article published yes Michael Piovarci author 62E473F4-5C99-11EA-A40E-AF823DDC885E0000-0002-5062-4474 Michael Foshey author Jie Xu author Timothy Erps author Vahid Babaei author Piotr Didyk author Szymon Rusinkiewicz author Wojciech Matusik author Bernd Bickel author 49876194-F248-11E8-B48F-1D18A9856A870000-0001-6511-9385 BeBi department Perception-Aware Appearance Fabrication project MATERIALIZABLE: Intelligent fabrication-oriented Computational Design and Modeling project 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, direct ink writing printer. https://research-explorer.ista.ac.at/download/11442/11467/2022_ACM_acceptedversion_Piovarci.pdf application/pdfno Association for Computing Machinery2022 eng 0730-0301 1557-7368 2201.11819 00083098920009110.1145/3528223.3530144 414 https://ista.ac.at/en/news/machine-learning-3d-printing-fluids/ Piovarci, Michael, et al. “Closed-Loop Control of Direct Ink Writing via Reinforcement Learning.” <i>ACM Transactions on Graphics</i>, vol. 41, no. 4, 112, Association for Computing Machinery, 2022, doi:<a href="https://doi.org/10.1145/3528223.3530144">10.1145/3528223.3530144</a>. Piovarci, M., Foshey, M., Xu, J., Erps, T., Babaei, V., Didyk, P., … Bickel, B. (2022). Closed-loop control of direct ink writing via reinforcement learning. <i>ACM Transactions on Graphics</i>. Association for Computing Machinery. <a href="https://doi.org/10.1145/3528223.3530144">https://doi.org/10.1145/3528223.3530144</a> M. Piovarci, M. Foshey, J. Xu, T. Erps, V. Babaei, P. Didyk, S. Rusinkiewicz, W. Matusik, B. Bickel, ACM Transactions on Graphics 41 (2022). Piovarci M, Foshey M, Xu J, et al. Closed-loop control of direct ink writing via reinforcement learning. <i>ACM Transactions on Graphics</i>. 2022;41(4). doi:<a href="https://doi.org/10.1145/3528223.3530144">10.1145/3528223.3530144</a> Piovarci, Michael, Michael Foshey, Jie Xu, Timothy Erps, Vahid Babaei, Piotr Didyk, Szymon Rusinkiewicz, Wojciech Matusik, and Bernd Bickel. “Closed-Loop Control of Direct Ink Writing via Reinforcement Learning.” <i>ACM Transactions on Graphics</i>. Association for Computing Machinery, 2022. <a href="https://doi.org/10.1145/3528223.3530144">https://doi.org/10.1145/3528223.3530144</a>. Piovarci M, Foshey M, Xu J, Erps T, Babaei V, Didyk P, Rusinkiewicz S, Matusik W, Bickel B. 2022. Closed-loop control of direct ink writing via reinforcement learning. ACM Transactions on Graphics. 41(4), 112. M. Piovarci <i>et al.</i>, “Closed-loop control of direct ink writing via reinforcement learning,” <i>ACM Transactions on Graphics</i>, vol. 41, no. 4. Association for Computing Machinery, 2022. 114422022-06-10T06:41:47Z2025-09-10T09:36:45Z