Distributed model predictive control for ink-jet 3D printing

This paper develops a closed-loop approach for ink-jet 3D printing. The control design is based on a distributed model predictive control scheme, which can handle constraints (such as droplet volume) as well as the large-scale nature of the problem. The high resolution of ink-jet 3D printing make centralized methods extremely time-consuming, thus a distributed implementation of the controller is developed. First a graph-based height evolution model that can capture the liquid flow dynamics is proposed. Then, a scalable closed-loop control algorithm is designed based on the model using Distributed MPC, that reduces computation time significantly. The performance and efficiency of the algorithm are shown to outperform open-loop printing and closed-loop printing with existing Centralized MPC methods through simulation results.

[1]  Zhi Wang,et al.  An application of spatial Iterative Learning Control to micro-additive manufacturing , 2016, 2016 American Control Conference (ACC).

[2]  M.B.G. Wassink,et al.  Enabling higher jet frequencies for an inkjet printhead using iterative learning control , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[3]  Kok Lay Teo,et al.  Optimization and control with applications , 2005 .

[4]  James B. Rawlings,et al.  Postface to “ Model Predictive Control : Theory and Design ” , 2012 .

[5]  Andrew G. Alleyne,et al.  Control of high-resolution electrohydrodynamic jet printing , 2011 .

[6]  J. Borwein,et al.  Two-Point Step Size Gradient Methods , 1988 .

[7]  Stephen P. Boyd,et al.  Fast Model Predictive Control Using Online Optimization , 2010, IEEE Transactions on Control Systems Technology.

[8]  Hod Lipson,et al.  Geometric feedback control of discrete‐deposition SFF systems , 2010 .

[9]  Sandipan Mishra,et al.  A Model-Based Layer-to-Layer Control Algorithm for Ink-Jet 3D Printing , 2014 .

[10]  Mohsen A. Jafari,et al.  Tool Path-Based Deposition Planning in Fused Deposition Processes , 2002 .

[11]  Sharon L.N. Ford,et al.  Additive Manufacturing Technology: Potential Implications for U.S. Manufacturing Competitiveness , 2014 .

[12]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[13]  Sandipan Mishra,et al.  A Layer-To-Layer Model and Feedback Control of Ink-Jet 3-D Printing , 2015, IEEE/ASME Transactions on Mechatronics.

[14]  Kira Barton,et al.  A new spatial Iterative Learning Control approach for improved micro-Additive Manufacturing , 2014, 2014 American Control Conference.

[15]  Brigitte Grondin-Perez,et al.  Thermal Building Simulation and Computer Generation of Nodal Models , 2012, ArXiv.

[16]  M Maarten Steinbuch,et al.  Using iterative learning control with basis functions to compensate medium deformation in a wide-format inkjet printer , 2014 .

[17]  Anders Rantzer,et al.  Distributed Model Predictive Control with suboptimality and stability guarantees , 2010, 49th IEEE Conference on Decision and Control (CDC).

[18]  Kye-Si Kwon,et al.  A waveform design method for high-speed inkjet printing based on self-sensing measurement , 2007 .

[19]  Sandipan Mishra,et al.  A predictive control algorithm for layer-to-layer ink-jet 3D printing , 2016, 2016 American Control Conference (ACC).