Aiming at the problems of low detection accuracy and high leakage rate in traditional detection algorithms, an improved YOLOv8 algorithm is proposed for automatic detection of fabric defects. A swin transformer block was added to the C2f module in the backbone network, which can transfer information between multiple attention layers in parallel to capture fabric defect information and improve the detection accuracy of small-sized defects. To enhance the model’s performance in detecting defects of various sizes, a bidirectional feature pyramid network (BiFPN) was incorporated into the neck. This allows for the assignment of different weights to defect features in different layers. A convolution block attention module (CBAM) was added to the feature fusion layer, enabling the model to automatically increase the weight of essential features and suppress nonessential features during training to solve the problem of leakage detection of small-sized defects due to occlusion and background confusion. The Wise-IoU (WIoU) loss function replaces the conventional loss function, addressing sample imbalance and directing the model to prioritize average-quality samples. This modification contributes to an overall improvement in the model’s performance. The results of the experiment proved that on the self-constructed fabric defect dataset, the algorithm in this paper achieved an accuracy of 97.7%, recall of 95.1%, and mAP of 96.8%, which are 4.4%, 9.4%, and 5.1% higher than those of the YOLOv8 algorithm, respectively. On the AliCloud Tianchi dataset, the algorithm achieves 52.3%, 49.2%, and 49.8% in terms of accuracy, recall, and mAP, respectively, which is an improvement of 4.4% in terms of accuracy, 2.8% in terms of recall, and 2.7% in terms of mAP compared with the baseline algorithm. The improved YOLOv8 algorithm has a high detection accuracy, low leakage rate, and a detection speed of 107.5 FPS, which aligns with the real-time defect detection speed in the industry.

Aiming at the existing fabric defect detection algorithms with their suboptimal detection accuracy, high model complexity, difficult deployment to the edge of the device, and insufficient ability to meet the problem of real-time detection of fabric defects, we propose a lightweight fabric defect detection model (LWFDD-YOLO) based on the improved YOLOv8n. First, a generalized efficient layer attention network with selective kernel attention (GELAN_SKA) is proposed to replace the C2f module, and selective kernel attention is added to the module to adjust the weights of the convolution kernel according to the different features of different scales, reduce the use of computational resources, thus improving the model’s detection performance and efficiency. Second, a cascaded group attention (CGA) mechanism is added to provide a different input segmentation for each head to enhance the feature diversity of the input attention heads and improve the model’s computational efficiency. An ultra-lightweight dynamic up-sampling operator (Dy_sample) is introduced, which employs a point-sampling-based approach to reduce the consumption of computational resources and improve model performance. Finally, the YOLOv8l framework is utilized to construct a complex model teacher network and the features learned from the teacher network are transferred to the lightweight network proposed in this paper, thus further improving the model performance of the algorithm. The experimental results show that on the self-built fabric defect dataset, the accuracy, recall, and mean average precision ( mAP ) of our algorithm reach 89.4%, 85.2%, and 87.9%, which are 8.9%, 7.2%, and 4.5% higher than the original model, respectively, the number of parameters of the model decreases by 23.4%, the amount of GFLOPs decreases by 25.6%, and the file size is only 9.2 MB. The detection speed can reach 163.4 FPS on GPU. On the AliCloud Tian Chi dataset, the accuracy, recall, and mAP were also improved by 7.4%, 2%, and 4.4%, respectively. The LWFDD-YOLO algorithm proposed in this study can realize real-time detection of fabric defects with relatively obvious improvement in accuracy, and the model requires less memory and is easier to deploy to edge devices, so can be used as a reference for real-time detection of fabric defects.

To satisfy the requirements of high speed, large capacity and constant winding, a textile winding rotor needs to work in a wide rotation speed range and frequently pass through critical speed points. Thus, the winding rotor adopts the flexible long shaft coupling structure and flexible support with rubber O-rings. This kind of rotor has a multi-coupling structure and frequency-dependent parameters characteristics, especially representative and universal in the dynamic analysis method of the high-speed rotor. In this paper, an approach was proposed to investigate the dynamic behavior of the winding rotor considering the flexible coupling and frequency-dependent supporting parameters. Firstly, a dynamic model of the winding rotor was established by using a Timoshenko beam element. Its dynamic behaviors were simulated by considering the time-varying rotation speed and the frequency-dependent parameters of flexible support. Secondly, a non-contact measuring device was developed for measuring the vibration displacement of the winding rotor in three different speed-up times. Finally, based on simulation and measurement data, how flexible support parameters and the speed-up time affect the winding rotor passing through the critical speed point of the rotor smoothly is revealed. The methods and findings reported here can be used for theoretical and experimental vibration analysis of other types of high-speed flexible rotors.

During the winding process, the filament moves at a high speed with multiple configurations and large deformations, and is acted upon by winding tension, contact force, transverse force, air resistance, and so on. Accurately predicting the trajectory and tension fluctuation of the filament under the high-speed running condition is the basis for regulating winding parameters and ensuring high-quality winding. This paper proposed a novel dynamic approach for modeling the polyester filament winding system. The filament element was established by absolute nodal coordinate formulation. The nonlinear spring and viscous damper elements were used to establish the contact model between the filament and the mechanical parts, and the mechanical model of the influence of the airflow on the filament was established. Through considering the moving filament and all the force factors, the dynamic model of the multi-body coupling system of the filament winding and the corresponding nonlinear dynamic equation were established, and the dynamic equations were solved using MATLAB. An example of the high-speed winding system was simulated and further analyzed, and the simulated trajectory of the moving filament was highly consistent with the experimental image record.

Yarn tension during ring spinning is influenced by the shape of the balloon. With the high speed of ring spinning, the existing balloon control technology will have problems with yarn breakage caused by increased tension and yarn wear during the spinning process. Therefore, this article takes the naturally formed multi-balloon as the research object and analyzes the force of the yarn in the balloon section to establish the mathematical model of the steady-state balloon section. Then we selected the appropriate parameters to compare the balloon trajectory graph obtained by simulation and experiment, to verify the validity of the mathematical model. Finally, the relationship between the yarn tension and the number of balloon segments under different balloon heights and spindle speeds, and the conditions for the stable shape of multi-balloon are discussed. It was found that under different balloon shapes, increasing the number of balloon segments can effectively reduce the yarn tension at the yarn guide, and the stable conditions of balloons with different numbers of segments under certain process conditions. The findings are of great significance to the subsequent research on the technological parameters of low-tension spinning and the structural design of ring-spinning machines.

Sliver quality in the spinning process can be enhanced significantly by using an autoleveling control system (ACS) of the drawing frame, which improves the uniformity of sliver linear density. Most existing research on closed-loop control systems focuses on theoretical analysis, and fixed-time open-loop control systems cannot accurately detect and control slivers. This paper develops and experimentally evaluates an open-loop fixed-length control system for the drawing frame to enhance the stability of the sliver thickness detection and improve the sliver quality, based on the fundamental autoleveling control equation. The proposed ACS adopts a digital signal processor (DSP) as its core, integrating key modules including a touchscreen, speed sensor, servo drive, sliver thickness sensor, and analog-to-digital converter. The autoleveling, communication, and online monitoring algorithms for sliver quality have all been implemented successfully. The proposed ACS is subsequently evaluated on an experimental platform to assess its performance. The proposed ACS reduces the CV _5m of the sliver by 93.10% compared with the unleveled sliver, improving sliver quality significantly. Compared with the existing advanced ACS, the proposed ACS reduces the root mean square error and average CV of sliver weight by 11.75% and 5.59%, respectively. This work provides theoretical and technical support for advancing domestic high-speed drawing frame development.