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arXiv AI By Caicheng Wang, Zili Wang, Shuyou Zhang, Yongzhe Xiang, Zheyi Li, Jianrong Tan

A self-learning framework for adaptive free-form pipe routing in aeroengines

Read the original on arXiv AI →

The paper introduces SLPR, a self‑learning framework that uses proximal policy optimization to optimize free‑form pipe routing in aeroengines. It incorporates a unified rule‑modeling system for obstacle detection, a potential energy table for rapid layout queries, and iteratively refines routing via agent‑environment interaction. Experiments show SLPR outperforms three baselines in pipe length, rule adherence, path complexity, and computational efficiency, and adapts quickly to changing design environments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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