.

Effects of yarn defects and specimen size on impact compressive damages of 3-D angle interlock woven composites

The objective of this work is to investigate yarn defects and specimen size on the impact compressive properties of 3-D angle interlock woven composites (AIWCs). The size effects on impact compressive properties were tested along in plane and out of plane directions. A new finite element model, with inherent defects in the geometrical model of yarns, was established to simulate impact compressive properties of the 3-D AIWCs. The model was further used to analyze size effects on impact compressive properties of 3-D AIWCs. We found the size effect on the 3-D AIWCs was not apparent both in experimental and numerical results; however, the random defects in yarns had a great effect on the compressive properties of 3-D AIWCs along different directions. The yarns effects will weaken the compressive stiffness and strength significantly.

Research of foreign fibers in cotton yarn defect model based on regression analysis

The detecting of foreign fiber may not be very effective, particularly around the detection zone where many types of foreign fibers may coexist. In order to eliminate the fibers more effectively, a model has been established to detect foreign fiber faults in yarn. Relevant data were collected through investigation of a number of standard samples, with the length and area of foreign fibers as the independent variables, and the number of defects as the dependent variable, which were combined using linear regression theory to establish a regression equation for different fiber defects. The equations to find the regression coefficients, which include the model fitting degree, the Durbin–Watson value, the standard error, and the Cook distance, were rigorously tested, and the regression equation was eventually compiled to produce the yarn faults model. When the fiber detection equipment recognizes fibers with a foreign profile, the calculated profile fiber size is used in a corresponding regression equation which obtains the defect points and compares them with each other, so that foreign fibers which are potentially more dangerous can be identified and preferentially eliminated. In order to verify the model, spinning experiments are performed. The actual defects from the experiment are compared with the predicted theoretical defects from the equation, and the prediction accuracy was found to reach more than 95%, showing that the foreign fiber yarn faults model, which lays a theoretical foundation for foreign fiber detection, is accurate and effective.