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Author ORCID Identifier
https://orcid.org/0000-0001-8330-6272
AccessType
Campus-Only Access for One (1) Year
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Mechanical Engineering
Year Degree Awarded
2023
Month Degree Awarded
May
First Advisor
Xian Du
Subject Categories
Controls and Control Theory
Abstract
This work focuses on advances in roll-to-roll microcontact printing by the establishment of learning-based modeling methods that guides the monitoring and control of the continuous microcontact printing process. Microcontact printing is an attractive cost-effective method of patterning micro- and nano-scale features via selective mechanical contact on flexible web substrates using stamps. Adapting microcontact printing to continuous, roll-to-roll platforms facilitates applications such as flexible electronics and wearables. However, a limitation of present continuous printing processes is that in-line metrology is unavailable for process monitoring and control. Therefore, this work aims to develop in-line metrology for print pattern quality monitoring of nano-thin monolayer print processes and use neural-network-based predictive control to control the printing processes. First, a real-time prediction of web tension based on contactless sensing and deep learning is developed. Second, a neural-network-based adaptive model predictive control for a flexure-based roll-to-roll contact printing system is designed. To further study the mechanisms of the microcontact printing process, an offline consistent optical surface inspection based on open environment droplet size-controlled condensation figures for print pattern quality monitoring is developed. Inline metrology for microcontact printing quality monitoring based on condensation figures is then applied. Finally, a case study of a novel autofocus method is presented to show the possibility of applying deep learning-based model predictive control in the control of the roll-to-roll microcontact printing process.
DOI
https://doi.org/10.7275/35076059
Recommended Citation
Yan, Jingyang, "MONITORING AND CONTROL OF THE ROLL-TO-ROLL MICROCONTACT PRINTING PROCESS THROUGH NEURAL NETWORK AND REAL-TIME SENSING" (2023). Doctoral Dissertations. 2873.
https://doi.org/10.7275/35076059
https://scholarworks.umass.edu/dissertations_2/2873
Creative Commons License
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