A service matching method for the cloud manufacturing of paper gravure printing machine doctor blades based on improved K-means clustering is proposed. This approach is aimed at the problem of poor accuracy of both service clustering and supply and demand matching in cloud-based doctor blade manufacturing for paper gravure printing machines. First, based on the improved K-means clustering algorithm, doctor blade cloud manufacturing services are clustered to form a set of services with high similarity within groups and low similarity between groups. Second, the extension theory is used to establish a correlation function to select the doctor blade cloud manufacturing service set with the highest correlation degree with processing demand to form a candidate service set. Finally, the analytic hierarchy process and grey relational analysis are used to select the best cloud manufacturing service based on the subjective demand preference of users to achieve the matching purpose. The experimental results demonstrate that the accuracy of this method in solving the manufacturing service problem of gravure printing machine doctor blades can exceed 90% in approximately 30 min.
With the rapid development of logistics automation and the digital transformation of the home appliance industry, damage to heavy appliance packaging cartons during storage and transportation has become increasingly frequent, adversely affecting product image and delivery quality. Common surface defects such as scratches, holes, and wet stains can easily lead to disputes and economic losses. Therefore, a highly efficient, automated, and terminaldeployable intelligent detection algorithm is urgently required to achieve accurate identification and recording of packaging damages. To address the limitations of YOLOv8n in carton surface damage detection-specifically, its constrained accuracy and the frequent occurrence of missed and false detections-the authors propose an age). The proposed model enhances detection performance while maintaining high efficiency through three key optimizations: introducing a large-kernel receptive field attention module (SPPF_LSKA) in the backbone to improve global context modeling; adopting the and incorporating a multi-path coordinate attention (MPCA) mechanism to strengthen key region perception. Experiments conducted on a self-constructed dataset containing three categories-scratches, holes, and wet stains-demonstrate that YOLOv8-PD achieves improvements of 1.4%, 0.9%, and 1.4% in mAP@0.5, Precision, and Recall, respectively, compared with the baseline YOLOv8n. These results validate the proposed method's superior accuracy and realtime performance in industrial application scenarios.
A hydrogel composed of cellulose nanofibrils (CNFs) exhibits a unique three-dimensional cross-linking network structure, high porosity, and a large specific surface area in addition to other characteristics. This makes it a promising biomass-based adsorbent for the effective adsorption of heavy metal ions. Oxidized cellulose nanofibrils (OCNFs) were synthesized through the oxidation of CNFs using sodium periodate. Complex hydrogels were fabricated by compounding OCNFs with cationic guar gum (CGG). The OCNF/CGG hydrogels were employed for the adsorption of the heavy metal ion Pb2+. The Pb2+ adsorbed OCNF/CGG hydrogels were subsequently transformed into porous carbon aerogels through freeze-drying and carbonization processes. The results indicated a strong electrostatic interaction between CGG and OCNF. Furthermore, the optimal pH range for the solution system was determined to be 3.5-6. With an increase in initial concentration, the maximum adsorption capacity of the OCNF/CGG hydrogel for Pb2+ was significantly improved. When the initial concentration of Pb2+ was elevated to 480 mg L-1, a substantial increase in the maximum adsorption capacity of the hydrogel for Pb2+ ions was observed. This demonstrates that the OCNF/CGG hydrogel exhibits excellent adsorption efficiency for Pb2+ ions. The adsorption process approached saturation at 60 min. The maximum adsorption capacity was found to be 908.4 mg g-1. Following freeze-drying and carbonization treatments, the Pb2+-adsorbed hydrogel was converted into a lead-doped carbon aerogel. The resulting carbon aerogel displayed favorable and stable electrochemical properties. The presence of lead significantly enhanced the electrochemical characteristics of the carbon aerogel, leading to improvements in both conductivity and pseudocapacitance. This study could broaden the application of hydrogels in treating water contaminated by heavy metal ions and provide new approaches for converting pollutants into printed electronics materials.
This paper focuses on the flexible packaging line for large length-to-diameter ratio heavy cylindrical products. It systematically analyzes the key bottleneck issues and proposes an optimized production line solution based on modular design. Based on the modular concept, the packaging process is decomposed into the loading module, inner packing processing module, packing module, sealing module, and stacking module. The functions of each module are clarified, and the connection sequence is optimized to achieve the compactness of the process flow and the efficient coordination of equipment. For the posture control, quality positioning, and safety protection requirements of single-root high-density materials during the packaging process, an "L-shaped + straight-line" layout scheme and dynamic scheduling strategy are proposed. A discrete-event model is constructed using the FlexSim simulation software. Through parameter calibration based on actual production data from 2022-2023, the effectiveness of the optimization scheme in improving equipment utilization and reducing buffer waiting time is verified, providing technical reference for the design of intelligent packaging lines for similar high-value and fragile materials. The proposed method and simulation framework are applicable to the optimization of production lines for imaging equipment and related materials.
This study develops a lightweight bionic energy-absorbing structure (loofah sponge bionic structure [LSBS]), inspired by the highly porous loofah sponge, suitable for additive manufacturing. The loofah sponge is partitioned into four functional regions and characterized by regional compression tests, based on which eleven main characteristic structures are extracted and integrated into a parametric 3D model. Finite element simulations in ANSYS Workbench 15.0, combined with structural specific strength and structural specific stiffness indices, are used to evaluate lightweight performance under static and compressive loading. The LSBS specimens are fabricated by DLP (UV-curable resin [UVCR]) and FDM (PLA) and tested in quasi-static compression. The PLA-LSBS exhibits markedly higher energy absorption than UVCR-LSBS, attaining 4.39 J & centerdot;g-1mass-specific energy absorption and 5.48 J & centerdot;cm-3 volume-specific energy absorption, with a 135.10% higher peak load and only 0.83 g extra mass. These results verify the effectiveness of the extracted loofah-inspired features and demonstrate a feasible pathway for designing lightweight, high-energy-absorbing structures via 3D printing.
This research develops a novel manufacturing approach for millimeter-wave feedhorns, utilizing additive manufacturing combined with electroless metallization. A corrugated horn antenna operating across the K-band spectrum was engineered and produced using polymer-based 3D printing, followed by internal surface silver deposition. This methodology achieved complex internal geometry consolidation in a single process, yielding a structure with merely 17% the mass of comparable steel counterparts. Electrical characterization demonstrated reflection coefficients predominantly exceeding-20 dB magnitude across 18.0-27.0 GHz alongside the attained gain values surpassing 14 dB within 18.0-24.0 GHz. The measured far-field radiation characteristics showed excellent correlation with computational electromagnetic models. The demonstrated technique presents transformative potential for the mass-efficient production of high-frequency components in next-generation small satellite constellations and compact radar platforms.
Color constancy algorithms play a crucial role in computer vision, and their performance needs to be accurately evaluated. However, recent years have seen scant systematic research on the correlation between human visual perception and objective distance measures for quantifying the performance of such algorithms. In this study, therefore, the authors systematically assessed the performance of 34 existing distance measures by psychophysical studies. Six classical color constancy algorithms and two recent algorithms were adopted to process over 110 images within 4 categories (Indoor, Human, Street, and Nature), and the influence of color space on the performance of distance measures was explored. Visual assessments obtained from 48 subjects were used to analyze the consistency between predictions of distance measures and human visual responses. It was found that the two most commonly used distance measures, the recovery angle error and the reproduction angle error in normalized RGB color space, exhibited high correlation with visual judgments, producing correlation coefficients of approximately measures across different color spaces were also observed. Dissistency with human perception, yielding correlation coefficients of approximately 0.88. In addition, it was found that specific scenes also influenced the accuracy of distance measures. Our study highlights the importance of selecting appropriate color spaces for evaluating color constancy algorithms and offers more insights for the optimization of distance measures in the future.
The widespread use of spot color inks in packaging printing has led to the accumulation of substantial remaining spot color inks, resulting in resource waste and environmental concerns. This study proposes a novel utilization method for remaining spot color inks that integrates Delaunay triangulation with the single-constant Kubelka-Munk (K-M) theory to achieve precise and efficient reuse. A color matching database was first established based on the spectral reflectance and colorimetric properties of base and remaining spot color inks. The Delaunay triangulation algorithm was applied enabling the identification of feasible ink combinations through tetramodel based on the single-constant K-M theory was developed to optimize ink formulations for given target colors. Experimental validation using multiple remaining spot color targets demonstrated that resource-efficient color management in industrial printing.
The extension of published and projected (IEC/ISO) international standards for inkjet printing that were specifically developed for printed and flexible electronics equipment is considered for their more general application to inkjet printing in industry and in research. A comparison of the inkjet printing equipment requirements between printed electronics (PE) and more general applications is made to provide some guidance to manufacturers, designers, and engineers potentially involved with industrial inkjet printing equipment standards compliance as integrators and/or end users. Reviews for applications and improved techniques of inkjet printing are cited, and an update is provided for international standards for PE inkjet equipment published since earlier reviews.
Accurate spot color matching is critical to printing applications, yet constructing an efficient ink base database remains a challenge due to the labor-intensive preparation of ink ladder samples. This study proposes a two-step optimization method to enhance the efficiency and accuracy of spot color prediction using the singlesimilarity screening via the Goodness-of-Fit Coefficient to select samples with consistent spectral behavior. The second step optimizes for K/S linearity, identifying concentrations (35% and 40%) that best align with the KM model's linearity assumption. Five target spot colors, created by mixing yellow, red, and blue base inks, were used to evaluate the method. The K/S values derived from three sample sets-all ladder samples, one-step optimized samples, and two-step optimized samples-were used to predict spectral reflectance and for one-step optimized samples, demonstrating superior accuracy. By reducing the required samples from 19 to 2 per ink, the method for industrial applications such as packaging and branding.
This research develops mathematical models of C, M, Y, K and CIE L*a*b* chromaticity values to minimize the errors between the predicted ratio and the actual ratio of process colors (cyan, magenta, yellow, black) on holographic paper by measuring and analyzing the data after mixing the basic inks according to proportion. It obtains the least squares estimation points by performing the multiple nonlinear regression analysis method in MATLAB. Moreover, by replacing the values of CIE L*a*b* chromaticity, the regression significance of the mathematical model is verified and the corresponding basic ink mass ratio is obtained. The results reveal that the RMSE, MAE, and R2 values of spectral prediction models, which are established by multiple nonlinear regression analysis, show small errors between the predicted and the actual outcomes.
Maintaining stable tension is essential for ensuring the slitting quality of lithium battery separators. In particular, the precision of tension control in the unwinding system is critical to both product quality and process stability. This study proposes an optimized disturbance rejection control (IGA-ADRC) to address the tension regulation challenges in the unwinding system of lithium battery separator slitting machines. First, based on the operating mechanism of the unwinding system, a dynamic model was developed that incorporates time-varying parameters, nonlinear behavior, and strong coupling characteristics. Second, an active disturbance rejection controller was designed and optimized using an immune genetic algorithm, based on the tension dynamics of the unwinding system. Finally, the effectiveness of the proposed control strategy was validated through both simulations and experimental results. Simulation and experimental results demonstrate that the IGA-ADRC reduces tension deviation by 59.1% compared to proportional-integral- derivative control (from +/- 1.1 N to +/- 0.45 N) and by 25% compared to conventional ADRC (from +/- 0.6 N to +/- 0.45 N) while improving response speed and overshoot suppression. The proposed IGA-ADRC method achieves superior performance in terms of tension regulation accuracy, system robustness, and disturbance rejection capabilities.
Quantitative relationships among solid density, dot gain, and relative contrast in offset printing were examined using quadratic regression modeling. By designing a scientific experimental program, 20 printed samples with fields of 50% and 75% dots were collected and accurately measured using a spectrophotometer. A quadratic regression model for four-color ink was developed using response surface analysis, emphasizing the nonlinear effects and interactions among parameters. Analysis showed all regression models achieved high significance (p < 0.001), with coefficients of determination (R-2) exceeding 0.85, indicating excellent fit and predictive power. The optimal solid density parameters for the four-color inks were identified through extreme value analysis: yellow, 0.990; magenta, 1.330; cyan, 1.420; and black, 1.750, where the relative contrast peaks. These findings provide a scientific basis for optimizing offset solid density and serve as valuable guidance to improve the quality and consistency of printed materials.
Accurate traffic flow forecasting plays a crucial role in alleviating road congestion and optimizing traffic management. Although numerous effective models have been proposed in existing research to predict future traffic flow, most models exhibit certain limitations in modeling spatiotemporal dependencies, especially in capturing multiscale spatiotemporal relationships. To address this, we propose a and Temporal Attention (STAIL-TA) for traffic flow prediction, which is designed for dynamic and interactive adaptive modeling of spatiotemporal features in traffic flow data. Specifically, we first design a feature augmentation layer that enhances the interaction of timebased features. Next, we introduce an interactive dynamic graph convolutional network, which uses an interactive learning strategy to simultaneously capture spatiotemporal characteristics of traffic data. Additionally, a new dynamic graph generation method is employed to design a dynamic graph convolutional block, which is capable of capturing the spatial correlations that change dynamically within the traffic network. Finally, we construct a novel temporal attention mechanism that effectively leverages local contextual information and is specifically designed for transforming numerical sequence representations. This enables the prediction model to capture the dynamic temporal dependencies of traffic flow better, thus facilitating long-term forecasting. The experimental results show that the STAILTA model improves the mean absolute error and root mean squared error on the PEMS-BAY dataset by 7.75%, 3.68% and 5.59%, 2.72% in the 15-minute and 30-minute predictions, respectively, when compared to the existing optimal baseline method, MRA-BGCN.
