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<p>Machining sequencing is a critical step in computer-aided process planning (CAPP) for numerical control (NC) machining, directly impacting efficiency and cost. Consequently, the machining sequencing method is crucial for practical manufacturing processes. However, existing sequencing methods face two main limitations: (1) they often treat machining features (MFs) as the smallest unit, neglecting that complex features require decomposition into sub-features and multiple feature machining steps (FMSs); (2) while exact methods are very time-consuming, prevailing approximate methods suffer from inconsistent performance, repeated iterative computations, and an inability to learn from experience. To address these challenges, this paper proposes a knowledge-guided deep reinforcement learning (DRL) framework for FMS-level sequencing. The method incorporates dynamic selection masks to enforce machining constraints and a process knowledge-based cost model to ensure efficient convergence. Leveraging the sequencing experience acquired during the learning process, this method can solve the machining sequencing problem within an acceptable computation time. Experiments demonstrate that the proposed method outperforms existing methods in both total cost and computation time, enabling more efficient machining of complex parts.</p>

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Knowledge-guided deep reinforcement learning framework for feature machining step sequencing

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Pengyu Wang,
  • Youpeng You,
  • Weichao Liu,
  • Dawei Ding,
  • Fengyu Xu

摘要

Machining sequencing is a critical step in computer-aided process planning (CAPP) for numerical control (NC) machining, directly impacting efficiency and cost. Consequently, the machining sequencing method is crucial for practical manufacturing processes. However, existing sequencing methods face two main limitations: (1) they often treat machining features (MFs) as the smallest unit, neglecting that complex features require decomposition into sub-features and multiple feature machining steps (FMSs); (2) while exact methods are very time-consuming, prevailing approximate methods suffer from inconsistent performance, repeated iterative computations, and an inability to learn from experience. To address these challenges, this paper proposes a knowledge-guided deep reinforcement learning (DRL) framework for FMS-level sequencing. The method incorporates dynamic selection masks to enforce machining constraints and a process knowledge-based cost model to ensure efficient convergence. Leveraging the sequencing experience acquired during the learning process, this method can solve the machining sequencing problem within an acceptable computation time. Experiments demonstrate that the proposed method outperforms existing methods in both total cost and computation time, enabling more efficient machining of complex parts.