This paper presents a learning based planner for computing optimized 3D printing toolpaths on prescribed graphs, the challenges of which include the varying graph structures on different models and the large scale of nodes & edges on a graph. We adopt an on-the-fly strategy to tackle these challenges, formulating the planner as a Deep Q-Network (DQN) based optimizer to decide the next 'best' node to visit. We construct the state spaces by the Local Search Graph (LSG) centered at different nodes on a graph, which is encoded by a carefully designed algorithm so that LSGs in similar configurations can be identified to re-use the earlier learned DQN priors for accelerating the computation of toolpath planning. Our method can cover different 3D printing applications by defining their corresponding reward functions. Toolpath planning problems in wire-frame printing, continuous fiber printing, and metallic printing are selected to demonstrate its generality. The performance of our planner has been verified by testing the resultant toolpaths in physical experiments. By using our planner, wire-frame models with up to 4.2k struts can be successfully printed, up to 93.3% of sharp turns on continuous fiber toolpaths can be avoided, and the thermal distortion in metallic printing can be reduced by 24.9%.

中文翻译：

---

基于学习的 Toolpath Planner 基于 3D 打印的各种图形


本文提出了一个基于学习的计划器，用于在规定的图表上计算优化的3D打印工具路径，其挑战包括不同模型上的不同图形结构以及图表上节点和边缘的大规模。我们采用动态策略来应对这些挑战，将规划器制定为基于深度 Q 网络 （DQN） 的优化器，以决定下一个要访问的“最佳”节点。我们通过以图形上不同节点为中心的本地搜索图 （LSG） 构建状态空间，该图形由精心设计的算法编码，以便可以识别具有相似配置的 LSG，以重用先前学习的 DQN 先验来加速刀具路径规划的计算。我们的方法可以通过定义相应的奖励函数来涵盖不同的 3D 打印应用。选择线框打印、连续纤维打印和金属打印中的刀具路径规划问题来演示其通用性。我们的规划器的性能已通过在物理实验中测试生成的刀具路径来验证。通过使用我们的规划器，可以成功打印具有高达 4.2k 支柱的线框模型，可以避免连续纤维刀具路径上高达 93.3% 的急转弯，并且可以将金属打印中的热变形减少 24.9%。