Development and Implementation of a Smart Factory for Educational Fiber Extrusion Device Production

This thesis presents the development and production of FrED (Fiber Extrusion Device), an educational manufacturing system designed to bridge the gap between theoretical instruction and hands-on practice in process control, computer vision, and smart manufacturing. Building on an existing prototype, this work focused on transitioning FrED from a proof-of-concept into a production-ready system by designing scalable workflows, improving hardware and software integration, and developing tools to ensure traceability and repeatability across builds. A major contribution of this thesis was the enhancement and implementation of a smart factory environment capable of supporting batch production. This included designing and deploying applications using Tulip Interfaces to manage inventory, guide subassembly processes, and monitor production metrics in real time. A modular SKU system and structured bin labeling framework were introduced to reduce errors, maintain version control, and support future growth. Station-specific apps were developed and refined to ensure consistent assembly and simplify onboarding across a rotating team of users. In parallel, this thesis contributed to the evaluation and refinement of a vision-based diameter measurement system using a low-cost USB camera. The system was analyzed under various operating conditions and its limitations under motion and variable lighting were quantified. Multiple image processing strategies were explored and robustness metrics were developed to inform future improvements. To ensure pedagogical relevance, the system was tested in user-facing workshops and public demo sessions. Feedback informed updates to both the assembly process and instructional content. By the end of the development cycle, the system supported the successful production of 35 complete FrED units, establishing a replicable model for small-scale manufacturing. This thesis demonstrates how modular digital infrastructure can enable scalable hardware deployment. It also highlights the practical challenges of transitioning from prototype to production and proposes tools and methods that can support broader adoption of smart manufacturing principles in learning environments.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Fillon, Marie

Advisor dc:contributor.advisor

• Anthony, Brian W.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/163452

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/163452