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The layout design of piping systems, particularly for free-form pipes, is crucial for achieving lightweight construction, structural precision, and operational reliability in aero-engine development. Existing routing methods exhibit performance limitations, with generated layouts often lacking the geometric continuity and smoothness required for manufacturability. To address these challenges, this study proposes a novel smooth free-form pipe routing (SFPR) framework, which integrates a proximal policy optimization (PPO) algorithm with a Non-Uniform Rational B-Spline (NURBS) curve parameterization. The SFPR framework optimizes the path smoothness by constraining length, curvature, torsion, and their first-order derivatives. Comparative experiments demonstrate that SFPR achieves robust obstacle avoidance and generates paths up to 24.5% shorter than a heuristic-based method. Further analysis reveals that SFPR reduces curvature and torsion amplitudes by 33.6% and 39.8%, respectively, compared to non-smooth frameworks, while stabilizing their spatial distributions. These advancements enable significant reductions in material consumption, enhanced manufacturability of free-form pipes, and mitigation of mechanical shock on forming dies caused by abrupt geometric transitions.

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Smooth Free-Form Pipe Routing Design in Aero-Engines: A Quartic NURBS-Driven Deep Reinforcement Learning Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Caicheng Wang,
  • Zili Wang,
  • Shuyou Zhang,
  • Yongzhe Xiang,
  • Zheyi Li,
  • Jianrong Tan

摘要

The layout design of piping systems, particularly for free-form pipes, is crucial for achieving lightweight construction, structural precision, and operational reliability in aero-engine development. Existing routing methods exhibit performance limitations, with generated layouts often lacking the geometric continuity and smoothness required for manufacturability. To address these challenges, this study proposes a novel smooth free-form pipe routing (SFPR) framework, which integrates a proximal policy optimization (PPO) algorithm with a Non-Uniform Rational B-Spline (NURBS) curve parameterization. The SFPR framework optimizes the path smoothness by constraining length, curvature, torsion, and their first-order derivatives. Comparative experiments demonstrate that SFPR achieves robust obstacle avoidance and generates paths up to 24.5% shorter than a heuristic-based method. Further analysis reveals that SFPR reduces curvature and torsion amplitudes by 33.6% and 39.8%, respectively, compared to non-smooth frameworks, while stabilizing their spatial distributions. These advancements enable significant reductions in material consumption, enhanced manufacturability of free-form pipes, and mitigation of mechanical shock on forming dies caused by abrupt geometric transitions.