Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning
Invention type: Technology
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Case number: #22652
Described is a model-free deep reinforcement learning (DRL) control system and technique. In embodiments, the DRL control system and technique may be used in a real-time manufacturing process. In embodiments, a DRL control system and technique may be used for controlling a fiber drawing system. The DRL-based control system predictively regulates a fiber diameter to track dynamically varying reference trajectories.
Researchers
Brian Anthony
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Donghyun Kim
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Sangwoon Kim
Departments: Department of Mechanical Engineering
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Communication Systems: Optical / Industrial Engineering & Automation: Manufacturing & Equipment
Impact Areas: Connected World
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dynamic control of a manufacturing process using deep reinforcement learning
United States of America | Published application
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