Session: 04-01 Emerging Technologies: Smart AM

Paper Number: 96840

96840 - Data-Driven Model Predictive Control for Roll to Roll Process Registration Error 

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 multi-layer printed electronics specified by device resolution (micrometer accuracy) 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 printing process is to align successively print patterns on the material and to ensure a quality print output through appropriately controlling various web handling process variables. Conventional model-based control methods require an accurate web-handling dynamic model and tension measurements to ensure control rules can be derived based on the model. For complex multi-stage R2R systems, physics-based state-space models are difficult to derive, and real-time tension measurements are not always acquirable. This paper presents a data-driven model predictive control (DD-MPC) method to reduce the register errors generated by a multi-stage R2R electronics printing system. We show that the DD-MPC can handle multi-input and multi-output systems, which may have couplings between inputs and outputs, and handle the situation when partial system states are not measurable. We also consider other constraints such as tension and input changing rate while designing the controller to reduce registration error.

Presenting Author: Xiaoning Jin Northeastern University

Presenting Author Biography: Prof. Xiaoning (Sarah) Jin is an assistant professor of Mechanical and Industrial Engineering at Northeastern University. Her research interests are in the area of modeling and analysis for intelligent and advanced manufacturing processes and systems, with a specialization in diagnostics and prognostics, control and predictive decision making. Her works have been applied to a variety of industry applications ranging from automotive manufacturing, roll-to-roll printing process monitoring, high-precision micro-/nano-scale manufacturing processes, smart operations and maintenance strategy for maritime equipment, etc. Prof. Jin’s research has been sponsored through multiple federal agencies, including NSF, Manufacturing USA Institutes and industry collaborators.

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