Closed-Loop Control of Direct Ink Writing via Reinforcement Learning
Closed-Loop Control of Direct Ink Writing via Reinforcement Learning
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DOI：
10.1145/3528223.3530144
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发表时间：
2022-07-01
影响因子：
6.2
通讯作者：
Bickel, Bernd
中科院分区：
文献类型：
--
作者：
Piovarci, Michal;Foshey, Michael;Bickel, Bernd
使增材制造能够采用各种新颖的功能材料，可能是这项技术的主要推动力。然而，使这种材料可打印需要由专业操作员进行艰苦的试错，因为它们通常倾向于表现出特殊的流变或滞后特性。即使成功找到工艺参数，由于批次之间的材料差异，也无法保证印刷到印刷的一致性。这些挑战使得闭环反馈成为一个有吸引力的选择，其中过程参数被动态调整。设计一个有效的控制器有几个挑战：沉积参数是复杂的和高度耦合的，文物发生后，长时间的视野，模拟沉积是计算成本高，学习硬件是棘手的。在这项工作中，我们证明了使用强化学习学习增材制造闭环控制策略的可行性。我们表明，近似的，但有效的，数值模拟是足够的，只要它允许学习的沉积转化为现实世界的经验的行为模式。结合强化学习，我们的模型可以用来发现优于基线控制器的控制策略。此外，恢复的策略具有最小的模拟到真实的差距。我们通过在使用低粘度和高粘度材料的单层打印机上应用我们的控制策略来展示这一点。
Enabling additive manufacturing to employ a wide range of novel, functional materials can be a major boost to this technology. However, making such materials printable requires painstaking trial-and-error by an expert operator, as they typically tend to exhibit peculiar rheological or hysteresis properties. Even in the case of successfully finding the process parameters, there is no guarantee of print-to-print consistency due to material differences between batches. These challenges make closed-loop feedback an attractive option where the process parameters are adjusted on-the-fly. There are several challenges for designing an efficient controller: the deposition parameters are complex and highly coupled, artifacts occur after long time horizons, simulating the deposition is computationally costly, and learning on hardware is intractable. In this work, we demonstrate the feasibility of learning a closed-loop control policy for additive manufacturing using reinforcement learning. We show that approximate, but efficient, numerical simulation is sufficient as long as it allows learning the behavioral patterns of deposition that translate to real-world experiences. In combination with reinforcement learning, our model can be used to discover control policies that outperform baseline controllers. Furthermore, the recovered policies have a minimal sim-to-real gap. We showcase this by applying our control policy in-vivo on a single-layer printer using low and high viscosity materials.