Deep Reinforcement Learning Control for Fiber Drawing Precision
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Solution Overview
Problem
Conventional manufacturing control systems require accurate numerical modeling of specific manufacturing processes, limiting their ability to handle varying geometries, materials, and fabrication methodologies, and struggle with stochastic behavior and non-linear dynamics.
Innovation Solution
A dynamic model control system based on deep reinforcement learning (DRL) that learns to control manufacturing processes without prior analytical or numerical models, enabling the production of articles with varying characteristics and adapting to changing conditions by tracking dynamically varying reference trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional control systems use accurate numerical modeling of specific manufacturing processes, then control precision is improved, but adaptability to varying geometries, materials, and fabrication methodologies deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static numerical models to dynamic model-free control using reinforcement learning. The controller continuously learns and adapts to changing process conditions, geometries, and materials in real-time, resolving the contradiction between maintaining precision and adapting to variability.
Solution Approach 2:
The patent changes the fundamental parameter of control from fixed numerical models to adaptive reinforcement learning policies. By modifying the control approach from model-based to learning-based, the system achieves both precision and adaptability across varying manufacturing conditions.
2Reliability
If conventional control systems rely on prior analytical or numerical models, then control performance is improved for known processes, but ability to handle stochastic behavior and non-linear dynamics deteriorates
Solution Approach 1:
The patent applies self-service by enabling the control system to automatically learn and adapt to stochastic behavior and non-linear dynamics through reinforcement learning. The system serves itself by continuously improving its control policy based on real-time feedback, without requiring pre-defined models of the complex dynamics.
Solution Approach 2:
The patent uses feedback extensively through the reinforcement learning mechanism, where the controller observes process outcomes and adjusts its actions to maximize cumulative reward. This closed-loop learning approach handles stochastic behavior and non-linear dynamics effectively while maintaining reliable control performance.
3Ease of operation
If traditional PID and MPC control systems are used, then implementation simplicity is maintained, but tracking performance for dynamically varying reference trajectories deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the reinforcement learning controller offline to learn optimal control policies for various operating conditions. This pre-learning phase enables the controller to achieve superior tracking performance during real-time operation without complex online computation, balancing simplicity and performance.
Data Source
AI summary
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.