Design for Manufacturing: A Manufacturability Knowledge-Integrated Reinforcement Learning Framework for Free-Form Pipe Routing in Aeroengines
A new framework for pipe routing in aeroengines has been proposed, integrating manufacturability knowledge with reinforcement learning. This approach aims to optimize the design process by addressing the disconnect between design and manufacturing practices. Experimental results indicate that the framework produces more efficient and manufacturable pipe paths compared to traditional methods.
- ▪The proposed framework is called Frenet-based pipe routing optimization (FPRO).
- ▪FPRO uses a boundary value problem formulation and incorporates manufacturing constraints into the design process.
- ▪Experimental validation shows that FPRO achieves better performance in terms of path length, obstacle avoidance, and manufacturability.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20644 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Machine Learning arXiv:2605.20644 (cs) [Submitted on 20 May 2026] Title:Design for Manufacturing: A Manufacturability Knowledge-Integrated Reinforcement Learning Framework for Free-Form Pipe Routing in Aeroengines Authors:Caicheng Wang, Zili Wang, Shuyou Zhang, Yongzhe Xiang, Zheyi Li, Liangyou Li, Jianrong Tan View a PDF of the paper titled Design for Manufacturing: A Manufacturability Knowledge-Integrated Reinforcement Learning Framework for Free-Form Pipe Routing in Aeroengines, by Caicheng Wang and 6 other authors View PDF Abstract:Design for manufacturing plays a critical role in advanced aeroengine development, where complex components necessitate careful consideration of manufacturability.
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