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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. 

High performance carbon fibers are widely used as fiber reinforcements in composite material systems for aerospace, automotive, and defense applications. Longitudinal tensile failure of such composite systems is a result of clustering of single fiber tensile failures occurring at the microscale, on the order of a few microns to a few hundred microns. Since fiber tensile strength at the microscale has a first order effect on composite strength, it is important to characterize the strength of single fibers at microscale gage lengths which is extremely challenging. An experimental technique based on a combination of transverse loading of single fibers under SEM with DIC is a potential approach to access microscale gage lengths. The SEM-DIC technique requires creation of uniform, random, and contrastive sub-microscale speckle pattern on the curved fiber surface for accurate strain measurements. In this paper, we investigate the formation of such sub-microscale speckle patterns on individual sized IM7 carbon fibers of nominal diameter 5.2 µm via sputter coating. Various process conditions such as working pressure, sputtering current, and coating duration are investigated for pattern creation on fiber surface using a gold-palladium (Au-Pd) target. A nanocluster type sub-microscale pattern is obtained on the fiber surface for different coating conditions. Numerical translation experiments are performed using the obtained patterns to study image correlation and identify a suitable pattern for SEM-DIC experiments. The pattern obtained at a working pressure of 120–140 mTorr with 50 mA current for a duration of 10 min is found to have an average speckle size of 53 nm and good contrast for image correlation. Rigid body translation SEM experiments for drift/distortion correction using a sized IM7 carbon fiber coated with the best patterning conditions showed that Stereo-SEM-DIC is needed for accurately characterizing fiber strain fields due to its curved surface. The effect of sputter coating on fiber tensile strength and strain is investigated via single fiber tensile tests. Results showed that there is no significant difference in the mean tensile strength and failure strain between uncoated and coated fibers (average increment in fiber diameter of ∼221 nm due to coating) at 5% significance level. SEM images of failure surfaces for uncoated and coated fibers also confirmed a tensile failure of fibers as observed for polyacrylonitrile PAN-based fibers in literature.