Reinforcement Learning for Synchronised Flow Control in a Dual-Gate Resin Infusion System

TL;DR

This paper introduces a reinforcement learning strategy using Proximal Policy Optimization to synchronize resin flow fronts in dual-inlet infusion processes, improving control and quality in composite manufacturing.

Contribution

The paper develops a novel RL-based control method for dual-gate resin infusion, addressing flow synchronization in complex, partially observable environments.

Findings

RL approach achieves accurate flow convergence

Improves process control in resin infusion

Potential to enhance composite manufacturing quality

Abstract

Resin infusion (RI) and resin transfer moulding (RTM) are critical processes for the manufacturing of high-performance fibre-reinforced polymer composites, particularly for large-scale applications such as wind turbine blades. Controlling the resin flow dynamics in these processes is critical to ensure the uniform impregnation of the fibre reinforcements, thereby preventing residual porosities and dry spots that impact the consequent structural integrity of the final component. This paper presents a reinforcement learning (RL) based strategy, established using process simulations, for synchronising the different resin flow fronts in an infusion scenario involving two resin inlets and a single outlet. Using Proximal Policy Optimisation (PPO), our approach addresses the challenge of managing the fluid dynamics in a partially observable environment. The results demonstrate the effectiveness of the RL approach in achieving an accurate flow convergence, highlighting its potential towards improving process control and product quality in composites manufacturing.