2026-09-20T05:00:48Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1558692024-08-02T03:26:56Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Anthony, Brian W.
Jaiswal, Somesh Sunil
Massachusetts Institute of Technology. Department of Mechanical Engineering
2024-08-01T19:02:30Z
2024-08-01T19:02:30Z
2024-05
2024-06-13T16:45:27.319Z
https://hdl.handle.net/1721.1/155869
This thesis explores the integration of the Desktop Fiber Extrusion Device (FrED) into smart manufacturing education, emphasizing its transformative potential in engineering curricula. The research focuses on the development and application of educational and research-grade FrED models, designed to provide hands-on learning experiences remotely, which is increasingly pertinent in the evolving landscape of engineering education. Through iterative design and implementation of control systems, including Proportional-Integral-Derivative (PID) and Deep Reinforcement Learning (DRL), the study enhances the operational precision and educational utility of FrED. Furthermore, the introduction of an innovative, low-cost tension sensor in the fiber extrusion process represents a significant enhancement in monitoring and controlling the mechanical properties of extruded fibers, which is critical for understanding manufacturing dynamics. The thesis also proposes a structured coursework framework titled "Remote Monitoring and Control in Smart Manufacturing" that utilizes FrED to teach key concepts of smart manufacturing. This coursework is designed to equip students with the skills to operate advanced manufacturing tools and analyze real-time data for process optimization. The findings demonstrate that FrED not only supports the theoretical and practical education of engineering students but also serves as a bridge to high-tech industrial applications, making it a pivotal tool in the digital transformation of manufacturing education. This work lays the groundwork for future research on the scalability of such educational tools and their integration into different educational settings globally, potentially democratizing access to cutting-edge engineering education.
M.Eng.
Massachusetts Institute of Technology
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-sa/4.0/
Enhancing Engineering Education: Integration of the Desktop Fiber Extrusion Device (FrED) for Hands-On Learning in Smart Manufacturing.
Thesis
application/pdf
Master
Master of Engineering in Advanced Manufacturing and Design
Publication
unknown
unknown
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<Abstract>This thesis explores the integration of the Desktop Fiber Extrusion Device (FrED) into smart manufacturing education, emphasizing its transformative potential in engineering curricula. The research focuses on the development and application of educational and research-grade FrED models, designed to provide hands-on learning experiences remotely, which is increasingly pertinent in the evolving landscape of engineering education. Through iterative design and implementation of control systems, including Proportional-Integral-Derivative (PID) and Deep Reinforcement Learning (DRL), the study enhances the operational precision and educational utility of FrED. Furthermore, the introduction of an innovative, low-cost tension sensor in the fiber extrusion process represents a significant enhancement in monitoring and controlling the mechanical properties of extruded fibers, which is critical for understanding manufacturing dynamics. The thesis also proposes a structured coursework framework titled "Remote Monitoring and Control in Smart Manufacturing" that utilizes FrED to teach key concepts of smart manufacturing. This coursework is designed to equip students with the skills to operate advanced manufacturing tools and analyze real-time data for process optimization. The findings demonstrate that FrED not only supports the theoretical and practical education of engineering students but also serves as a bridge to high-tech industrial applications, making it a pivotal tool in the digital transformation of manufacturing education. This work lays the groundwork for future research on the scalability of such educational tools and their integration into different educational settings globally, potentially democratizing access to cutting-edge engineering education.</Abstract>
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