A Machine Learning Approach to Improve Diameter Control in Desktop Fiber Extrusion Processes

A machine learning approach to controlling the diameter of a desktop fiber extrusion process with a PLC is developed and evaluated against the performance of PID control. The deep reinforcement learning model can learn how to control the output diameter of the process based on a given target without any knowledge of the system dynamics. The model learns how to control the output diameter after being trained on hours of data recorded from an open loop control process. After training the model can receive sensory information from a PLC, calculate an action based on the desired target and send the action to the PLC to execute.

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

2024

Author dc:creator

• Patrick, Keeghan J.

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/1721.1/153677

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153677