In fabricating functional layers, including thin-film transistors and conductive electrodes, using roll-to-roll (R2R) processing on polymer-based PET film, the instability of the slot-die coating meniscus under a high-speed web impedes functional layer formation with the desired thickness and width. The thickness profiles of the functional layers significantly impact the performance of the final products. In this study, we introduce an electrohydrodynamic (EHD)-based voltage application module to a slot-die coater to ensure the uniformity of the cross-machine direction (CMD) thickness profile within the functional layer and enable a stable, high-speed R2R process. The module can effectively control the spreadability of the meniscus by utilizing variations in the surface tension of the ink. The effectiveness of the EHD module was experimentally verified by applying a high voltage to a slot-die coater while keeping other process variables constant. As the applied voltage increases, the CMD thickness deviation reduces by 64.5%, and the production rate significantly increases (up to 300%), owing to the formation of a stable coated layer. The introduction of the EHD-based application module to the slot-die coater effectively controlled the spreadability of the meniscus, producing large-area functional layers.

Roll-to-roll (R2R) manufacturing depends on a system’s capability to deposit high-quality coatings with precise thickness, width, and uniformity. Therefore, consistent maintenance requires the immediate and accurate detection of coating defects. This study proposes a primary color selection (PCS) method to detect edge defects in R2R systems. This method addresses challenges associated with training data demands, complexity, and defect adaptability through a vision data-centric approach, ensuring precise edge coating defect detection. Using color information, high accuracy was achieved while minimizing data capacity requirements and processing time. Precise edge detection was facilitated by accurately distinguishing coated and noncoated regions by selecting the primary color channel based on color variability. The PCS method achieved superior accuracy (95.8%), outperforming the traditional weighted sum method (78.3%). This method is suitable for real-time detection in manufacturing systems and mitigates edge coating defects, thus facilitating quality control and production optimization.

Roll misalignment in roll-to-roll (R2R) processes is a critical cause of lateral displacement and tension imbalance, leading to dimensional instability and surface defects in polymer films. Conventional analyses based on beam or camber models often require complex calibration and additional sensing, which can limit their applicability in real-time production environments. This study introduces a diagnostic approach that estimates web misalignment directly from side-to-side tension differences measured in roll-to-roll (R2R) systems. The method eliminates the need for additional sensors and complex geometric calibration, simplifying system setup. The correlation between tension imbalance, lateral displacement, and the equivalent misalignment angle was experimentally established. Our approach produced accurate predictions across various process conditions, including different roll misalignments and applied tensions, and we found that reducing tension “hunting” further enhances prediction stability. This study demonstrates that the proposed tension-based approach can complement existing systems and reduce the reliance on complex external sensing for diagnostic checks of misalignment. By simplifying alignment diagnostics, the method provides a practical route to enhance process setup, reduce downtime, and improve the uniformity of polymer films in continuous manufacturing.

In light of ongoing advancements in smart manufacturing, there is a growing need for intelligent fault diagnosis methods that maintain reliability under noisy, high-variability operating conditions. Conventional feature selection strategies often struggle when data contain outliers or suboptimal feature subsets, limiting their diagnostic utility. This study introduces a density-based feature space optimization (DBFSO) framework that integrates feature selection with localized density estimation to enhance feature space separability and classifier efficiency. Using k-nearest neighbor density estimation, the method identifies and removes low-density feature vectors associated with noise or outlier behavior, thereby sharpening the feature space and improving class discriminability. Experiments using roll-to-roll (R2R) manufacturing data under mechanical disturbances demonstrate that DBFSO improves classification accuracy by up to 36–40% when suboptimal feature subsets are used and reduces training time by 60–71% due to reduced feature space volume. Even with already-optimized feature sets, DBFSO provides consistent performance gains and increased robustness against operational variability. Additional validation using a bearing fault dataset confirms that the framework generalizes across domains, yielding improved accuracy and significantly more compact, noise-resistant feature representations. These findings highlight DBFSO as an effective preprocessing strategy for intelligent fault diagnosis in intelligent manufacturing systems.