Twofold Variation Propagation Modeling and Analysis for Roll-to-Roll Manufacturing Systems

Abstract

Roll-to-roll (R2R) manufacturing has emerged as one of the most cost-effective manufacturing techniques for high-volume production of flexible printing electronics, graphene films, thin-film batteries, and solar cells, as opposed to low-volume batch production. However, in practice, real-time monitoring of the R2R process and in situ measurement of fabricated materials during production are challenging because of the high speed and dynamic variation of the web. In addition, due to the lack of cost-effective sensors and in-line metrology systems with a large range and high resolution, effective quality control and defect diagnosis are difficult to achieve. To address these challenges, this paper aims to develop analytical models that can serve as virtual sensing and metrology tools to quantify process state variation and estimate product quality in R2R processes with limited accessibility of in situ sensor. First, the quality variation propagation mechanism in R2R processes is investigated. Second, a hybrid multistage modeling method is proposed to characterize the twofold variation propagation—product-centric and process-centric variations, and its relationship with product quality in R2R processes. Note to Practitioner s—The novel modeling method developed in this paper employs both physics-based analysis (e.g., web handling system dynamics) and regression methods [e.g., censored regression and linear/logistic regression (LG)] using multisensor signals. The estimation results from the model can serve as virtual sensing and virtual metrology tools to increase the system visibility and be applied for process monitoring and error detection in real time. A print registration unit of elastic film in a roll-to-roll system is employed to demonstrate and validate the proposed modeling method in terms of the accuracy of state estimates and quality prediction. Based on the comparison results, the hybrid modeling method shows higher accuracy in state estimation and quality prediction than do the models with data-driven methods only.