Development of Process Control Framework Incorporating Deep Reinforcement Learning for Desktop Fiber Extrusion Device via PLC Implementation

Abstract

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.