用强化学习同步双入口注树脂过程，提升复合材料成型质量
Reinforcement Learning for Synchronised Flow Control in a Dual-Gate Resin Infusion System
- 基于PPO算法优化双入口树脂流动同步性
- 实现多路流体精准汇聚，减少干斑与孔隙
- 适合复合材料智能制造与工艺优化场景
树脂注入（RI）和树脂传递模塑（RTM）是制造高性能纤维增强聚合物复合材料的关键工艺，尤其适用于风力涡轮机叶片等大型部件。控制树脂流动动力学对确保纤维充分浸润至关重要，以避免残留孔隙和干斑，影响最终构件的结构完整性。本文提出一种基于强化学习（RL）的策略，利用过程仿真建立双树脂入口、单出口场景下的流动同步控制方法。采用近端策略优化（PPO）算法，解决部分可观测环境中的流体动力学调控难题。结果表明，该方法能有效实现多路树脂流动的精确收敛，展现出在复合材料制造中提升工艺控制与产品质量的巨大潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。