ABSTRACT

This Historically Black Colleges and Universities - Excellence in Research (HBCU-EiR) grant supports research that contributes new knowledge related to quality assurance for additive manufacturing, promoting both the progress of science and the advancement of national prosperity. Inkjet printing is one representative additive manufacturing process based on thermal or acoustic formation and ejection of liquid droplets through a nozzle. Its great promise has been demonstrated in electronics, energy, healthcare and biomedical industries. However, inkjet printing is sensitive to environmental, material, mechanical and electronical factors, and the process can easily deviate from the desirable working status, resulting in defective parts. This tends to lead to material and energy waste and affects the structural health and functional integrity of many important engineering systems. This award supports fundamental research to provide needed knowledge for the development of a holistic framework involving neural networks for quality assurance in inkjet printing. This project holds the potential to significantly improve productivity, quality and material efficiency for inkjet-based additive manufacturing processes, thus benefiting the U.S. economy and society. Using a multi-disciplinary approach involving manufacturing, computer vision, control theory, and machine learning, this research helps broaden participation of underrepresented groups in research and promotes engineering education.

The goal of this project is to establish a comprehensive framework that seamlessly integrates in-process video-based monitoring with closed-loop control and compensation to effectively detect and subsequently correct the process drift and anomalies toward high-quality inkjet printing. The framework consists of three synergic digital twins based on neural networks, a technique that mimics the operations of a human brain in the artificial intelligence field. The first digital twin aims at closed-loop control of the kinematic and morphological status of the micro droplets. The second digital twin focuses on closed-loop control of the geometrical and morphological status of the printed patterns. The third digital twin determines and implements compensation strategies for defective patterns. Specific objectives are to 1) identify methodology for creation and integration of digital twins to maintain desirable droplet status, obtain required patterns and implement effective compensation, 2) derive practical guidelines of using neural network in quality assurance, including input selection and preparation, network design and optimization, output selection and usage, and transferability and adaptability, and 3) understand the relationship between material properties, control variables, in-process parameters and print outcome in inkjet printing from the perspectives of neural network. This project is expected to provide fundamental understanding of the design, development, and implementation of cyber-physical systems in additive manufacturing. The developed framework can be adapted to other macro- and micro-scale additive manufacturing processes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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