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1. A Review of Advanced Roll-to-Roll Manufacturing: System Modeling and Control

   https://doi.org/10.1115/1.4067053  

   Martin, Christopher; Zhao, Qishen; Patel, Anjali; Velasquez, Enrique; Chen, Dongmei; Li, Wei (April 2025, Journal of Manufacturing Science and Engineering)

   Abstract Roll-to-roll (R2R) manufacturing is a highly efficient industrial method for continuously processing flexible webs through a series of rollers. With advancements in technology, R2R manufacturing has emerged as one of the most economical production methods for advanced products, such as flexible electronics, renewable energy devices, and 2D materials. However, the development of cost-effective and efficient manufacturing processes for these products presents new challenges, including higher precision requirements, the need for improved in-line quality control, and the integration of material processing dynamics into the traditional web handling system. This paper reviews the state of the art in advanced R2R manufacturing, focusing on modeling and control, and highlights research areas that need further development. 

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2. PLDI-Based Convexification for Roll-to-Roll Dry Transfer Control

   https://doi.org/10.1016/j.ifacol.2021.11.217  

   Martin, C.; Zhao, Q.; Bakshi, S.; Chen, D.; Li, W. (January 2021, IFAC-PapersOnLine)
3. Roll‐to‐Roll Compatible Topochemical Wetting Control for Metamaterial Printing

   https://doi.org/10.1002/adom.202302785  

   Donie, Yidenekachew_J; Ramamurthy, Maya; Chakraborty, Rohan_D; Francis, Lorraine_F; Frisbie, C_Daniel; Ferry, Vivian_E (January 2024, Advanced Optical Materials)

   Abstract The widespread utilization of metamaterials, despite their immense transformative potential, faces challenges regarding scalability in mass production. To address these limitations, an additive method that leverages liquid inks and selective wetting to produce scalable and cost‐effective metamaterials is presented. UV‐based imprinting lithography is utilized to fabricate surface energy‐modulated patterns, enabling precise solution patterning. This approach, unlike conventional UV‐based imprinting lithography, not only accurately produces the negative replica of the stamp topography during UV‐induced crosslinking but also transfers a hydrophobic layer onto the raised surfaces of the imprinted hydrophilic layer, resulting in 3D shapes with spatially modulated surface energy. In the second process step, a functional ink is dragged over the patterned substrate where it dewets to fill the hydrophilic recesses. This innovative process enables high‐speed metamaterial production, with ink deposition speeds up to 12 m min⁻¹. The method accommodates a wide range of inks, including metals, dielectrics, and semiconductors, providing meticulous control over vertical structures such as pattern thickness and hetero‐multilayer formation. Additionally, it offers flexibility in creating metamaterials on free‐standing ultra‐thin sheets, introducing desirable attributes like foldability and disposability. The effectiveness of this approach is validated through the fabrication and characterization of metallic metamaterials. 

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4. MODEL BASED REPETITIVE CONTROL FOR PEELING FRONT GEOMETRY CONTROL IN A ROLL-TO-ROLL PEELING PROCESS

   Zhao, Qishen; Martin, Christopher; Chen, Dongmei; Li, Wei (July 2022, Proceedings of the 2022 International Symposium on Flexible Automation)

   Roll-to-roll(R2R) peeling is an innovative method that transfers flexible electronics and 2D materials from the flexible substrate where they are grown to the end-use substrate. This process enables the full potential of R2R 2D material fabrication methods in a continuous, high-throughput, and environment friendly manner. During the R2R peeling process, the device patterning causes periodic changes in the adhesion energy between the device and substrate. This periodic disturbance can degrade the quality of the final product if not properly controlled. Current control methods used for the R2R peeling process do not explicitly reject the periodic disturbance. It is therefore desirable to develop a controller that is capable of performing periodic disturbance rejection. This paper presents a model-based repetitive controller that integrates a frequency estimation of the disturbance into the R2R peeling control to maintain the optimal peeling process performance. A linear estimator using system identification techniques is employed. The simulation results show that the developed controller achieves better R2R process performance when compared to a conventional model-based controller. 

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5. Data-Driven Model Predictive Control for Roll-to-Roll Process Register Error

   https://doi.org/10.1115/IAM2022-96840  

   Shah, Karan; He, Anqi; Wang, Zifeng; Du, Xian; Jin, Xiaoning (October 2022, Proceedings of the 2022 International Additive Manufacturing Conference. 2022 International Additive Manufacturing Conference. Lisbon, Portugal.)

   Abstract Roll-to-Roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based products, as opposed to sheet-to-sheet (S2S) approach suited for low-volume work. However, meeting the tight alignment tolerance requirements of additive multi-layer printed electronics specified by device resolution that is usually at micrometer scale has become a major challenge in R2R flexible electronics printing, preventing the fabrication technology from being transferred from conventional S2S to high-speed R2R production. Print registration in a R2R process is to align successive print patterns on the flexible substrate and to ensure quality printed devices through effective control of various process variables. Conventional model-based control methods require an accurate web-handling dynamic model and real-time tension measurements to ensure control laws can be faithfully derived. For complex multistage R2R systems, physics-based state-space models are difficult to derive, and real-time tension measurements are not always acquirable. In this paper, we present a novel data-driven model predictive control (DD-MPC) method to minimize the multistage register errors effectively. We show that the DD-MPC can handle multi-input and multi-output systems and obtain the plant model from sensor data via an Eigensystem Realization Algorithm (ERA) and Observer Kalman filter identification (OKID) system identification method. In addition, the proposed control scheme works for systems with partially measurable system states. 

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