
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.

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 single-constant Kubelka–Munk (KM) model. The first step employs spectral similarity 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 CIE Lab values, with color differences (ΔE) calculated against measured values. The two-step optimized samples achieved the lowest average ΔE value of 3.08 compared to 7.38 for all samples and 4.59 for one-step optimized samples, demonstrating superior accuracy. By reducing the required samples from 19 to 2 per ink, the method significantly enhances efficiency without compromising precision. These findings highlight the importance of spectral consistency and K/S linearity for reliable color matching and offer a practical solution for industrial applications such as packaging and branding.

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 control strategy based on immune genetic algorithm-optimized active 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 (R2) 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.

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 in the CIE L∗a∗b∗ space to construct a 3D color gamut structure, enabling the identification of feasible ink combinations through tetrahedral inclusion analysis. Subsequently, an inverse color matching model 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 the predicted and practical color differences (ΔE) remained below 3. This approach not only improves the reuse rate of the remaining spot color inks but also offers a systematic and scalable solution for resource-efficient color management in industrial printing.

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.

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 terminal-deployable 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 improved object detection algorithm, YOLOv8-PD (Packaging Damage). 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 Wise-IoU loss function to refine bounding box regression accuracy; 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 real-time performance in industrial application scenarios.

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.

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⋅g−1 mass-specific energy absorption and 5.48 J⋅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.