Sensor-based Prediction of Thermally Induced Lateral Dynamics in Roll-to-roll Web Processing

In roll-to-roll (R2R) manufacturing, lateral web displacement in the cross-machine direction (CMD) affects coating uniformity, registration accuracy, and product quality. During drying, nonuniform thermal conditions cause asymmetric thermal expansion, generating geometric imperfections and complex lateral dynamics that are difficult to predict using conventional models. This study proposes a sensor-based thermomechanical framework for predicting thermally induced lateral web displacement in R2R drying processes. CMD temperature distributions measured by distributed in-situ thermocouples are incorporated into an extended lateral dynamics model to represent temperature-dependent geometric variations and their interaction with web tension. By directly integrating sensor-measured thermal fields, the framework provides a physics-based interpretation of lateral web behavior under asymmetric heating conditions. Experimental validation was performed using an industrial-scale R2R coating system equipped with temperature and edge-position sensors. Results show that the proposed model predicts lateral displacement more accurately than conventional approaches based on uniform-temperature assumptions, particularly under significant CMD temperature gradients. Statistical analysis further identifies web speed as a key factor influencing lateral displacement. The proposed framework provides a practical approach for sensor-based monitoring and predictive assessment of lateral web dynamics in R2R manufacturing, enabling evaluation of thermally induced process variability and supporting improved process control and product quality.