Ink-jet 3D printing is a promising technology for additive manufacturing, with the potential for impacting a wide variety of industries. In traditional ink-jet 3D printing, the part is built up by depositing droplets layer upon layer in an open-loop manner. Droplet and edge dimensions are typically predicted experimentally and are assumed to remain constant through the printing process. However, there is no guarantee of consistent droplet shape and dimensions or the smoothness of the finished parts due to uncertainties in the manufacturing process. To address this issue, we propose a model-based feedback control law for ink-jet 3D printing that uses a height sensor for measuring profile height after each layer for determining the appropriate layer patterns for subsequent layers. Towards this goal, a simple model describing the relationship between profile height change and droplet deposition in the layer building process is first proposed and experimentally identified. Based on this model, a closed-loop layer-to-layer control algorithm is then developed for the ink-jet printing process. Specifically, the proposed algorithm uses a model prediction control algorithm to minimize the difference between the predicted height and the desired height and the predicted surface unevenness after a fixed number of layers. Experimental results show that the algorithm is able to achieve more consistent shapes between layers, reduced edge shrinking of the part, and smoother surface of the top layer.
Development and In Silico Evaluation of a Model-Based Closed-Loop Fluid Resuscitation Control Algorithm