Deep reinforcement learning-based tool path generation for reducing non-cutting motion in multiisland cavities

Multi-island cavities are characterized by complex geometries, irregular boundaries, and discretely distributed islands. Tool paths generated by traditional zigzag cutting often suffer from frequent tool retractions, excessive non-cutting motion, and interference caused by islands. To address these issues, this paper proposes a deep reinforcement learning-based tool path generation method. First, the machining region of a complex cavity is discretized into driving lines, and continuous line groups are constructed through geometric processing and a grouping algorithm. In this way, the tool path generation problem is transformed into a multi-stage sequential decision-making problem, and a Markov Decision Process model is established. On this basis, a Deep Q-Network with prioritized experience replay is employed, enabling the agent to learn the endpoint visiting sequence through interaction with the environment and thereby reduce the total non-cutting motion distance. Compared with the local heuristic method and the classical zigzag scan algorithm, the proposed method reduces the total noncutting motion distance and the number of tool retractions, especially for complex multi-island cavities. The results indicate that the proposed method has good adaptability and global optimization capability, providing a new solution for tool path generation in zigzag cutting of complex aero-engine cavities.