In order to solve the problem of low response frequency and poor consistency of conventional yarn grippers in weft accumulators, in this study, a piezoelectric yarn gripper is used instead of conventional yarn grippers and the motion characteristics of its actuator are studied. This gripper uses a bimorph piezoelectric bending actuator with a low-cost, well integrated self-sensing method based on charge measurement. The modeling of the piezoelectric micromanipulator is based on the piezoelectric and Euler–Bernoulli beam equations. The static and dynamic characteristics of the piezoelectric actuator as well as the self-sensing capability were experimentally tested. The experimental results show that the maximum output displacement at the end of the piezoelectric actuator is 834 μm, and the maximum output force is 388 μN at 150 V driving voltage. The stability and consistency of its response are also very good, with a response speed of 24 ms. The self-sensing test of the output force also proved the feasibility of the self-sensing method used, with an error of 0.74%. The piezoelectric yarn gripper studied in this paper is promising for practical clamping applications.

Solid ink density is an important control parameter in the manufacturing process of offset prints—the size of which has a significant impact on the color performance of the prints—in which the determination of the optimal solid ink density is critical for the pre-press phase of industrial production. Compared with the traditional method of determining the optimal solid ink density, the current printing equipment used to determine the optimal solid ink density will be faster at improving industrial production efficiency and product quality. To improve the efficiency of determining the optimal solid ink density, the Random Forest algorithm was applied for the first time to the prediction task of solid ink density in offset printing. An optimal solid ink density prediction model for lithographic offset printing is established, and the L*a*b* colorimetric values of CMY three-color prints are used as inputs for training through hyperparameter optimization of the model. The experimental data show that the relevant evaluation metrics MAE, RMSE, MSE, and R² of the model are within the reliable range. A comparison between the proposed prediction model and several mainstream machine-learning algorithms indicates that the Random Forest model performs superiorly in both the coefficient of determination (R²) and the mean squared error (MSE). Specifically, the Random Forest model achieved an R² value of 0.969, reflecting improvements of 27.5%, 1.89%, 3.8%, and 34.02% compared to artificial neural network, gradient boosting, polynomial regression, and support vector regression models, respectively. In terms of MSE, the model reduced prediction error by 87.1%, 36.2%, 55.4%, and 89.2%, respectively, when compared with the same models. This approach has proven to provide both theoretical support and a practical pathway for enhancing the level of intelligence in pre-press process control, demonstrating significant practical application value.

The identification of the yarn roll’s margin represents a critical phase in the automated production of textiles. At present, conventional visual detection techniques are inadequate for accurately measuring, filtering out background noise, and generalizing the margin of the yarn roll. To address this issue, this study constructed a semantic segmentation dataset for the yarn roll and proposed a new method for detecting the margin of the yarn roll based on deep learning. By replacing the encoder component of the U-Net with the initial 13 convolutional layers of VGG16 and incorporating pre-trained weights, we constructed a VGG-UNet model that is well suited for yarn roll segmentation. A comparison of the results obtained on the test set revealed that the model achieved an average Intersection over Union (IoU) of 98.70%. Subsequently, the contour edge point set was obtained through the application of traditional image processing techniques, and contour fitting was performed. Finally, the actual yarn roll margin was calculated based on the relationship between pixel dimensions and actual dimensions. The experiments demonstrate that the margin of the yarn roll can be accurately measured with an error of less than 3 mm. This is particularly important in situations where the margin is narrow, as the detection accuracy remains high. This study provides significant technical support and a theoretical foundation for the automation of the textile industry.

Extracting standardized digital design patterns from real knitted fabric images is critical for textile reverse engineering and digital archiving. Unlike smooth graphics, knitted fabrics exhibit high-frequency textures from yarn loop interlacing, introducing significant grayscale variations within same-color regions. Existing algorithms struggle to distinguish these from pattern edges, causing color quantization and segmentation failures. To suppress yarn texture while preserving edges between color blocks, we propose an adaptive pattern extraction method using Bayesian-optimized bilateral filtering. The primary contribution lies in providing a domain-specific, application-focused integrated framework. Specifically, (1) a knitting-texture-aware multidimensional evaluation parameter is constructed by integrating physical-cause-based texture features (gray-level co-occurrence matrix (GLCM) contrast, homogeneity, and Laplacian variance) with perception-based edge preservation metrics (the Sobel operator and the structural similarity index (SSIM)), enabling accurate discrimination between yarn-level texture noise and pattern-level color block boundaries—a distinction that generic image quality metrics cannot make. (2) Then, this domain-specific objective function is embedded within a Bayesian optimization framework to achieve automatic, zero-shot, per-image parameter adaptation across different knitting processes, without requiring any external training data. K-means color quantization maps in continuous tones to discrete classes, generating standardized patterns meeting knitting requirements. Experiments on 316 samples covering six processes show our method outperforms standard denoising and advanced algorithms like relative total variation (RTV), achieving an average SSIM of 0.83 and PSNR of 26.92 dB, reducing processing time from 15–30 min to 21 s per image, providing efficient automation for knitted Computer-Aided Design (CAD) systems.

Patterns entered into knitting CAD have thousands or tens of thousands of different colors, which need to be merged by color-separation algorithms. However, for degraded patterns, the current color-separation algorithms cannot achieve the desired results, and the clustering quantity parameter needs to be managed manually. In this paper, we propose a fast and automatic FCM color-separation algorithm based on superpixels, which first uses the Real-ESRGAN blind super-resolution network to clarify the degraded patterns and obtain high-resolution images with clear boundaries. Then, it uses the improved MMGR-WT superpixel algorithm to pre-separate the high-resolution images and obtain superpixel images with smooth and accurate edges. Subsequently, the number of superpixel clusters is automatically calculated by the improved density peak clustering (DPC) algorithm. Finally, the superpixels are clustered using fast fuzzy c-means (FCM) based on a color histogram. The experimental results show that not only is the algorithm able to automatically determine the number of colors in the pattern and achieve the accurate color separation of degraded patterns, but it also has lower running time. The color-separation results for 30 degraded patterns show that the segmentation accuracy of the color-separation algorithm proposed in this paper reaches 95.78%.