Abstract: 3D printing is an advanced manufacturing technology, the optimized path planning method of 3D printing can improve efficiency or molding quality. For the traditional path planning methods are short of handling complex thin-walled structures, an reinforcement learning-based path planning method named Q-path is proposed for complex thin-walled structures. First of all, based on the path planning in 3D printing is a filling task, the path planning task in reinforcement learning is converted to a traversal problem. In order to improve printing efficiency and molding quality, the optimization goal is to minimize the total cost of printing, and the constraint conditions in the reinforcement learning are designed according to the optimization goal, that is, to minimize the number of start, stop and turns of the print head. This paper establishes a simulation environment of a slice, uses the Q-learning algorithm with the above constraints, learning is guided by calculating the value of the total cost to find the optimal path plan. Experimental results show that this method is better than the traditional method for 3D printing path planning in printing complex thin-walled structures.

Key words: 3D printing, reinforcement learning, path planning, Q-learning

摘要： 3D打印是一项先进的制造技术，通过优化其中路径规划方案可以提高效率或成型质量。由于用于3D打印路径规划的传统方法在打印复杂薄壁结构时效果不佳，该文结合强化学习的智能性，提出了一种适用于复杂薄壁结构的路径规划方法。基于3D打印中的路径规划是填充任务，将强化学习中的路径规划任务转换为全遍历问题。为提高打印效率和成型质量，以最小化打印总成本为优化目标，根据优化目标设计强化学习中的约束条件，即最小化打印头的启停和转弯次数。建立单层切片的仿真环境，采用带有上述约束条件的Q-learning算法，通过计算总成本的值来引导学习，寻找最优路径方案。实验结果表明，该方法在打印复杂薄壁结构上的表现优于用于3D打印路径规划的传统方法。

关键词: 3D打印, 强化学习, 路径规划, Q-学习