This article develops and experimentally validates a distributed predictive control algorithm for closed-loop control of inkjet 3-D printing to handle constraints, e.g., droplet volume bounds, as well as the large-scale nature of the 3-D printing problem. The large number of decision variables, i.e., droplet volumes at each grid point, in high resolution inkjet 3-D printing makes centralized methods extremely time-consuming, thus, a distributed implementation of the controller is necessary. First, a graph-based height evolution model that captures the liquid spreading dynamics is described. Based on this model, a scalable closed-loop control algorithm using distributed model predictive control (MPC) that can reduce computation time significantly is designed and experimentally implemented. The performance and efficiency of the algorithm are shown to outperform open-loop printing and closed-loop printing with existing centralized MPC methods.
- Additive manufacturing (AM)
- inkjet 3-D printing
- model predictive control (MPC)