Shanhui Liu

Stable tension is critical for the coating quality of lithium-ion battery electrodes. As the origin of tension control, the unwinding system’s control accuracy governs the stability of downstream processes and the final yield. To achieve the required precision, we propose a control strategy that combines reinforcement learning and fuzzy PID. We first derived a nonlinear time-varying dynamic model of the unwinding tension system based on the unwinding mechanism. Leveraging this model, we then designed a reinforcement-learning-based fuzzy PID controller. Finally, we validated the performance of the proposed control strategy through simulations and experiments. Simulations and experiments confirm that, under varying coil radius, the reinforcement-learning-based fuzzy PID controller outperforms both the conventional PID and fuzzy PID controllers in robustness, effectively accommodating the effects of time-varying tension system parameters. Moreover, this method substantially improves the dynamic performance of the unwinding system, with pronounced overshoot suppression, and demonstrates superior robustness and disturbance rejection capabilities.

Conventional pneumatic bending actuators cannot perform the in-grasp twisting required to detach mature Agaricus bisporus from the cultivation substrate. Manual harvesting was characterized by measuring fingertip contact forces, fruiting-body rotation, and motion timing using flexible-film force sensors and an inertial measurement unit. The resulting grasp–twist–lift characteristics informed the design of a three-finger segmented variable-angle symmetric oblique-chamber (SVA-SOC) pneu-net soft gripper. Each SVA-SOC actuator integrates upper parallel chambers and lower symmetric oblique chambers that are synchronously pressurized to generate inward bending for grasping; after grasp establishment, differential pressure between the lower oblique chamber groups induces torsional deformation while the grasping pressure is maintained. A reduced-order model based on the Yeoh constitutive model and segmented constant-curvature assumption was validated against finite-element simulations and experiments, yielding root mean square error (RMSE) values of 12.82° and 3.36° for bending and twisting angles, respectively. At 85 kPa, a single actuator generated bending and tangential contact forces of 1.41 and 1.31 N, respectively. The three-finger gripper achieved a maximum target rotation of 44.59°, an initial grasping force of 5.38 N, and a pull-off force of 18.59 N. In 50 harvesting trials, grasping, harvesting, and undamaged harvesting rates were 100%, 94% and 86%, respectively, indicating feasibility for low-damage harvesting of mature Agaricus bisporus.

To address the collaborative optimization problem caused by the strong coupling between 2D sheet metal nesting and flexible shop floor scheduling in the sheet metal manufacturing process for color-separation ovens of satellite-type flexographic printing presses, this paper proposes a collaborative optimization method for nesting and scheduling based on a dual-flow graph attention network and proximal policy optimization. This problem involves inherent structural and resource symmetry in manufacturing operations and is further complicated by process precedence constraints, multi-resource competition, human–machine collaboration, assembly dependencies, and the dynamic coupling between nesting decisions and downstream production takt. Based on the composition of oven components and actual production methods, a collaborative optimization model integrating nesting and scheduling was constructed; a discrete-event simulation-driven joint scheduling environment was established to uniformly model the nesting, cutting, flexible machining, and assembly processes. On this basis, the collaborative nesting–scheduling process was formalized as a Markov decision process, clearly defining the state space, action space, reward function, and state transition mechanism. To enhance the state representation capabilities of the reinforcement learning agent in a high-dimensional discrete action space and under strongly constrained dynamic scheduling scenarios, this paper embeds a dual-stream graph attention network into the PPO framework to develop the DS-GAT-PPO algorithm. This algorithm simultaneously captures spatial nesting relationships among parts as well as temporal dynamic features such as equipment load, worker fatigue, and production takt time, thereby generating feasible and efficient joint scheduling plans. Experimental results show that the proposed method can reduce completion time by approximately 10.9–33.8% while maintaining a high sheet utilization rate; in a comparison of reinforcement learning algorithms, the proposed method achieved better completion times in most test cases and reduced the number of convergence steps by approximately 6.67–66.67%, validating its effectiveness in improving solution quality, convergence efficiency, and scheduling stability. These findings provide a foundation for further research and practical applications of collaborative nesting–scheduling optimization in dynamic manufacturing environments.

To address the low production efficiency and insufficient cross-enterprise collaboration in wallboard outsourcing for coating equipment manufacturing, this study proposes a wallboard manufacturing service composition optimization method based on an improved NSGA-III algorithm. First, the service composition optimization problem in wallboard manufacturing is analyzed, and a mathematical model for wallboard outsourcing service composition optimization is established. Next, an improved NSGA-III method integrating Latin hypercube sampling, greedy local search, and Lévy flight-based global search strategies is proposed, alongside a similarity measurement method for the objective-space structure based on statistical features and distribution differences, which is used to select benchmark functions that closely match the characteristics of actual business data. Finally, comparative experiments using benchmark functions and real-world business data are conducted to verify the effectiveness and superiority of the proposed method in solving service composition optimization problems. The experimental results demonstrate that the proposed method achieves excellent performance in both solution quality and computational efficiency, significantly improving the utilization of wallboard outsourcing manufacturing resources across enterprises and facilitating efficient business process flows.

To address the issues of uneven grayscale distribution, weak defect features, and small target scales on the coating surface of LCD polarizers during manufacturing, an improved YOLOv10-N-based method is proposed for surface defect detection. First, a polarizer coating defect dataset is constructed based on the LCD polarizer coating process and the characteristics of coating defects. Adaptive median filtering is then employed for image denoising, while a particle-swarm-optimization-based improved histogram equalization method is adopted for image enhancement. Next, the Scale-aware Pyramid Pooling (SCPP) module is introduced into the C2f module of the backbone network to construct the C2f_SCPP feature extraction module, thereby improving the model’s ability to detect coating defects with different morphologies through multi-scale semantic feature fusion. In addition, rotation-equivariant convolution PreCM is incorporated into the SPPF module of the backbone network to build the SPPF_PreCM module, which effectively suppresses feature redundancy and scale conflicts while strengthening the representation of tiny defects. Finally, while retaining the original Distribution Focal Loss (DFL) branch of YOLOv10, WIoU is used to replace CIoU as the IoU loss term in bounding box regression, thereby improving localization accuracy and accelerating model convergence during training. Experimental results show that, compared with YOLOv10-N, the proposed method improves mAP@0.5 and mAP@0.5:0.95 by 1.8 and 2.8 percentage points, respectively, demonstrating its effectiveness for polarizer coating defect detection. However, its generalization capability under diverse production environments, varying illumination conditions, and complex noise scenarios still requires further investigation.

Register accuracy is an important indicator for measuring the quality of multicolor gravure printing. However, the accuracy of the photoelectric sensor register error detection does not meet the register control requirements for light-colored printed electronic products. To address this issue, the authors propose a high-precision register error detection method based on machine vision for circular matrix color marks. First, image denoising is carried out based on the characteristics of circular matrix color marks. Then a combined coarse–fine detection method is employed, and an improved Canny operator is used to detect pixel-level edge points in color marks. The eight-neighborhood edge tracking is performed by connecting the edges through morphological dilation processing. Then, based on pixel-level edges, the Zernike moment method is used to obtain subpixel-level edge points. Finally, the least squares method is utilized to fit the color mark center based on subpixel edges, and the register errors in both horizontal and vertical directions are obtained based on the distances between each color mark and the reference color mark center. Experimental results show that the proposed register error detection method based on matrix color marks achieves an average register error of 0.026 pixels. This method not only enhances the accuracy of register error detection but also demonstrates stability against noise and varying lighting conditions, meeting the demand for register error detection in complex gravure printing equipment.

For printing equipment manufacturing enterprises, the selection of an enterprise resource planning (ERP) system involves multiple factors, including function matching degree, technical performance, cost-effectiveness, and others. Therefore, ERP system selection becomes a complex multi-criteria decision-making (MCDM) problem. In this study, a method for ERP system selection suitable for printing equipment manufacturing enterprises is proposed. First, a set of evaluation criteria covering five dimensions—functionality, technicality, implementability, economy, and reputation—is established through literature review. Second, the Fuzzy Analytical Hierarchy Process (FAHP) is used to determine the weights of the evaluation criteria. Then, the gray relational analysis (GRA)-enhanced Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is applied to rank the alternatives. Finally, a dual verification mechanism combining weight perturbation and parameter sensitivity is designed to test the robustness of the ranking results. Experimental results show that Alternative 2 is the best ERP system. Furthermore, key evaluation criteria, such as core functionality coverage and purchase price, are considered to be relatively important. The study demonstrates that this model, through methodological innovation and verification mechanism design, effectively integrates subjective and objective information. It provides a solution that is both scientifically sound and practically applicable for ERP system selection in printing equipment manufacturing enterprises.

To address the challenges of small fastener targets, complex backgrounds, and the low efficiency of traditional manual inspection in coating workshop scenarios, this paper proposes an improved Cascade R-CNN-based fastener detection method. A VOC-format dataset was constructed covering three target categories—Marking-painted fastener, Fastener, and Fallen off—which represents typical inspection scenarios of coating equipment under diverse operating conditions and enhances the adaptability of the model. Within the Cascade R-CNN framework, three improvements were introduced: the Convolutional Block Attention Module (CBAM) was integrated into the ResNet-101 backbone to enhance feature representation of small objects; anchor scales were reduced to better align with the actual size distribution of fasteners; and Soft-NMS was adopted in place of conventional NMS to effectively reduce missed detections in overlapping regions. Experimental results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 96.60% on the self-constructed dataset, with both Precision and Recall exceeding 95%, significantly outperforming Faster R-CNN and the original Cascade R-CNN. The method enables accurate detection and missing-state recognition of fasteners in complex backgrounds and small-object scenarios, providing reliable technical support for the automation and intelligence of printing equipment inspection.

In response to the low efficiency of collaborative processing of sewing machine cases at the part level in network collaborative manufacturing, this paper proposes a sewing machine cases manufacturing service composition optimization method based on an improved multi-objective artificial hummingbird algorithm. The structure and production process of sewing machine cases are analyzed; a framework for service composition optimization in the sewing machine cases manufacturing service platform is established; the required manufacturing resource service composition is determined; and a dual-objective service composition optimization mathematical model that considers Quality of Service (QoS) indicators and flexibility indicators is constructed. Opposition-based learning strategies, roulette wheel selection strategies, and improved differential evolution strategies are embedded in the multi-objective artificial hummingbird algorithm, and the improved artificial hummingbird algorithm (ORAHA_DE) is used to solve the sewing machine cases manufacturing service composition optimization model. The experimental results show the effectiveness and superiority of this composition optimization method in solving the sewing machine cases manufacturing composition optimization problem while avoiding entrapment in a local optimum during the solution process, thereby achieving the composition optimization of sewing machine cases collaborative manufacturing services.

Wallboard outsourcing is a critical task in cloud-based manufacturing, where demand enterprises seek suitable suppliers for machining services through online platforms. However, the recommendation process faces significant challenges, including sparse rating data, unstructured textual descriptions from suppliers, and complex, non-linear user preferences. To address these issues, this paper proposes AttVAE-PMF, a novel recommendation method based on dual-channel neural networks and probabilistic matrix factorization. Specifically, an attention-enhanced long short-term memory (LSTM) is employed to extract semantic features from free-text supplier descriptions, while a variational autoencoder (VAE) is used to model latent preferences from sparse demand-side ratings. These two types of latent representations are then fused via probabilistic matrix factorization (PMF) to complete the rating matrix and infer enterprise preferences. Experiments conducted on both the wallboard dataset and the MovieLens-100K dataset demonstrate that AttVAE-PMF outperforms baseline methods—including PMF, DLCRS, and SSAERec—in terms of convergence speed and robustness to data sparsity, validating its effectiveness in handling sparse and heterogeneous information in wallboard outsourcing recommendation scenarios.