With the diversification of spinning order varieties and process parameters, the conventional method of determining production plans through trial spinning no longer satisfies the processing requirements of enterprises. Currently, deficiencies exist in predicting spinning quality relying on manual experience and traditional methods. The back propagation (BP) neural network within the realm of deep learning theory faces challenges in handling time series data, while the long short-term memory (LSTM) neural network, despite its intricate mechanism, exhibits an overall lower predictive accuracy. Consequently, a more precise predictive methodology is imperative to assist production personnel in efficiently ascertaining cotton-blending schemes and processing parameters, thereby elevating the production efficiency of the enterprise. In response to this challenge, we propose an attention-GRU-based cotton yarn quality prediction model. By employing the attention mechanism, the model is directed towards the input features most significantly impacting yarn quality. Real-world performance indicators of raw cotton and process parameters are utilized to predict yarn tensile strength. A comparative analysis is conducted against prediction results of BP, LSTM, and gated recurrent unit (GRU) neural networks that do not incorporate the attention mechanism. The outcomes reveal that the GRU model enhanced with the attention mechanism demonstrates reductions of 56.3%, 38.5%, and 36.4% in root mean square error (RMSE), along with 0.367%, 0.158%, and 0.190% in mean absolute percentage error (MAPE), respectively. The model attains a coefficient of determination R-squared of 0.954, indicating a high degree of fitness. This study underscores the potential of the proposed attention-GRU model in refining cotton yarn quality prediction and its consequential implications for process optimization and enhanced production efficiency within textile enterprises.

Production scheduling in a knitting workshop is an important method to improve production efficiency, reduce costs and improve service. In order to achieve a reasonable allocation of parallel machines as well as cooperation between different machines within the workshop, thereby ensuring the optimal scheduling of production plans, this paper proposes a scheduling method using an improved genetic algorithm (IGA) based on tabu search. Firstly, the production scheduling model of a knitting workshop is established. Secondly, an IGA based on the minimum processing time rule, the priority idle machine rule and the production order ranking code is used to optimize the solution. Finally, an experiment platform for knitting workshop production is built to verify the proposed scheduling method. The experimental results show that the proposed IGA based on tabu search performs well in terms of preconvergence speed, global search capability and local search capability. The IGA converges faster than the traditional genetic algorithm by about 25%, reduces the redundancy time of scheduling, meets the production requirements of the knitting intelligent workshop and has a good reference value for promoting the intelligent development of knitting production.

The constitutive equation of mechanics, namely the stress–strain model of the material, is used to describe the mathematical expression of the mechanical properties of the material (stress–strain–intensity–time relationship). In the cutting simulation of metals, the material constitutive model needs to be established. To study the cutting mechanism of gray cast iron using the finite element method (FEM), a series of split Hopkinson press bar (SHPB) tests are carried out to achieve the stress–strain curves of gray cast iron under different strain rates and temperatures. In the cutting simulation of gray cast iron, the Johnson–Cook (JC) constitutive equation is used to reflect the mechanical behavior during the cutting process. The calculation of JC constants is analyzed deeply and obtained from the SHPB test data. One orthogonal finite element model is developed with the JC material model to reveal the cutting mechanism of gray cast iron. The chip formation, stress distribution, temperature distribution, and cutting force are investigated through the cutting simulation. At the same time, cutting experiments of gray cast iron are carried out for validating the simulation results. The results of the cutting simulation and experiments show a good agreement.

The output voltage of inverters is influenced by nonlinear factors such as dead time and voltage drops, injecting low-order harmonics. This results in fifth and seventh harmonic distortions in the stator current, causing periodic torque ripples and significantly affecting the control precision of Permanent Magnet Synchronous Motors (PMSMs). To address this issue, this paper proposes a control strategy named quasi-proportional-resonant sliding mode control (QPR-SMC). Initially, sliding mode control is employed as the current controller to enhance disturbance rejection capability and provide a rapid dynamic response. Subsequently, a quasi-proportional-resonant controller is introduced to extract the sixth harmonic component from the current, which is then used as a compensation term for the sliding mode control surface. Finally, the current tracking error and the compensation term are combined as inputs to the sliding mode control law, forming a current error-proportional resonant-sliding mode control surface. This approach enhances the harmonic suppression capability of the system. The results demonstrate that the proposed method effectively reduces the fifth and seventh harmonic components in the three-phase current and mitigates motor jitter by suppressing the sixth harmonic in the d–q coordinate system.

Online detection of yarn roll’s margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

The automatic detection of the thread roll’s margin is one of the kernel problems in the textile field. As the traditional detection method based on the thread’s tension has the disadvantages of high cost and low reliability, this paper proposes a technology that installs a camera on a mobile robot and uses computer vision to detect the thread roll‘s margin. Before starting, we define a thread roll‘s margin as follows: The difference between the thread roll‘s radius and the bobbin’s radius. Firstly, we capture images of the thread roll‘s end surface. Secondly, we obtain the bobbin’s image coordinates by calculating the image’s convolutions with a Circle Gradient Operator. Thirdly, we fit the thread roll and bobbin’s contours into ellipses, and then delete false detections according to the bobbin’s image coordinates. Finally, we restore every sub-image of the thread roll by a perspective transformation method, and establish the conversion relationship between the actual size and pixel size. The difference value of the two concentric circles’ radii is the thread roll’s margin. However, there are false detections and these errors may be more than 19.4 mm when the margin is small. In order to improve the precision and delete false detections, we use deep learning to detect thread roll and bobbin’s radii and then can calculate the thread roll’s margin. After that, we fuse the two results. However, the deep learning method also has some false detections. As such, in order to eliminate the false detections completely, we estimate the thread roll‘s margin according to thread consumption speed. Lastly, we use a Kalman Filter to fuse the measured value and estimated value; the average error is less than 5.7 mm.