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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23923 |
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| _version_ | 1866908428288393216 |
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| author | Camacho-Sánchez, Miguel García-Torres, Fernando Lisegaard, Jesper John del Amor, Rocío Mohanty, Sankhya Naranjo, Valery |
| author_facet | Camacho-Sánchez, Miguel García-Torres, Fernando Lisegaard, Jesper John del Amor, Rocío Mohanty, Sankhya Naranjo, Valery |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Synchronised Flow Control in a Dual-Gate Resin Infusion System Camacho-Sánchez, Miguel García-Torres, Fernando Lisegaard, Jesper John del Amor, Rocío Mohanty, Sankhya Naranjo, Valery Machine Learning Artificial Intelligence 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. |
| title | Reinforcement Learning for Synchronised Flow Control in a Dual-Gate Resin Infusion System |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.23923 |