Meniscus Morphology-Based Prediction of Backup Roll Eccentricity for Stable Slot-Die Coating on Polymer Films

In roll-to-roll slot-die coating systems, backup roll eccentricity induces periodic fluctuations in key process parameters, leading to coating defects. Therefore, accurate prediction of backup roll eccentricity during operation is essential for maintaining stable coating quality. However, conventional methods based on web tension signals have limitations in clearly distinguishing and quantitatively evaluating subtle eccentricities. To address this issue, this study proposes a data-driven framework for predicting backup roll eccentricity using meniscus image information. Representative morphological features are defined to describe the global shape, local shape, and curvature characteristics of the meniscus. Since these features are sensitive to eccentricity-induced process variations, they can serve as effective indicators for eccentricity prediction. The defined features are used to train regression models, and the model with the highest predictive accuracy is selected. Experimental results confirm that the proposed meniscus-based approach significantly outperforms conventional tension-based methods. The proposed method achieves an average Root Mean Square Error (RMSE) of approximately 0.63 μm, an average Normalized Root Mean Square Error (NRMSE) of approximately 0.34, and a coefficient of determination (R²) greater than 0.93 across all test cases. These results demonstrate the feasibility of robust process monitoring using a simple vision sensor configuration.