Development of Process Control Framework Incorporating Deep Reinforcement Learning for Desktop Fiber Extrusion Device via PLC Implementation
Optical fiber has revolutionized communication, and the market has experienced rapid growth in the last ten years. It can transmit information at high speeds with minimal loss over long distances due to its structure. Fiber extrusion, a common manufacturing method in the industry, involves controlli...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/153730 |
| _version_ | 1826216392335032320 |
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| author | Zhang, Yutong |
| author2 | Anthony, Brian |
| author_facet | Anthony, Brian Zhang, Yutong |
| author_sort | Zhang, Yutong |
| collection | MIT |
| description | Optical fiber has revolutionized communication, and the market has experienced rapid growth in the last ten years. It can transmit information at high speeds with minimal loss over long distances due to its structure. Fiber extrusion, a common manufacturing method in the industry, involves controlling the fiber diameter during its formation. In this thesis, a control framework for a desk-top fiber extrusion device is developed, incorporating Deep Reinforcement Learning. By improving the mechanical design of the desk-top fiber extrusion device and implementing PID controllers over the system on the Allen-Bradley PLC, the coefficient of variation in the fiber extrusion process is reduced to 0.1. A communication path is established based on open platform communication unified architecture (OPC UA), enabling the external devices to access the data in the PLC. Using a Deep Reinforcement Learning model on a separate PC, the process is controlled to have a coefficient of variation of 0.13, with the potential to reduce the response time. |
| first_indexed | 2024-09-23T16:46:54Z |
| format | Thesis |
| id | mit-1721.1/153730 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:46:54Z |
| publishDate | 2024 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1537302024-03-14T03:02:24Z Development of Process Control Framework Incorporating Deep Reinforcement Learning for Desktop Fiber Extrusion Device via PLC Implementation Zhang, Yutong Anthony, Brian Massachusetts Institute of Technology. Department of Mechanical Engineering Optical fiber has revolutionized communication, and the market has experienced rapid growth in the last ten years. It can transmit information at high speeds with minimal loss over long distances due to its structure. Fiber extrusion, a common manufacturing method in the industry, involves controlling the fiber diameter during its formation. In this thesis, a control framework for a desk-top fiber extrusion device is developed, incorporating Deep Reinforcement Learning. By improving the mechanical design of the desk-top fiber extrusion device and implementing PID controllers over the system on the Allen-Bradley PLC, the coefficient of variation in the fiber extrusion process is reduced to 0.1. A communication path is established based on open platform communication unified architecture (OPC UA), enabling the external devices to access the data in the PLC. Using a Deep Reinforcement Learning model on a separate PC, the process is controlled to have a coefficient of variation of 0.13, with the potential to reduce the response time. M.Eng. 2024-03-13T13:30:03Z 2024-03-13T13:30:03Z 2024-02 2024-02-15T21:17:19.917Z Thesis https://hdl.handle.net/1721.1/153730 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Zhang, Yutong Development of Process Control Framework Incorporating Deep Reinforcement Learning for Desktop Fiber Extrusion Device via PLC Implementation |
| title | Development of Process Control Framework
Incorporating Deep Reinforcement Learning for Desktop
Fiber Extrusion Device via PLC Implementation |
| title_full | Development of Process Control Framework
Incorporating Deep Reinforcement Learning for Desktop
Fiber Extrusion Device via PLC Implementation |
| title_fullStr | Development of Process Control Framework
Incorporating Deep Reinforcement Learning for Desktop
Fiber Extrusion Device via PLC Implementation |
| title_full_unstemmed | Development of Process Control Framework
Incorporating Deep Reinforcement Learning for Desktop
Fiber Extrusion Device via PLC Implementation |
| title_short | Development of Process Control Framework
Incorporating Deep Reinforcement Learning for Desktop
Fiber Extrusion Device via PLC Implementation |
| title_sort | development of process control framework incorporating deep reinforcement learning for desktop fiber extrusion device via plc implementation |
| url | https://hdl.handle.net/1721.1/153730 |
| work_keys_str_mv | AT zhangyutong developmentofprocesscontrolframeworkincorporatingdeepreinforcementlearningfordesktopfiberextrusiondeviceviaplcimplementation |